Datasets:

ZhuofengLi
/

survey-sections-2k

Dataset card Data Studio Files Files and versions Community

Split (1)

train · 2k rows

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.

survey_title stringlengths 19 197	section_num int64 3 56	references stringlengths 4 1.34M	section_outline stringlengths 531 9.08k
A Review of Localization and Tracking Algorithms in Wireless Sensor Networks	20	--- paper_title: Localization algorithms of Wireless Sensor Networks: a survey paper_content: In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs. --- paper_title: Cognitive radio, software defined radio, and adaptiv wireless systems paper_content: Preface. Chapter 1: Introducing Adaptive, Aware, and Cognitive Radios Bruce Fette. Chapter 2: Cognitive Networks Ryan W. Thomas, Daniel H. Friend, Luiz A. DaSilva, Allen B. MacKenzie. Chapter 3: Cognitive Radio Architecture Joseph Mitola III. Chapter 4: Software Defined Radio Architectures for Cognitive radios H. Arslan, H. celebi. Chapter 5: Value-Creation and Migration in Adaptive and Cognitive Radio Systems Keith E. Nolan, Francis J. Mullany, Eamonn Ambrose, Linda E. Doyle. Chapter 6: Codes and Games for Dynamic Spectrum Access Yiping Xing, Harikeshwar Kushwaha, K.P. Subbalakshmi, R. Chandramouli. Chapter 7: Efficiency and Coexistence Strategies for Cognitive Radio Sai Shankar N. Chapter 8: Enabling Cognitive Radio Through Sensing, Awareness, and Measurements H. Arslan, S. yarkan. Chapter 9: Spectrum Sensing for Cognitive Radio Applications H. Arslan, T. Yucek. Chapter 10: Location Information Management Systems for Cognitive Wireless Networks H. Arslan, H. Celebi. Chapter 11: OFDM for Cognitive Radio: Merits and Challenges H. Arslan, H. A. Mahmoud, T.Yucek. Chapter 12: UWB Cognitive Radio H. Arslan, M.E. Sahin. Chapter 13: Applications of Cognitive radio H. Arslan, S. Ahmed. Chapter 14: Cross-layer Adaptation and Optimization for Cognitive Radio H. Arslan, S. Yarkan. Index. --- paper_title: An Efficient Compartmental Model for Real-Time Node Tracking Over Cognitive Wireless Sensor Networks paper_content: In this paper, an efficient compartmental model for real-time node tracking over cognitive wireless sensor networks is proposed. The compartmental model is developed in a multi-sensor fusion framework with cognitive bandwidth utilization. The multi-sensor data attenuation model using radio, acoustic, and visible light signal is first derived using a sum of exponentials model. A compartmental model that selectively combines the multi-sensor data is then developed. The selection of individual sensor data is based on the criterion of bandwidth utilization. The parameters of the compartmental model are computed using the modified Prony estimator, which results in high tracking accuracies. Additional advantages of the proposed method include lower computational complexity and asymptotic distribution of the estimator. Cramer-Rao bound and elliptical error probability analysis are also discussed to highlight the advantages of the compartmental model. Experimental results for real-time node tracking in indoor environment indicate a significant improvement in tracking performance when compared to state-of-the-art methods in literature. --- paper_title: An Efficient Compartmental Model for Real-Time Node Tracking Over Cognitive Wireless Sensor Networks paper_content: In this paper, an efficient compartmental model for real-time node tracking over cognitive wireless sensor networks is proposed. The compartmental model is developed in a multi-sensor fusion framework with cognitive bandwidth utilization. The multi-sensor data attenuation model using radio, acoustic, and visible light signal is first derived using a sum of exponentials model. A compartmental model that selectively combines the multi-sensor data is then developed. The selection of individual sensor data is based on the criterion of bandwidth utilization. The parameters of the compartmental model are computed using the modified Prony estimator, which results in high tracking accuracies. Additional advantages of the proposed method include lower computational complexity and asymptotic distribution of the estimator. Cramer-Rao bound and elliptical error probability analysis are also discussed to highlight the advantages of the compartmental model. Experimental results for real-time node tracking in indoor environment indicate a significant improvement in tracking performance when compared to state-of-the-art methods in literature. --- paper_title: Localization of Wireless Sensor Networks in the Wild: Pursuit of Ranging Quality paper_content: Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. Our real-world experience from GreenOrbs, a sensor network system deployed in a forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper, we propose CDL, a Combined and Differentiated Localization approach for localization that exploits the strength of range-free approaches and range-based approaches using received signal strength indicator (RSSI). A critical observation is that ranging quality greatly impacts the overall localization accuracy. To achieve a better ranging quality, our method CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented and evaluated CDL by extensive real-world experiments in GreenOrbs and large-scale simulations. Our experimental and simulation results demonstrate that CDL outperforms current state-of-art localization approaches with a more accurate and consistent performance. For example, the average location error using CDL in GreenOrbs system is 2.9 m, while the previous best method SISR has an average error of 4.6 m. --- paper_title: Localization system for wireless networks paper_content: Positioning in wireless environments is crucial for the continuity of rich and mobile multimedia applications. A good position accuracy is particularly difficult to obtain in indoor or mixed indoor-outdoor scenarios. An efficient positioning system must accurately localize any mobile terminal/user in these demanding environments, being at the same time low-cost and easy to deploy. This paper proposes a real time tracking system for Wi-Fi networks based on trilateration calculated from RSSI values that are returned by the different wireless network interfaces. A fundamental characteristic of the system is the fact that it operates in passive mode, which means that there is no relationship between the positioning system and the terminal/user whose position is being calculated. The characteristics of the developed application are presented, together with the most important considerations that were taken into account during its development phase. The accuracy of the proposed system is evaluated by applying it to different scenarios and the results obtained prove that the application is able to achieve a good precision level, in spite of being a low cost solution that is very easy to deploy in practice. --- paper_title: Indoor Positioning System Using Visible Light and Accelerometer paper_content: Indoor positioning system is a critical part in location-based services. Highly precise positioning systems can support different mobile applications in future wireless systems. Positioning systems using existing wireless networks have low deployment costs, but the position error can be up to several meters. While there are positioning systems proposed in the literature that have low position error, they require extra hardware and are therefore costly to deploy. In this paper, we propose an indoor positioning system based on visible light communications (VLC). In contrast to existing works on VLC for positioning, our system estimates the location of the receiver in three dimensions even without: 1) the knowledge of the height of the receiver from ground; and 2) requiring the alignment of the receiver’s normal with the LED’s normal. Our system has low installation cost as it uses existing lighting sources as transmitters. Light sensor and accelerometer, which can be found in most smartphones, are used at the receiver’s side. They are used to measure the received light intensity and the orientation of the smartphone. A low-complexity algorithm is then used to find out the receiver’s position. Our system does not require the knowledge of the LED transmitters’ physical parameters. Experimental results show that our system achieves average position errors of less than 0.25 m. --- paper_title: Distributed Angle Estimation for Localization in Wireless Sensor Networks paper_content: In this paper, we design a new distributed angle estimation method for localization in wireless sensor networks (WSNs) under multipath propagation environment. We employ a two-antenna anchor that can emit two linear chirp waves simultaneously, and propose to estimate the angle of departure (AOD) of the emitted waves at each receiving node via frequency measurement of the local received signal strength indication (RSSI) signal. An improved estimation method is further proposed where multiple parallel arrays are adopted to provide the space diversity. The proposed methods rely only on radio transceivers and do not require frequency synchronization or precise time synchronization between the transceivers. More importantly, the angle is estimated at each sensor in a completely distributed manner. The performance analysis is derived and simulations are presented to corroborate the proposed studies. --- paper_title: Constrained Least Squares Algorithm for TOA-Based Mobile Location under NLOS Environments paper_content: This paper presents a mobile station (MS) location method using constrained least-squares (CLS) estimation in the non-line-sight (NLOS) conditions. Three or more time-of-arrival (TOA) measurements of a signal traveling between a MS and base stations (BSs) are necessary for its localization. However, when some of the measurements are from NLOS paths, the location errors can be very large. We propose a method that mitigates possible large TOA error measurements caused by NLOS. This method does not depend on a particular distribution of the NLOS error. Simulation results show that the location accuracy is significantly improved over traditional algorithms, even under highly NLOS conditions. geometrical approach, the geometric relationship between the mobile device and its reference is exploited to establish the Euclidean distance between them and to identify the physical location of the device. In this paper, we propose a novel least-square (LS) approach combining with geometrical relationship. It firstly adjusts the NLOS-corrupted range measurements to approach their LOS values, and then minimizes a constrained least- squares function incorporating the known relation between the intermediate variable and the position coordinate, based on the technique of Lagrange multipliers. This algorithm does not require the distinction between NLOS and LOS BSs (6), and the knowledge of the statistics of measurement noise and NLOS errors. Our approach also has the advantage of requiring no modifications to the subscriber equipment. The location estimation can be performed at either the MS if it has the functionality or at special location units in the network. The remainder of this paper is organized as follows. The proposed algorithm is outlined in Section II and the simulation results and performance analysis are discussed in Section III. Finally, conclusions are drawn in Section IV. --- paper_title: RF Localization and tracking of mobile nodes in Wireless Sensors Networks: Architectures, Algorithms and Experiments paper_content: In this paper we address the problem of localizing, tracking ::: and navigating mobile nodes associated to operators acting in a ?xed ::: wireless sensor network (WSN) using only RF information. We propose ::: two alternative and somehow complementary strategies: the ?rst one is ::: based on an empirical map of the Radio Signal Strength (RSS) distribu- ::: tion generated by the WSN and on the stochastic model of the behavior ::: of the mobile nodes, while the second one is based on a maximum like- ::: lihood estimator and a radio channel model for the RSS. We compare ::: the two approaches and highlight pros and cons for each of them. Fi- ::: nally, after implementing them into two real-time tracking systems, we ::: analyze their performance on an experimental testbed in an industrial ::: indoor environment. --- paper_title: A new hybrid algorithm on TDOA localization in wireless sensor network paper_content: A hybrid algorithm for TDOA localization is proposed in this paper. It has well combined the advantages of genetic algorithm and quasi-Newton algorithm. The hybrid algorithm has sufficiently displayed the characteristics of genetic algorithm's group searching and quasi-Newton method's local strong searching. At the same time it effectively overcomes the problem of high sensitivity to initial point of quasi-Newton method and shortcoming of genetic algorithm which reduces the searching efficiency in later period. The experimental results show that if the parameters are assumed reasonably the hybrid algorithm has extremely stability, higher localization rate and localization precision than genetic algorithm and quasi-Newton algorithm. --- paper_title: Experimental analysis of RSSI-based indoor localization with IEEE 802.15.4 paper_content: This paper presents a comparison between some of the most used ranging localization methods based on the Received Signal Strength Indicator (RSSI) in low-power IEEE 802.15.4 wireless sensor networks. In particular, the Trilateration, the Min-Max and the Maximum-Likelihood algorithms have been compared using only a limited number of reference nodes. In order to perform an exhaustive comparison we carried out tests in an indoor environment: dozens of RSSI values for every estimation have been gathered and cleaned from outliers values. Our results show that it is possible to some extent to obtain positioning information from nodes equipped with IEEE 802.15.4 radio modules, given the position and the number of reference nodes. --- paper_title: Joint Node Localization and Time-Varying Clock Synchronization in Wireless Sensor Networks paper_content: The problems of node localization and clock synchronization in wireless sensor networks are naturally tied from a statistical signal processing perspective. In this work, we consider the joint estimation of an unknown node's location and clock parameters by incorporating the effect of imperfections in node oscillators, which render a time varying nature to the clock parameters. In order to alleviate the computational complexity associated with the optimal maximum a-posteriori estimator, a simpler approach based on the Expectation-Maximization (EM) algorithm is proposed which iteratively estimates the clock parameters using a Kalman smoother in the E-step, and the location of the unknown node in the M-step. The convergence and the mean square error (MSE) performance of the proposed algorithm are evaluated using simulation studies which demonstrate the high fidelity of the proposed joint estimation approach. --- paper_title: Angle-of-arrival localization based on antenna arrays for wireless sensor networks q paper_content: Among the large number of contributions concerning the localization techniques for wireless sensor networks (WSNs), there is still no simple, energy and cost efficient solution suitable in outdoor scenarios. In this paper, a technique based on antenna arrays and angle-of-arrival (AoA) measurements is carefully discussed. While the AoA algorithms are rarely considered for WSNs due to the large dimensions of directional antennas, some system configurations are investigated that can be easily incorporated in pocket-size wireless devices. A heuristic weighting function that enables decreasing the location errors is introduced. Also, the detailed performance analysis of the presented system is provided. The localization accuracy is validated through realistic Monte-Carlo simulations that take into account the specificity of propagation conditions in WSNs as well as the radio noise effects. Finally, trade-offs between the accuracy, localization time and the number of anchors in a network are addressed. --- paper_title: Network-based wireless location: challenges faced in developing techniques for accurate wireless location information paper_content: Wireless location refers to the geographic coordinates of a mobile subscriber in cellular or wireless local area network (WLAN) environments. Wireless location finding has emerged as an essential public safety feature of cellular systems in response to an order issued by the Federal Communications Commission (FCC) in 1996. The FCC mandate aims to solve a serious public safety problem caused by the fact that, at present, a large proportion of all 911 calls originate from mobile phones, the location of which cannot be determined with the existing technology. However, many difficulties intrinsic to the wireless environment make meeting the FCC objective challenging. These challenges include channel fading, low signal-to-noise ratios (SNRs), multiuser interference, and multipath conditions. In addition to emergency services, there are many other applications for wireless location technology, including monitoring and tracking for security reasons, location sensitive billing, fraud protection, asset tracking, fleet management, intelligent transportation systems, mobile yellow pages, and even cellular system design and management. This article provides an overview of wireless location challenges and techniques with a special focus on network-based technologies and applications. --- paper_title: Adaptive AOA/TOA Localization Using Fuzzy Particle Filter for Mobile WSNs paper_content: Location-awareness is crucial and becoming increasingly important to many applications in wireless sensor networks. This paper presents a network-based positioning system and outlines recent work in which we have developed an efficient principled approach to localize a mobile sensor using time of arrival (TOA) and angle of arrival (AOA) information employing multiple seeds in the line-of-sight scenario. By receiving the periodic broadcasts from the seeds, the mobile target sensors can obtain adequate observations and localize themselves automatically. Based on the distance measurements and the initial position estimate, adaptive fuzzy control scheme is applied to solve the localization adjustment problem. The simulations show that the proposed approach provides adaptive flexibility and robust improvement in position estimation. --- paper_title: Localization of WSN node based on Time of Arrival using Ultra wide band spectrum paper_content: In this paper, the proposed methodology calculates the distance between two nodes in Wireless Sensor Network for localization purpose. The methodology is a variant of Time of Arrival (TOA) methodology. The simulations are done in Matlab using Ultra wide band spectrum and Gaussian monocycle pulses. The measured distances are compared with set distances and mean square errors are calculated. Finally, the set distance and measured distances are compared for various transmission frequencies within UWB spectrum. --- paper_title: 3-D mobile node localization using constrained volume optimization over ad-hoc sensor networks paper_content: This paper proposes a three dimensional mobile node localization and obstacle avoidance mechanism in ad-hoc sensor networks (AHSN). The localization task is performed through constrained volume optimization. Obstacle avoidance is achieved through weighted distribution method. Constrained volume optimization is performed by minimizing the squared location error and imposing the distance and boundary conditions. Obstacle avoidance is achieved by choosing the angular direction which minimizes the cost function. The solution to the constrained optimization problems ensures that the method is robust to change in environmental conditions and NLOS issues. Additionally, the algorithm is scalable and follows a distributed approach to localization. The performance of this method is assessed by deploying nodes in both indoor and outdoor environments. Improved localization accuracy is noted when compared to conventional methods in terms of statistical location estimates and Cramer-Rao lower bound localization error analysis. --- paper_title: Energy efficient optimal node-source localization using mobile beacon in ad-hoc sensor networks paper_content: In this paper, a single mobile beacon based method to localize nodes using principle of maximum power reception is proposed. Optimal positioning of the mobile beacon for minimum energy consumption is also discussed. In contrast to existing methods, the node localization is done with prior location of only three nodes. There is no need of synchronization, as there is only one mobile anchor and each node communicates only with the anchor node. Also, this method is not constrained by a fixed sensor geometry. The localization is done in a distributed fashion, at each sensor node. Experiments on node-source localization are conducted by deploying sensors in an ad-hoc manner in both outdoor and indoor environments. Localization results obtained herein indicate a reasonable performance improvement when compared to conventional methods. --- paper_title: Hybrid TOA/AOA-Based Mobile Localization with and without Tracking in CDMA Cellular Networks paper_content: This paper proposes a hybrid TOA/AOA (Time of Arrival/Angle of Arrival)-based localization algorithm for Code Division Multiple Access (CDMA) networks. The algorithm extends the Taylor Series Least Square (TS-LS) method originally developed for TOA-based systems to incorporate AOA measurements. In addition, tracking algorithms utilizing velocity and acceleration measurements are investigated. Simulation results illustrate that the proposed TOA/AOA TS-LS can provide better performance than conventional schemes in localization accuracy and in reduced likelihood of encountering non-convergence problem compared with TOA TS-LS. Tracking algorithms using the Extended and Unscented Kalman Filter (EKF and UKF) can track the objects relatively well, further decreasing the positioning error. UKF is found to provide closer tracking of the trajectory than EKF, for it truly captures the statistical mean and variance of the noises. --- paper_title: A simple and efficient estimator for hyperbolic location paper_content: An effective technique in locating a source based on intersections of hyperbolic curves defined by the time differences of arrival of a signal received at a number of sensors is proposed. The approach is noniterative and gives an explicit solution. It is an approximate realization of the maximum-likelihood estimator and is shown to attain the Cramer-Rao lower bound near the small error region. Comparisons of performance with existing techniques of beamformer, spherical-interpolation, divide and conquer, and iterative Taylor-series methods are made. The proposed technique performs significantly better than spherical-interpolation, and has a higher noise threshold than divide and conquer before performance breaks away from the Cramer-Rao lower bound. It provides an explicit solution form that is not available in the beamforming and Taylor-series methods. Computational complexity is comparable to spherical-interpolation but substantially less than the Taylor-series method. > --- paper_title: NLOS mitigation in TOA-based localization using semidefinite programming paper_content: In this work, time-of-arrival (TOA)-based wireless sensor localization in non-line-of-sight (NLOS) environments is investigated. In such environments, the accuracy of localization techniques is significantly degraded. While previous work often assumes some knowledge of the NLOS environment, we assume that the estimator knows neither which connections are NLOS nor the distribution of the NLOS errors. It is shown that the maximum likelihood estimator using only LOS connections provides a lower bound on the estimation accuracy. Furthermore, a novel NLOS mitigation technique based on semidefinite programming (SDP) is proposed. The proposed SDP technique estimates the source location jointly with the NLOS biases. The performance of the proposed estimator is compared with the aforementioned lower bound and with previous algorithms through computer simulations. Simulation results show that the proposed SDP estimator outperforms the other algorithms substantially, especially in severe NLOS environments. --- paper_title: Indoor node localization using geometric dilution of precision in ad-hoc sensor networks paper_content: In this paper, a new method for sensor node localization using geometric dilution of precision (GDOP) is described. The proposed methods are not constrained by fixed geometry. They can be used for robust node localization under both LOS and NLOS conditions in an ad-hoc sensor network. These methods are robust since they utilize measurements obtained from both LOS and NLOS conditions. Extensive simulations and real indoor deployments are used to evaluate the performance of the proposed node localization methods based on GDOP. The localization accuracy of these algorithms is reasonably better when compared to similar methods in literature. --- paper_title: Novel Robust Direction-of-Arrival-Based Source Localization Algorithm for Wideband Signals paper_content: Source localization for wideband signals using acoustic sensor networks has drawn much research interest recently. The maximum-likelihood is the predominant objective for a wide variety of source localization approaches, and we have previously proposed an expectation-maximization (EM) algorithm to solve the source localization problem. In this paper, we tackle the source localization problem based on the realistic assumption that the sources are corrupted by spatially-non-white noise. We explore the respective limitations of our recently proposed algorithm, namely EM source localization algorithm, and design a new direction-of-arrival (DOA) estimation based (DEB) source localization algorithm. We also derive the Cramer-Rao lower bound (CRLB) analysis and the computational complexity study for the aforementioned source localization schemes. Through Monte Carlo simulations and our derived CRLB analysis, it is demonstrated that our proposed DEB algorithm significantly outperforms the previous EM method in terms of both source localization accuracy and computational complexity. --- paper_title: Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment paper_content: In this paper, we analyze the accuracy of indoor localization measurement based on a wireless sensor network. The position estimation procedure is based on the received-signal-strength measurements collected in a real indoor environment. Two different classes of low-computational-effort algorithms based on the centroid concept are considered, i.e., the weighted centroid localization method and the relative-span exponential weighted localization method. In particular, different sources of measurement uncertainty are analyzed by means of theoretical simulations and experimental results. --- paper_title: A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques paper_content: Localization of a wireless device using the time-of-arrivals (TOAs) from different base stations has been studied extensively in the literature. Numerous localization algorithms with different accuracies, computational complexities, a-priori knowledge requirements, and different levels of robustness against non-line-of-sight (NLOS) bias effects also have been reported. However, to our best knowledge, a detailed unified survey of different localization and NLOS mitigation algorithms is not available in the literature. This paper aims to give a comprehensive review of these different TOA-based localization algorithms and their technical challenges, and to point out possible future research directions. Firstly, fundamental lower bounds and some practical estimators that achieve close to these bounds are summarized for line-of-sight (LOS) scenarios. Then, after giving the fundamental lower bounds for NLOS systems, different NLOS mitigation techniques are classified and summarized. Simulation results are also provided in order to compare the performance of various techniques. Finally, a table that summarizes the key characteristics of the investigated techniques is provided to conclude the paper. --- paper_title: Performance comparison of localization techniques for sequential WSN discovery paper_content: In this paper, the performance of different localization algorithms are compared in the context of the sequential Wireless Sensor Network (WSN) discovery problem. Here, all sensor nodes are at unknown locations except for a very small number of so called anchor nodes at known locations. The locations of nodes are sequentially estimated such that when the location of a given node is found, it may be used to localize others. The underlying performance of such an approach is largely dependent upon the localization technique employed. In this paper, several well-known localization techniques are presented using a united notation. These methods are time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), direction of arrival (DOA) and large aperture array (LAA) localization. The performance of a sequential network discovery process is then compared when using each of these localization algorithms. These algorithms are implemented in the Java-DSP software package as part of a localization toolbox. (5 pages) --- paper_title: Localization of acoustic beacons using iterative null beamforming over ad-hoc wireless sensor networks paper_content: In this paper an iterative method to localize and separate multiple audio beacons using the principles of null beam forming is proposed. In contrast to standard methods, the source separation is done optimally by putting a null on all the other sources while obtaining an estimate of a particular source. Also, this method is not constrained by fixed sensor geometry as is the case with general beamforming methods. The wireless sensor nodes can therefore be deployed in any random geometry as required. Experiments are performed to estimate the location and also the power spectral density of the separated sources. The experimental results indicate that the method can be used in ad-hoc, flexible and low-cost wireless sensor network deployment. --- paper_title: Overview of Radiolocation in CDMA Cellular Systems paper_content: Applications for the location of subscribers of wireless services continue to expand. Consequently, location techniques for wireless technologies are being investigated. With code-division multiple access (CDMA) being deployed by a variety of cellular and PCS providers, developing an approach for location in CDMA networks is imperative. This article discusses the applications of location technology, the methods available for its implementation in CDMA networks, and the problems that are encountered when using CDMA networks for positioning. --- paper_title: Angle-of-arrival localization based on antenna arrays for wireless sensor networks q paper_content: Among the large number of contributions concerning the localization techniques for wireless sensor networks (WSNs), there is still no simple, energy and cost efficient solution suitable in outdoor scenarios. In this paper, a technique based on antenna arrays and angle-of-arrival (AoA) measurements is carefully discussed. While the AoA algorithms are rarely considered for WSNs due to the large dimensions of directional antennas, some system configurations are investigated that can be easily incorporated in pocket-size wireless devices. A heuristic weighting function that enables decreasing the location errors is introduced. Also, the detailed performance analysis of the presented system is provided. The localization accuracy is validated through realistic Monte-Carlo simulations that take into account the specificity of propagation conditions in WSNs as well as the radio noise effects. Finally, trade-offs between the accuracy, localization time and the number of anchors in a network are addressed. --- paper_title: Robust and Low Complexity Source Localization in Wireless Sensor Networks Using Time Difference of Arrival Measurement paper_content: Wireless source localization has found a number of applications in wireless sensor networks. In this work, we investigate robust and low complexity solutions to the problem of source localization based on the time-difference of arrivals (TDOA) measurement model. By adopting a min-max approximation to the maximum likelihood source location estimation, we develop two low complexity algorithms that can be reliably and rapidly solved through semi-definite relaxation. Our approach hinges on the use of a reference sensor node which can be optimized according to the Cramer-Rao lower bound or selected heuristically. Our low complexity estimate can be used either as the final location estimation output or as the initial point for other traditional search algorithms. --- paper_title: Wireless Sensor Network Localization Techniques paper_content: Wireless sensor network localization is an important area that attracted significant research interest. This interest is expected to grow further with the proliferation of wireless sensor network applications. This paper provides an overview of the measurement techniques in sensor network localization and the one-hop localization algorithms based on these measurements. A detailed investigation on multi-hop connectivity-based and distance-based localization algorithms are presented. A list of open research problems in the area of distance-based sensor network localization is provided with discussion on possible approaches to them. --- paper_title: Localization of irregular Wireless Sensor Networks based on multidimensional scaling paper_content: In many applications of Wireless Sensor Networks (WSN), it is crucial to know the location of sensor nodes. Although several methods have been proposed, most of them have poor performance in irregularly shaped networks. MDS-MAP is one of the localization methods based on multidimensional scaling (MDS) technique. It uses the connectivity information to derive the location of the nodes in the network. In presence of additional data such as estimated distances between adjacent neighbors, it can also enhance the localization precision. Since MDS-MAP uses the length of the shortest path as Euclidian distance between the nodes, it is sensitive to the shape of the network. In this paper we present MDS-MAP(I), a modified algorithm based on MDS-MAP which improves the localization task in irregular networks. The simulation results show that the algorithm is more reliable in various topologies and achieves a significant performance improvement upon existing method. --- paper_title: Improved MDS-based localization paper_content: It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. MDS-MAP is a recent localization method based on multidimensional scaling (MDS). It uses connectivity information - who is within communications range of whom - to derive the locations of the nodes in the network, and can take advantage of additional data, such as estimated distances between neighbors or known positions for certain anchor nodes, if they are available. However, MDS-MAP is an inherently centralized algorithm and is therefore of limited utility in many applications. In this paper, we present a new variant of the MDS-MAP method, which we call MDS-MAP(P) standing for MDS-MAP using patches of relative maps, that can be executed in a distributed fashion. Using extensive simulations, we show that the new algorithm not only preserves the good performance of the original method on relatively uniform layouts, but also performs much better than the original on irregularly-shaped networks. The main idea is to build a local map at each node of the immediate vicinity and then merge these maps together to form a global map. This approach works much better for topologies in which the shortest path distance between two nodes does not correspond well to their Euclidean distance. We also discuss an optional refinement step that improves solution quality even further at the expense of additional computation. --- paper_title: MDS and Trilateration Based Localization in Wireless Sensor Network paper_content: Localization of sensor nodes is crucial in Wireless Sensor Network because of applications like surveillance, tracking, navigation etc. Various optimization techniques for localization have been proposed in literature by different researchers. In this paper, we propose a two phase hybrid approach for localization using Multidi- mensional Scaling and trilateration, namely, MDS with refinement using trilateration. Trilateration refines the estimated locations obtained by the MDS algorithm and hence acts as a post optimizer which improves the accuracy of the estimated positions of sensor nodes. Through extensive simulations, we have shown that the proposed algorithm is more robust to noise than previous approaches and provides higher accuracy for estimating the positions of sensor nodes. --- paper_title: ALESSA: MDS - based localization algorithm for Wireless Sensor Networks paper_content: Self - localization in Wireless Sensor Networks (WSN) should be precise and reliable. Alternative Least-Square Scaling Algorithm (ALESSA) is a recently proposed centralized Multidimensional Scaling (MDS)-based localization algorithm, which uses an iterative approach to solve for the coordinates of discrete points. While ALESSA converges most of the time, like most iterative algorithm, it can be trapped in local minima causing large errors in the location estimates. In this paper, we propose the reseeding of the initial random estimates to improve the convergence of the algorithm. Performance of the proposed algorithm is evaluated under different network topologies with limited connectivity. We also analyzed the effects of low Signal to Noise Ratio (SNR) and the population of the nodes deployed in the network to the algorithm's localization precision. Simulation results show that at 26 dB SNR reseeding always results in convergence with the estimation errors within 5 % of the reference communication range. Analysis and test runs also verified that our algorithm provides accurate and consistent localization estimates under range-based localization with limited network connectivity, even with a low SNR. The algorithm also performs well with limited number of nodes. --- paper_title: Geolocation Techniques: Principles and Applications paper_content: Basics of Distributed and Cooperative Radio and Non-Radio Based Geolocation provides a detailed overview of geolocation technologies. The book covers the basic principles of geolocation, including ranging techniques to localization technologies, fingerprinting and localization in wireless sensor networks. This book also examines the latest algorithms and techniques such as Kalman Filtering, Gauss-Newton Filtering and Particle Filtering. --- paper_title: Localization algorithms of Wireless Sensor Networks: a survey paper_content: In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs. --- paper_title: Monte Carlo localization for mobile wireless sensor networks paper_content: Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors' radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy. --- paper_title: DV Based Positioning in Ad Hoc Networks paper_content: Many ad hoc network protocols and applications assume the knowledge of geographic location of nodes. The absolute position of each networked node is an assumed fact by most sensor networks which can then present the sensed information on a geographical map. Finding position without the aid of GPS in each node of an ad hoc network is important in cases where GPS is either not accessible, or not practical to use due to power, form factor or line of sight conditions. Position would also enable routing in sufficiently isotropic large networks, without the use of large routing tables. We are proposing APS --- a localized, distributed, hop by hop positioning algorithm, that works as an extension of both distance vector routing and GPS positioning in order to provide approximate position for all nodes in a network where only a limited fraction of nodes have self positioning capability. --- paper_title: GPS-less Low Cost Outdoor Localization For Very Small Devices paper_content: Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, lightweight, untethered, and unobtrusive. The problem of localization, that is, determining where a given node is physically located in a network, is a challenging one, and yet extremely crucial for many of these applications. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the reliance on GPS of all nodes in these networks. We review localization techniques and evaluate the effectiveness of a very simple connectivity metric method for localization in outdoor environments that makes use of the inherent RF communications capabilities of these devices. A fixed number of reference points in the network with overlapping regions of coverage transmit periodic beacon signals. Nodes use a simple connectivity metric, which is more robust to environmental vagaries, to infer proximity to a given subset of these reference points. Nodes localize themselves to the centroid of their proximate reference points. The accuracy of localization is then dependent on the separation distance between two-adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90 percent of our data points is within one-third of the separation distance. However, future work is needed to extend the technique to more cluttered environments. --- paper_title: An improved DV-Hop localization algorithm for wireless sensor networks paper_content: Aiming at the positioning problem of wireless sensor network node location, an improved DV-hop positioning algorithm is proposed in this paper, together with its basic principle and realization issues. The proposed method can improve location accuracy without increasing hardware cost for sensor node. Simulation results show that it has good positioning accuracy and coverage. The influences of anchor nodes on the DV-hop algorithm are also explored in the paper. --- paper_title: Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks paper_content: A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to the existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers the enhanced capability of multiple source localization. A multiresolution search algorithm and an expectation-maximization (EM) like iterative algorithm are proposed to expedite the computation of source locations. The Crame/spl acute/r-Rao Bound (CRB) of the ML source location estimate has been derived. The CRB is used to analyze the impacts of sensor placement to the accuracy of location estimates for single target scenario. Extensive simulations have been conducted. It is observed that the proposed ML method consistently outperforms existing acoustic energy based source localization methods. An example applying this method to track military vehicles using real world experiment data also demonstrates the performance advantage of this proposed method over a previously proposed acoustic energy source localization method. --- paper_title: DuRT: Dual RSSI Trend Based Localization for Wireless Sensor Networks paper_content: Localization is a key issue in wireless sensor networks. The geographical location of sensors is important information that is required in sensor network operations such as target detection, monitoring, and rescue. These methods are classified into two categories, namely range-based and range-free. Range-based localizations achieve high location accuracy by using specific hardware or using absolute received signal strength indicator (RSSI) values, whereas range-free approaches obtain location estimates with lower accuracy. Because of the hardware and energy constraints in sensor networks, RSSI offers a convenient method to find the position of sensor nodes. However, in the presence of channel noise, fading, and attenuation, it is not possible to estimate the actual location. In this paper, we propose an RSSI-based localization scheme that considers the trend of RSSI values obtained from beacons to estimate the position of sensor nodes. Through applying polynomial modeling on the relationship between received RSSI and distance, we are able to locate the maximum RSSI point on the anchor trajectory. Using two such trajectories, the sensor position can be determined by calculating the intersection point of perpendiculars passing through the maximum RSSI point on each trajectory. In addition, we devised schemes to improve the localization method to perform under a variety of cases such as single trajectory, unavailability of RSSI trends, and so. The advantage of our scheme is that it does not rely on absolute RSSI values and hence, can be applied in dynamic environments. In simulations, we demonstrate that the proposed localization scheme achieves higher location accuracy compared with existing localization approaches. --- paper_title: Localization for mobile sensor networks paper_content: Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions. --- paper_title: Organizing a Global Coordinate System from Local Information on an Ad Hoc Sensor Network paper_content: We demonstrate that it is possible to achieve accurate localization and tracking of a target in a randomly placed wireless sensor network composed of inexpensive components of limited accuracy. The crucial enabler for this is a reasonably accurate local coordinate system aligned with the global coordinates. We present an algorithm for creating such a coordinate system without the use of global control, globally accessible beacon signals, or accurate estimates of inter-sensor distances. The coordinate system is robust and automatically adapts to the failure or addition of sensors. Extensive theoretical analysis and simulation results are presented. Two key theoretical results are: there is a critical minimum average neighborhood size of 15 for good accuracy and there is a fundamental limit on the resolution of any coordinate system determined strictly from local communication. Our simulation results show that we can achieve position accuracy to within 20% of the radio range even when there is variation of up to 10% in the signal strength of the radios. The algorithm improves with finer quantizations of inter-sensor distance estimates: with 6 levels of quantization position errors better than 10% are achieved. Finally we show how the algorithm gracefully generalizes to target tracking tasks. --- paper_title: Range-free localization schemes for large scale sensor networks paper_content: Wireless Sensor Networks have been proposed for a multitude of location-dependent applications. For such systems, the cost and limitations of the hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute point-to-point distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. In this paper, we present APIT, a novel localization algorithm that is range-free. We show that our APIT scheme performs best when an irregular radio pattern and random node placement are considered, and low communication overhead is desired. We compare our work via extensive simulation, with three state-of-the-art range-free localization schemes to identify the preferable system configurations of each. In addition, we study the effect of location error on routing and tracking performance. We show that routing performance and tracking accuracy are not significantly affected by localization error when the error is less than 0.4 times the communication radio radius. --- paper_title: A new weighted centroid localization algorithm in wireless sensor networks paper_content: Nodes in a sensor network are often randomly distributed. To assign measurements to locations, each node has to determine its own position. Algorithms for positioning in wireless sensor networks are classified into two groups: approximate and exact. In this paper, we propose a range-based approximate positioning approach which is almost the combination of WCL and EBTB. Then, compare it with two other approximate positioning approaches (WCL with time complexity of O(n)) and EBTB with time complexity of O(nn) and an exact positioning approach (QR Factorization with time complexity of O(nnn)). Finally, it will be shown that EWCL (with time complexity of O(nn)) is the best localization algorithm with respect to the three other localization algorithms when the noise is high and its accuracy is close to the accuracy of QR when the noise is medium. --- paper_title: Monte Carlo localization for mobile wireless sensor networks paper_content: Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors' radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy. --- paper_title: An adaptive anchor navigation algorithm for localization in MANET paper_content: Automatic anchor navigation for sensor node localization in mobile networks is a challenging task. Previous work on anchor navigation has mostly concentrated on static node localization. This paper proposes an adaptive anchor navigation algorithm to localize sensor nodes in a mobile ad-hoc networks (MANET). The novelty of the proposed algorithm lies in fact that an anchor chooses an optimal position at every instant. This adaptive position selection of the anchor ensures low energy consumption. On the other hand, an optimal path mechanism proposed herein constrains the anchor to traverse in a region densely populated with nodes. This ensures that the path traversed by the anchors is minimal. In order to localize the nodes, the ratio of grid benefit to distance (GBD) within the probable region of anchor is first computed. Subsequently, an optimality criterion is used to decide the next anchor location. Localization is then performed using geometric methods. Experimental results on node localization in a mobile network scenario indicate a reasonable performance improvement when compared to conventional methods. --- paper_title: Optimal anchor guiding algorithms for maximal node localization in mobile sensor networks paper_content: Localization of mobile nodes is a challenging problem especially when both anchor and node are mobile. In this paper, algorithms for optimal anchor guiding, to localize maximum number of nodes, are proposed. The algorithms are based on the principle of jointly maximizing a grid benefit criterion and the number of nodes localized. The advantage of these algorithms is that, that both the anchors and nodes can be deployed randomly and can traverse the region with varying speeds. Additionally, the optimal path for the anchor is decided in such a way that the maximum number of nodes are localized. The proposed algorithms are extensively analyzed for their performance by conducting experiments on both, NI and Crossbow set-up. The results obtained from localization error analysis indicate that the algorithms discussed in this work perform reasonably better than the similar conventional algorithms available in literature. Additional analysis performed on energy consumption indicate that the proposed algorithms are energy efficient. --- paper_title: Localization for mobile sensor networks paper_content: Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions. --- paper_title: An improved DV-Hop localization algorithm for wireless sensor networks paper_content: Aiming at the positioning problem of wireless sensor network node location, an improved DV-hop positioning algorithm is proposed in this paper, together with its basic principle and realization issues. The proposed method can improve location accuracy without increasing hardware cost for sensor node. Simulation results show that it has good positioning accuracy and coverage. The influences of anchor nodes on the DV-hop algorithm are also explored in the paper. --- paper_title: Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks paper_content: A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to the existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers the enhanced capability of multiple source localization. A multiresolution search algorithm and an expectation-maximization (EM) like iterative algorithm are proposed to expedite the computation of source locations. The Crame/spl acute/r-Rao Bound (CRB) of the ML source location estimate has been derived. The CRB is used to analyze the impacts of sensor placement to the accuracy of location estimates for single target scenario. Extensive simulations have been conducted. It is observed that the proposed ML method consistently outperforms existing acoustic energy based source localization methods. An example applying this method to track military vehicles using real world experiment data also demonstrates the performance advantage of this proposed method over a previously proposed acoustic energy source localization method. --- paper_title: Multi-sensor data fusion methods for indoor localization under collinear ambiguity paper_content: Sensor node localization in mobile ad-hoc sensor networks is a challenging problem. Often, the anchor nodes tend to line up in a linear fashion in a mobile sensor network when nodes are deployed in an ad-hoc manner. This paper discusses novel node localization methods under the conditions of collinear ambiguity of the anchors. Additionally, the work presented herein also describes a methodology to fuse data available from multiple sensors for improved localization performance under conditions of collinear ambiguity. In this context, data is first acquired from multiple sensors sensing different modalities. The data acquired from each sensor is used to compute attenuation models for each sensor. Subsequently, a combined multi-sensor attenuation model is developed. The fusion methodology uses a joint error optimization approach on the multi-sensor data. The distance between each sensor node and anchor is itself computed using the differential power principle. These distances are used in the localization of sensor nodes under the condition of collinear ambiguity of anchors. Localization error analysis is also carried out in indoor conditions and compared with the Cramer-Rao lower bound. Experimental results on node localization using simulations and real field deployments indicate reasonable improvements in terms of localization accuracy when compared to methods likes MLAR and MGLR. --- paper_title: Sensor node tracking using semi-supervised Hidden Markov Models paper_content: In this paper, a novel method for mobile sensor node tracking using semi-supervised Hidden Markov Models (HMM) is discussed. A new methodology to develop a combined attenuation model from data gathered from multiple sensors is also described. Observations emitted from the nodes are sparsely measured over the network area with beacons placed on the boundaries. HMMs are trained using observations measured at each grid point. The distances between a node passing through a specific grid point and beacons are estimated using likelihood maximization. The local location co-ordinates of the node positions are then computed by solving a constrained volume optimization problem. Quaternion rotation is used to finally obtain global coordinates of the node location. Several standard manoeuvres of mobile nodes are first simulated. Similar manoeuvres are also recorded from real field deployments. The experimental results are obtained for node localization and tracking from these experiments. Results indicate an improvement in the localization accuracy, when compared to the conventional localization methods. --- paper_title: Semi-supervised Laplacian regularized least squares algorithm for localization in wireless sensor networks paper_content: In this paper, we propose a new approach for localization in wireless sensor networks based on semi-supervised Laplacian regularized least squares algorithm. We consider two kinds of localization data: signal strength and pair-wise distance between nodes. When nodes are close within their physical location space, their localization data vectors should be similar. We first propose a solution using the alignment criterion to learn an appropriate kernel function in terms of the similarities between anchors, and the kernel function is used to measure the similarity between pair-wise sensor nodes in the networks. We then propose a semi-supervised learning algorithm based upon manifold regularization to obtain the locations of the non-anchors. We evaluate our algorithm under various network topology, transmission range and signal noise, and analyze its performance. We also compare our approach with several existing approaches, and demonstrate the high efficiency of our proposed algorithm in terms of location estimation error. --- paper_title: RF Localization and tracking of mobile nodes in Wireless Sensors Networks: Architectures, Algorithms and Experiments paper_content: In this paper we address the problem of localizing, tracking ::: and navigating mobile nodes associated to operators acting in a ?xed ::: wireless sensor network (WSN) using only RF information. We propose ::: two alternative and somehow complementary strategies: the ?rst one is ::: based on an empirical map of the Radio Signal Strength (RSS) distribu- ::: tion generated by the WSN and on the stochastic model of the behavior ::: of the mobile nodes, while the second one is based on a maximum like- ::: lihood estimator and a radio channel model for the RSS. We compare ::: the two approaches and highlight pros and cons for each of them. Fi- ::: nally, after implementing them into two real-time tracking systems, we ::: analyze their performance on an experimental testbed in an industrial ::: indoor environment. --- paper_title: Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength paper_content: Indoor wireless systems often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors for location-based applications. As such, these applications could benefit greatly from NLOS identification and mitigation techniques. These techniques have been primarily investigated for ultra-wide band (UWB) systems, but little attention has been paid to WiFi systems, which are far more prevalent in practice. In this study, we address the NLOS identification and mitigation problems using multiple received signal strength (RSS) measurements from WiFi signals. Key to our approach is exploiting several statistical features of the RSS time series, which are shown to be particularly effective. We develop and compare two algorithms based on machine learning and a third based on hypothesis testing to separate LOS/NLOS measurements. Extensive experiments in various indoor environments show that our techniques can distinguish between LOS/NLOS conditions with an accuracy of around 95%. Furthermore, the presented techniques improve distance estimation accuracy by 60% as compared to state-of-the-art NLOS mitigation techniques. Finally, improvements in distance estimation accuracy of 50% are achieved even without environment-specific training data, demonstrating the practicality of our approach to real world implementations. --- paper_title: A tutorial on hidden Markov models and selected applications in speech recognition paper_content: This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. > --- paper_title: A RSS-EKF localization method using HMM-based LOS/NLOS channel identification paper_content: Knowing channel sight condition is important as it has a great impact on localization performance. In this paper, a RSS-based localization algorithm, which jointly takes into consideration the effect of channel sight conditions, is investigated. In our approach, the channel sight conditions experience by a moving target to all sensors is modeled as a hidden Markov model (HMM), with the quantized measured RSSs as its observation. The parameters of HMM are obtained by an off-line training assuming that the LOS/NLOS can be identified during the training phase. With the HMM matrices, a forward-only algorithm can be utilized for real time sight conditions identification. The target is localized by extended Kalman Filter (EKF) by suitably combining with the sight conditions. Simulation results show that our proposed localization strategy can provide good identification to channel sight conditions, hence results in a better localization estimation. --- paper_title: Convex position estimation in wireless sensor networks paper_content: A method for estimating unknown node positions in a sensor network based exclusively on connectivity-induced constraints is described. Known peer-to-peer communication in the network is modeled as a set of geometric constraints on the node positions. The global solution of a feasibility problem for these constraints yields estimates for the unknown positions of the nodes in the network. Providing that the constraints are tight enough, simulation illustrates that this estimate becomes close to the actual node positions. Additionally, a method for placing rectangular bounds around the possible positions for all unknown nodes in the network is given. The area of the bounding rectangles decreases as additional or tighter constraints are included in the problem. Specific models are suggested and simulated for isotropic and directional communication, representative of broadcast-based and optical transmission respectively, though the methods presented are not limited to these simple cases. --- paper_title: Walkie-Markie: Indoor Pathway Mapping Made Easy paper_content: We present Walkie-Markie - an indoor pathway mapping system that can automatically reconstruct internal pathway maps of buildings without any a-priori knowledge about the building, such as the floor plan or access point locations. Central to Walkie-Markie is a novel exploitation of the WiFi infrastructure to define landmarks (WiFi-Marks) to fuse crowdsourced user trajectories obtained from inertial sensors on users' mobile phones. WiFi-Marks are special pathway locations at which the trend of the received WiFi signal strength changes from increasing to decreasing when moving along the pathway. By embedding these WiFi-Marks in a 2D plane using a newly devised algorithm and connecting them with calibrated user trajectories, Walkie-Markie is able to infer pathway maps with high accuracy. Our experiments demonstrate that Walkie-Markie is able to reconstruct a high-quality pathway map for a real office-building floor after only 5-6 rounds of walks, with accuracy gradually improving as more user data becomes available. The maximum discrepancy between the inferred pathway map and the real one is within 3m and 2.8m for the anchor nodes and path segments, respectively. --- paper_title: Anchor-Based Localization via Interval Analysis for Mobile Ad-Hoc Sensor Networks paper_content: Location awareness is a fundamental requirement for many applications of sensor networks. This paper proposes an original technique for self-localization in mobile ad-hoc networks. This method is adapted to the limited computational and memory resources of mobile nodes. The localization problem is solved in an interval analysis framework. The propagation of the estimation errors is based on an interval formulation of a state space model, where observations consist of anchor-based connectivities. The problem is then formulated as a constraint satisfaction problem where a simple Waltz algorithm is applied in order to contract the solution. This technique yields a guaranteed and robust online estimation of the mobile node positions. Observation errors as well as anchor node imperfections are taken into consideration in a simple and computational-consistent way. Multihop anchor-based and backpropagated localizations are also made possible in our method. Simulation results on mobile node trajectories corroborate the efficiency of the proposed technique and show that it outperforms the particle filtering methods. --- paper_title: Localization from mere connectivity paper_content: It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. We present an algorithm that uses connectivity information who is within communications range of whom to derive the locations of the nodes in the network. The method can take advantage of additional information, such as estimated distances between neighbors or known positions for certain anchor nodes, if it is available. The algorithm is based on multidimensional scaling, a data analysis technique that takes O(n3) time for a network of n nodes. Through simulation studies, we demonstrate that the algorithm is more robust to measurement error than previous proposals, especially when nodes are positioned relatively uniformly throughout the plane. Furthermore, it can achieve comparable results using many fewer anchor nodes than previous methods, and even yields relative coordinates when no anchor nodes are available. --- paper_title: Locating in fingerprint space: wireless indoor localization with little human intervention paper_content: Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1600m2, and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey. --- paper_title: Anchor-Free Distributed Localization in Sensor Networks paper_content: Many sensor network applications require that each node’s sensor stream be annotated with its physical location in some common coordinate system. Manual measurement and configuration methods for obtaining location don’t scale and are error-prone, and equipping sensors with GPS is often expensive and does not work in indoor and urban deployments. Sensor networks can therefore benefit from a self-configuring method where nodes cooperate with each other, estimate local distances to their neighbors, and converge to a consistent coordinate assignment. This paper describes a fully decentralized algorithm called AFL (Anchor-Free Localization) where nodes start from a random initial coordinate assignment and converge to a consistent solution using only local node interactions. The key idea in AFL is fold-freedom, where nodes first configure into a topology that resembles a scaled and unfolded version of the true configuration, and then run a force-based relaxation procedure. We show using extensive simulations under a variety of network sizes, node densities, and distance estimation errors that our algorithm is superior to previously proposed methods that incrementally compute the coordinates of nodes in the network, in terms of its ability to compute correct coordinates under a wider variety of conditions and its robustness to measurement errors. --- paper_title: GPS-less Low Cost Outdoor Localization For Very Small Devices paper_content: Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, lightweight, untethered, and unobtrusive. The problem of localization, that is, determining where a given node is physically located in a network, is a challenging one, and yet extremely crucial for many of these applications. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the reliance on GPS of all nodes in these networks. We review localization techniques and evaluate the effectiveness of a very simple connectivity metric method for localization in outdoor environments that makes use of the inherent RF communications capabilities of these devices. A fixed number of reference points in the network with overlapping regions of coverage transmit periodic beacon signals. Nodes use a simple connectivity metric, which is more robust to environmental vagaries, to infer proximity to a given subset of these reference points. Nodes localize themselves to the centroid of their proximate reference points. The accuracy of localization is then dependent on the separation distance between two-adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90 percent of our data points is within one-third of the separation distance. However, future work is needed to extend the technique to more cluttered environments. --- paper_title: Gaussian Process Regression for Fingerprinting based Localization paper_content: Abstract In this paper, Gaussian process regression (GPR) for fingerprinting based localization is presented. In contrast to general regression techniques, the GPR not only infers the posterior received signal strength (RSS) mean but also the variance at each fingerprint location. The GPR does take into account the variance of input i.e., noisy RSS data. The hyper-parameters of GPR are estimated using trust-region-reflective algorithm. The Cramer-Rao bound is analysed to highlight the performance of the parameter estimator. The posterior mean and variance of RSS data is utilized in fingerprinting based localization. The principal component analysis is employed to choose the k strongest wi-fi access points (APs). The performance of the proposed algorithm is validated using using real field deployments. Accuracy improvements of 10% and 30% are observed in two sites compared to the Horus fingerprinting approach. --- paper_title: WiFi-SLAM Using Gaussian Process Latent Variable Models paper_content: WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization. --- paper_title: Improved MDS-based localization paper_content: It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. MDS-MAP is a recent localization method based on multidimensional scaling (MDS). It uses connectivity information - who is within communications range of whom - to derive the locations of the nodes in the network, and can take advantage of additional data, such as estimated distances between neighbors or known positions for certain anchor nodes, if they are available. However, MDS-MAP is an inherently centralized algorithm and is therefore of limited utility in many applications. In this paper, we present a new variant of the MDS-MAP method, which we call MDS-MAP(P) standing for MDS-MAP using patches of relative maps, that can be executed in a distributed fashion. Using extensive simulations, we show that the new algorithm not only preserves the good performance of the original method on relatively uniform layouts, but also performs much better than the original on irregularly-shaped networks. The main idea is to build a local map at each node of the immediate vicinity and then merge these maps together to form a global map. This approach works much better for topologies in which the shortest path distance between two nodes does not correspond well to their Euclidean distance. We also discuss an optional refinement step that improves solution quality even further at the expense of additional computation. --- paper_title: No need to war-drive: Unsupervised indoor localization paper_content: We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone’s accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that “sense” these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1.69m. War-driving is not necessary, neither are floorplans ‐ the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment. --- paper_title: Simulated Annealing based Wireless Sensor Network Localization with Flip Ambiguity Mitigation paper_content: Accurate self-localization capability is highly desirable in wireless sensor networks. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase simulated annealing based localization (SAL) algorithm to address the issue. Simulated annealing (SA) is a technique for combinatorial optimization problems and it is robust against being trapped into local minima. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed scheme gives accurate and consistent location estimates of the nodes and mitigate errors due to flip ambiguities. --- paper_title: Fusion of Radio and Camera Sensor Data for Accurate Indoor Positioning paper_content: Indoor positioning systems have received a lot of attention recently due to their importance for many location-based services, e.g. indoor navigation and smart buildings. Lightweight solutions based on WiFi and inertial sensing have gained popularity, but are not fit for demanding applications, such as expert museum guides and industrial settings, which typically require sub-meter location information. In this paper, we propose a novel positioning system, RAVEL (Radio And Vision Enhanced Localization), which fuses anonymous visual detections captured by widely available camera infrastructure, with radio readings (e.g. WiFi radio data). Although visual trackers can provide excellent positioning accuracy, they are plagued by issues such as occlusions and people entering/exiting the scene, preventing their use as a robust tracking solution. By incorporating radio measurements, visually ambiguous or missing data can be resolved through multi-hypothesis tracking. We evaluate our system in a complex museum environment with dim lighting and multiple people moving around in a space cluttered with exhibit stands. Our experiments show that although the WiFi measurements are not by themselves sufficiently accurate, when they are fused with camera data, they become a catalyst for pulling together ambiguous, fragmented, and anonymous visual tracklets into accurate and continuous paths, yielding typical errors below 1 meter. --- paper_title: Improving Simultaneous Localization and Mapping for pedestrian navigation and automatic mapping of buildings by using online human-based feature labeling paper_content: In this paper we present an extension to odometry based SLAM for pedestrians that incorporates human-reported measurements of recognizable features, or “places” in an environment. The method which we have called “PlaceSLAM” builds on the Simultaneous Localization and Mapping (SLAM) principle in that a spatial representation of such places can be built up during the localization process. We see an important application to be in mapping of new areas by volunteering pedestrians themselves, in particular to improve the accuracy of “FootSLAM” which is based on human step estimation (odometry). We present a description of various flavors of PlaceSLAM and derive a Bayesian formulation and particle filtering implementation for the most general variant. In particular we distinguish between two important cases which depend on whether the pedestrian is required to report a place's identifier or not. Our results based on experimental data show that our approach can significantly improve the accuracy and stability of FootSLAM and this with very little additional complexity. After mapping has been performed, users of such improved FootSLAM maps need not report places themselves. --- paper_title: The horus wlan location determination system paper_content: This report presents a general analysis for the performance of WLAN location determination systems. In particular, we present an analytical method for calculating the average distance error and probability of error of WLAN location determination systems. These expressions are obtained with no assumptions regarding the distribution of signal strength or the probability of the user being at a specific location, which is usually taken to be a uniform distribution over all the possible locations in current WLAN location determination systems. We use these expressions to find the optimal strategy to estimate the user location and to prove formally that probabilistic techniques give more accuracy than deterministic techniques, which has been taken for granted without proof for a long time. The analytical results are validated through simulation experiments. We also study the effect of the assumption that the user position follows a uniform distribution over the set of possible locations on the accuracy of WLAN location determination systems. The results show that knowing the probability distribution of the user position can reduce the number of access points required to obtain a given accuracy. However, with a high density of access points, the performance of a WLAN location determination system is consistent under different probability distributions for the user position. --- paper_title: Place Lab: Device Positioning Using Radio Beacons in the Wild paper_content: Location awareness is an important capability for mobile computing. Yet inexpensive, pervasive positioning—a requirement for wide-scale adoption of location-aware computing—has been elusive. We demonstrate a radio beacon-based approach to location, called Place Lab, that can overcome the lack of ubiquity and high-cost found in existing location sensing approaches. Using Place Lab, commodity laptops, PDAs and cell phones estimate their position by listening for the cell IDs of fixed radio beacons, such as wireless access points, and referencing the beacons' positions in a cached database. We present experimental results showing that 802.11 and GSM beacons are sufficiently pervasive in the greater Seattle area to achieve 20-30 meter median accuracy with nearly 100% coverage measured by availability in people's daily lives. --- paper_title: Crowdsourced indoor localization for diverse devices through radiomap fusion paper_content: Crowdsourcing is an emerging field that allows to tackle difficult problems by soliciting contributions from common people, rather than trained professionals. In the post-pc era, where smartphones dominate the personal computing market offering both constant mobility and large amounts of spatiotemporal sensory data, crowdsourcing is becoming increasingly popular. In this context, crowdsourcing stands as the only viable solution for collecting the large amount of location-related network data required to support location-based services, e.g., the signal strength radiomap of a fingerprinting localization system inside a multi-floor building. However, this benefit does not come for free, because crowdsourcing also poses new challenges in radiomap creation. We focus on the problem of device diversity that occurs frequently as the contributors usually carry heterogeneous mobile devices that report network measurements very differently. We demonstrate with simulations and experimental results that the traditional signal strength values from the surrounding network infrastructure are not suitable for crowdsourcing the radiomap. Moreover, we present an alternative approach, based on signal strength differences, that is far more robust to device variations and maintains the localization accuracy regardless of the number of contributing devices. --- paper_title: Hybrid maximum depth-kNN method for real time node tracking using multi-sensor data paper_content: In this paper, a hybrid maximum depth - k Nearest Neighbour (hybrid MD-kNN) method for real time sensor node tracking and localization is proposed. The method combines two individual location hypothesis functions obtained from generalized maximum depth and generalized kNN methods. The individual location hypothesis functions are themselves obtained from multiple sensors measuring visible light, humidity, temperature, acoustics, and link quality. The hybridMD-kNN method therefore combines the lower computational power of maximum depth and outlier rejection ability of kNN method to realize a robust real time tracking method. Additionally, this method does not require the assumption of an underlying distribution under non-line-of-sight (NLOS) conditions. Additional novelty of this method is the utilization of multivariate data obtained from multiple sensors which has hitherto not been used. The affine invariance property of the hybrid MD-kNN method is proved and its robustness is illustrated in the context of node localization. Experimental results on the Intel Berkeley research data set indicates reasonable improvements over conventional methods available in literature. --- paper_title: Indoor location fingerprinting with heterogeneous clients paper_content: Heterogeneous wireless clients measure signal strength differently. This is a fundamental problem for indoor location fingerprinting, and it has a high impact on the positioning accuracy. Mapping-based solutions have been presented that require manual and error-prone calibration for each new client. This article presents hyperbolic location fingerprinting, which records fingerprints as signal strength ratios between pairs of base stations instead of absolute signal strength values. This article also presents an automatic mapping-based method that avoids calibration by learning from online measurements. The evaluation shows that the solutions can address the signal strength heterogeneity problem without requiring extra manual calibration. --- paper_title: RADAR: an in-building RF-based user location and tracking system paper_content: The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy. --- paper_title: Localization algorithms of Wireless Sensor Networks: a survey paper_content: In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs. --- paper_title: A Particle-Filtering Approach for Vehicular Tracking Adaptive to Occlusions paper_content: In this paper, we propose a new particle-filtering approach for handling partial and total occlusions in vehicular tracking situations. Our proposed method, which is named adaptive particle filter (APF), uses two different operation modes. When the tracked vehicle is not occluded, the APF uses a normal probability density function (pdf) to generate the new set of particles. Otherwise, when the tracked vehicle is under occlusion, the APF generates the new set of particles using a Normal-Rayleigh pdf. Our approach was designed to detect when a total occlusion starts and ends and to resume vehicle tracking after disocclusions. We have tested our APF approach in a number of traffic surveillance video sequences with encouraging results. Our proposed approach tends to be more accurate than comparable methods in the literature, and at the same time, it tends to be more robust to target occlusions. --- paper_title: Survey of Target Tracking Protocols Using Wireless Sensor Network paper_content: Target tracking is one of the non trivial applications of wireless sensor network which is set up in the areas of field surveillance, habitat monitoring, indoor buildings, and intruder tracking. Various approaches have been investigated for tracking the targets, considering diverse metrics like scalability, overheads, energy consumption and target tracking accuracy. This paper for the first time contributes a survey of target tracking protocols for sensor networks and presents their classification in a precise manner. The five main categories explored in this paper are, hierarchical, tree-based, prediction- based, mobicast message-based tracking and hybrid methods. To be more precise, the survey promotes overview of recent research literature along with their performance comparison and evaluation based on simulation with real data. Certainly this task is challenging and not straight forward due to differences in estimations, parameters and performance metrics, therefore the paper concludes with open research challenges. --- paper_title: Dynamic clustering for acoustic target tracking in wireless sensor networks paper_content: We devise and evaluate a fully decentralized, light-weight, dynamic clustering algorithm for target tracking. Instead of assuming the same role for all the sensors, we envision a hierarchical sensor network that is composed of 1) a static backbone of sparsely placed high-capability sensors which assume the role of a cluster head (CH) upon triggered by certain signal events and 2) moderately to densely populated low-end sensors whose function is to provide sensor information to CHs upon request. A cluster is formed and a CH becomes active, when the acoustic signal strength detected by the CH exceeds a predetermined threshold. The active CH then broadcasts an information solicitation packet, asking sensors in its vicinity to join the cluster and provide their sensing information. We address and devise solution approaches (with the use of Voronoi diagram) to realize dynamic clustering: (I1) how CHs operate with one another to ensure that only one CH (preferably the CH that is closes to the target) is active with high probability, (I2) when the active CH solicits for sensor information, instead of having all the sensors in its vicinity reply, only a sufficient number of sensors respond with nonredundant, essential information to determine the target location, and (I3) both the packets that sensors send to their CHs and packets that CHs report to subscribers do not incur significant collision. Through both probabilistic analysis and ns-2 simulation, we use with the use of Voronoi diagram, the CH that is usually closes to the target is (implicitly) selected as the leader and that the proposed dynamic clustering algorithm effectively eliminates contention among sensors and renders more accurate estimates of target locations as a result of better quality data collected and less collision incurred. --- paper_title: A survey: localization and tracking mobile targets through wireless sensors network paper_content: Wireless sensor network applications have been deployed widely. Sensor networks involve sensor nodes which are very small in size. They are low in cost, and have a low battery life. Sensor nodes are capable of solving a variety of collaborative problems, such as, monitoring and surveillance. One of the critical components in wireless sensor networks is the localizing tracking sensor or mobile node. In this paper we will discuss the various location system techniques and categorize these techniques based on the communication between nodes into centralized and decentralized localization techniques. The tracking techniques are categorized into four main types. Each type will be compared and discussed in detail. We will suggest ways of implementing the techniques and finally carry out an evaluation. --- paper_title: Greedy node localization in mobile sensor networks using Doppler frequency shift paper_content: The principle of Doppler frequency shift can be utilized for node localization when mobile nodes are introduced into a sensor network. In this paper, a greedy method for mobile node localization using the principle of Doppler frequency shift is presented. A localization framework which accounts for multiple nodes and multiple reception paths is first developed. Subsequently, a greedy approach to anchor path guidance for maximal node localization is proposed. The method is advantageous both in terms of energy consumption and the number of nodes localized. Experiments on mobile node localization are conducted to illustrate the effectiveness of the proposed method. --- paper_title: Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network paper_content: Two novel distributed particle filters with Gaussian mixer approximation are proposed to localize and track multiple moving targets in a wireless sensor network. The distributed particle filters run on a set of uncorrelated sensor cliques that are dynamically organized based on moving target trajectories. These two algorithms differ in how the distributive computing is performed. In the first algorithm, partial results are updated at each sensor clique sequentially based on partial results forwarded from a neighboring clique and local observations. In the second algorithm, all individual cliques compute partial estimates based only on local observations in parallel, and forward their estimates to a fusion center to obtain final output. In order to conserve bandwidth and power, the local sufficient statistics (belief) is approximated by a low dimensional Gaussian mixture model (GMM) before propagating among sensor cliques. We further prove that the posterior distribution estimated by distributed particle filter convergence almost surely to the posterior distribution estimated from a centralized Bayesian formula. Moreover, a data-adaptive application layer communication protocol is proposed to facilitate sensor self-organization and collaboration. Simulation results show that the proposed DPF with GMM approximation algorithms provide robust localization and tracking performance at much reduced communication overhead. --- paper_title: Classification of Object Tracking Techniques in Wireless Sensor Networks paper_content: Object tracking is one of the killer applications for wireless sensor networks (WSN) in which the network of wireless sensors is assigned the task of tracking a particular object. The network employs the object tracking techniques to continuously report the position of the object in terms of Cartesian coordinates to a sink node or to a central base station. A family tree of object tracking techniques has been prepared.In this paper we have summarized the object tracking techniques available so far in wireless sensor networks. --- paper_title: Monte Carlo localization for mobile wireless sensor networks paper_content: Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors' radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy. --- paper_title: Distributed tracking in wireless ad hoc sensor networks paper_content: Abstract : Target tracking is an important application for wireless ad hoc sensor networks. Because of the energy and communication constraints imposed by the size of the sensors, the processing has to be distributed over the sensor nodes. This paper discusses issues associated with distributed multiple target tracking for ad hoc sensor networks and examines the applicability of tracking algorithms developed for traditional networks of large sensors. when data association is not an issue, the standard pre- predict/update structure in single target tracking can be used to assign individual tracks to the sensor nodes based on their locations. Track ownership will have to be carefully migrated, using for example information driven sensor tasking, to minimize the need for communication when targets move. when data association is needed in tracking multiple interacting targets, clusters of tracks should be assigned to groups of collaborating nodes. Some recent examples of this type of distributed processing are given. Keywords: Wireless ad hoc sensor networks, multiple target tracking, distributed tracking --- paper_title: A Hybrid Cluster-Based Target Tracking Protocol for Wireless Sensor Networks paper_content: Target tracking is a typical and important application of wireless sensor networks (WSNs). In consideration of the network scalability and energy efficiency for target tracking in large-scale WSNs, it has been employed as an effective solution by organizing the WSNs into clusters. However, tracking a moving target in cluster-based WSNs suffers a boundary problem when the target moves across or along the boundaries of clusters, as the static cluster membership prevents sensors in different clusters from sharing information. In this paper, we propose a novel mobility management protocol, called hybrid cluster-based target tracking (HCTT), which integrates on-demand dynamic clustering into a cluster-based WSN for target tracking. By constructing on-demand dynamic clusters at boundary regions, nodes from different static clusters that detect the target can temporarily share information, and the tracking task can be handed over smoothly from one static cluster to another. As the target moves, static clusters and on-demand dynamic clusters alternately manage the target tracking task. Simulation results show that the proposed protocol performs better in tracking the moving target when compared with other typical target tracking protocols. --- paper_title: A survey of secure target tracking algorithms for wireless sensor networks paper_content: Tracking a target as it moves in monitored area has become an increasingly important application for wireless sensor networks (WSNs). Target tracking algorithms continuously report the position of the target in terms of its coordinates to a sink node or a central base station. Due to the rapid development of WSNs, there is no standardized classification of target tracking algorithms. Some of those classifications consider scalability, energy consumption and accuracy; other classification considers network topology, position of target, mobility of target/object etc. In this paper, we have considered target tracking algorithms of WSNs from the security point of view. We have compared and discussed problem of security in the most important target tracking algorithms for WSNs. To the best of our knowledge, this is the first study that analyses the target tracking algorithms in terms of security. --- paper_title: A protocol for tracking mobile targets using sensor networks paper_content: With recent advances in device fabrication technology, economical deployment of large scale sensor networks, capable of pervasive monitoring and control of physical systems have become possible. Scalability, low overhead anti distributed functionality are some of the key requirements for any protocol designed for such large scale sensor networks. In this paper, we present a protocol, Distributed Predictive Tracking, for one of the most likely applications for sensor networks: tracking moving targets. The protocol uses a clustering based approach for scalability and a prediction based tracking mechanism to provide a distributed and energy efficient solution. The protocol is robust against node or prediction failures which may result in temporary loss of the target and recovers from such scenarios quickly and with very little additional energy use. Using simulations we show that the proposed architecture is able to accurately track targets with random movement patterns with accuracy over a wide range of target speeds. --- paper_title: Spatiotemporal multicast in sensor networks paper_content: Sensor networks often involve the monitoring of mobile phenomena. We believe this task can be facilitated by a spatiotemporal multicast protocol which we call "mobicast". Mobicast is a novel spatiotemporal multicast protocol that distributes a message to nodes in a delivery zone that evolves over time in some predictable manner. A key advantage of mobicast lies in its ability to provide reliable and just-in-time message delivery to mobile delivery zones on top of a random network topology. Mobicast can in theory achieve good spatiotemporal delivery guarantees by limiting communication to a mobile forwarding zone whose size is determined by the global worst-case value associated with a compactness metric defined over the geometry of the network (under a reasonable set of assumptions). In this work, we first studied the compactness properties of sensor networks with uniform distribution. The results of this study motivate three approaches for improving the efficiency of spatiotemporal multicast in such networks. First, spatiotemporal multicast protocols can exploit the fundamental tradeoff between delivery guarantees and communication overhead in spatiotemporal multicast. Our results suggest that in such networks, a mobicast protocol can achieve relatively high savings in message forwarding overhead by slightly relaxing the delivery guarantee, e.g., by optimistically choosing a forwarding zone that is smaller than the one needed for a 100% delivery guarantee. Second, spatiotemporal multicast may exploit local compactness values for higher efficiency for networks with non uniform spatial distribution of compactness. Third, for random uniformly distributed sensor network deployment, one may choose a deployment density to best support spatiotemporal communication. We also explored all these directions via simulation and results are presented in this paper. --- paper_title: Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization paper_content: Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m. --- paper_title: An Adaptive Particle Filter for Indoor Robot Localization paper_content: This paper develops an adaptive particle filter for indoor mobile robot localization, in which two different resampling operations are implemented to adjust the number of particles for fast and reliable computation. Since the weight updating is usually much more computationally intensive than the prediction, the first resampling-procedure so-called partial resampling is adopted before the prediction step, which duplicates the large weighted particles while reserves the rest obtaining better estimation accuracy and robustness. The second resampling, adopted before the updating step, decreases the number of particles through particle merging to save updating computation. In addition to speeding up the filter, sample degeneracy and sample impoverishment are counteracted. Simulations on a typical 1D model and for mobile robot localization are presented to demonstrate the validity of our approach. --- paper_title: Performance analysis based on least squares and extended Kalman filter for localization of static target in wireless sensor networks paper_content: Abstract Wireless sensor network localization is an essential problem that has attracted increasing attention due to wide demands such as in-door navigation, autonomous vehicle, intrusion detection, and so on. With the a priori knowledge of the positions of sensor nodes and their measurements to targets in the wireless sensor networks (WSNs), i.e. posterior knowledge, such as distance and angle measurements, it is possible to estimate the position of targets through different algorithms. In this contribution, two commonly-used approaches based on least-squares and Kalman filter are described and analyzed for localization of one static target in the WSNs with distance, angle, or both distance and angle measurements, respectively. Noting that the measurements of these sensors are generally noisy of certain degree, it is crucial and interesting to analyze how the accuracy of localization is affected by the sensor errors and the sensor network, which may help to provide guideline on choosing the specification of sensors and designing the sensor network. In addition, the problem of optimal sensor placement is also addressed to minimize the localization error. To this end, theoretical analysis have been made for the different methods based on three typical types of measurement noise: bounded noise, uniformly distributed noise, and Gaussian white noise. Simulation results illustrate the performance comparison of these different methods, the theoretical analysis and simulations and the optimal sensor geometry which may be meaningful and guideful in practice. --- paper_title: Dynamic clustering for object tracking in wireless sensor networks paper_content: Object tracking is an important feature of the ubiquitous society and also a killer application of wireless sensor networks. Nowadays, there are many researches on object tracking in wireless sensor networks under practice, however most of them cannot effectively deal with the trade-off between missing-rate and energy efficiency. In this paper, we propose a dynamic clustering mechanism for object tracking in wireless sensor networks. With forming the cluster dynamically according to the route of moving, the proposed method can not only decrease the missing-rate but can also decrease the energy consumption by reducing the number of nodes that participate in tracking and minimizing the communication cost, thus can enhance the lifetime of the whole sensor networks. The simulation result shows that our proposed method achieves lower energy consumption and lower missing-rate. --- paper_title: Tracking and Predicting Moving Targets in Hierarchical Sensor Networks paper_content: Target tracking is an important application of newly developed wireless sensor networks (WSN). Much work has been done on this topic using a plane network architecture. We propose a scheme, namely hierarchical prediction strategy (HPS), for target prediction in hierarchical sensor networks. The network is divided into clusters, which are composed of one cluster-head and many normal nodes, by Voronoi division. For an existing target, cluster-heads only selectively activate nearby sensor nodes to perform tracking. Moreover, Recursive Least Square technique is used to predict the target trajectory and help activate next-round sensor nodes. Extended simulations show the properties of the proposed network architecture and the efficiency of the prediction scheme. --- paper_title: Voronoi-Based Sensor Network Engineering for Target Tracking Using Wireless Sensor Networks paper_content: Recent advances in integrated electronic devices motivated the use of wireless sensor networks in many applications including target surveillance and tracking. A number of sensor nodes are scattered within a sensitive region to detect the presence of intruders and forward subsequent events to the analysis center(s). Obviously, the sensor deployment should guarantee an optimal event detection rate. This paper proposes a tracking framework based on Voronoi tessellations. Two mobility models are proposed to control the coverage degree according to target presence. The objective is to set a non-uniform coverage within the monitored zone to allow detecting the target by multiple sensor nodes. Moreover, we introduce an algorithm to discover redundant nodes (which do not provide additional information about target position). This algorithm is shown to be effective in reducing the energy consumption using an activity scheduling approach. --- paper_title: Localization for mobile sensor networks paper_content: Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions. --- paper_title: A Survey on Target Tracking Techniques in Wireless Sensor Networks paper_content: Target Tracking as it moves through a sensor network has become an increasingly important application in Wireless Sensor Networks. This paper examines some of the target tracking techniques in use today. An analysis of each technique is presented along with it advantages, problems and possible improvements. There are seven main categories explored in this paper. The survey promotes overview of recent research literature along with their performance comparison and evaluation. --- paper_title: Distributed tracking and classification of targets with sensor networks paper_content: Localization and tracking of target is an important application of the wireless sensor network. In this paper, we propose to apply a classification algorithm to sensor network nodes aimed at target tracking. Localization and tracking is based on a distributed method that enables us to simplify the signal processing and makes a more robust system. Also we address to group sensors, each group or cluster is led by one of them (leader node/sensor). This sensor is responsible for processing all the information about the target and estimate its position. Simulation results show that this classification algorithm reduces the estimate error in tracking targets. --- paper_title: Prediction-based strategies for energy saving in object tracking sensor networks paper_content: In order to fully realize the potential of sensor networks, energy awareness should be incorporated into every stage of the network design and operation. In this paper, we address the energy management issue in a sensor network killer application - object tracking sensor networks (OTSNs). Based on the fact that the movements of the tracked objects are sometimes predictable, we propose a prediction-based energy saving scheme, called PES, to reduce the energy consumption for object tracking under acceptable conditions. We compare PES against the basic schemes we proposed in the paper to explore the conditions under which PES is most desired. We also test the effect of some parameters related to the system workload, object moving behavior and sensing operations on PES through extensive simulation. Our results show that PES can save significant energy under various conditions. --- paper_title: Dynamic clustering for acoustic target tracking in wireless sensor networks paper_content: We devise and evaluate a fully decentralized, light-weight, dynamic clustering algorithm for target tracking. Instead of assuming the same role for all the sensors, we envision a hierarchical sensor network that is composed of 1) a static backbone of sparsely placed high-capability sensors which assume the role of a cluster head (CH) upon triggered by certain signal events and 2) moderately to densely populated low-end sensors whose function is to provide sensor information to CHs upon request. A cluster is formed and a CH becomes active, when the acoustic signal strength detected by the CH exceeds a predetermined threshold. The active CH then broadcasts an information solicitation packet, asking sensors in its vicinity to join the cluster and provide their sensing information. We address and devise solution approaches (with the use of Voronoi diagram) to realize dynamic clustering: (I1) how CHs operate with one another to ensure that only one CH (preferably the CH that is closes to the target) is active with high probability, (I2) when the active CH solicits for sensor information, instead of having all the sensors in its vicinity reply, only a sufficient number of sensors respond with nonredundant, essential information to determine the target location, and (I3) both the packets that sensors send to their CHs and packets that CHs report to subscribers do not incur significant collision. Through both probabilistic analysis and ns-2 simulation, we use with the use of Voronoi diagram, the CH that is usually closes to the target is (implicitly) selected as the leader and that the proposed dynamic clustering algorithm effectively eliminates contention among sensors and renders more accurate estimates of target locations as a result of better quality data collected and less collision incurred. --- paper_title: Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks paper_content: Target tracking in wireless sensor networks can be considered as a milestone of a wide range of applications to permanently report, through network sensors, the positions of a mobile target to the base station during its move across a certain path. While tracking a mobile target, a lot of open challenges arise and need to be investigated and maintained which mainly include energy efficiency and tracking accuracy. In this paper, we propose three algorithms for tracking a mobile target in wireless sensor network utilizing cluster-based architecture, namely adaptive head, static head, and selective static head. Our goal is to achieve a promising tracking accuracy and energy efficiency by choosing the candidate sensor nodes nearby the target to participate in the tracking process while preserving the others in sleep state. Through Matlab simulation, we investigate the performance of the proposed algorithms in terms of energy consumption, tracking error, sensor density, as well as target speed. The results show that the adaptive head is the most efficient algorithm in terms of energy consumption while static and selective static heads algorithms are preferred as far as the tracking error is concerned especially when the target moves rapidly. Furthermore, the effectiveness of our proposed algorithms is verified through comparing their results with those obtained from previous algorithms. --- paper_title: Prediction-based cluster management for target tracking in wireless sensor networks paper_content: The key impediments to a successful wireless sensor network (WSN) application are the energy and the longevity constraints of sensor nodes. Therefore, two signal processing oriented cluster management strategies, the proactive and the reactive cluster management, are proposed to efficiently deal with these constraints. The former strategy is designed for heterogeneous WSNs, where sensors are organized in a static clustering architecture. A non-myopic cluster activation rule is realized to reduce the number of hand-off operations between clusters, while maintaining desired estimation accuracy. The proactive strategy minimizes the hardware expenditure and the total energy consumption. On the other hand, the main concern of the reactive strategy is to maximize the network longevity of homogeneous WSNs. A Dijkstra-like algorithm is proposed to dynamically form active cluster based on the relation between the predictive target distribution and the candidate sensors, considering both the energy efficiency and the data relevance. By evenly distributing the energy expenditure over the whole network, the objective of maximizing the network longevity is achieved. The simulations evaluate and compare the two proposed strategies in terms of tracking accuracy, energy consumption and execution time. Copyright © 2010 John Wiley & Sons, Ltd. --- paper_title: CODA: A Continuous Object Detection and Tracking Algorithm for Wireless Ad Hoc Sensor Networks paper_content: Wireless sensor networks make possible many new applications in a wide range of application domains. One of the primary applications of such networks is the detection and tracking of continuously moving objects, such as wild fires, biochemical materials, and so forth. This study supports such applications by developing a continuous object detection and tracking algorithm, designated as CODA, based on a hybrid static/dynamic clustering technique. The CODA mechanism enables each sensor node to detect and track the moving boundaries of objects in the sensing field. The numerical results obtained using a Qualnet simulator confirm the effectiveness and robustness of the proposed approach. --- paper_title: Target localization based on energy considerations in distributed sensor networks paper_content: Wireless distributed sensor networks (DSNs) are important for a number of strategic applications such as coordinated target detection, surveillance, and localization. Energy is a critical resource in wireless sensor networks and system lifetime needs to be prolonged through the use of energy-conscious sensing strategies during system operation. We propose an energy-aware target detection and localization strategy for cluster-based wireless sensor networks. The proposed method is based on an a posteriori algorithm with a two-step communication protocol between the cluster head and the sensors within the cluster. Based on a limited amount of data received from the sensor nodes, the cluster head executes a localization procedure to determine the subset of sensors that must be queried for detailed target information. This approach reduces both energy consumption and communication bandwidth requirements, and prolongs the lifetime of the wireless sensor network. Simulation results show that a large amount of energy is saved during target localization using this strategy. --- paper_title: A dynamic tracking mechanism for mobile target in wireless sensor networks paper_content: How to save energy for target tracking in WSNs is an important issue. The basic idea of energy-saving method is using sleep mechanism. However, if nodes enter sleep state too much, targets may lose easily from the sleeping nodes. It is high consuming the energy to find back these losing targets. Thus, it would notice accuracy of tracing targets when saving energy. In this proposal, it offers a scheme about trace of moving target. It uses cluster scheme and algorithm, which apply different strengths and energy-consuming according to different possibility of losing targets. The possibility of lost is aimed to different acceleration of targets and wake up different number of nodes. Lowering losing rate of target can own energy-saving and improved performance. --- paper_title: Information-driven dynamic sensor collaboration paper_content: This article overviews the information-driven approach to sensor collaboration in ad hoc sensor networks. The main idea is for a network to determine participants in a "sensor collaboration" by dynamically optimizing the information utility of data for a given cost of communication and computation. A definition of information utility is introduced, and several approximate measures of the information utility are developed for reasons of computational tractability. We illustrate the use of this approach using examples drawn from tracking applications. --- paper_title: RARE: An Energy-Efficient Target Tracking Protocol for Wireless Sensor Networks paper_content: Energy efficiency for target tracking in wireless sensor networks is very important and can be improved by reducing the number of nodes involved in communications. We propose two algorithms, RARE-area and RARE-node to reduce the number of nodes participating in tracking and so increase energy efficiency. The RARE-area algorithm ensures that only nodes that receive a given quality of data participate in tracking and the RARE-node algorithm ensures that any nodes with redundant information do not participate in tracking. Simulation studies show significant energy savings are obtained with implementation of either the RARE-area algorithm alone or both RARE-area and RARE-node algorithms together. --- paper_title: Distributed event localization and tracking with wireless sensors ⋆ paper_content: In this paper we present the distributed event localization and tracking algorithm DELTA that solely depends on light measurements. Based on this information and the positions of the sensors, DELTA is able to track a moving person equipped with a flashlight by dynamically building groups and electing well located nodes as group leaders. Moreover, DELTA supports object localization. The gathered data is sent to a monitoring entity in a fixed network which can apply pattern recognition techniques to determine the legitimacy of the moving person. DELTA enables object tracking with minimal constraints on both sensor hardware and the moving object. We show the feasibility of the algorithm running on the limited hardware of an existing sensor platform. --- paper_title: Distributed tracking and classification of targets with sensor networks paper_content: Localization and tracking of target is an important application of the wireless sensor network. In this paper, we propose to apply a classification algorithm to sensor network nodes aimed at target tracking. Localization and tracking is based on a distributed method that enables us to simplify the signal processing and makes a more robust system. Also we address to group sensors, each group or cluster is led by one of them (leader node/sensor). This sensor is responsible for processing all the information about the target and estimate its position. Simulation results show that this classification algorithm reduces the estimate error in tracking targets. --- paper_title: Survey of Target Tracking Protocols Using Wireless Sensor Network paper_content: Target tracking is one of the non trivial applications of wireless sensor network which is set up in the areas of field surveillance, habitat monitoring, indoor buildings, and intruder tracking. Various approaches have been investigated for tracking the targets, considering diverse metrics like scalability, overheads, energy consumption and target tracking accuracy. This paper for the first time contributes a survey of target tracking protocols for sensor networks and presents their classification in a precise manner. The five main categories explored in this paper are, hierarchical, tree-based, prediction- based, mobicast message-based tracking and hybrid methods. To be more precise, the survey promotes overview of recent research literature along with their performance comparison and evaluation based on simulation with real data. Certainly this task is challenging and not straight forward due to differences in estimations, parameters and performance metrics, therefore the paper concludes with open research challenges. --- paper_title: Distributed tracking in wireless ad hoc sensor networks paper_content: Abstract : Target tracking is an important application for wireless ad hoc sensor networks. Because of the energy and communication constraints imposed by the size of the sensors, the processing has to be distributed over the sensor nodes. This paper discusses issues associated with distributed multiple target tracking for ad hoc sensor networks and examines the applicability of tracking algorithms developed for traditional networks of large sensors. when data association is not an issue, the standard pre- predict/update structure in single target tracking can be used to assign individual tracks to the sensor nodes based on their locations. Track ownership will have to be carefully migrated, using for example information driven sensor tasking, to minimize the need for communication when targets move. when data association is needed in tracking multiple interacting targets, clusters of tracks should be assigned to groups of collaborating nodes. Some recent examples of this type of distributed processing are given. Keywords: Wireless ad hoc sensor networks, multiple target tracking, distributed tracking --- paper_title: Dual prediction-based reporting for object tracking sensor networks paper_content: As one of the wireless sensor network killer applications, object tracking sensor networks (OTSNs) disclose many opportunities for energy-aware system design and implementations. We investigate prediction-based approaches for performing energy efficient reporting in OTSNs. We propose a dual prediction-based reporting mechanism (called DPR), in which both sensor nodes and the base station predict the future movements of the mobile objects. Transmissions of sensor readings are avoided as long as the predictions are consistent with the real object movements. DPR achieves energy efficiency by intelligently trading off multihop/long-range transmissions of sensor readings between sensor nodes and the base station with one-hop/short-range communications of object movement history among neighbor sensor nodes. We explore the impact of several system parameters and moving behavior of tracked objects on DPR performance, and also study two major components of DPR: prediction models and location models through simulations. Our experimental results show that DPR is able to achieve considerable energy savings under various conditions and outperforms existing reporting mechanisms. --- paper_title: Energy-aware location error handling for object tracking applications in wireless sensor networks paper_content: Developing an efficient object tracking system has been an interesting challenge in wireless sensor network communities. Due to the severe resource constraints of sensor hardware, the accuracy of the tracking system could be compromised by the processing power or energy consumption. A sophisticated tracking algorithm is therefore not applicable to sensor applications, and any tracking system should explicitly consider the energy issue. In this paper, we present energy-aware location error handling techniques, namely error avoidance and error correction, to prevent and handle errors efficiently. Real situations such as an unexpected change in the mobile event's direction, failure of event detection, or transmission failure of an error message are considered in the design of the proposed mechanisms. The prototype system is built with real sensor hardware, and the functionality is validated in real experiments. The experimental evaluation, together with simulation analysis, shows that the proposed mechanism saves energy while achieving good tracking accuracy. --- paper_title: Optimal tracking interval for predictive tracking in wireless sensor network paper_content: An important application of wireless sensor networks is tracking moving objects. Prediction-based techniques have been proposed to reduce the power consumption in wireless sensor networks by limiting the sensor active time. This paper proposes a quantitative method to optimize the power efficiency by analyzing the effect of prediction on the energy consumption in such networks. To our best knowledge, our efforts are the first attempt made to calculate the optimal tracking interval for a given predictive tracking algorithm. Based on this method, the lifetime and power efficiency of a sensor network can be effectively improved. --- paper_title: A protocol for tracking mobile targets using sensor networks paper_content: With recent advances in device fabrication technology, economical deployment of large scale sensor networks, capable of pervasive monitoring and control of physical systems have become possible. Scalability, low overhead anti distributed functionality are some of the key requirements for any protocol designed for such large scale sensor networks. In this paper, we present a protocol, Distributed Predictive Tracking, for one of the most likely applications for sensor networks: tracking moving targets. The protocol uses a clustering based approach for scalability and a prediction based tracking mechanism to provide a distributed and energy efficient solution. The protocol is robust against node or prediction failures which may result in temporary loss of the target and recovers from such scenarios quickly and with very little additional energy use. Using simulations we show that the proposed architecture is able to accurately track targets with random movement patterns with accuracy over a wide range of target speeds. --- paper_title: On localized prediction for power efficient object tracking in sensor networks paper_content: Energy is one of the most critical constraints for sensor network applications. In this paper, we exploit the localized prediction paradigm for power-efficient object tracking sensor network. Localized prediction consists of a localized network architecture and a prediction mechanism called dual prediction, which achieve power savings by allowing most of the sensor nodes stay in sleep mode and by reducing the amount of long-range transmissions. Performance evaluation, based on mathematical analysis, shows that localized prediction can significantly reduce the power consumption in object tracking sensor networks. --- paper_title: Prediction-based strategies for energy saving in object tracking sensor networks paper_content: In order to fully realize the potential of sensor networks, energy awareness should be incorporated into every stage of the network design and operation. In this paper, we address the energy management issue in a sensor network killer application - object tracking sensor networks (OTSNs). Based on the fact that the movements of the tracked objects are sometimes predictable, we propose a prediction-based energy saving scheme, called PES, to reduce the energy consumption for object tracking under acceptable conditions. We compare PES against the basic schemes we proposed in the paper to explore the conditions under which PES is most desired. We also test the effect of some parameters related to the system workload, object moving behavior and sensing operations on PES through extensive simulation. Our results show that PES can save significant energy under various conditions. --- paper_title: DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks paper_content: Most existing work on sensor networks concentrates on finding efficient ways to forward data from the information source to the data centers, and not much work has been done on collecting local data and generating the data report. This paper studies this issue by proposing techniques to detect and track a mobile target. We introduce the concept of dynamic convoy tree-based collaboration, and formalize it as a multiple objective optimization problem which needs to find a convoy tree sequence with high tree coverage and low energy consumption. We propose an optimal solution which achieves 100% coverage and minimizes the energy consumption under certain ideal situations. Considering the real constraints of a sensor network, we propose several practical implementations: the conservative scheme and the prediction-based scheme for tree expansion and pruning; the sequential and the localized reconfiguration schemes for tree reconfiguration. Extensive experiments are conducted to compare the practical implementations and the optimal solution. The results show that the prediction-based scheme outperforms the conservative scheme and it can achieve similar coverage and energy consumption to the optimal solution. The experiments also show that the localized reconfiguration scheme outperforms the sequential reconfiguration scheme when the node density is high, and the trend is reversed when the node density is low. --- paper_title: Mobile object tracking in wireless sensor networks paper_content: Wireless sensor network is an emerging technology that enables remote monitoring objects and environment. This paper proposes a protocol to track a mobile object in a sensor network dynamically. The previous researches almost focus on how to track object accurately and they do not consider the query for mobile sources. Additionally, they need not report the tracking information to user. The work is concentrated on mobile user how to query target tracks and obtain the target position effectively. The mobile user can obtain the tracking object position without broadcast query. The user is moving and approaching the target when he/she knows the target's position. Wireless sensor networks can assist user to detect target as well as keep the movement information of the target. Sensor nodes establish face structure to track the designated target and keep target tracks. The source follows the tracks to approaching target. To chase the object quick and maintain an accurate tracking route, the sensors cooperate together to shorten the route between target and source dynamically. A source can quickly approach a target along a shortened route. Finally, we compare the proposed scheme with three flooding-based query methods. By the simulation results, the proposed protocol has better performance than that of flooding-based query methods. --- paper_title: Efficient location tracking using sensor networks paper_content: We apply sensor networks to the problem of tracking moving objects. We describe a publish-and-subscribe tracking method, called scalable tracking using networked sensors (STUN), that scales well to large numbers of sensors and moving objects by using hierarchy. We also describe a method, called drain-and-balance (DAB), for building efficient tracking hierarchies, computed from expected characteristics of the objects movement patterns. DAB is shown to perform well by running it on 1D and 2D sensor network topologies, and comparing it to schemes, which do not utilize movement information. --- paper_title: Efficient in-network moving object tracking in wireless sensor networks paper_content: The rapid progress of wireless communication and embedded microsensing MEMS technologies has made wireless sensor networks possible. In light of storage in sensors, a sensor network can be considered as a distributed database, in which one can conduct in-network data processing. An important issue of wireless sensor networks is object tracking, which typically involves two basic operations: update and query. This issue has been intensively studied in other areas, such as cellular networks. However, the in-network processing characteristic of sensor networks has posed new challenges to this issue. In this paper, we develop several tree structures for in-network object tracking which take the physical topology of the sensor network into consideration. The optimization process has two stages. The first stage tries to reduce the location update cost based on a deviation-avoidance principle and a highest-weight-first principle. The second stage further adjusts the tree obtained in the first stage to reduce the query cost. The way we model this problem allows us to analytically formulate the cost of object tracking given the update and query rates of objects. Extensive simulations are conducted, which show a significant improvement over existing solutions --- paper_title: Voronoi-Based Sensor Network Engineering for Target Tracking Using Wireless Sensor Networks paper_content: Recent advances in integrated electronic devices motivated the use of wireless sensor networks in many applications including target surveillance and tracking. A number of sensor nodes are scattered within a sensitive region to detect the presence of intruders and forward subsequent events to the analysis center(s). Obviously, the sensor deployment should guarantee an optimal event detection rate. This paper proposes a tracking framework based on Voronoi tessellations. Two mobility models are proposed to control the coverage degree according to target presence. The objective is to set a non-uniform coverage within the monitored zone to allow detecting the target by multiple sensor nodes. Moreover, we introduce an algorithm to discover redundant nodes (which do not provide additional information about target position). This algorithm is shown to be effective in reducing the energy consumption using an activity scheduling approach. --- paper_title: Evaluations of target tracking in wireless sensor networks paper_content: Target tracking is one of the most important applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to maximize the lifetime of the sensor network. There exists a demand for self-organizing and routing capabilities in the sensor network. Existing methods attempting to achieve these requirements, such as the LEACH-based algorithms, however, suffer either redundancy in data and sensor node deployment, or complex computation incurred in the sensor nodes. Those drawbacks result in energy use inefficiency and/or complex computation overhead. OCO, or Optimized Communication and Organization, is an algorithm that ensures maximum accuracy of target tracking, efficient energy dissipation, and low computation overhead on the sensor nodes. Simulation evaluations of OCO are compared with other two methods under various scenarios. --- paper_title: Structures for in-network moving object tracking in wireless sensor networks paper_content: One important application of wireless sensor networks is the tracking of moving objects. The recent progress has made it possible for tiny sensors to have more computing power and storage space. Therefore, a sensor network can be considered as a distributed database, on which one can conduct in-network data processing. This paper considers in-network moving object tracking in a sensor network. This typically consists of two operations: location update and query. We propose a message-pruning tree structure that is an extension of the earlier work (H.T. Kung and D. Vlah, March 2003), which assumes the existence of a logical structure to connect sensors in the network. We formulate this problem as an optimization problem. The formulation allows us to take into account the physical structure of the sensor network, thus leading to more efficient solutions than in the previous paper of H.T. Kung and D. Vlah (March 2003) in terms of communication costs. We evaluate updating and querying costs through simulations. --- paper_title: Distributed sensor activation algorithm for target tracking with binary sensor networks paper_content: Target tracking with wireless sensor networks (WSNs) has been a hot research topic recently. Many works have been done to improve the algorithms for localization and prediction of a moving target with smart sensors. However, the results are frequently difficult to implement because of hardware limitations. In this paper, we propose a practical distributed sensor activation algorithm (DSA2) that enables reliable tracking with the simplest binary-detection sensors. In this algorithm, all sensors in the field are activated with a probability to detect targets or sleep to save energy, the schedule of which depends on their neighbor sensors' behaviors. Extensive simulations are also shown to demonstrate the effectiveness of the proposed algorithm. Great improvement in terms of energy-quality tradeoff and excellent robustness of the algorithm are also emphasized in the simulations. --- paper_title: Adaptive Sensor Activation Algorithm for Target Tracking in Wireless Sensor Networks paper_content: Target tracking is an important application of wireless sensor networks where energy conservation plays an important role. In this paper, we propose an energy-efficient sensor activation protocol based on predicted region technique, called predicted region sensor activation algorithm (PRSA). The proposed algorithm predicts the moving region of target in the next time interval instead of predicting the accurate position, by analyzing current location and velocity of the target. We take these nodes within the predicted region as waiting-activation nodes and establish activation strategy. The fewest essential number of sensor nodes within the predicted region will be activated to monitor the target. Thus, the number of nodes that was involved in tracking the target will be decreased to save energy and prolong the network’s operational lifetime. The simulation results demonstrate the effectiveness of the proposed algorithm. --- paper_title: Quality Tradeoffs in Object Tracking with Duty-Cycled Sensor Networks paper_content: Extending the lifetime of wireless sensor networks requires energy-conserving operations such as duty-cycling. However, such operations may impact the effectiveness of high fidelity real-time sensing tasks, such as object tracking, which require high accuracy and short response times. In this paper, we quantify the influence of different duty-cycle schemes on the efficiency of bearings-only object tracking. Specifically, we use the Maximum Likelihood localization technique to analyze the accuracy limits of object location estimates under different response latencies considering variable network density and duty-cycle parameters. Moreover, we study the tradeoffs between accuracy and response latency under various scenarios and motion patterns of the object. We have also investigated the effects of different duty-cycled schedules on the tracking accuracy using acoustic sensor data collected at Aberdeen Proving Ground, Maryland, by the U.S. Army Research Laboratory (ARL). --- paper_title: Space-time Coordinated Distributed Sensing Algorithms for Resource Efficient Narrowband Target Localization and Tracking paper_content: Distributed sensing has been used for enhancing signal to noise ratios for space-time localization and tracking of remote objects using phased array antennas, sonar, and radio signals. The use of these technologies in identifying mobile targets in a field, emitting acoustic signals, using a network of low-cost narrow band acoustic micro-sensing devices randomly dispersed over the region of interest, presents unique challenges. The effects of wind, turbulence, and temperature gradients and other environmental effects can decrease the signal to noise ratio by introducing random errors that cannot be removed through calibration. This paper presents methods for dynamic distributed signal processing to detect, identify, and track targets in noisy environments with limited resources. Specifically, it evaluates the noise tolerance of adaptive beamforming and compares it to other distributed sensing approaches. Many source localization and direction-of-arrival (DOA) estimation methods based on beamforming using acoustic sensor array have been proposed. We use the approximate maximum likelihood parameter estimation method to perform DOA estimation of the source in the frequency domain. Generally, sensing radii are large and data from the nodes are transmitted over the network to a centralized location where beamforming is done. These methods therefore depict low tolerance to environmental noise. Knowledge based localized distributed processing methods have also been developed for distributed in-situ localization and target tracking in these environments. These methods, due to their reliance only on local sensing, are not significantly affected by spatial perturbations and are robust in tracking targets in low SNR environments. Specifically, Dynamic Space-time Clustering (DSTC)-based localization and tracking algorithm has demonstrated orders of magnitude improvement in noise tolerance with nominal impact on performance. We also propose hybrid algorithms for energy efficient robust performance in very noisy environments. This paper compares the performance of hybrid algorithms with sparse beamforming nodes supported by randomly dispersed DSTC nodes to that of beamforming and DSTC algorithms. Hybrid algorithms achieve relative high accuracy in noisy environments with low energy consumption. Sensor data from a field test in the Marine base at 29 Palms, CA, were analyzed for validating the results in this paper. The results were compared to “ground truth” data obtained from GPS receivers on the vehicles. --- paper_title: Spatiotemporal multicast in sensor networks paper_content: Sensor networks often involve the monitoring of mobile phenomena. We believe this task can be facilitated by a spatiotemporal multicast protocol which we call "mobicast". Mobicast is a novel spatiotemporal multicast protocol that distributes a message to nodes in a delivery zone that evolves over time in some predictable manner. A key advantage of mobicast lies in its ability to provide reliable and just-in-time message delivery to mobile delivery zones on top of a random network topology. Mobicast can in theory achieve good spatiotemporal delivery guarantees by limiting communication to a mobile forwarding zone whose size is determined by the global worst-case value associated with a compactness metric defined over the geometry of the network (under a reasonable set of assumptions). In this work, we first studied the compactness properties of sensor networks with uniform distribution. The results of this study motivate three approaches for improving the efficiency of spatiotemporal multicast in such networks. First, spatiotemporal multicast protocols can exploit the fundamental tradeoff between delivery guarantees and communication overhead in spatiotemporal multicast. Our results suggest that in such networks, a mobicast protocol can achieve relatively high savings in message forwarding overhead by slightly relaxing the delivery guarantee, e.g., by optimistically choosing a forwarding zone that is smaller than the one needed for a 100% delivery guarantee. Second, spatiotemporal multicast may exploit local compactness values for higher efficiency for networks with non uniform spatial distribution of compactness. Third, for random uniformly distributed sensor network deployment, one may choose a deployment density to best support spatiotemporal communication. We also explored all these directions via simulation and results are presented in this paper. --- paper_title: VE-mobicast: A variant-egg-based mobicast routing protocol for sensornets paper_content: In this paper, we present a new "spatiotemporal multicast" protocol for supporting applications which require spatiotemporal coordination in sensornets. To simultaneously consider the factors of moving speed and direction, this work mainly investigates a new mobicast routing protocol, called variant-egg-based mobicast (VE-mobicast), by utilizing the variant-egg shape of the forwarding zone to achieve a high predicted accuracy. The contributions of our protocol are summarized as follows: (1) it builds a new shape of a forwarding zone, called the variant-egg, to adaptively and efficiently determine the location and shape of the forwarding zone to maintain the same number of wake-up sensor nodes; (2) it is a fully distributed algorithm which reduces the communication overhead of determining the forwarding zone and the mobicast message forwarding overhead; (3) it can improve the predicted accuracy of the forwarding zone by considering the factors of moving speed and direction. Finally, the simulation results illustrate the performance achievements, compared to existing mobicast routing protocols. --- paper_title: HVE-mobicast: a hierarchical-variant-egg-based mobicast routing protocol for wireless sensornets paper_content: In this paper, we propose a new mobicast routing protocol, called the HVE-mobicast (hierarchical-variant-egg-based mobicast) routing protocol, in wireless sensor networks (WSNs). Existing protocols for a spatiotemporal variant of the multicast protocol called a "mobicast" were designed to support a forwarding zone that moves at a constant velocity, $\stackrel{\rightarrow}{v}$ , through sensornets. The spatiotemporal characteristic of a mobicast is to forward a mobicast message to all sensor nodes that are present at time t in some geographic zone (called the forwarding zone) Z, where both the location and shape of the forwarding zone are a function of time over some interval (t start ,t end ). Mobicast routing protocol aims to provide reliable and just-in-time message delivery for a mobile sink node. To consider the mobile entity with the different moving speed, a new mobicast routing protocol is investigated in this work by utilizing the cluster-based approach. The message delivery of nodes in the forwarding zone of the HVE-mobicast routing protocol is transmitted by two phases; cluster-to-cluster and cluster-to-node phases. In the cluster-to-cluster phase, the cluster-head and relay nodes are distributively notified to wake them up. In the cluster-to-node phase, all member nodes are then notified to wake up by cluster-head nodes according to the estimated arrival time of the delivery zone. The key contribution of the HVE-mobicast routing protocol is that it is more power efficient than existing mobicast routing protocols, especially by considering different moving speeds and directions. Finally, simulation results illustrate performance enhancements in message overhead, power consumption, needlessly woken-up nodes, and successful woken-up ratio, compared to existing mobicast routing protocols. --- paper_title: Reliable mobicast via face-aware routing paper_content: This paper presents a novel protocol for a spatiotemporal variant of multicast called mobicast, designed to support message delivery in ad hoc sensor networks. The spatiotemporal character of mobicast relates to the obligation to deliver a message to all the nodes that will he present at time t in some geographic zone Z, where both the location and shape of the delivery zone are a function of time over some interval (t start, tend). The protocol, called face-aware routing (FAR), exploits ideas adapted from existing applications of face routing to achieve reliable mobicast delivery. The key features of the protocol are a routing strategy, which uses information confined solely to a node's immediate spatial neighborhood, and a forwarding schedule, which employs only local topological information. Statistical results shows that, in uniformly distributed random disk graphs, the spatial neighborhood size is usually less than 20. This suggests that FAR is likely to exhibit a low average memory cost. An estimation formula for the average size of the spatial neighborhood in a random network is another analytical result reported in this paper. This paper also presents a novel and low cost distributed algorithm for spatial neighborhood discovery ---	Title: A Review of Localization and Tracking Algorithms in Wireless Sensor Networks Section 1: INTRODUCTION Description 1: This section introduces the challenges, importance, and applications of localization and tracking in wireless sensor networks. Section 2: BROAD CLASSIFICATION OF LOCALIZATION METHODS Description 2: This section provides a broad classification of localization methods including range-free and range-based methods, and discusses their differences and applications. Section 3: Range-based Localization Methods Description 3: This subsection details the various range-based localization methods such as Received Signal Strength (RSS), Angle-of-Arrival (AOA), Time-of-Arrival (TOA), and Time-Difference-of-Arrival (TDOA). Section 4: Time-of-Arrival (TOA)-based Localization Description 4: This subsection explores TOA-based localization methods and their formulation including challenges related to synchronization. Section 5: Angle-of-Arrival (AOA)-based Localization Description 5: This subsection discusses AOA-based localization methods and their utilization of angle information with the associated costs and hardware requirements. Section 6: Received Signal Strength (RSS) based Localization Description 6: This subsection examines RSS-based localization methods, including model dependencies and error considerations. Section 7: Range-free Localization Methods Description 7: This section explains various range-free localization techniques and their subdivision into local techniques and hop-counting methods. Section 8: OVERVIEW OF IMPLEMENTATION METHODS Description 8: This section provides an overview of different implementation methods for localization such as machine learning-based methods, centralized and distributed methods, fingerprinting, and multi-sensor data fusion techniques. Section 9: BROAD CLASSIfiCATION OF TRACKING METHODS Description 9: This section broadly classifies tracking methods into cluster-based, tree-based, activation-based, and mobicast-based tracking methods. Section 10: Cluster-based Tracking Methods Description 10: This subsection delves into cluster-based tracking methods, exploring static, dynamic, and spatio-temporal approaches. Section 11: Prediction-based Tracking Methods Description 11: This subsection discusses energy-efficient prediction-based tracking approaches, detailing their architecture and limitations. Section 12: Tree-based Tracking Methods Description 12: This subsection covers tree-based tracking methods, focusing on hierarchical organization and convoy trees. Section 13: Activation-based Tracking Methods Description 13: This subsection explains different activation-based tracking algorithms and their energy consumption implications. Section 14: Mobicast-based Tracking Methods Description 14: This subsection explores spatio-temporal methods for tracking using mobicast and the relevance of geographic zone messaging. Section 15: EXPERIMENTAL SETUP FOR LOCALIZATION AND TRACKING Description 15: This section reviews various experimental kits and setups available for localization and tracking applications, highlighting their features and applications. Section 16: National Instruments Wireless Sensor Nodes Description 16: This subsection discusses the features and applications of National Instruments Wireless Sensor Nodes. Section 17: Crossbow Motes Description 17: This subsection details the Crossbow motes setup and their application in research. Section 18: SensWiz Networks Kit Description 18: This subsection covers the SensWiz Networks Kit and its application. Section 19: Hand-Held Devices Description 19: This subsection discusses the use of hand-held devices like smartphones and tablets for localization and tracking purposes. Section 20: CONCLUSION Description 20: This concluding section summarizes the key points and findings of the survey, discussing the trade-offs and applications of different localization and tracking methods.
EURASIP Journal on Applied Signal Processing 2003:10, 941–952 c ○ 2003 Hindawi Publishing Corporation Physically Informed Signal Processing Methods for Piano Sound Synthesis: A Research Overview	7	--- paper_title: Commuted Piano Synthesis paper_content: The \commuted piano synthesis" algorithm is described, based on a simpli ed acoustic model of the piano. The model includes multiple coupled strings, a nonlinear hammer, and an arbitrarily large soundboard and enclosure. Simpli cations are employed which greatly reduce computational complexity. Most of the simpli cations are made possible by the commutativity of linear, time-invariant systems. Special care is given to the felt-covered hammer which is highly nonlinear and therefore does not commute with other components. In its present form, a complete, two-key piano can be synthesized in real time on a single 25MHz Motorola DSP56001 signal processing chip. --- paper_title: Structured audio and effects processing in the MPEG-4 multimedia standard paper_content: While previous generations of the MPEG multimedia standard have focused primarily on coding and transmission of content digitally sampled from the real world,MPEG-4 contains extensive support for structured, syntheticand synthetic/natural hybrid coding methods. An overviewis presented of the "Structured Audio" and "AudioBIFS"components of MPEG-4, which enable the description ofsynthetic soundtracks, musical scores, and effects algorithmsand the compositing, manipulation, and synchronization ofreal and synthetic audio sources. A discussion of the separation of functionality between the systems layer and the audiotoolset of MPEG-4 is presented, and prospects for efficientDSP-based implementations are discussed. --- paper_title: Structured Audio: Creation, Transmission, and Rendering of Parametric Sound Representations paper_content: Structured audio representations are semantic and symbolic descriptions that are useful for ultralow-bit-rate transmission, flexible synthesis, and perceptually based manipulation and retrieval of sound. We present an overview of techniques for transmitting and synthesizing sound represented in structured format, and for creating structured representations from audio waveforms. We discuss applications for structured audio in virtual environments, music synthesis, gaming, content-based retrieval, interactive broadcast, and other multimedia contexts. --- paper_title: A Tutorial on Digital Sound Synthesis Techniques paper_content: Progress in electronics and computer technology has led to an ever-increasing utilization of digital techniques for musical sound production. Some of these are the digital equivalents of techniques employed in analog synthesizers and in other fields of electrical engineering. Other techniques have been specifically developed for digital music devices and are peculiar to these. This paper introduces the fundamentals of the main digital synthesis techniques. Mathematical developments have been restricted in the exposition and can be found in the papers listed in the references. To simplify the discussion, whenever possible, the techniques are presented with reference to continuous signals. Sound synthesis is a procedure used to produce a sound without the help of acoustic instruments. In digital synthesis, a sound is represented by a sequence of numbers (samples). Hence, a digital synthesis technique consists of a computing procedure or mathematical formula, which computes each sample value. Normally, the synthesis formula depends on some values, that is, parameters. Frequency and amplitude are examples of such parameters. Parameters can be constant or slowly time variant during the sound. Time-variant parameters are also called control functions. Synthesis techniques can be classified as (1) generation techniques (Fig. la), which directly produce the signal from given data, and (2) transformation techniques (Fig. Ib), which can be divided into two stages, the generation of one or more simple signals and their modification. Often, more or less elaborate combinations of these techniques are employed. Fixed-Waveform Synthesis --- paper_title: Understanding Musical Sound with Forward Models and Physical Models paper_content: This research report describes an approach to parameter estimation for physical models of sound-generating systems using distal teachers and foward models (Jordan & Rumelhart, 1992; Jordan, 1990). The general problem is to find an inverse model of a sound-generating system that transforms sounds to action parameters; these parameters constitute a model-base description of the sound. We first show that a two-layer feedforward model is capable of performing inverse mappings for a simple physical model of a violin string. We refer to this learning strategy as direct inverse modeling; it requires an explicit teacher and it is only suitable for convex regions of the parameter space. A model of two strings was implemented that had non-convex regions in its parameter space. We show how the direct modeling strategy failed at the task of learning the inverse model in this case and that forward models can be used, in conjunction with distal teachers, to bias the learning of an inverse model so that non-convex regions are mapped to single-point solutions in the parameter space. Our results show that forward models are appropriate for learning to map sounds to parametric representations --- paper_title: Synchronizing Computer Graphics Animation and Audio paper_content: Our prototype authoring tool synchronizes computer graphics animation, narration and background music. It lets users visualize the contents of a background music track and narration along a single time line and displays an animation's scenario sharing the same time line. A graphical user interface supports interactive editing of both background music and narration without any loss in quality. --- paper_title: Principles of Digital Waveguide Models of Musical Instruments paper_content: Basic principles of digital waveguide modeling of musical instruments are presented in a tutorial introduction intended for graduate students in electrical engineering with a solid background in signal processing and acoustics. The vibrating string is taken as the principal illustrative example, but the formulation is unified with that for acoustic tubes. Modeling lossy stiff strings using delay lines and relatively low-order digital filters is described. Various choices of wave variables are discussed, including velocity waves, force waves, and root-power waves. Signal scattering at an impedance discontinuity is derived for an arbitrary number of waveguides intersecting at a junction. Various computational forms are discussed, including the Kelly-Lochbaum, one-multiply, and normalized scattering junctions. A relatively new three-multiply normalized scattering junction is derived using a two-multiply transformer to normalize a one-multiply scattering junction. Conditions for strict passivity of the model are discussed. Use of commutativity of linear, time-invariant elements to greatly reduce computational cost is described. Applications are summarized, and models of the clarinet and bowed-string are described in some detail. The reed-bore and bow-string interactions are modeled as nonlinear scattering junctions attached to the bore/string acoustic waveguide. --- paper_title: Hysteretic model of the grand piano hammer felt paper_content: The experimental relationships of dynamic force versus grand piano hammer felt deformation show the significant influence of hysteresis characteristics. To explain this phenomenon, a new mathematical model of the hammer felt is proposed. In this model the hammer felt is considered as a nonlinear history‐dependent (hysteretic) material with an exponential kernel function. The numerical simulation of interaction of the hammer with a fixed target was used to identify the nonlinear and hysteresis parameters of the felt, and good agreement with experiments was achieved. Also, this model is used here for the analysis of interaction of the hammer with a real grand piano string. --- paper_title: Numerical simulations of piano strings. II. Comparisons with measurements and systematic exploration of some hammer‐string parameters paper_content: A physical model of the piano string, using finite difference methods, has recently been developed. [Chaigne and Askenfelt, J. Acoust. Soc. Am. 95, 1112–1118 (1994)]. The model is based on the fundamental equations of a damped, stiff string interacting with a nonlinear hammer, from which a numerical finite difference scheme is derived. In the present study, the performance of the model is evaluated by systematic comparisons between measured and simulated piano tones. After a verification of the accuracy of the method, the model is used as a tool for systematically exploring the influence of string stiffness, relative striking position, and hammer‐string mass ratio on string waveforms and spectra. --- paper_title: Piano string excitation. VI: Nonlinear modeling paper_content: Accurate modeling of the piano string–hammer interaction requires that the nonlinearity of the force‐displacement relation for the hammer be recognized and included. Two models that accomplish this with numerical integration are described, and sample results are presented. The first model is valid for a perfectly flexible string, where nondispersive traveling waves can be conveniently followed with a digital delay line. The second model uses standing waves instead to describe the vibrations of a stiff string, with the stiffness itself justifying truncation of what would otherwise be a poorly converging normal‐mode summation. Predictions with these models give significantly better agreement with data than did previous calculations with completely linear models. --- paper_title: Numerical simulations of piano strings. I. A physical model for a struck string using finite difference methods paper_content: The first attempt to generate musical sounds by solving the equations of vibrating strings by means of finite difference methods (FDM) was made by Hiller and Ruiz [J. Audio Eng. Soc. 19, 462–472 (1971)]. It is shown here how this numerical approach and the underlying physical model can be improved in order to simulate the motion of the piano string with a high degree of realism. Starting from the fundamental equations of a damped, stiff string interacting with a nonlinear hammer, a numerical finite difference scheme is derived, from which the time histories of string displacement and velocity for each point of the string are computed in the time domain. The interacting force between hammer and string, as well as the force acting on the bridge, are given by the same scheme. The performance of the model is illustrated by a few examples of simulated string waveforms. A brief discussion of the aspects of numerical stability and dispersion with reference to the proper choice of sampling parameters is also incl... --- paper_title: Traveling Wave Implementation of a Lossless Mode-coupling Filter and the Wave Digital Hammer paper_content: Monochloro-substituted saturated compounds may be prepared by condensing a saturated compound such as cycloalkyl hydrocarbon with a chloromonoolefin possessing not more than 4 carbon atoms and having the chlorine atom attached to one of the doubly-bonded carbon atoms in the presence of a free radical-generating catalyst and a promoter comprising a hydrogen chloride compound. --- paper_title: Elimination of delay-free loops in discrete-time models of nonlinear acoustic systems paper_content: Nonlinear acoustic systems are often described by means of nonlinear maps acting as instantaneous constraints on the solutions of a system of linear differential equations. This description leads to discrete-time models exhibiting noncomputable loops. We present a solution to this computability problem by means of geometrical transformation of the nonlinearities and algebraic transformation of the time-dependent equations. The proposed solution leads to stable and accurate simulations even at relatively low sampling rates. --- paper_title: Numerical simulations of piano strings. II. Comparisons with measurements and systematic exploration of some hammer‐string parameters paper_content: A physical model of the piano string, using finite difference methods, has recently been developed. [Chaigne and Askenfelt, J. Acoust. Soc. Am. 95, 1112–1118 (1994)]. The model is based on the fundamental equations of a damped, stiff string interacting with a nonlinear hammer, from which a numerical finite difference scheme is derived. In the present study, the performance of the model is evaluated by systematic comparisons between measured and simulated piano tones. After a verification of the accuracy of the method, the model is used as a tool for systematically exploring the influence of string stiffness, relative striking position, and hammer‐string mass ratio on string waveforms and spectra. --- paper_title: Numerical simulations of piano strings. I. A physical model for a struck string using finite difference methods paper_content: The first attempt to generate musical sounds by solving the equations of vibrating strings by means of finite difference methods (FDM) was made by Hiller and Ruiz [J. Audio Eng. Soc. 19, 462–472 (1971)]. It is shown here how this numerical approach and the underlying physical model can be improved in order to simulate the motion of the piano string with a high degree of realism. Starting from the fundamental equations of a damped, stiff string interacting with a nonlinear hammer, a numerical finite difference scheme is derived, from which the time histories of string displacement and velocity for each point of the string are computed in the time domain. The interacting force between hammer and string, as well as the force acting on the bridge, are given by the same scheme. The performance of the model is illustrated by a few examples of simulated string waveforms. A brief discussion of the aspects of numerical stability and dispersion with reference to the proper choice of sampling parameters is also incl... --- paper_title: Commuted Piano Synthesis paper_content: The \commuted piano synthesis" algorithm is described, based on a simpli ed acoustic model of the piano. The model includes multiple coupled strings, a nonlinear hammer, and an arbitrarily large soundboard and enclosure. Simpli cations are employed which greatly reduce computational complexity. Most of the simpli cations are made possible by the commutativity of linear, time-invariant systems. Special care is given to the felt-covered hammer which is highly nonlinear and therefore does not commute with other components. In its present form, a complete, two-key piano can be synthesized in real time on a single 25MHz Motorola DSP56001 signal processing chip. --- paper_title: Physical Modeling of Plucked String Instruments with Application to Real-Time Sound Synthesis paper_content: An efficient approach for real-time synthesis of plucked string instruments using physical modeling and DSP techniques is presented. Results of model-based resynthesis are illustrated to demonstrate that high-quality synthetic sounds of several string instruments can be generated using the proposed modeling principles. Real-time implementation using a signal processor is described, and several aspects of controlling physical models of plucked string instruments are studied. --- paper_title: Principles of Digital Waveguide Models of Musical Instruments paper_content: Basic principles of digital waveguide modeling of musical instruments are presented in a tutorial introduction intended for graduate students in electrical engineering with a solid background in signal processing and acoustics. The vibrating string is taken as the principal illustrative example, but the formulation is unified with that for acoustic tubes. Modeling lossy stiff strings using delay lines and relatively low-order digital filters is described. Various choices of wave variables are discussed, including velocity waves, force waves, and root-power waves. Signal scattering at an impedance discontinuity is derived for an arbitrary number of waveguides intersecting at a junction. Various computational forms are discussed, including the Kelly-Lochbaum, one-multiply, and normalized scattering junctions. A relatively new three-multiply normalized scattering junction is derived using a two-multiply transformer to normalize a one-multiply scattering junction. Conditions for strict passivity of the model are discussed. Use of commutativity of linear, time-invariant elements to greatly reduce computational cost is described. Applications are summarized, and models of the clarinet and bowed-string are described in some detail. The reed-bore and bow-string interactions are modeled as nonlinear scattering junctions attached to the bore/string acoustic waveguide. --- paper_title: Development and Calibration of a Guitar Synthesizer paper_content: A specific efficient implementation for the synthesis of acoustic guitar tones is described which is based on recent digital waveguide synthesis techniques. Enhanced analysis methods are used for calibrating the synthesis model based on digitized tones. The lowest resonances of the guitar body are implemented with separate parametric second-order resonators that run at a lower sampling rate than the string model. --- paper_title: Perceptual study of decay parameters in plucked string synthesis paper_content: A listening experiment was conducted to study the audibility of variation of decay parameters in plucked string synthesis. A digital commuted-waveguide-synthesis model was used to generate the test sounds. The decay of each tone was parameterized with an overall and a frequency-dependent decay parameter. Two different fundamental frequencies, tone durations, and types of excitation signals were used totalling in eight test sets for both parameters. The results indicate that variations between 25% and 40% in the time constant of decay are inaudible. This suggests that large deviations in decay parameters can be allowed from a perceptual viewpoint. The results are applied in model-based audio processing. --- paper_title: Robust loss filter design for digital waveguide synthesis of string tones paper_content: A robust loss filter design method is presented for digital waveguide string models, which can be used with high filter orders. The method aims at minimizing the decay time error in partials of the synthetic tone. This is achieved by a new weighting function based on the first-order Taylor series approximation of the decay time errors. Smoothing of decay time data and requiring the design to be minimum-phase are also proposed to facilitate the stability of the design. The new method is applicable to analysis-based sound synthesis of piano and guitar tones, for example. --- paper_title: Physical Modeling of Plucked String Instruments with Application to Real-Time Sound Synthesis paper_content: An efficient approach for real-time synthesis of plucked string instruments using physical modeling and DSP techniques is presented. Results of model-based resynthesis are illustrated to demonstrate that high-quality synthetic sounds of several string instruments can be generated using the proposed modeling principles. Real-time implementation using a signal processor is described, and several aspects of controlling physical models of plucked string instruments are studied. --- paper_title: Synchronizing Computer Graphics Animation and Audio paper_content: Our prototype authoring tool synchronizes computer graphics animation, narration and background music. It lets users visualize the contents of a background music track and narration along a single time line and displays an animation's scenario sharing the same time line. A graphical user interface supports interactive editing of both background music and narration without any loss in quality. --- paper_title: Simple and robust method for the design of allpass filters using least-squares phase error criterion paper_content: We consider a simple scheme for the design of allpass filters for approximation (or equalization) of a given phase function using a least-squares error criterion. Assuming that the desired phase response is prescribed at a discrete set of frequency points, we formulate a general least-squares equation-error solution with a possible weight function. Based on the general formulation and detailed analysis of the introduced error, we construct a new algorithm for phase approximation. In addition to iterative weighting of the equation error, the nominal value of the desired group delay is also adjusted iteratively to minimize the total phase error measure in equalizer applications. This new feature essentially eliminates the difficult choice of the nominal group delay which is known to have a profound effect on the stability of the designed allpass filter. The proposed method can be used for highpass and bandpass equalization as well, where the total phase error can be further reduced by introducing an adjustable-phase offset in the optimization. The performance of the algorithm is analyzed in detail with examples. First we examine the approximation of a given phase function. Then we study the equalization of the nonlinear phase of various lowpass filters. Also, a bandpass example is included. Finally we demonstrate the use of the algorithm for the design of approximately linear-phase recursive filters as a parallel connection of a delay line and an allpass filter. > --- paper_title: Quality of Piano Tones paper_content: A synthesizer was constructed to produce simultaneously 100 pure tones with means for controlling the intensity and frequency of each one of them. The piano tones were analyzed by conventional apparatus and methods and the analysis set into the synthesizer. The analysis was considered correct only when a jury of eight listeners could not tell which were real and which were synthetic tones. Various kinds of synthetic tones were presented to the jury for comparison with real tones. A number of these were judged to have better quality than the real tones. According to these tests synthesized piano like tones were produced when the attack time was less than 0.01 sec. The decay can be as long as 20 sec for the lower notes and be less than 1 sec for the very high ones. The best quality is produced when the partials decrease in level at the rate of 2 db per 100 cps increase in the frequency of the partial. The partials below middle C must be inharmonic in frequency to be piano like. --- paper_title: Audibility of the timbral effects of inharmonicity in stringed instrument tones paper_content: Listening tests were conducted to find the audibility of inharmonicity in musical sounds produced by stringed instruments, such as the piano or the guitar. The audibility threshold of inharmonicity was measured at five fundamental frequencies. Results show that the detection of inharmonicity is strongly dependent on the fundamental frequency f0. A simple model is presented for estimating the threshold as a function of f0. The need to implement inharmonicity in digital sound synthesis is discussed. --- paper_title: Bandwidth of perceived inharmonicity for physical modeling of dispersive strings paper_content: The influence of accurate reproduction of inharmonicity on the perceived quality of piano tones is investigated. Acoustic piano tones were resynthesized by changing the bandwidth of correct positioning of partials. Cutoff frequencies of inharmonicity for different pitches have been established by psychoacoustic experimentation. The results are applicable to the design of dispersive resonators in sound synthesis by physical modeling. --- paper_title: Circulant and elliptic feedback delay networks for artificial reverberation paper_content: The feedback delay network (FDN) has been proposed for digital reverberation, The digital waveguide network (DWN) is also proposed with similar advantages. This paper notes that the commonly used FDN with an N/spl times/N orthogonal feedback matrix is isomorphic to a normalized digital waveguide network consisting of one scattering junction joining N reflectively terminated branches. Generalizations of FDNs and DWNs are discussed. The general case of a lossless FDN feedback matrix is shown to be any matrix having unit-modulus eigenvalues and linearly independent eigenvectors. A special class of FDNs using circulant matrices is proposed. These structures can be efficiently implemented and allow control of the time and frequency behavior. Applications of circulant feedback delay networks in audio signal processing are discussed. --- paper_title: Resynthesis of Coupled Piano String Vibrations Based on Physical Modeling paper_content: This paper presents a technique to resynthesize the sound generated by the vibrations of two piano strings tuned to a very close pitch and coupled at the bridge level. Such a mechanical system produces doublets of components, thus generating beats and double decays on the amplitudes of the partials. We design a digital waveguide model by coupling two elementary digital waveguides. This model is able to reproduce perceptually relevant sounds. The parameters of the model are estimated from the analysis of real signals collected directly on the strings by laser velocimetry. Sound transformations can be achieved by modifying relevant parameters in order to simulate various physical situations. --- paper_title: Pedal resonance effect simulation device for digital pianos paper_content: The present invention relates to a pedal resonance effect simulation device for digital pianos consisting of a resonance effect circuit for the simulation of the resonance effects in the strings of a traditional mechanical piano, coupled with a reference model which varies the resonance contributions of the various strings, which are equivalent to those of a mechanical piano, by using delay lines with a method which considers the position of the resonance pedal pressed by the performer upon reproduction. --- paper_title: Coupled piano strings paper_content: The admittance of the piano bridge has a crucial effect on piano tone by coupling together the strings belonging to one note into a single dynamical system. In this paper, we first develop theoretical expressions that show how the rate of energy transmission to the bridge as a function of time (including the phenomena of beats and ’’aftersound’’) depends on bridge admittance, hammr irregularities, and the exact state in which the piano is tuned. We then present experimental data showing the effects of mutual string coupling on beats and aftersound, as well as the great importance of the two polarizations of the string motion. The function of the una corda pedal in controlling the aftersound is explained, and the stylistic possibilities of a split damper are pointed out. The way in which an excellent tuner can use fine tuning of the unisons to make the aftersound more uniform is discussed. --- paper_title: Accurate and efficient modeling of beating and two-stage decay for string instrument synthesis paper_content: In this paper a novel modeling method is presented for beating and two-stage decay. Here, one digital waveguide is used for each note and some resonators are run in parallel to simulate the beating and two-stage decay of those partials, where these phenomena are most prominent. The resonator bank is implemented by using the multi-rate approach, resulting in a decrease of computational cost by a factor of 10. By taking the advantage of the characteristics of the resonators, relatively simple upsampling and downsampling filters are used. Two different filtering approaches are presented and compared with respect to computational complexity. Examples are shown with the application to piano sound synthesis. --- paper_title: Circulant and elliptic feedback delay networks for artificial reverberation paper_content: The feedback delay network (FDN) has been proposed for digital reverberation, The digital waveguide network (DWN) is also proposed with similar advantages. This paper notes that the commonly used FDN with an N/spl times/N orthogonal feedback matrix is isomorphic to a normalized digital waveguide network consisting of one scattering junction joining N reflectively terminated branches. Generalizations of FDNs and DWNs are discussed. The general case of a lossless FDN feedback matrix is shown to be any matrix having unit-modulus eigenvalues and linearly independent eigenvectors. A special class of FDNs using circulant matrices is proposed. These structures can be efficiently implemented and allow control of the time and frequency behavior. Applications of circulant feedback delay networks in audio signal processing are discussed. ---	Title: Physically Informed Signal Processing Methods for Piano Sound Synthesis: A Research Overview Section 1: INTRODUCTION Description 1: Write an introduction to the paper, outlining the primary motivation, context, and objectives of physical modeling in piano sound synthesis. Section 2: ACOUSTICS AND MODEL STRUCTURE Description 2: Discuss the general aspects of piano acoustics and the structure of the physical model used for piano sound synthesis. Section 3: THE HAMMER Description 3: Delve into the physical and numerical modeling of the piano hammer, discussing hammer-string interaction and various implementation approaches. Section 4: THE STRING Description 4: Describe different string modeling techniques with a focus on digital waveguide methods, including loss filter design, dispersion simulation, and string coupling. Section 5: RADIATION MODELING Description 5: Address the modeling of the soundboard and the radiation properties, including different implementation strategies and computational considerations. Section 6: CONCLUSIONS Description 6: Summarize the main stages of development in physical modeling for the piano, highlighting key computational aspects and discussing the importance of perceptual and stability considerations. Section 7: ACKNOWLEDGMENTS Description 7: Provide acknowledgments for any contributing individuals or organizations and mention funding sources where applicable.
A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications	11	--- paper_title: Detection and classification of surface defects of gun barrels using computer vision and machine learning paper_content: Abstract This work proposes a machine vision based approach for the detection and classification of the surface defects such as normal wear, corrosive pitting, rust and erosion that are usually present in used gun barrels. Surface images containing the defective regions of several used gun barrels were captured in a non-destructive manner using a Charge-Coupled Device (CCD) camera attached with a miniature microscopic probe. Among the captured images, normal wear appeared as bright and the rest of the three defects appeared as dark. Therefore, the classification has been carried out in two stages. Various segmentation methods were tested and extended maxima transform gave the best result. The defective area was calculated in metric units. Multiple textural features based on histogram and gray level co-occurrence matrix were extracted from the segmented images and ranked them automatically using the sequential forward feature selection method in order to select the best minimal features for the classification purpose. Many classifiers based on Bayes, k -Nearest Neighbor, Artificial Neural Network and Support Vector Machine (SVM) were tested and the results demonstrated the efficacy of SVM for this application. All these steps were carried out at six different scales of image sizes and the best scale was selected for the entire analysis based on the segmentation and classification accuracy. The introduction of this Gaussian scale spacing concept could reduce the computation without compromising on the accuracy. Overall, the methodology forms a novel framework for surface defect detection and classification that has a potential to automate the inspection process. --- paper_title: Image recognition technology in rotating machinery fault diagnosis based on artificial immune paper_content: By using image recognition technology, this paper presents a new fault diagnosis method for rotating machinery with artificial immune algorithm. This method focuses on the vibration state parameter image. The main contribution of this paper is as follows: firstly, 3-D spectrum is created with raw vibrating signals. Secondly, feature information in the state parameter image of rotating machinery is extracted by using Wavelet Packet transformation. Finally, artificial immune algorithm is adopted to diagnose rotating machinery fault. On the modeling of 600MW turbine experimental bench, rotor`s normal rate, fault of unbalance, misalignment and bearing pedestal looseness are being examined. It`s demonstrated from the diagnosis example of rotating machinery that the proposed method can improve the accuracy rate and diagnosis system robust quality effectively. --- paper_title: Achievements and Challenges in Machine Vision-Based Inspection of Large Concrete Structures paper_content: Large concrete structures need to be inspected in order to assess their current physical and functional state, to predict future conditions, to support investment planning and decision making, and to allocate limited maintenance and rehabilitation resources. Current procedures in condition and safety assessment of large concrete structures are performed manually leading to subjective and unreliable results, costly and time-consuming data collection, and safety issues. To address these limitations, automated machine vision-based inspection procedures have increasingly been proposed by the research community. This paper presents current achievements and open challenges in vision-based inspection of large concrete structures. First, the general concept of Building Information Modeling is introduced. Then, vision-based 3D reconstruction and as-built spatial modeling of concrete civil infrastructure are presented. Following that, the focus is set on structural member recognition as well as on concrete damage detection and assessment exemplified for concrete columns. Although some challenges are still under investigation, it can be concluded that vision-based inspection methods have significantly improved over the last 10 years, and now, as-built spatial modeling as well as damage detection and assessment of large concrete structures have the potential to be fully automated. --- paper_title: A Vision-Based Dynamic Rotational Angle Measurement System for Large Civil Structures paper_content: In this paper, we propose a vision-based rotational angle measurement system for large-scale civil structures. Despite the fact that during the last decade several rotation angle measurement systems were introduced, they however often required complex and expensive equipment. Therefore, alternative effective solutions with high resolution are in great demand. The proposed system consists of commercial PCs, commercial camcorders, low-cost frame grabbers, and a wireless LAN router. The calculation of rotation angle is obtained by using image processing techniques with pre-measured calibration parameters. Several laboratory tests were conducted to verify the performance of the proposed system. Compared with the commercial rotation angle measurement, the results of the system showed very good agreement with an error of less than 1.0% in all test cases. Furthermore, several tests were conducted on the five-story modal testing tower with a hybrid mass damper to experimentally verify the feasibility of the proposed system. --- paper_title: Monitoring global earthquake-induced demands using vision-based sensors paper_content: A vision-based approach is evaluated for its applicability as a new sensing technology for measuring earthquake-induced motions. The approach evaluated in this paper is advantageous since it requires very limited physical attachment to the structure of interest, is high-speed, high-resolution, and does not introduce additional mass or otherwise modify the properties of the structure. A demonstration experiment is described in which four digital high-speed, high-resolution, charge-coupled-device cameras outfitted with red light-emitting diodes are used to track 21 reflective (nearly) mass less spherical elements discretely mounted on a scale five-story steel frame structure. The structure is mounted on a large bi-axial shake table and subjected to different earthquake motions. A total of eleven conventional (wired) transducers [linear variable displacement transducers and accelerometers] are also discretely mounted on the structure, providing a unique comparison with the vision-based approach. Results from these experiments show that the nonintrusive vision-based approach is extremely promising in terms of its ability to capture inter-story drift, floor level velocities, and accelerations, provided proper post-processing of the dynamic data occurs. --- paper_title: An advanced vision-based system for real-time displacement measurement of high-rise buildings paper_content: This paper introduces an advanced vision-based system for dynamic real-time displacement measurement of high-rise buildings using a partitioning approach. The partitioning method is based on the successive estimation of relative displacements and rotational angles at several floors using a multiple vision-based displacement measurement system. In this study, two significant improvements were made to realize the partitioning method: (1) time synchronization, (2) real-time dynamic measurement. Displacement data and time synchronization information are wirelessly transferred via a network using the TCP/IP protocol. The time synchronization process is periodically conducted by the master system to guarantee the system time at the master and slave systems are synchronized. The slave system is capable of dynamic real-time measurement and it is possible to economically expand measurement points at slave levels using commercial devices. To verify the accuracy and feasibility of the synchronized multi-point vision-based system and partitioning approach, many laboratory tests were carried out on a three-story steel frame model. Furthermore, several tests were conducted on a five-story steel frame tower equipped with a hybrid mass damper to experimentally confirm the effectiveness of the proposed system. --- paper_title: Horizontal Roadway Curvature Computation Algorithm Using Vision Technology paper_content: Horizontal roadway curvature data is essential for roadway safety analysis. However, collecting such data is time-consuming, costly, and dangerous using traditional, manual surveying methods. It is especially difficult to perform such manual measurement when roadways have high traffic volumes. Thus, it would be valuable for transportation agencies if roadway curvature data could be computed from photographic images taken using low-cost digital cameras. This is the first article that develops an algorithm using emerging vision technology to acquire horizontal roadway curvature data from roadway images to perform roadway safety assessment. The proposed algorithm consists of 4 steps: 1) curve edges image processing, 2) mapping edge positions from an image domain to the real-world domain, 3) calibrating camera parameters, and 4) calculating the curve radius and center from curve points. The proposed algorithm was tested on roadways having various levels of curves and using different image sources to demonstrate its capability. The ground truth curvatures for 2 cases were also collected to evaluate the error of the proposed algorithm. The test results are very promising, and the computed curvatures are especially accurate for curves of small radii (less than 66 m/200 ft) with less than 1.0% relative errors with respect to the ground truth data. The proposed algorithm can be used as an alternative method that complements the traditional measurement methods used by state DOTs to collect roadway curvature data. --- paper_title: A preliminary study on the response of steel structures using surveillance camera image with vision-based method during the Great East Japan Earthquake paper_content: Abstract Steel structures had experienced an intense and long transient response caused by the great earthquake motion during the Great East Japan Earthquake on 11th March 2011. Lots of non-structural damages such as ceiling fallen and glass broken are the main damage characteristics of steel structures although there were rarely structural damages. Some opened earthquake records and videos were adopted in this study in order to investigate the seismic behavior of the steel structures. An application of vision-based method has been carried out on a video of surveillance camera provided by Japan Broadcasting Corporation (Nippon Housou Kyoukai) to obtain the displacement response and natural period of the high-rise steel buildings in Tokyo. --- paper_title: Safety Monitoring of Railway Tunnel Construction Using FBG Sensing Technology paper_content: In comparison with above-ground structures, the investigation of underground space structures still faces great challenges because of the extremely complicated constitutive relationships of the soils or rocks. Implementation of structural health monitoring (SHM) systems on the underground structures such as tunnels commencing from the construction stage may be of help in understanding their operational behaviors and long-term trends. This paper explores the application of the fiber Bragg grating (FBG) sensing technology for safety monitoring during railway tunnel construction. An FBG-based temperature monitoring system is first developed for real-time temperature measurement of the frozen soils during freezing construction of a metro-tunnel cross-passage. Through in-situ deployment of FBG-based liquid-level sensors, the subgrade settlement of a segment of a high-speed rail line is then monitored in an automatic manner during construction of an undercrossing tunnel. The field results indicate that the FBG ... --- paper_title: Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data paper_content: Abstract This paper aims at developing a monitoring-based method for fatigue life assessment of steel bridges with use of long-term monitoring data of dynamic strain. A standard daily stress spectrum is derived by statistically analyzing the stress spectra accounting for highway traffic, railway traffic, and typhoon effects. The optimal number of daily strain data for derivation of the standard daily stress spectrum is determined by examining the predominant factors which affect the prediction of fatigue life. With the continuously measured dynamic strain responses from the instrumented Tsing Ma Bridge carrying both highway and railway traffic, the proposed method is exemplified to evaluate the fatigue life of fatigue-critical welded details on the bridge. --- paper_title: Multistep Explicit Stereo Camera Calibration Approach to Improve Euclidean Accuracy of Large-Scale 3D Reconstruction paper_content: AbstractThe spatial accuracy of point clouds generated from a mobile stereo camera set is very sensitive to the intrinsic and extrinsic camera parameters (i.e., camera calibration) used in stereo image-based three-dimensional (3D) reconstruction methods. The existing camera calibration algorithms induce a significant amount of error owing to poor estimation accuracy in camera parameters when they are used for large-scale scenes such as mapping civil infrastructure. This leads to higher uncertainties in the location of 3D points, and may result in the failure of the whole reconstruction process. This paper proposes a novel procedure to address this problem. It hypothesizes that a set of multiple calibration parameters created by videotaping a moving calibration pattern along a specific path can increase overall calibration accuracy and ultimately enhance the Euclidean accuracy of the generated point cloud. This is achieved by using conventional camera calibration algorithms to perform separate estimations ... --- paper_title: A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure paper_content: Visual inspection of civil infrastructure is essential for condition assessment.We focus on concrete bridges, tunnels, underground pipes, and asphalt pavements.Accordingly, we review the latest computer vision based defect detection methods.Using computer vision most relevant types of defects can be automatically detected.Automatic defect properties retrieval and assessment has not been achieved yet. To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research. --- paper_title: Local positioning systems versus structural monitoring: a review paper_content: SUMMARY ::: ::: Structural monitoring and structural health monitoring could take advantage from different devices to record the static or dynamic response of a structure. A positioning system provides displacement information on the location of moving objects, which is assumed to be the basic support to calibrate any structural mechanics model. The global positioning system could provide satisfactory accuracy in absolute displacement measurements. But the requirements of an open area position for the antennas and a roofed room for its data storage and power supply limit its flexibility and its applications. Several efforts are done to extend its field of application. The alternative is local positioning system. Non-contact sensors can be easily installed on existing infrastructure in different locations without changing their properties: several technological approaches have been exploited: laser-based, radar-based, vision-based, etc. In this paper, a number of existing options, together with their performances, are reviewed. Copyright © 2014 John Wiley & Sons, Ltd. --- paper_title: Multi-image stitching and scene reconstruction for evaluating defect evolution in structures paper_content: It is well-recognized that civil infrastructure monitoring approaches that rely on visual approaches will continue to be an important methodology for condition assessment of such systems. Current inspection standards for structures such as bridges require an inspector to travel to a target structure site and visually assess the structure's condition. A less time-consuming and inexpensive alternative to current visual monitoring methods is to use a system that could inspect structures remotely and also more frequently. This article presents and evaluates the underlying technical elements for the development of an integrated inspection software tool that is based on the use of inexpensive digital cameras. For this purpose, digital cameras are appropriately mounted on a structure (e.g., a bridge) and can zoom or rotate in three directions (similar to traffic cameras). They are remotely controlled by an inspector, which allows the visual assessment of the structure's condition by looking at images captured by... --- paper_title: Yarn features extraction using image processing and computer vision: a study with cotton and polyester yarns paper_content: Abstract The aim of this paper is the development of a new technological solution, for the automatic characterization of the yarn mass parameters (linear mass, diameter, and hairiness) based on image processing and computer vision techniques. A preliminary study for the detection and distinction between loop and protruding fibers is also presented. A custom-made application developed in LabVIEW from National Instruments with the IMAQ Vision Toolkit was used to acquire, analyze and process the yarn images. Some experimental results using cotton and polyester yarns were performed and compared with a commercial solution for validation. The presented approach allows a correct yarn parameterization improving products’ quality in the textile industry. --- paper_title: Experimental Study of Dynamic Crack Growth in Unidirectional Graphite/Epoxy Composites using Digital Image Correlation Method and High-speed Photography paper_content: In this work, fracture behavior of multilayered unidirectional graphite/epoxy composite (T800/3900-2) materials is investigated. Rectangular coupons with a single-edged notch are studied under geometrically symmetric loading configurations and impact loading conditions. The notch orientation parallel to or at an angle to the fiber orientation is considered to produce mode-I or mixed-mode (mode-I and -II) fracture. Feasibility of studying stress-wave induced crack initiation and rapid crack growth in fiber-reinforced composites using the digital image correlation method and high-speed photography is demonstrated. Analysis of photographed random speckles on specimen surface provides information pertaining to crack growth history as well as surface deformations in the crack-tip vicinity. Measured deformation fields are used to estimate mixed-mode fracture parameters and examine the effect of fiber orientation (β) on crack initiation and growth behaviors. The samples show differences in fracture responses dep... --- paper_title: Close-range photogrammetry applications in bridge measurement: Literature review paper_content: Abstract Close-range photogrammetry has found many diverse applications in the fields of industry, biomechanics, chemistry, biology, archaeology, architecture, automotive, and aerospace, as well as accident reconstruction. Although close-range photogrammetry has not been as popular in bridge engineering as in other fields, the investigations that have been conducted demonstrate the potential of this technique. The availability of inexpensive, off-the-shelf digital cameras and soft-copy, photogrammetry software systems has made close-range photogrammetry much more feasible and affordable for bridge engineering applications. To increase awareness of the use of this powerful non-contact, non-destructive technique in the bridge engineering field, this paper presents a literature review on the basic development of close-range photogrammetry and briefly describes previous work related to bridge deformation and geometry measurement; structural test monitoring; and historic documentation. The major aspects of photogrammetry bridge measurement are covered starting from the late 1970s and include a description of measurement types, cameras, targets, network control, and software. It is shown that early applications featured the use of metric cameras (specially designed for photogrammetry purposes), diffuse targets (non-retroreflective), stereoscopic photogrammetry network layout, and analog analytical tools, which transformed over time to the use of non-metric cameras, retro-reflective targets, highly convergent network layout, and digital computerized analytical tools. --- paper_title: Monitoring-Based Fatigue Reliability Assessment of Steel Bridges: Analytical Model and Application paper_content: A fatigue reliability model which integrates the probability distribution of hot spot stress range with a continuous probabilistic formulation of Miner's damage cumulative rule is developed for fatigue life and reliability evaluation of steel bridges with long-term monitoring data. By considering both the nominal stress obtained by measurements and the corresponding stress concentration factor (SCF) as random variables, a probabilistic model of the hot spot stress is formulated with the use of the S-N curve and the Miner's rule, which is then used to evaluate the fatigue life and failure probability with the aid of structural reliability theory. The proposed method is illustrated using the long-term strain monitoring data from the instrumented Tsing Ma Bridge. A standard daily stress spectrum accounting for highway traffic, railway traffic, and typhoon effects is derived by use of the monitoring data. Then global and local finite element models (FEMs) of the bridge are developed for numerically calculating the SCFs at fatigue-susceptible locations, while the stochastic characteristics of SCF for a typical welded T-joint are obtained by full-scale model experiments of a railway beam section of the bridge. A multimodal probability density function (PDF) of the stress range is derived from the monitoring data using the finite mixed Weibull distributions in conjunction with a hybrid parameter estimation algorithm. The failure probability and reliability index versus fatigue life are achieved from the obtained joint PDF of the hot spot stress in terms of the nominal stress and SCF random variables. --- paper_title: Modeling of Stress Spectrum Using Long-Term Monitoring Data and Finite Mixture Distributions paper_content: This study focuses on how to exploit long-term monitoring data of structural strain for analytical modeling of multimodal rainflow-counted stress spectra by use of the method of finite mixture distributions in conjunction with a hybrid mixture parameter estimation algorithm. The long-term strain data acquired from an instrumented bridge carrying both highway and railway traffic is used to verify the procedure. A wavelet-based filtering technique is first applied to eliminate the temperature effect inherent in the measured strain data. The stress spectrum is obtained by extracting the stress range and mean stress from the stress time histories with the aid of a rainflow counting algorithm. By synthesizing the features captured from daily stress spectra, a representative sample of stress spectrum accounting for multiple loading effects is derived. Then, the modeling of the multimodal stress range is performed by use of finite mixed normal, lognormal, and Weibull distributions, with the best mixed distributi... --- paper_title: Vision Metrology And Three Dimensional Visualization In Structural Testing And Monitoring paper_content: This paper describes initial work carried out to advance the Erse of vision metrology and three dimensional visualization techniques in structural testing and monitoring. A description is given of a methodology for semi-automatically generating dynamic three dimensional computer graphics models, which is employed to provide a three dimensional spatial framework for conventional engineering instrumentation. The use of techniques developed for two structural measurement applications is explained and opportunities for current and future research are reported. --- paper_title: Dynamic displacement measurement of large-scale structures based on the Lucas–Kanade template tracking algorithm paper_content: Abstract The development of optics and computer technologies enables the application of the vision-based technique that uses digital cameras to the displacement measurement of large-scale structures. Compared with traditional contact measurements, vision-based technique allows for remote measurement, has a non-intrusive characteristic, and does not necessitate mass introduction. In this study, a high-speed camera system is developed to complete the displacement measurement in real time. The system consists of a high-speed camera and a notebook computer. The high-speed camera can capture images at a speed of hundreds of frames per second. To process the captured images in computer, the Lucas–Kanade template tracking algorithm in the field of computer vision is introduced. Additionally, a modified inverse compositional algorithm is proposed to reduce the computing time of the original algorithm and improve the efficiency further. The modified algorithm can rapidly accomplish one displacement extraction within 1 ms without having to install any pre-designed target panel onto the structures in advance. The accuracy and the efficiency of the system in the remote measurement of dynamic displacement are demonstrated in the experiments on motion platform and sound barrier on suspension viaduct. Experimental results show that the proposed algorithm can extract accurate displacement signal and accomplish the vibration measurement of large-scale structures. --- paper_title: Systematic errors in digital image correlation due to undermatched subset shape functions paper_content: Digital image correlation techniques are commonly used to measure specimen displacements by finding correspondences between an image of the specimen in an undeformed or reference configuration and a second image under load. To establish correspondences between the two images, numerical techniques are used to locate an initially square image subset in a reference image within an image taken under load. During this process, shape functions of varying order can be applied to the initially square subset. Zero order shape functions permit the subset to translate rigidly, while first-order shape functions represent an affine transform of the subset that permits a combination of translation, rotation, shear and normal strains. --- paper_title: Accurate and robust ROI localization in a camshift tracking application paper_content: Camshift has been well accepted as one of the most popular methods for object tracking. However, it fails to address complex situations, such as similar color interference, object occlusion, and illumination changes. In this paper, we enhance Camshift to enable it to handle the above-mentioned problems. A two-dimensional (2D) histogram of the hue and luminance is used for the color features of the target. To reduce the influence from irrelevant background pixels, a Flood-fill operation is implemented. The obtained 2D target model can precisely describe the target as well as achromatic points. A similarity score is evaluated to prevent similar color interference. When a target's colors are not distinguishable from the background colors, motion information will contribute to the tracking task. Finally, an average rate change is adopted to maintain progressive but not abrupt changes in the window size. The proposed algorithm has been extensively tested. The results are satisfactory while maintaining the processing in real time. --- paper_title: A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification paper_content: Dynamic displacement of structures is an important index for in-service structural condition and behavior assessment, but accurate measurement of structural displacement for large-scale civil structures such as long-span bridges still remains as a challenging task. In this paper, a vision-based dynamic displacement measurement system with the use of digital image processing technology is developed, which is featured by its distinctive characteristics in non-contact, long-distance, and high-precision structural displacement measurement. The hardware of this system is mainly composed of a high-resolution industrial CCD (charge-coupled-device) digital camera and an extended-range zoom lens. Through continuously tracing and identifying a target on the structure, the structural displacement is derived through cross-correlation analysis between the predefined pattern and the captured digital images with the aid of a pattern matching algorithm. To validate the developed system, MTS tests of sinusoidal motions under different vibration frequencies and amplitudes and shaking table tests with different excitations (the El-Centro earthquake wave and a sinusoidal motion) are carried out. Additionally, in-situ verification experiments are performed to measure the mid-span vertical displacement of the suspension Tsing Ma Bridge in the operational condition and the cable-stayed Stonecutters Bridge during loading tests. The obtained results show that the developed system exhibits an excellent capability in real-time measurement of structural displacement and can serve as a good complement to the traditional sensors. --- paper_title: Vision-based structural displacement measurement: System performance evaluation and influence factor analysis paper_content: Abstract In the past decade, the emerging machine vision-based measurement technology has gained great concerns among civil engineers due to its overwhelming merits of non-contact, long-distance, and high-resolution. A critical issue regarding to the measurement performance and accuracy of the vision-based system is how to identify and eliminate the systematic and unsystematic error sources. In this paper, a vision-based structural displacement measurement system integrated with a digital image processing approach is developed. The performance of the developed vision-based system is evaluated by comparing the results simultaneously obtained by the vision-based system and those measured by the magnetostrictive displacement sensor (MDS). A series of experiments are conducted on a shaking table to examine the influence factors which will affect the accuracy and stability of the vision-based system. It is demonstrated that illumination and vapor have a critical effect on the measurement results of the vision-based system. --- paper_title: Multi-point displacement monitoring of bridges using a vision-based approach paper_content: To overcome the drawbacks of the traditional contact-type sensor for structural displacement measurement, the vision-based technology with the aid of the digital image processing algorithm has received increasing concerns from the community of structural health monitoring (SHM). The advanced vision-based system has been widely used to measure the structural displacement of civil engineering structures due to its overwhelming merits of non-contact, long-distance, and high-resolution. However, seldom currently-available vision-based systems are capable of realizing the synchronous structural displacement measurement for multiple points on the investigated structure. In this paper, the method for vision-based multi-point structural displacement measurement is presented. A series of moving loading experiments on a scale arch bridge model are carried out to validate the accuracy and reliability of the vision-based system for multi-point structural displacement measurement. The structural displacements of five points on the bridge deck are measured by the vision-based system and compared with those obtained by the linear variable differential transformer (LVDT). The comparative study demonstrates that the vision-based system is deemed to be an effective and reliable means for multi-point structural displacement measurement. --- paper_title: Image-based structural dynamic displacement measurement using different multi-object tracking algorithms paper_content: With the help of advanced image acquisition and processing technology, the vision-based measurement methods have been broadly applied to implement the structural monitoring and condition identification of civil engineering structures. Many noncontact approaches enabled by different digital image processing algorithms are developed to overcome the problems in conventional structural dynamic displacement measurement. This paper presents three kinds of image processing algorithms for structural dynamic displacement measurement, i.e., the grayscale pattern matching (GPM) algorithm, the color pattern matching (CPM) algorithm, and the mean shift tracking (MST) algorithm. A vision-based system programmed with the three image processing algorithms is developed for multi-point structural dynamic displacement measurement. The dynamic displacement time histories of multiple vision points are simultaneously measured by the vision-based system and the magnetostrictive displacement sensor (MDS) during the laboratory shaking table tests of a three-story steel frame model. The comparative analysis results indicate that the developed vision-based system exhibits excellent performance in structural dynamic displacement measurement by use of the three different image processing algorithms. The field application experiments are also carried out on an arch bridge for the measurement of displacement influence lines during the loading tests to validate the effectiveness of the vision-based system. --- paper_title: Calibration methodology of a vision system for measuring the displacements of long-deck suspension bridges paper_content: SUMMARY ::: ::: Structural health monitoring is an emergent powerful diagnostic tool that can be used to identify and to prevent possible failures of the various components that comprise an infrastructure. In a suspension bridge, the measurement of the vertical and transversal displacements plays an important role for its safety evaluation. Considering the restrictions usually found on these structures, an enhanced solution comprises a non-contact vision-based measurement system with dynamic response, accuracy and amplitude range well suited to the physical phenomenon to measure. ::: ::: ::: ::: A methodology to perform the vision system calibration is described, which can be carried out in situ, while the deck is moving, requiring little effort and a minimum set of information. The key idea is to use the factorization method to get an initial estimation of the object shape and camera's parameters, and to incorporate the knowledge about the distances between the calibration targets in a non-linear optimization process to achieve a metric shape reconstruction and optimize the camera's parameters estimation. ::: ::: ::: ::: Results obtained by numerical simulation experiments are reported, showing the negligible influence of noisy calibration data in the calibration performance and also the robustness of the calibration process on the camera's parameters estimation. Tests carried out to assess the calibration and tracking of the bridge deck motion showed that, even in an environmental severely affected by noise, it is possible to measure the vertical and transversal displacements of the bridge deck with excellent levels of accuracy. Copyright © 2011 John Wiley & Sons, Ltd. --- paper_title: Dynamic displacement measurement of large-scale structures based on the Lucas–Kanade template tracking algorithm paper_content: Abstract The development of optics and computer technologies enables the application of the vision-based technique that uses digital cameras to the displacement measurement of large-scale structures. Compared with traditional contact measurements, vision-based technique allows for remote measurement, has a non-intrusive characteristic, and does not necessitate mass introduction. In this study, a high-speed camera system is developed to complete the displacement measurement in real time. The system consists of a high-speed camera and a notebook computer. The high-speed camera can capture images at a speed of hundreds of frames per second. To process the captured images in computer, the Lucas–Kanade template tracking algorithm in the field of computer vision is introduced. Additionally, a modified inverse compositional algorithm is proposed to reduce the computing time of the original algorithm and improve the efficiency further. The modified algorithm can rapidly accomplish one displacement extraction within 1 ms without having to install any pre-designed target panel onto the structures in advance. The accuracy and the efficiency of the system in the remote measurement of dynamic displacement are demonstrated in the experiments on motion platform and sound barrier on suspension viaduct. Experimental results show that the proposed algorithm can extract accurate displacement signal and accomplish the vibration measurement of large-scale structures. --- paper_title: Vision-Based Displacement Sensor for Monitoring Dynamic Response Using Robust Object Search Algorithm paper_content: This study develops a vision-based displacement measurement system for remote monitoring of vibration of large-size structures such as bridges and buildings. The system consists of one or multiple video cameras and a notebook computer. With a telescopic lens, the camera placed at a stationary point away from a structure captures images of an object on the structure. The structural displacement is computed in real time through processing the captured images. A robust object search algorithm developed in this study enables accurate measurement of the displacement by tracking existing features on the structure without requiring a conventional target panel to be installed on the structure. The efficacy of the system in remote measurement of dynamic displacements was demonstrated through a shaking table test and a field experiment on a long-span bridge. --- paper_title: Vertical Displacement Measurements for Bridges Using Optical Fiber Sensors and CCD Cameras — A Preliminary Study paper_content: Bridge managers all over the world are always looking for simple ways to measure bridge vertical displacements for structural health monitoring. However, traditional methods to obtain such data are either tedious or expensive. There is a need to develop a simple, inexpensive, and yet practical method to measure bridge vertical displacements. This paper proposes two methods using either optical fiber (FBG) sensors or a charge-coupled-device (CCD) camera, respectively, for vertical displacement measurements of bridges. The FBG sensor method is based on the measured horizontal strains together with the identified curvature functions obtained by a self-developed FBG Tilt sensor. CCD cameras use a large number of pixels to form an image. The CCD camera method utilizes image processing techniques for pixel identification and subsequent edge detection. A preliminary study to validate the proposed methods in laboratory was presented. The tests include applying the methods to determine the vertical displacements s... --- paper_title: Vibration Monitoring of Multiple Bridge Points by Means of a Unique Vision-Based Measuring System paper_content: Bridge static and dynamic vibration monitoring is a key activity for both safety and maintenance purposes. The development of vision-based systems allows to use this type of devices for remote estimation of a bridge vibration, simplifying the measuring system installation. The uncertainty of this type of measurements is strongly related to the experimental conditions (mainly the pixel-to-millimeters conversion, the target texture, the camera characteristics and the image processing technique). In this paper two different types of cameras are used to monitor the response of a bridge to a train pass-by. The acquired images are analyzed using three different image processing techniques (Pattern Matching, Edge Detection and Digital Image Correlation) and the results are compared with a reference measurement, obtained by a laser interferometer providing single point measurements. Tests with different zoom levels are shown and the corresponding uncertainty values are estimated. As the zoom level decreases it is possible not only to measure the displacement of one point of the bridge, but also to grab images from a wide structure portion in order to recover displacements of a large number of points in the field of view. The extreme final solution would be having wide area measurements with no targets, to make measurements really easy, with clear advantages, but also with some drawbacks in terms of uncertainty to be fully comprehended. --- paper_title: Remote sensing of building structural displacements using a microwave interferometer with imaging capability paper_content: Phase interference of microwave images has been experimented for remote submillimeter-accuracy detection of structural displacements of a real-scale building, subject to tensional stress. The images are obtained by a synthetic-aperture interferometric radar, making use of continuous-wave step-frequency waveform. Phase information of the synthesized microwave images is exploited for detecting displacements of the illuminated structure. --- paper_title: Seismic structural displacement measurement using a line-scan camera: camera-pattern calibration and experimental validation paper_content: This article examines high-speed, line-scan cameras as a robust and high-speed displacement sensor for a range of seismic monitoring applications. They have the additional benefit of requiring no invasive mechanisms or added processing to provide a high-resolution output measure, and do not interfere architecturally. Following the method proposed by Lim et al. for measuring foundation pile movements, multiple displacements and motions of any structure can be determined in real time at rates over 1 kHz using only one high-speed, line-scan camera and a special pattern. This resolution is more than sufficient for structural monitoring and control problems. Moreover, a novel edge tracking algorithm is proposed that enables high-resolution measurement of large motions using relatively low-resolution, line-scan cameras. Further, as the accuracy of the measurement results depends directly on camera-pattern calibration and satisfying the assumptions made by Lim et al., an easy-to-implement calibration procedure is developed that ensures the accuracy of the measurement results. Finally, versatility of the total measurement procedure is examined through both harmonic and random vibration experiments with a suite of different input motions applied to a computer-controlled cart. Comparing the input and the measured motions confirms that vision-based structural displacement measurement utilising a high-speed, line-scan camera offers a robust, high-resolution and low-cost means of measuring structural vibration displacements. --- paper_title: 3D displacement measurement model for health monitoring of structures using a motion capture system paper_content: Abstract Unlike 1D or 2D displacement measurement sensors, a motion capture system (MCS) can determine the movement of markers in any direction precisely. In addition, an MCS can overcome the limitations of the sampling frequency in 3D measurements by terrestrial laser scanning (TLS) and global positioning system (GPS). This paper presents a method to measure three dimensional (3D) structural displacements using a motion capture system (MCS) with a high accuracy and sampling rate. The MCS measures 2D coordinates of a number of markers with multiple cameras; these measurements are then used to calculate the 3D coordinates of markers. Therefore, unlike previous 1D or 2D displacement measurement sensors, the MCS can determine precisely the movement of markers in any direction. In addition, since the MCS cameras can monitor several markers, measurement points are increased by the addition of more markers. The effectiveness of the proposed model was tested by comparing the displacements measured in a free vibration experiment of a 3-story structure with a height of 2.1 m using both the MCS and laser displacement sensors. --- paper_title: Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system paper_content: Abstract This article describes the development of a non-contact dynamic displacement measurement system for railway bridges based on video technology. The system, consisting of a high speed video camera, an optical lens, lighting lamps and a precision target, can perform measurements with acquisition frame rates ranging from 64 fps to 500 fps, and be articulated with other measurement systems, which promotes its integration in structural health monitoring (SHM) systems. Preliminary tests of the system have shown that the measurements’ precision can be affected by: (i) movements of the camera stand and, therefore, rigid supports should be used and the camera should be protected from air flows; (ii) the distortion of the field of view, caused by the flow of heat waves generated by IR incandescent lighting and, therefore, the operating time of the lamps should be limited. The system was used to measure the displacement of a railway bridge’s deck, induced by the passage of trains at speeds between 120 km/h and 180 km/h, yielding a very good agreement between the results of displacement measurement obtained with the video system and with a LVDT. The achieved precision was below 0.1 mm for distances from the camera to the target up to 15 m, and in the order of 0.25 mm for a distance of 25 m. The application of an image processing technique at subpixel level resulted in real precisions generally inferior to the theoretical precisions. --- paper_title: Speckle pattern quality assessment for digital image correlation paper_content: Abstract To perform digital image correlation (DIC), each image is divided into groups of pixels known as subsets or interrogation cells. Larger interrogation cells allow greater strain precision but reduce the spatial resolution of the data field. As such the spatial resolution and measurement precision of DIC are limited by the resolution of the image. In the paper the relationship between the size and density of speckles within a pattern is presented, identifying that the physical properties of a pattern have a large influence on the measurement precision which can be obtained. These physical properties are often overlooked by pattern assessment criteria which focus on the global image information content. To address this, a robust morphological methodology using edge detection is devised to evaluate the physical properties of different speckle patterns with image resolutions from 23 to 705 pixels/mm. Trends predicted from the pattern property analysis are assessed against simulated deformations identifying how small changes to the application method can result in large changes in measurement precision. An example of the methodology is included to demonstrate that the pattern properties derived from the analysis can be used to indicate pattern quality and hence minimise DIC measurement errors. Experiments are described that were conducted to validate the findings of morphological assessment and the error analysis. --- paper_title: Systematic errors in digital image correlation caused by intensity interpolation paper_content: Recently, digital image correlation as a tool for surface defor- mation measurements has found widespread use and acceptance in the field of experimental mechanics. The method is known to reconstruct displacements with subpixel accuracy that depends on various factors such as image quality, noise, and the correlation algorithm chosen. How- ever, the systematic errors of the method have not been studied in detail. We address the systematic errors of the iterative spatial domain cross- correlation algorithm caused by gray-value interpolation. We investigate the position-dependent bias in a numerical study and show that it can lead to apparent strains of the order of 40% of the actual strain level. Furthermore, we present methods to reduce this bias to acceptable lev- els. © 2000 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(00)00911-9) --- paper_title: Recognition of camera-captured low-quality characters using motion blur information paper_content: Camera-based character recognition has gained attention with the growing use of camera-equipped portable devices. One of the most challenging problems in recognizing characters with hand-held cameras is that captured images undergo motion blur due to the vibration of the hand. Since it is difficult to remove the motion blur from small characters via image restoration, we propose a recognition method without de-blurring. The proposed method includes a generative learning method in the training step to simulate blurred images by controlling blur parameters. The method consists of two steps. The first step recognizes the blurred characters based on the subspace method, and the second one reclassifies structurally similar characters using blur parameters estimated from the camera motion. We have experimentally proved that the effective use of motion blur improves the recognition accuracy of camera-captured characters. --- paper_title: Study of systematic errors in strain fields obtained via DIC using heterogeneous deformation generated by plastic FEA paper_content: In this article, systematic errors that arise in the derivation of strain fields based on displacement fields obtained by digital image correlation (DIC) are analysed. Attention is paid to errors that arise from different implementations of the DIC technique. In particular, we investigate the influence of the shape function, the interpolation order and the subset size on the derived strains. In addition, we focus on errors that can be directly attributed to the derivation of the strain fields, e.g. the strain-window size and the strain-window interpolation order. The errors are estimated using numerically deformed images that were obtained by imposing finite element (FE) displacement fields on an undeformed image yielding plastic deformation of the specimen. This FE procedure simulates realistic experimental heterogeneous deformations at various load steps. It is shown that the errors on the strain fields can be substantially reduced if conscious choices in the abovementioned implementations are made. --- paper_title: The errors in digital image correlation due to overmatched shape functions paper_content: In subset-based digital image correlation (DIC), a proper shape function must be chosen to approximate the underlying displacement field of the target subsets to ensure an accurate subset matching. Shape functions with varying orders of Taylor's expansions (e.g. zero-, first- and second-order) have been proposed. However, since the actual deformation occurring in the deformed subsets cannot be known a priori in most practical measurements, problems of mismatch (undermatched or overmatched) inevitably arise, which lead to additional errors in the measurement displacements. Although systematic errors due to undermatched shape functions have been thoroughly studied, the displacement errors associated with overmatched shape functions are still not sufficiently clear to us. In this paper, the systematic and random errors caused by the use of overmatched shape functions are first examined using numerical translation tests with precisely controlled subpixel motions. The results reveal that overmatched shape functions will not introduce additional systematic error but can induce increased random error, while the latter is negligibly small when a larger subset is used. Thus, it can be made explicit that second-order shape functions, capable of depicting more complex local deformation, can be used for practical DIC applications as a default, because this effectively eliminates the possible systematic error associated with the use of first-order shape functions, while the possibly increased random errors are small enough if using a relatively larger subset. Two sets of images with inhomogeneous deformation are also used to verify the accuracy of DIC measurements using second-order shape functions. --- paper_title: Blind Deblurring and Denoising of Images Corrupted by Unidirectional Object Motion Blur and Sensor Noise paper_content: Low light photography suffers from blur and noise. In this paper, we propose a novel method to recover a dense estimate of spatially varying blur kernel as well as a denoised and deblurred image from a single noisy and object motion blurred image. A proposed method takes the advantage of the sparse representation of double discrete wavelet transform-a generative model of image blur that simplifies the wavelet analysis of a blurred image-and the Bayesian perspective of modeling the prior distribution of the latent sharp wavelet coefficient and the likelihood function that makes the noise handling explicit. We demonstrate the effectiveness of the proposed method on moderate noise and severely blurred images using simulated and real camera data. --- paper_title: Dynamics-based motion de-blurring for a PZT-driven, compliant camera orientation mechanism paper_content: This paper proposes a method for removing motion blur from images captured by a fast-moving robot eye. Existing image techniques focused on recovering blurry images due to camera shake with long exposure time. In addition, previous studies relied solely on properties of the images or used external sensors to estimate a blur kernel, or point spread function PSF. This paper focuses on estimating a latent image from the blur images taken by the robotic camera orientation system. A PZT-driven, compliant camera orientation system was employed to demonstrate the effectiveness of this approach. Discrete switching commands were given to the robotic system to create a rapid point-to-point motion while suppressing the vibration with a faster response. The blurry images were obtained when the robotic system created a rapid point-to-point motion, like human saccadic motion. This paper proposes a method for estimating the PSF in knowledge of system dynamics and input commands, resulting in a faster estimation. The proposed method was investigated under various motion conditions using the single-degree-of-freedom camera orientation system to verify the effectiveness and was compared with other approaches quantitatively and qualitatively. The experiment results show that overall the performance metric of the proposed method was 27.77% better than conventional methods. The computation time of the proposed method was 50 times faster than that of conventional methods. --- paper_title: Assessment of measuring errors in DIC using deformation fields generated by plastic FEA paper_content: In this article, systematic errors that arise from different implementations of digital image correlation (DIC) techniques are analyzed. In particular, we investigate the influence of the adopted correlation function, the interpolation order, the shape function and the subset size on the derived displacements. These errors are estimated using numerically deformed images that were obtained by imposing finite element (FE) displacement fields on an undeformed image yielding plastic deformation of the specimen. This FE procedure simulates realistic experimental heterogeneous deformations at various load steps. It is shown that DIC is able to reproduce these displacements up to a satisfactory level if conscious choices in the above-mentioned implementations are made. --- paper_title: Lens distortion correction for digital image correlation by measuring rigid body displacement paper_content: A method of lens distortion correction is proposed in order to improve the measurement accuracy of digital image correlation for two-dimensional displacement measurement. The amounts of lens distortion are evaluated from displacement distributions obtained in a rigid body in-plane translation or rotation test. After detecting the lens distortion, its coefficient is determined using the method of least squares. Then, the corrected displacement distributions are obtained. The effectiveness of the proposed method is demonstrated by applying the correction method to an in-plane translation test and tension tests. The experimental results show that the proposed distortion correction method eliminates the effect of lens distortion from measured displacements. --- paper_title: Systematic errors in digital image correlation due to undermatched subset shape functions paper_content: Digital image correlation techniques are commonly used to measure specimen displacements by finding correspondences between an image of the specimen in an undeformed or reference configuration and a second image under load. To establish correspondences between the two images, numerical techniques are used to locate an initially square image subset in a reference image within an image taken under load. During this process, shape functions of varying order can be applied to the initially square subset. Zero order shape functions permit the subset to translate rigidly, while first-order shape functions represent an affine transform of the subset that permits a combination of translation, rotation, shear and normal strains. --- paper_title: Use of rigid-body motion for the investigation and estimation of the measurement errors related to digital image correlation technique paper_content: The aim of this work is to investigate the sources of errors related to digital image correlation (DIC) technique applied to strain measurements. The knowledge of such information is important before the measured kinematic fields can be exploited. After recalling the principle of DIC, some sources of errors related to this technique are listed. Both numerical and experimental tests, based on rigid-body motion, are proposed. These tests are simple and easy-to-implement. They permit to quickly assess the errors related to lighting, the optical lens (distortion), the CCD sensor, the out-of-plane displacement, the speckle pattern, the grid pitch, the size of the subset and the correlation algorithm. The errors sources that cannot be uncoupled were estimated by amplifying their contribution to the global error. The obtained results permit to address a classification of the error related to the used equipment. The paper ends by some suggestions proposed in order to minimize the errors. --- paper_title: Experimental Analysis of the Errors due to Polynomial Interpolation in Digital Image Correlation paper_content: Digital image correlation attempts to estimate displacement fields by digitally correlating two images acquired before and after motion. To do so, pixel intensity has to be interpolated at non-integer locations. The ideal interpolator is the sinc, but as it requires infinite support, it is not normally used and is replaced by polynomials. Polynomial interpolation produces visually appealing results but introduces positional errors in the signal, thus causing the digital image correlation algorithms to converge to incorrect results. In this work, an experimental campaign is described, that aims to characterise the errors introduced by interpolation, focusing in particular on the systematic error and the standard deviation of displacements. --- paper_title: The systematic error in digital image correlation induced by self-heating of a digital camera paper_content: The systematic strain measurement error in digital image correlation (DIC) induced by self-heating of digital CCD and CMOS cameras was extensively studied, and an experimental and data analysis procedure has been proposed and two parameters have been suggested to examine and evaluate this. Six digital cameras of four different types were tested to define the strain errors, and it was found that each camera needed between 1 and 2 h to reach a stable heat balance, with a measured temperature increase of around 10 ?C. During the temperature increase, the virtual image expansion will cause a 70?230 ?? strain error in the DIC measurement, which is large enough to be noticed in most DIC experiments and hence should be eliminated. --- paper_title: Study of optimal subset size in digital image correlation of speckle pattern images paper_content: This paper investigates the effect of subset size, associated with image pattern quality and subset displacement functions, on the accuracy of deformation measurements by digital image correlation(DIC). A concept of subset entropy is introduced in this work to quantify the subset image pattern quality for DIC analysis and its effectiveness was demonstrated by experimental studies. By employing white-light images with almost uniform subset entropy and first-order displacement functions, the effect of subset size on DIC analysis was investigated for the deformation cases of translation, uniform deformation, and simulated quadratic deformation, respectively. The results show that the chosen subset size must be large enough for precise displacement measurements when subset displacement functions match underlying actual deformation. On the other hand, optimal subset size in DIC for nonhomgeneous deformation measurements appears as a result of a tradeoff between the influence of random errors and systematic errors. --- paper_title: Motion-blur evaluation: A comparison of approaches paper_content: — In this paper, the results obtained from two independent evaluations of motion-blur effects with respect to the agreement between the two different approaches used, imaging and non-imaging, are analyzed. The measurements have been carried out in different laboratories by different operators without the prior intention of a subsequent analysis as presented here. The resulting data is analyzed to quantify the repeatability of each instrument and, in a second step, the comparability of results from the two approaches is investigated. The imaging approach used in these experiments is based on a stationary high-speed camera with temporal oversampling and numerical image-data processing to obtain the intensity distribution on the retina of an observer under the condition of smooth pursuit eye tracking. Results from that approach are compared to results obtained from the evaluation of step responses acquired with optical transient recorders by frame-period convolution. Measurements are carried out with a first LCD monitor with test patterns of both contrast polarities, with three velocities of translation, and four levels of gray. A second object of measurement is used for investigation of the effect of operator intervention in the process of evaluation of the imaging approach, especially on the determination of the reference levels that are needed for evaluation of the normalized blurred edge (NBE). Possible sources of uncertainties are identified for all approaches and instruments. Based on the analysis of that data, the practicability of step-response-based evaluations of the “blurred edge width/time” compared to the results obtained using the high-speed imaging approach, as long as there is no motion-dependent image processing, are confirmed. --- paper_title: Restoration of TDI camera images with motion distortion and blur paper_content: Abstract Platform movement during exposure of imaging system severely degrades image quality. In the case of Time delay and integration (TDI) camera, abnormal movements cause not only image blur but also distortion, for image Point Spread Function (PSF) is space-variant. In this paper, we present a motion degradation model of TDI image, and provide a method to restore such degraded image. While a TDI camera is imaging, it outputs images row by row (or line by line) along the scanning axis, and our method processes in the same track. We firstly calculate the space-invariant PSF of each row using the movement information of the TDI camera. Then, we substitute pixels of the row and the ones of their neighbor rows together with the PSF into standard Richardson–Lucy algorithm. By deconvoluting we get the restored pixels of the row. The same operations are executed for all rows of the degraded TDI image. Finally, a restored image can be reconstructed from those restored rows. Both simulated and experimental results prove the effectiveness of our method. --- paper_title: Adaptive subset offset for systematic error reduction in incremental digital image correlation paper_content: Abstract Digital image correlation (DIC) relies on a high correlation between the intensities in the reference image and the target image. When decorrelation occurs due to large deformation or viewpoint change, incremental DIC is utilized to update the reference image and use the correspondences in this renewed image as the reference points in subsequent DIC computation. As each updated reference point is derived from previous correlation, its location is generally of sub-pixel accuracy. Conventional subset which is centered at the point results in subset points at non-integer positions. Therefore, the acquisition of the intensities of the subset demands interpolation which is proved to introduce additional systematic error. We hereby present adaptive subset offset to slightly translate the subset so that all the subset points fall on integer positions. By this means, interpolation in the updated reference image is totally avoided regardless of the non-integer locations of the reference points. The translation is determined according to the decimal of the reference point location, and the maximum are half a pixel in each direction. Such small translation has no negative effect on the compatibility of the widely used shape functions, correlation functions and the optimization algorithms. The results of the simulation and the real-world experiments show that adaptive subset offset produces lower measurement error than the conventional method in incremental DIC when applied in both 2D-DIC and 3D-DIC. --- paper_title: Recovering ball motion from a single motion-blurred image paper_content: Motion blur often affects the ball image in photographs and video frames in many sports such as tennis, table tennis, squash and golf. In this work, we operate on a single calibrated image depicting a moving ball over a known background, and show that motion-blurred ball images, usually unwelcome in computer vision, bear more information than a sharp image. We provide techniques for extracting such information ranging from low-level image processing to 3D reconstruction, and present a number of experiments and possible applications, such as ball localization with speed and direction measurement from a single image, and ball trajectory reconstruction from a single long-exposure photograph. --- paper_title: Long Deck Suspension Bridge Monitoring: The Vision System Calibration Problem paper_content: : Structural Health Monitoring is an emergent powerful diagnostic tool that can be used to identify and to prevent possible failures of the various components that comprise an infrastructure. In the particular case of a suspension bridge, the measurement of the vertical and transversal displacements plays an important role for its safety evaluation. Taking into account the restrictions usually found on these structures, an enhanced solution comprises a non-contact vision-based measurement system with dynamic response, accuracy and amplitude range well suited to the physical phenomenon to measure. The paper describes a methodology to perform the vision system calibration that can be carried out in-situ, while the deck is moving, requiring little effort and a minimum set of information. Specifically, only a set of active targets fixed on the deck and the knowledge about the distances between them is required. Results related to the performance evaluation, obtained by numerical simulation and by real experiments with a reduced structure model, are presented and they show that, even in an environment severely affected by disturbance noise, it is possible to measure the vertical and transversal displacements with a standard accuracy better than 10 mm. --- paper_title: Study of image characteristics on digital image correlation error assessment paper_content: In this paper, errors related to digital image correlation (DIC) technique applied to measurements of displacements are estimated. This work is based on the generation of synthetic images representative of real speckle patterns. With these images, various parameters are treated in order to determine their impact on the measurement error. These parameters are related to the type of deformation imposed on the speckle, the speckle itself (encoding of the image and image saturation) or the software (subset size). --- paper_title: Error estimation in measuring strain fields with DIC on planar sheet metal specimens with a non-perpendicular camera alignment paper_content: Abstract The determination of strain fields based on displacement components obtained via 2D-DIC is subject to several errors that originate from various sources. In this contribution, we study the impact of a non-perpendicular camera alignment to a planar sheet metal specimen's surface. The errors are estimated in a numerical experiment. To this purpose, deformed images – that were obtained by imposing finite element (FE) displacement fields on an undeformed image – are numerically rotated for various Euler angles. It is shown that a 3D-DIC stereo configuration induces a substantial compensation for the introduced image-plane displacement gradients. However, higher strain accuracy and precision are obtained – up to the level of a perfect perpendicular alignment – in a proposed “rectified” 2D-DIC setup. This compensating technique gains benefit from both 2D-DIC (single camera view, basic amount of correlation runs, no cross-camera matching nor triangulation) and 3D-DIC (oblique angle compensation). Our conclusions are validated in a real experiment on SS304. --- paper_title: Measurement of sinusoidal vibration from motion blurred images paper_content: Previous vision-based methods usually measure vibration from large sequence of unblurred images recorded by high-speed video or stroboscopic photography. In this paper, we propose a novel method for sinusoidal vibration measurement based on motion blurred images. We represent the motion blur information in images by the relationship between the geometric moments of the motion blurred images and the motion, and estimate the vibration parameters from this motion blur cue. We need only one motion blurred image and an unblurred image or two successive frames of blurred images to calculate the parameters of low-frequency vibration as well as the amplitude and direction of high-frequency vibration, while unblurred-image-based techniques rely on much more images to obtain the same results and existing motion-blurred-image-based approaches only estimate the amplitude of high-frequency vibration. Experimental results with both simulated and real vibrations of low and high frequencies are employed to demonstrate the effectiveness of the proposed scheme. --- paper_title: Quality assessment of speckle patterns for digital image correlation paper_content: Digital image correlation (DIC) is an optical-numerical full-field displacement measuring technique, which is nowadays widely used in the domain of experimental mechanics. The technique is based on a comparison between pictures taken during loading of an object. For an optimal use of the method, the object of interest has to be covered with painted speckles. In the present paper, a comparison is made between three different speckle patterns originated by the same reference speckle pattern. A method is presented for the determination of the speckle size distribution of the speckle patterns, using image morphology. The images of the speckle patterns are numerically deformed based on a finite element simulation. Subsequently, the displacements are measured with DIC-software and compared to the imposed ones. It is shown that the size of the speckles combined with the size of the used pixel subset clearly influences the accuracy of the measured displacements. --- paper_title: Dynamic testing of a laboratory model via vision-based sensing paper_content: In the class of not-contact sensors, the techniques of vision-based displacement estimation enable one to gather dense global measurements of static deformation as well as of dynamic response. They are becoming more and more available thanks to the ongoing technology developments. In this work, a vision system, which takes advantage of fast-developing digital image processing and computer vision technologies and provides high sample rate, is implemented to monitor the 2D plane vibrations of a reduced scale frame mounted on a shaking table as available in a laboratory. The physical meanings of the camera parameters, the trade-off between the system resolution and the field-of-view, and the upper limitation of marker density are discussed. The scale factor approach, which is widely used to convert the image coordinates measured by a vision system in the unit of pixels into space coordinates, causes a poor repeatability of the experiment, an unstable experiment precision, and therefore a global poor flexibility. To overcome these problems, two calibrations approaches are introduced: registration and direct linear transformation. Based on the constructed vision-based displacement measurement system, several experiments are carried out to monitor the motion of a scale-reduced model on which dense markers are glued. The experiment results show that the proposed system can capture and successfully measure the motion of the laboratory model within the required frequency band. --- paper_title: Use of Digital Image Processing in the Monitoring of Deformations in Building Structures. paper_content: Digital image processing was applied to develop a simple, robust and economical measuring system for detecting deformations in building structures. Selected points of the structure are captured by electronic cameras at regular intervals and their positions are measured in the digital image. The target points, in this case light-emitting diodes (LEDs), as well as the cameras used for measuring operate in the infrared range, which means that the quality of the images is not affected by the lighting conditions. Tests in the laboratory showed that using this technique it is possible to track targets over distances typical for building structures within a matter of millimeters. The practical applicability of the method was verified in a pilot project, for which the gym of the Staffelsee High School in Murnau in the district of Garmisch-Partenkirchen was chosen. The wide-span timber roof structure of the building was equipped with a system based on digital image processing gauging the movement of three points on each of the four main beams. Reference measurements can be taken with a built-in laser gauge. To be able to observe the correlation between weather conditions and deformation, the system is complemented by a weather station with snow-cushions on the roof of the building. Furthermore, the monitoring system is connected to multiple alarming devices. --- paper_title: Real-Time Displacement Measurement of a Flexible Bridge Using Digital Image Processing Techniques paper_content: In this study, real-time displacement measurement of bridges was carried out by means of digital image processing techniques. This is innovative, highly cost-effective and easy to implement, and yet maintains the advantages of dynamic measurement and high resolution. First, the measurement point is marked with a target panel of known geometry. A commercial digital video camera with a telescopic lens is installed on a fixed point away from the bridge (e.g., on the coast) or on a pier (abutment), which can be regarded as a fixed point. Then, the video camera takes a motion picture of the target. Meanwhile, the motion of the target is calculated using image processing techniques, which require a texture recognition algorithm, projection of the captured image, and calculation of the actual displacement using target geometry and the number of pixels moved. Field tests were carried out for the verification of the present method. The test results gave sufficient dynamic resolution in amplitude as well as the frequency. Use of this technology for a large suspension bridge is discussed considering the characteristics of such bridges having low natural frequencies within 3 Hz and the maximum displacement of several centimeters. --- paper_title: A simple image-based strain measurement method for measuring the strain fields in an RC-wall experiment paper_content: A simple image-based method for measuring plane strain fields on the surface of specimens in earthquake engineering experiments was developed. This method integrated camera calibration, stereo triangulation, image metric rectification and image template matching techniques to develop a method that was cost-effective, easy to apply and provided a satisfactory level of measurement accuracy. A zero-strain test conducted using this method showed that the measurement accuracy achieved was 0.04 pixels. That is, the relative displacement accuracy achieved was 0.005 mm and the strain accuracy was 0.001. This level of accuracy was achieved using eight-mega-pixel digital cameras to measure a 17 cm × 28 cm measurement region. Cracks that were 0.012 mm wide were identified in the concrete by examining the displacement fields calculated through the application of this image-based method in an RC-wall experiment. Copyright © 2011 John Wiley & Sons, Ltd. --- paper_title: A vision-based approach for the direct measurement of displacements in vibrating systems paper_content: This paper reports the results of an analytical and experimental study to develop, calibrate, implement and evaluate the feasibility of a novel vision-based approach for obtaining direct measurements of the absolute displacement time history at selectable locations of dispersed civil infrastructure systems such as long-span bridges. The measurements were obtained using a highly accurate camera in conjunction with a laser tracking reference. Calibration of the vision system was conducted in the lab to establish performance envelopes and data processing algorithms to extract the needed information from the captured vision scene. Subsequently, the monitoring apparatus was installed in the vicinity of the Vincent Thomas Bridge in the metropolitan Los Angeles region. This allowed the deployment of the instrumentation system under realistic conditions so as to determine field implementation issues that need to be addressed. It is shown that the proposed approach has the potential of leading to an economical and robust system for obtaining direct, simultaneous, measurements at several locations of the displacement time histories of realistic infrastructure systems undergoing complex three-dimensional deformations. --- paper_title: Monitoring of a civil structure’s state based on noncontact measurements paper_content: In this article, the comparison of two noncontact measurement methods dedicated to civil engineering structures’ state examination is presented. The vision-based method computes the displacement field of the analyzed structure by means of the digital image correlation coefficient. The system consists of one or more high-resolution digital cameras mounted on a head or on portable tripods. The developed methodology and created software application embedded in an MS-Windows operating system are presented. The second system measures the deflection of the structures by means of a radar interferometer. In both cases, it is possible to measure many points on the structure simultaneously. This article presents a comparison of the displacement field measurement performed on a field setup, as well as the span of a steel bridge designed for tram traffic. Both systems are described, with special attention given to their application in measurements of civil engineering structures. This article demonstrates a prelimina... --- paper_title: A Synchronized Multipoint Vision-Based System for Displacement Measurement of Civil Infrastructures paper_content: This study presents an advanced multipoint vision-based system for dynamic displacement measurement of civil infrastructures. The proposed system consists of commercial camcorders, frame grabbers, low-cost PCs, and a wireless LAN access point. The images of target panels attached to a structure are captured by camcorders and streamed into the PC via frame grabbers. Then the displacements of targets are calculated using image processing techniques with premeasured calibration parameters. This system can simultaneously support two camcorders at the subsystem level for dynamic real-time displacement measurement. The data of each subsystem including system time are wirelessly transferred from the subsystem PCs to master PC and vice versa. Furthermore, synchronization process is implemented to ensure the time synchronization between the master PC and subsystem PCs. Several shaking table tests were conducted to verify the effectiveness of the proposed system, and the results showed very good agreement with those from a conventional sensor with an error of less than 2%. --- paper_title: Analysis of the structural behavior of a membrane using digital image processing paper_content: Abstract This article presents a methodology for the experimental analysis of thin membranes using digital image processing techniques. The methodology is particularly suitable for structures that cannot be monitored using conventional systems, particularly those systems that involve contact with the structure. This methodology consists of a computer vision system that integrates the digital image acquisition and processing techniques on-line using special programming routines. Because the membrane analyzed is very thin and displays large displacements/strains, the use of any conventional sensor based on contact with the structure would not be possible. The methodology permits the measurement of large displacements at several points of the membrane simultaneously, thus enabling the acquisition of the global displacement field. The accuracy of the acquired displacement field is a function of the number of cameras and measured points. The second step is to estimate the strains and stresses on the membrane from the measured displacements. The basic idea of the methodology is to generate global two-dimensional functions that describe the strains and stresses at any point of the membrane. Two constitutive models are considered in the analysis: the Hookean and the neo-Hookean models. Favorable comparisons between the experimental and numerical results attest to the accuracy of the proposed experimental procedure, which can be used for both artificial and natural membranes. --- paper_title: A vision-based system for remote sensing of bridge displacement paper_content: This study proposed the vision-based system which remotely measures dynamic displacement of bridges in real-time using digital image processing techniques. This system has a number of innovative features including a high resolution in dynamic measurement, remote sensing, cost-effectiveness, real-time measurement and visualization, ease of installation and operation and no electro-magnetic interference. The digital video camera combined with a telescopic device takes a motion picture of the target installed on a measurement location. Meanwhile, the displacement of the target is calculated using an image processing technique, which requires a target recognition algorithm, projection of the captured image, and calculation of the actual displacement using target geometry and number of pixels moved. For the purpose of verification, a laboratory test using shaking table test and field application on a bridge with open-box girders were carried out. The test results gave sufficient dynamic resolution in frequency as well as the amplitude. --- paper_title: Structural dynamic displacement vision system using digital image processing paper_content: Abstract This study introduces dynamic displacement vision system (DDVS), which is applicable for imaging unapproachable structures using a hand-held digital video camcorder and is more economical than the existing contact and contactless measurement methods of dynamic displacement and deformation. This proposed DDVS method is applied to the Region of Interest (ROI) resizing and coefficient updating at each time step to improve the accuracy of the measurement from the digital image. Thus, after evaluating the algorithms conducted in this study by the static and dynamic verification, the measurement's usability by calculating the dynamic displacement of the masonry specimen, and the two-story steel frame specimen is evaluated under uniaxial seismic loading. The algorithm of the proposed method in this study, despite the relatively low resolution during frozen, slow, and seismic motions, has precision and usability that can replace the existing displacement transducer. Moreover, the method can be effectively applied to even fast behavior for multi-measurement positions like the seismic simulation test using large scale specimen. DDVS, using the consecutive images of the structures with an economic, hand-held digital video camcorder is a more economical displacement sensing concept than the existing contact and contactless measurement methods. --- paper_title: NONCONTACT PHOTOGRAMMETRIC MEASUREMENT OF VERTICAL BRIDGE DEFLECTION paper_content: This paper reports on the results from a study of vertical deflection measurement of bridges using digital close-range terrestrial photogrammetry (DCRTP). The study consisted of a laboratory and two field exercises. In the laboratory exercise, photogrammetric measurements of a 11.6 m (38 ft) steel beam loaded at midspan were made and compared with dial gauge readings and elastic beam theory. In the first field exercise, the initial camber and dead load deflection of 31.1 m (102 ft) prestressed concrete bridge girders were measured photogrammetrically and compared with level rod and total station readings. A comparison of the photogrammetric measurements with the dead load deflection diagram is also made. In the second field exercise, the vertical deflection of a 14.9 m (49 ft) noncomposite steel girder bridge loaded with two dump trucks was measured. Photogrammetric results are compared with deflections estimated using elastic finite-element analysis, level rod readings, and curvature-based deflection measurements. The paper is concluded with a discussion of work in progress to further improve the accuracy of DCRTP in the field. --- paper_title: A Vision-Based Sensor for Noncontact Structural Displacement Measurement paper_content: Conventional displacement sensors have limitations in practical applications. This paper develops a vision sensor system for remote measurement of structural displacements. An advanced template matching algorithm, referred to as the upsampled cross correlation, is adopted and further developed into a software package for real-time displacement extraction from video images. By simply adjusting the upsampling factor, better subpixel resolution can be easily achieved to improve the measurement accuracy. The performance of the vision sensor is first evaluated through a laboratory shaking table test of a frame structure, in which the displacements at all the floors are measured by using one camera to track either high-contrast artificial targets or low-contrast natural targets on the structural surface such as bolts and nuts. Satisfactory agreements are observed between the displacements measured by the single camera and those measured by high-performance laser displacement sensors. Then field tests are carried out on a railway bridge and a pedestrian bridge, through which the accuracy of the vision sensor in both time and frequency domains is further confirmed in realistic field environments. Significant advantages of the noncontact vision sensor include its low cost, ease of operation, and flexibility to extract structural displacement at any point from a single measurement. --- paper_title: BRIDGE DEFLECTION MEASUREMENT USING DIGITAL IMAGE CORRELATION paper_content: Digital image correlation technique is used for measuring vertical deflections of bridge girders during a bridge load testing. A bridge is loaded by a heavy cargo truck on the bridge road. Then, the deflection distribution is measured by digital image correlation. The applicability of digital image correlation to bridge deflection measurement is investigated by comparing the results obtained by digital image correlation with those obtained by displacement transducers. The effect of random pattern on an object surface is also investigated by measuring with and without random pattern. The results show that the deflection distributions of the bridge obtained by digital image correlation agree well with those obtained by the displacement transducers when the random pattern is attached on the bridge surface. In addition, it is found that the deflections can be measured even if the artificial random pattern is not applied to the surface of the bridge girder. It is emphasized that noncontact displacement measurement is possible by simple and easy procedure with digital image correlation for the structural evaluation of infrastructures. --- paper_title: Vision-based displacement measurement method for high-rise building structures using partitioning approach paper_content: The horizontal displacement of a high-rise building structure is usually considered as one of the major indicators to assess the structural safety. It is, however, difficult to directly measure the displacement of such a flexible structure due to the inaccessibility to a reference point usually needed for conventional displacement sensors and the huge size of the structure. In order to resolve this issue, a novel vision-based displacement measurement technique is proposed by employing a partitioning approach (i.e., successive estimation of relative displacements and rotational angles throughout a large flexible structure). A series of the experimental tests using two webcams installed on a flexible steel column structure have been conducted to validate the feasibility of the proposed method. The test results showed that the difference between the displacement measured from the proposed method and the exact value is less than 0.5%. Therefore, the proposed method could be considered as one promising candidate for measuring the displacement of high-rise building structures. --- paper_title: Vision-based algorithms for damage detection and localization in structural health monitoring paper_content: Summary ::: ::: Deflection curve can be used to detect and localize damage in civil engineering structures. In this paper, a vision-based method applied for in-plane displacement field measurement of cantilever beams is presented. The deflection curve of the analyzed structure is computed by means of the digital image correlation. Damage is introduced into the structure. Resulting deflection curves are used as an input to the novel damage detection algorithms: line segments method and voting method. The algorithms are then compared with the second derivative method and assessed through the probability of detection procedure. Copyright © 2015 John Wiley & Sons, Ltd. --- paper_title: Investigation of the dynamic characteristic of bridge structures using a computer vision method paper_content: A new method for the investigation of the dynamic characteristic of bridges has been developed. It is based on the photogrammetric principle; however, the viewing system is equipped with an additional reference system, which decreases the sensitivity to vibrations and an analysis system which enables image analysis. The method is used for monitoring and real-time measurement of the displacement of chosen points at bridge structures. It has applications particularly in the case of the measurement of hard-to-access places on bridges. The instrumentation, methodology and engineering examples of its application are presented. --- paper_title: An optical approach to structural displacement measurement and its application paper_content: A number of optical devices are commercially available now for measurement. Some of them, such as laser devices and still and video cameras with high resolution, may be used effectively and efficiently for measuring structural displacement. This paper presents an approach to this type of application using a charge-coupled-device camera. It can acquire digitized images for low cost, to be used to identify structural displacement via digital signal processing. It is shown that this approach’s resolution for point measurement is comparable with traditional sensors such as dial gages. Furthermore, it offers a new capability of displacement measurement for a large number of points on a structure, and it can provide spatially intensive displacement data. This kind of data may be used for structural damage detection and health monitoring, as suggested and demonstrated herein. --- paper_title: Flexible Videogrammetric Technique for Three-Dimensional Structural Vibration Measurement paper_content: A videogrammetric technique is proposed for measuring three-dimensional structural vibration response in the laboratory. The technique is based on the principles of close-range digital photogrammetry and computer vision. Two commercial-grade digital video cameras are used for image acquisition. To calibrate these cameras and to overcome potential lens distortion problems, an innovative two-step calibration process including individual and stereo calibration is proposed. These calibrations are efficiently done using a planar pattern arbitrarily shown at a few different orientations. This special characteristic makes it possible to perform an on-site calibration that provides flexibility in terms of using different camera settings to suit various application conditions. To validate the proposed technique, three tests, including sinusoidal motion of a point, wind tunnel test of a cross-section bridge model, and a three-story building model under earthquake excitation, are performed. Results indicate that the proposed videogrammetric technique can provide fairly accurate displacement measurement for all three tests. The proposed technique is shown to be a good complement to the traditional sensors for measuring two- or three-dimensional vibration response in the low-frequency range. --- paper_title: Development and application of a vision-based displacement measurement system for structural health monitoring of civil structures paper_content: For structural health monitoring (SHM) of civil infrastructures, displacement is a good descriptor of the structural behavior under all the potential disturbances. However, it is not easy to measure displacement of civil infrastructures, since the conventional sensors need a reference point, and inaccessibility to the reference point is sometimes caused by the geographic conditions, such as a highway or river under a bridge, which makes installation of measuring devices time-consuming and costly, if not impossible. To resolve this issue, a visionbased real-time displacement measurement system using digital image processing techniques is developed. The effectiveness of the proposed system was verified by comparing the load carrying capacities of a steel-plate girder bridge obtained from the conventional sensor and the present system. Further, to simultaneously measure multiple points, a synchronized vision-based system is developed using master/slave system with wireless data communication. For the purpose of verification, the measured displacement by a synchronized vision-based system was compared with the data measured by conventional contact-type sensors, linear variable differential transformers (LVDT) from a laboratory test. --- paper_title: Monocular Computer Vision Method for the Experimental Study of Three-Dimensional Rocking Motion paper_content: AbstractThe rocking problem is applicable to a wide variety of structural and nonstructural elements. The current applications range from bridge pier and shallow footing design to hospital and data center equipment, even art preservation. Despite the increasing number of theoretical and simulation studies of rocking motion, few experimental studies exist. Of those that have been published, most are focused on a reduced version of the problem introducing modifications to the physical problem with the purpose of eliminating either sliding, uplift, or the three-dimensional (3D) response of the body. However, all of these phenomena may affect the response of an unrestrained rocking body. The intent of this work is to present a computer vision method that allows for the experimental measurement of the rigid body translation and rotation time histories in three dimensions. Experimental results obtained with this method will be presented to demonstrate that it obtains greater than 97% accuracy when compared agai... --- paper_title: A paired visual servoing system for 6-DOF displacement measurement of structures paper_content: In the previous study, a paired structured light system which incorporates lasers, cameras, and screens was proposed, and experimental tests validated the potential of a displacement measurement system for large structures. However, the estimation of relative translational and rotational displacements between two sides was based on an assumption that there is zero initial displacement and that three laser beams are always on the screens. In this paper, a calibration method is proposed to offset the initial displacement using the first captured image. The calibration matrix derived from the initial offset is used for subsequent displacement estimation. A newly designed 2-DOF manipulator for each side is visually controlled to prevent the laser beams from leaving the screen. As the manipulator actively controls the laser beams to target the center of the screen, it contains all three laser points within the bounds of the screen. To verify the feasibility of the proposed system, various simulations and experimental tests were performed. The results show that the proposed visually servoed paired structured light system solves the main problem with the former system and that it can be utilized to enlarge the estimation range of the displacement. --- paper_title: A four-camera videogrammetric system for 3-D motion measurement of deformable object paper_content: Abstract A four-camera videogrammetric system with large field-of-view is proposed for 3-D motion measurement of deformable object. Four high-speed commercial-grade cameras are used for image acquisition. Based on close-range photogrammetry, an accurate calibration method is proposed and verified for calibrating the four cameras simultaneously, where a cross target as calibration patterns with feature points pasted on its two-sides is used. The key issues of the videogrammetric processes including feature point recognition and matching, 3-D coordinate and displacement reconstruction, and motion parameters calculation are discussed in detail. Camera calibration experiment indicates that the proposed calibration method, with a re-projection error less than 0.05 pixels, has a considerable accuracy. Accuracy evaluation experiments prove that the accuracy of the proposed system is up to 0.5 mm on length dynamic measurement within 5000 mm×5000 mm field-of-view. Motion measurement experiment on an automobile tire is conducted to validate performance of our system. The experimental results show that the proposed four-camera videogrammetric system is available and reliable for position, trajectory, displacement and speed measurement of deformable moving object. --- paper_title: A new low-cost displacements monitoring system based on Kinect sensor paper_content: We present a new system for structural health monitoring. In particular, the reason we have developed the system is related to structural monitoring during (potentially) destructive tests. Structural response is usually sensed by sensors such as accelerometers, LVDTs and all the instruments which need to be placed in contact with the structure in order to work properly. This can cause problems during (potentially) destructive tests, like the ones performed on shaking tables, an important class of experiments aimed at monitoring the behavior of certain structures under the action of a seismic force. Thus, in the present paper, we focus our attention on designing a new displacements monitoring system, which does not need to be in direct contact with the structure in order to work. The goal is to use a really promising technology in the field of 3D space monitoring to build a system suitable to replace classical sensors such as LVDTs and accelerometers, usually adopted in displacements monitoring. This technology is based on computer vision and low-cost sensors; this allows us to protect monitoring systems from damage due to contact with the structure under testing. Results show that our system is able to estimate the position of the selected points on a test structure with good accuracy, with respect to a traditional high-precision sensor. --- paper_title: A New Position Measurement System Using a Motion-Capture Camera for Wind Tunnel Tests paper_content: Considering the characteristics of wind tunnel tests, a position measurement system that can minimize the effects on the flow of simulated wind must be established. In this study, a motion-capture camera was used to measure the displacement responses of structures in a wind tunnel test, and the applicability of the system was tested. A motion-capture system (MCS) could output 3D coordinates using two-dimensional image coordinates obtained from the camera. Furthermore, this remote sensing system had some flexibility regarding lab installation because of its ability to measure at relatively long distances from the target structures. In this study, we performed wind tunnel tests on a pylon specimen and compared the measured responses of the MCS with the displacements measured with a laser displacement sensor (LDS). The results of the comparison revealed that the time-history displacement measurements from the MCS slightly exceeded those of the LDS. In addition, we confirmed the measuring reliability of the MCS by identifying the dynamic properties (natural frequency, damping ratio, and mode shape) of the test specimen using system identification methods (frequency domain decomposition, FDD). By comparing the mode shape obtained using the aforementioned methods with that obtained using the LDS, we also confirmed that the MCS could construct a more accurate mode shape (bending-deflection mode shape) with the 3D measurements. --- paper_title: Using Specific Displacements to Analyze Motion without Calibration paper_content: Considering the field of un-calibrated image sequences and self-calibration, this paper analyzes the use of specific displacements (such as fixed axis rotation, pure translations,...) or specific sets of camera parameters. This allows to induce affine or metric constraints, which can lead to self-calibration and 3D reconstruction.A uniformed formalism for such models already developed in the literature plus some novel models are developed here. A hierarchy of special situations is described, in order to tailor the most appropriate camera model to either the actual robotic device supporting the camera, or to tailor the fact we only have a reduced set of data available.This visual motion perception module leads to the estimation of a minimal 3D parameterization of the retinal displacement for a monocular visual system without calibration, and leads to self-calibration and 3D dynamic analysis.The implementation of these equations is analyzed and experimented. --- paper_title: A stereoscopic digital speckle photography system for 3-D displacement field measurements paper_content: Stereoscopic digital speckle photography offers a technique to measure object shapes and 3-D displacement fields in experimental mechanics. The system measures the displacement of a random white light speckle pattern, which somehow is present on the object surface, using digital correlation. This paper describes a general physical model for stereo imaging systems. A camera calibration algorithm, which takes the distortion in the lenses into account, is also presented and evaluated by real experiments. Standard deviations of small deformations as low as 1% of the pixel size for in-plane deformations and 6% of the pixel size for the out-of-plane component are reported. Using the calibration algorithm described, the main source of errors is random errors originating from the correlation algorithm. --- paper_title: Three-Dimensional Structural Translation and Rotation Measurement Using Monocular Videogrammetry paper_content: Measuring displacement for large-scale structures has always been an important yet challenging task. In most applications, it is not feasible to provide a stationary platform at the location where its displacements need to be measured. Recently, image-based technique for three-dimensional (3D) displacement measurement has been developed and proven to be applicable to civil engineering structures. Most of these developments, however, use two or more cameras and require sophisticated calibration using a total station. In this paper, we present a single-camera approach that can simultaneously measure both 3D translation and rotation of a planar target attached on a structure. The intrinsic parameters of the camera are first obtained using a planar calibration board arbitrarily positioned around the target location. The obtained intrinsic parameters establish the relationship between the 3D camera coordinates and the two-dimensional image coordinates. These parameters can then be used to extract the rotation and translation of the planar target using recorded image sequence. The proposed technique is illustrated using two laboratory tests and one field test. Results show that the proposed monocular videogrammetric technique is a simple and effective alternative method to measure 3D translation and rotation for civil engineering structures. It should be noted that the proposed technique cannot measure translation along the direction perpendicular to the image plane. Hence, proper caution should be taken when placing target and camera. --- paper_title: Nontarget Stereo Vision Technique for Spatiotemporal Response Measurement of Line-Like Structures paper_content: With continuous advancement in optical, electronics, and computer technology, commercial digital cameras now are equipped with a high pixel resolution at a reasonable cost. Image sequences recorded by these cameras contain both spatial and temporal informa- tion of the target object; hence they can be used to extract the object's dynamic responses and characteristics. This paper presents a nontarget stereo vision technique to measure response of a line-like structure simultaneously in both spatial and temporal domain. The technique uses two digital cameras to acquire image sequences of a line-like structure. It adopts a simple nondimensional length matching approach and the epipolar geometry to establish point correspondences within an image sequence and between two image sequences, respectively. After reconstructing a spatio-temporal displacement response from the two image sequences, wavelet transform is then used to extract the modal characteristics of the structure. The technique is illustrated using two free vibration tests: a steel cantilever beam and a bridge stay cable. Results show that the technique can measure the spatiotemporal responses, natural frequencies, and mode shapes of the two structures quite accurately. This image-based technique is a low-cost and simple-to-use approach that not only can complement current response measurement sensors but also offer special advantages which are hard to obtain using these traditional sensors. --- paper_title: 3D displacement measurement model for health monitoring of structures using a motion capture system paper_content: Abstract Unlike 1D or 2D displacement measurement sensors, a motion capture system (MCS) can determine the movement of markers in any direction precisely. In addition, an MCS can overcome the limitations of the sampling frequency in 3D measurements by terrestrial laser scanning (TLS) and global positioning system (GPS). This paper presents a method to measure three dimensional (3D) structural displacements using a motion capture system (MCS) with a high accuracy and sampling rate. The MCS measures 2D coordinates of a number of markers with multiple cameras; these measurements are then used to calculate the 3D coordinates of markers. Therefore, unlike previous 1D or 2D displacement measurement sensors, the MCS can determine precisely the movement of markers in any direction. In addition, since the MCS cameras can monitor several markers, measurement points are increased by the addition of more markers. The effectiveness of the proposed model was tested by comparing the displacements measured in a free vibration experiment of a 3-story structure with a height of 2.1 m using both the MCS and laser displacement sensors. --- paper_title: Three-Dimensional Acceleration Measurement Using Videogrammetry Tracking Data paper_content: In order to evaluate the feasibility of multi-point, non-contact, acceleration measurement, a high-speed, precision videogrammetry system has been assembled from commercially-available components and software. Consisting of three synchronized 640 × 480 pixel monochrome progressive scan CCD cameras each operated at 200 frames per second, this system has the capability to provide surface-wide position-versus-time data that are filtered and twice-differentiated to yield the desired acceleration tracking at multiple points on a moving body. The oscillating motion of targets mounted on the shaft of a modal shaker were tracked, and the accelerations calculated using the videogrammetry data were compared directly to conventional accelerometer measurements taken concurrently. Although differentiation is an inherently noisy operation, the results indicate that simple mathematical filters based on the well-known Savitzky and Golay algorithms, implemented using spreadsheet software, remove a significant component of the noise, resulting in videogrammetry-based acceleration measurements that are comparable to those obtained using the accelerometers. --- paper_title: Experimental validation of non-contacting measurement method using LED-optical displacement sensors for vibration stress of small-bore piping paper_content: Abstract There have been many reports of fatigue failures of small-bore piping systems such as drain piping, vent piping and instrumentation piping in nuclear power plants that arise from vibration sources such as pumps. To prevent the failures, integrity evaluation of piping is conducted by measuring and analyzing vibration stress in the piping. But, a more efficient and economical measurement method is desirable to evaluate the vibration fatigue in small-bore piping. In this study, a non-contacting measurement method was proposed that is based on optical displacement sensors using light emission diodes (LEDs) to measure the vibration stress. The applicability of the method was discussed based on the vibration experiments using pipe elements and a mock-up piping system. From the experimental results, the proposed method was clarified to be sufficiently applicable and practically useful for the vibration measurement and stress evaluation in small-bore piping systems. --- paper_title: High resolution digital image correlation measurements of strain accumulation in fatigue crack growth paper_content: Abstract Microstructure plays a key role in fatigue crack initiation and growth. Consequently, measurements of strain at the microstructural level are crucial to understanding fatigue crack behavior. The few studies that provide such measurements have relatively limited resolution or areas of observation. This paper provides quantitative, full-field measurements of plastic strain near a growing fatigue crack in Hastelloy X, a nickel-based superalloy. Unprecedented spatial resolution for the area covered was obtained through a novel experimental technique based on digital image correlation (DIC). These high resolution strain measurements were linked to electron backscatter diffraction (EBSD) measurements of grain structure (both grain shape and orientation). Accumulated plastic strain fields associated with fatigue crack growth exhibited inhomogeneities at two length scales. At the macroscale, the plastic wake contained high strain regions in the form of asymmetric lobes associated with past crack tip plastic zones. At high magnification, high resolution DIC measurements revealed inhomogeneities at, and below, the grain scale. Effective strain not only varied from grain to grain, but also within individual grains. Furthermore, strain localizations were observed in slip bands within grains and on twin and grain boundaries. A better understanding of these multiscale heterogeneities could help explain variations in fatigue crack growth rate and crack path and could improve the understanding of fatigue crack closure and fracture in ductile metals. --- paper_title: Assessing steel strains on reinforced concrete members from surface cracking patterns paper_content: Abstract The measurement of steel strains in reinforced concrete structures is often critical to characterise the stresses along the member. In this scope, this manuscript describes the development of a method to assess steel strains inside concrete members using solely surface measurements. These measurements were obtained using photogrammetry and image processing. The technique was validated using two concrete ties monitored with strain gauges placed inside the steel bars. The experimental results showed that one of the most important parameters affecting the accuracy of the technique is the measurement of crack widths. In comparison, the concrete strain has little effect on the final results. The technique is particularly advantageous since it is non-contact and does not impact on the bond conditions. It also does not require accessing the reinforcements. As a main conclusion, this work showed the feasibility of estimating strains inside the structure using surface measurements. This technique will benefit in the near future from further improvements namely in what concerns the camera resolution. --- paper_title: New Parameters to Describe High-Temperature Deformation of Prestressing Steel Determined Using Digital Image Correlation paper_content: AbstractThis paper describes the results from a series of high-temperature tension tests on prestressing steel under sustained load (creep tests). Both steady-state and transient heating regimes ar... --- paper_title: Computer Vision-Based Technique to Measure Displacement in Selected Soil Tests paper_content: The paper investigates the accuracy of normal case photography as a means of measuring linear deformations of soil specimens. The results of using this approach were compared with results from conventional procedures for two soil tests, namely unconfined compression and direct shear tests. Charge-coupled-device (CCD) video cameras were used to measure deformation or strain in soil specimens. A personal computer based digital vision system was used to obtain accurately measured linear displacement data. Using remolded soil specimens, comparisons between displacement measurements using American Society for Testing and Materials (ASTM) conventional methods and normal case photography methods showed that use of the latter method is promising and could be used as a substitute for strain gages. Experimental investigation showed that differences between displacement measurements using conventional ASTM procedures and the computer vision technique were consistently within 0.04 plus or minus 0.15 to 0.3 plus or minus 0.23 mm for unconfined compression tests and direst shear tests, respectively. This was compatible with the image scale where one pixel on the image domain was equivalent to about 0.4 mm on object space coordinates. Statistical correlations between strains by the two methods supported this result. Image scale and resolution were found to be the two major factors affecting the accuracy of the measurements. The results of this work can further the search for more fully automated soil testing measurements. --- paper_title: Full-field measurements of heterogeneous deformation patterns on polymeric foams using digital image correlation paper_content: The ability of a digital image correlation technique to capture the heterogeneous deformation fields appearing during compression of ultra-light open-cell foams is presented in this article. Quantitative characterization of these fields is of importance to understand the mechanical properties of the collapse process and the energy dissipation patterns in this type of materials. The present algorithm is formulated in the context of multi-variable non-linear optimization where a merit function based on a local average of the deformation mapping is minimized implicitly. A parallel implementation utilizing message passing interface for distributed-memory architectures is also discussed. Estimates for optimal size of the correlation window based on measurement accuracy and spatial resolution are provided. This technique is employed to reveal the evolution of the deformation texture on the surface of open-cell polyurethane foam samples of different relative densities. Histograms of the evolution of surface deformation are extracted, showing the transition from unimodal to bimodal and back to unimodal. These results support the interpretation that the collapse of light open-cell foams occurs as a phase transition phenomenon. --- paper_title: Application of three-dimensional digital image correlation to the core-drilling method paper_content: We present a non-destructive technique for the determination ofin situ stresses in concrete structures, reterred to as the core-drilling method. The method is similar to the American Society for Testing and Materials (ASTM) hole-drilling strain gage method, except that the core-drilling method is formulated in the current work are performed with traditional photogrammetry, and the more novel (and more accurate) three-dimensional digital image correlation. In this paper we review the background elasticity theory and we discuss the results of verification experiments on steel plates. Calculated normal stresses are within 17% of applied values for photogrammetry, and 7% for three-dimensional digital image correlation. --- paper_title: Image-based stress and strain measurement of wood in the split-Hopkinson pressure bar paper_content: The properties of wood must be considered when designing mechanical pulping machinery. The composition of wood within the annual ring is important. This paper proposes a novel image-based method to measure stress and planar strain distribution in soft, heterogeneous materials. The main advantage of this method in comparison to traditional methods that are based on strain gauges is that it captures local strain gradients and not only average strains. Wood samples were subjected to compression at strain rates of 1000–2500 s−1 in an encapsulated split-Hopkinson device. High-speed photography captured images at 50 000–100 000 Hz and different magnifications to achieve spatial resolutions of 2.9 to 9.7 µm pixels−1. The image-based analysis utilized an image correlation technique with a method that was developed for particle image velocimetry. The image analysis gave local strain distribution and average stress as a function of time. Two stress approximations, using the material properties of the split-Hopkinson bars and the displacement of the transmitter bar/sample interface, are presented. Strain gauges on the bars of the split-Hopkinson device give the reference average stress and strain. The most accurate image-based stress approximation differed from the strain gauge result by 5%. --- paper_title: Calibration and evaluation of optical systems for full-field strain measurement paper_content: The design and testing of a reference material for the calibration of optical systems for strain measurement is described, together with the design and testing of a standardized test material that allows the evaluation and assessment of fitness for purpose of the most sophisticated optical system for strain measurement. A classification system for the steps in the measurement process is also proposed and allows the development of a unified approach to diagnostic testing of components or sub-systems in an optical system for strain measurement based on any optical technique. The results described arise from a European study known as SPOTS whose objectives were to begin to fill the gap caused by a lack of standards. --- paper_title: Measurement of Local Deformations in Steel Monostrands Using Digital Image Correlation paper_content: The local deformation mechanisms in steel monostrands have a significant influence on their fatigue life and failure mode. However, the observation and quantification of deformations in monostrands experiencing axial and transverse deformations is challenging because of their complex geometry, difficulties with the placement of strain gauges in the vicinity of the anchorage, and, most importantly, the relatively small magnitude of deformation occurring in the monostrand. This paper focuses on the measurement of localized deformations in high-strength steel monostrands using the digital image correlation (DIC) technique. The presented technique enables the measurement of individual wire strains along the length of the monostrand and also provides quantitative information on the relative movement between individual wires, leading to a more in-depth understanding of the underlying fatigue mechanisms. To validate the proposed image-based measurement method, two different tests were performed, with the one correlation method showing good agreement. Data collected from the DIC technique creates a basis for the analysis of the fretting and localized bending behavior of the monostrand and provides relevant information on the internal state of displacement of the monostrand under bending load. --- paper_title: On the use of digital image correlation for slip measurement during coupon scale fretting fatigue experiments paper_content: Abstract Digital image correlation (DIC) is a full field three dimensional measurement technique that can quantify displacements and strains of a surface. In this paper, digital image correlation is used as a slip measurement technique during coupon scale fretting fatigue experiments. Slip measured with the novel DIC technique is compared to conventional slip measurement techniques as clip gauges and modified clip gauge measurements proposed by Wittkowsky et al. Slip measurements with the DIC system show lower slip values and higher tangential contact stiffness’s compared to (modified) clip gauge measurements. Slip measured with DIC is obtained closer to the contact compared to clip gauges, eliminating the influence of elastic deformations or fitting parameters. During the fretting fatigue experiments are two equal contacts simultaneously tested. However, the slip of both contacts is not identical with outliers of more than 10% difference in slip amplitude. --- paper_title: Application of digital photogrammetry techniques in identifying the mode shape ratios of stay cables with multiple camcorders paper_content: Abstract A simple digital photogrammetry technique using artificial targets is developed to conduct the synchronized ambient vibration measurements at different locations of a stay cable with multiple camcorders for the identification of mode shape ratios. To compensate the restrictions imposed by the adoption of budget equipment, elaborate adjustments in scale, synchronization, and baselines are performed. Based on the on-site measurements for three stay cables, it is verified that the proposed methodology can effectively measure the ambient vibration responses of stay cables and attain the same order of accuracy in the identification of mode shape ratios as that of high-resolution velocimeters. --- paper_title: Cost‐effective vision‐based system for monitoring dynamic response of civil engineering structures paper_content: This study develops a cost-effective vision-based displacement measurement system for real-time monitoring of dynamic responses of large-size civil engineering structures such as bridges and buildings. The system simply consists of a low-cost digital camcorder and a notebook computer equipped with digital image-processing software developed in this study. A target panel with predesigned marks is attached to a structure, whose movement is captured by the digital camcorder placed at a fixed point away from the measurement point. The captured images are streamed to the notebook computer and processed by the software to compute displacement in real time. The efficacy of the system in measuring dynamic responses was demonstrated through laboratory tests, seismic shaking table tests on a steel building frame, and a field experiment on a bridge. In order to simultaneously measure displacements of multiple points, this study further developed a time synchronization system, in which TCP/IP protocol is employed for communications. The effectiveness of the time synchronization system was also experimentally verified. The vision-based system developed in this study is simple, cost-effective, easy to install, and capable of real-time measurement of dynamic responses, making the system ideal for monitoring civil engineering structures. Copyright © 2009 John Wiley & Sons, Ltd. --- paper_title: Nontarget Image-Based Technique for Small Cable Vibration Measurement paper_content: In this paper, a proof-of-concept image-based technique is proposed for measuring small cable vibration. The technique analyzes an image sequence of a vibrating cable segment captured by a camera. An optical flow method is used to calculate variation of optical intensity of an arbitrary selected region of interest ROI on the cable image sequence. The obtained optical flow vector provides the direction of vibration for the ROI on the cable segment, which then can be used to estimate displacement of the ROI on the image plane. Furthermore, actual displacement of the ROI can be extracted when some conditions are met. The proposed technique is validated both in the laboratory using a rigid pipe and in the field on a small pedestrian bridge cable. Results show that the technique is able to measure the pipe motion and the cable vibration accurately. The proposed technique requires only one commercial camera, and no prior camera calibration is needed. In addition, the use of an optical flow method eliminates the need to attach any target to the cable and makes the technique very easy to implement. Despite these advantages, the technique still needs further development before it can be applied to long-span bridge cables. --- paper_title: Extracting modal parameters of a cable on shaky motion pictures paper_content: Abstract A set of modal parameters of a cable are extracted from a motion picture captured by a digital camera operated with shaking hands. It is difficult to identify the center of the targets attached to the cable surface from the blurred motion image of the cable, because of the high-speed motion of the cable, low sampling frequency of the camera, and the effect of shaking hands on the motion pictures. This paper proposes a multi-template matching algorithm to solve these difficulties. In addition, a sensitivity-based system identification algorithm is proposed for extracting the natural frequencies and the damping ratios from ambient cable vibration data. Three sets of vibration tests are performed to examine the validity of the proposed algorithms. The results show that the proposed approach of using these two algorithms is fairly feasible for extracting modal parameters from severely blurred motion pictures. --- paper_title: Structural damage detection using digital video imaging technique and wavelet transformation paper_content: Damage in structures may render risk of catastrophic failure. Identifying damages and their locations is termed as damage detection. In this paper, use of digital video imaging is proposed for detecting damage in structures. The theory of measuring structural vibration using high-resolution images is presented first, based on sub-pixel edge identification. Then a concept of mode shape difference function is developed for structural damage detection. A laboratory test program was carried out to implement these concepts using a high-speed digital video camera. The images were analyzed to obtain displacement time series at sub-pixel resolution. Mode shapes were obtained from the time series to find the mode shape difference functions between the damaged and the reference states. They were subjected to wavelet transformation for determining the damage locations. Results show that the proposed approach is able to identify the introduced damage cases and their locations. --- paper_title: Measurement of rivulet movement on inclined cables during rain–wind induced vibration paper_content: Abstract The large amplitude vibration of stay cables has been observed in several cable-stayed bridges under the simultaneous occurrence of rain and wind, which is called rain–wind induced vibration (RWIV). During RWIV, the upper rivulet oscillating circumferentially on the inclined cable surface is widely considered to have an important role in this phenomenon. However, the small size of rivulets and high sensitivity to wind flow make the measurement of the rivulet movement challenging. This study proposes a digital image processing method to measure the rivulet movement in wind tunnel tests. RWIV of a cable model was excited during the test and a digital video camera was used to record the video clips of the rivulets, from which the time history of the rivulet movement along the entire cable is identified through image processing. The oscillation amplitude, equilibrium position, and dominant frequency of the upper rivulet are investigated. Results demonstrated that the proposed non-contact, non-intrusive measurement method is cost-effective and has good resolution in measuring the rivulet vibration. Finally the rivulet vibration characteristics were also studied when the cable was fixed. Comparison demonstrates the relation between the upper rivulet and cable vibration. --- paper_title: Damage Detection Using Optical Measurements and Wavelets paper_content: The paper presents an application of the wavelet transform for damage detection based on optical measurements. A number of important issues, that needs to be considered when image sequences are used for vibration analysis, are discussed. These include: correspondence of image features from image to image, image calibration and spatial resolution. The principles of image edge detection are discussed and a comparison between the wavelet approach and the classical method is presented. A novel damage detection method based on optically measured modeshape data is proposed. The method is illustrated using a simple cantilever beam experiment. The major advantage of the method is the significantly increased number of discrete points used to describe modeshapes. This is in contrast to classical techniques where in practice a small number of measurement points are obtained from a limited number of sensors. --- paper_title: A New Position Measurement System Using a Motion-Capture Camera for Wind Tunnel Tests paper_content: Considering the characteristics of wind tunnel tests, a position measurement system that can minimize the effects on the flow of simulated wind must be established. In this study, a motion-capture camera was used to measure the displacement responses of structures in a wind tunnel test, and the applicability of the system was tested. A motion-capture system (MCS) could output 3D coordinates using two-dimensional image coordinates obtained from the camera. Furthermore, this remote sensing system had some flexibility regarding lab installation because of its ability to measure at relatively long distances from the target structures. In this study, we performed wind tunnel tests on a pylon specimen and compared the measured responses of the MCS with the displacements measured with a laser displacement sensor (LDS). The results of the comparison revealed that the time-history displacement measurements from the MCS slightly exceeded those of the LDS. In addition, we confirmed the measuring reliability of the MCS by identifying the dynamic properties (natural frequency, damping ratio, and mode shape) of the test specimen using system identification methods (frequency domain decomposition, FDD). By comparing the mode shape obtained using the aforementioned methods with that obtained using the LDS, we also confirmed that the MCS could construct a more accurate mode shape (bending-deflection mode shape) with the 3D measurements. --- paper_title: Application of modal analysis supported by 3D vision-based measurements paper_content: In the paper, applications of the 3D vision techniques to the modal analysis method are presented. The goal of the project was to develop a methodology for vibration amplitude measurements and a software tool for modal analysis based on visual data. For this purpose, dedicated procedures and algorithms based on the vision technique methods were developed. 3D measurements of vibrations and structure geometry were obtained by application and developing passive 3D vision techniques. The amplitude of vibrations was calculated for selected points on the structure. Necessary vision data were received from the high-speed digital camera "X-Stream Vision" in form of "avi" files that were used as the input data for the developed software tool. The amplitude of vibration displacements obtained from vision-based measurements were treated as the input data for operational modal analysis algorithms. In this domain, the Balanced Realization algorithm has been used. --- paper_title: MULTI-POINT MEASUREMENT OF STRUCTURAL VIBRATION USING PATTERN RECOGNITION FROM CAMERA IMAGE paper_content: Modal testing requires measuring the vibration of many points, for which an accelerometer, a gab sensor and laser vibrometer are generally used. Conventional modal testing requires mounting of these sensors to all measurement points in order to acquire the signals. However, this can be disadvantageous because it requires considerable measurement time and effort when there are many measurement points. In this paper, we propose a method for modal testing using a camera image. A camera can measure the vibration of many points at the same time. However, this task requires that the measurement points be classified frame by frame. While it is possible to classify the measurement points one by one, this also requires much time. Therefore, we try to classify multiple points using pattern recognition. The feasibility of the proposed method is verified by a beam experiment. The experimental results demonstrate that we can obtain good results. --- paper_title: Experimental methodology for the dynamic analysis of slender structures based on digital image processing techniques paper_content: There are several problems in engineering where the structural response may be affected by conventional devices for displacement measurements. This work presents an alternative methodology for the experimental dynamic analysis of structures, which cannot be monitored by conventional sensors. This methodology is based on digital image acquisition and processing techniques. The importance of this methodology is that it enables one to perform non-contact displacement measurements, without introducing undesirable changes in the structure's behavior, as for example, in models with an extremely reduced scale. The proposed methodology uses sticking markers of a negligible mass in relation to the structure, facilitating the implementation of the instrumentation of the experimental test. To evaluate the efficiency, precision and accuracy of this methodology, two different experimental tests are carried out and the obtained results are favorably compared with the theoretical results and with the ones provided by conventional sensors. This has shown to be a highly cost-effective and easy to implement experimental procedure for the analysis of very flexible structures, and yet maintains the advantage of dynamic measurements with high resolution. --- paper_title: Digital image processing for non-linear system identification paper_content: Abstract Emerging digital image processing techniques demonstrate their potential applications in engineering mechanics, particularly in the area of system identification involving non-linear characteristics of mechanical and structural systems. The objective of this study is to demonstrate the proof-of-concept that the techniques permit the identification non-intrusively and remotely. First, the efficacy of the digital image processing method is shown by identifying the friction behavior between two solids that are assumed to be governed by the Coulomb friction model. The inverse analysis is carried out to validate the proposed method of identifying model parameters. Studies further illustrate that the digital imaging procedure and inverse analysis algorithms developed for the friction problem can be extended for identification of non-linear mechanical and structural characteristics. One illustration utilizes the second example, where relative motion between a model structure and a shaking table is measured by digitally processing the analogue image provided by a video tape recorded during a shaking table test of a model structure base isolated by a hybrid isolation device consisting of friction and elastomeric components. Third, constitutive relationship for a non-linear elastomeric membrane is identified by digitally processing images of its deformed states under tension. The relationship is postulated to follow the Mooney–Rivlin function. Schemes developed are verified with iterative non-linear finite-element program that is valid in the finite deformation range. Emerging cost-effective hardware and software systems for high-performance data acquisition and processing are quite promising to the implementation of the techniques by removing most, if not all, of its existing limitations. --- paper_title: Remote nondestructive evaluation technique using infrared thermography for fatigue cracks in steel bridges paper_content: Long-standing infrastructure is subject to structural deterioration. In this respect, steel bridges suffer fatigue cracks, which necessitate immediate inspection, structural integrity evaluation or repair. However, the inaccessibility of such structures makes inspection time consuming and labour intensive. Therefore, there is an urgent need for developing high-performance nondestructive evaluation (NDE) methods to assist in effective maintenance of such structures. Recently, use of infrared cameras in nondestructive testing has been attracting increasing interest, as they provide highly efficient remote and wide area measurements. This paper first reviews the current situation of nondestructive inspection techniques used for fatigue crack detection in steel bridges, and then presents remote NDE techniques using infrared thermography developed by the author for fatigue crack detection and structural integrity assessments. Furthermore, results of applying fatigue crack evaluation to a steel bridge using the newly developed NDE techniques are presented. --- paper_title: Automatic concrete health monitoring: assessment and monitoring of concrete surfaces paper_content: To predict the degradation of concrete structures is extremely challenging. The typical approach combines periodic visual inspections with required non-destructive tests. However, this methodology only discretely evaluates few areas of the structure, being also time consuming and subject to human error. Therefore, a new method designated ‘automatic concrete health monitoring’ is herein presented which aims at automatically characterising and monitoring the state of conservation of concrete surfaces by combining photogrammetry, image processing and multi-spectral analysis. The method was designed to (i) characterise crack pattern, displacement and strain fields; (ii) map damages and (iii) assess and define restoration tasks. --- paper_title: Image-Based Monitoring of Open Gears of Movable Bridges for Condition Assessment and Maintenance Decision Making paper_content: AbstractMovable bridges are unique structures due to the complex interaction between their structural, mechanical, and electrical systems with an intricate interrelation creating several challenges related to operation and maintenance. Continuous monitoring of the critical parts of these structures is essential to track and evaluate their performance for improving maintenance operations and reducing the associated costs. Open gears are one of the most critical components of movable bridges. Proper and regular maintenance of these gears is vitally important to ensure a safe, reliable, and cost-effective operation. In this study, a practical and low-cost monitoring approach is presented to track the lubrication level in an open gear of a movable bridge by using video cameras. Two unique indices are developed for monitoring of the open gear by investigating two different image processing methods in a comparative fashion. The first methodology is based on an edge detection algorithm that utilizes a Sobel grad... --- paper_title: Improvement of Crack-Detection Accuracy Using a Novel Crack Defragmentation Technique in Image-Based Road Assessment paper_content: AbstractA common problem of crack-extraction algorithms is that extracted crack image components are usually fragmented in their crack paths. A novel crack-defragmentation technique, MorphLink-C, is proposed to connect crack fragments for road pavement. It consists of two subprocesses, including fragment grouping using the dilation transform and fragment connection using the thinning transform. The proposed fragment connection technique is self-adaptive for different crack types, without involving time-consuming computations of crack orientation, length, and intensity. The proposed MorphLink-C is evaluated using realistic flexible pavement images collected by the Florida Department of Transportation (FDOT). Statistical hypothesis tests are conducted to analyze false positive and negative errors in crack/no-crack classification using an artificial neural network (ANN) classifier associated with feature subset selection methods. The results show that MorphLink-C improves crack-detection accuracy and reduces... --- paper_title: Vision-Based Automated Crack Detection for Bridge Inspection paper_content: The visual inspection of bridges demands long inspection time and also makes it difficult to access all areas of the bridge. This paper presents a visual-based crack detection technique for the automatic inspection of bridges. The technique collects images from an aerial camera to identify the presence of damage to the structure. The images are captured without controlling angles or positioning of cameras so there is no need for calibration. This allows the extracting of images of damage sensitive areas from different angles to increase detection of damage and decrease false-positive errors. The images can detect cracks regardless of the size or the possibility of not being visible. The effectiveness of this technique can be used to successfully detect cracks near bolts. --- paper_title: Integrated Vision-Based System for Automated Defect Detection in Sewer Closed Circuit Television Inspection Videos paper_content: AbstractThis paper discusses the development of a general framework and software system to support automated analysis of sewer inspection closed-circuit television (CCTV) videos. The proposed system aims primarily to support the off-site review and quality control process of the videos and to enable efficient reevaluation of archived CCTV videos to extract historical sewer condition data. Automated analysis of sewer CCTV videos poses several challenges including the nonuniformity of camera motion and illumination conditions inside the sewer. The paper presents a novel algorithm for optical flow-based camera motion tracking to automatically identify, locate, and extract a limited set of video segments, called regions of interest (ROI), that likely include defects, thus reducing the time and computational requirements needed for video processing. The proposed algorithm attempts to recover the operator actions during the inspection session, which would enable determining the location and relative severity of... --- paper_title: Concrete Crack Assessment Using Digital Image Processing and 3D Scene Reconstruction paper_content: AbstractTraditional crack assessment methods for concrete structures are time consuming and produce subjective results. The development of a means for automated assessment employing digital image processing offers high potential for practical implementation. However, two problems in two-dimensional (2D) image processing hinder direct application for crack assessment, as follows: (1) the image used for the digital image processing has to be taken perpendicular to the surface of the concrete structure, and (2) the working distance used in retrieving the imaging model has to be measured each time. To address these problems, this paper proposes a combination of 2D image processing and three-dimensional (3D) scene reconstruction to locate the 3D position of crack edges. In the proposed algorithm, first the precise crack information is obtained from the 2D images after noise elimination and crack detection using image processing techniques. Then, 3D reconstruction is conducted employing several crack images to ... --- paper_title: An efficient image-based damage detection for cable surface in cable-stayed bridges paper_content: Abstract Since cable members are the major structural components of cable bridges, they should be properly inspected for surface damage and inside defects such as corrosion and/or breakage of wires. This study introduces an efficient image-based damage detection system that can automatically identify damages to the cable surface through image processing techniques and pattern recognition. The damage detection algorithm combines image enhancement techniques with principal component analysis (PCA) algorithm. Images from three cameras attached to a cable climbing robot are wirelessly transmitted to a server computer located on a stationary cable support. To improve the overall quality of the images, this study utilizes an image enhancement method together with a noise removal technique. Next the input images are projected into PCA sub-space, the Mahalanobis square distance is used to determine the distances between the input images and sample patterns. The smallest distance is found to be a match for an input image. The proposed damage detection algorithm was verified through laboratory tests on three types of cables. Results of the tests showed that the proposed system could be used to detect damage to bridge cables. --- paper_title: Sensor Networks, Computer Imaging, and Unit Influence Lines for Structural Health Monitoring: Case Study for Bridge Load Rating paper_content: In this paper, a novel methodology for structural health monitoring of a bridge is presented with implementations for bridge load rating using sensor and video image data from operating traffic. With this methodology, video images are analyzed by means of computer vision techniques to detect and track vehicles crossing the bridge. Traditional sensor data are correlated with computer images to extract unit influence lines (UILs). Based on laboratory studies, UILs can be extracted for a critical section with different vehicles by means of synchronized video and sensor data. The synchronized computer vision and strain measurements can be obtained for bridge load rating under operational traffic. For this, the following are presented: a real life bridge is instrumented and monitored, and the real-life data are processed under a moving load. A detailed finite-element model (FEM) of the bridge is also developed and presented along with the experimental measurements to support the applicability of the approach for load rating using UILs extracted from operating traffic. The load rating of the bridges using operational traffic in real life was validated with the FEM results of the bridge and the simulation of the operational traffic on the bridge. This approach is further proven with different vehicles captured with video and measurements. The UILs are used for load rating by multiplying the UIL vector of the critical section with the load vector from the HL-93 design truck. The load rating based on the UIL is compared with the FEM results and indicates good agreement. With this method, it is possible to extract UILs of bridges under regular traffic and obtain load rating efficiently. --- paper_title: Combined Imaging Technologies for Concrete Bridge Deck Condition Assessment paper_content: Evaluating the condition of concrete bridge decks is an increasingly important challenge for transportation agencies and bridge inspection teams. Closing the bridge to traffic, safety, and time consuming data collection are some of the major issues during a visual or in-depth bridge inspection. To date, several nondestructive testing technologies have shown promise in detecting subsurface deteriorations. However, the main challenge is to develop a data acquisition and analysis system to obtain and integrate both surface and subsurface bridge health indicators at higher speeds. Recent developments in imaging technologies for bridge decks and higher-end cameras allow for faster image collection while driving over the bridge deck. This paper will focus on deploying nondestructive imaging technologies such as the three-dimensional (3D) optical bridge evaluation system (3DOBS) and thermal infrared (IR) imagery on a bridge deck to yield both surface and subsurface indicators of condition, respectively. Spall and delamination maps were generated from the optical and thermal IR images. Integration of the maps into ArcGIS, a professional geographic information system (GIS), allowed for a streamlined analysis that included integrating and combining the results of the complimentary technologies. Finally, ground truth information was gathered through coring several locations on a bridge deck to validate the results obtained by nondestructive evaluation. This study confirms the feasibility of combining the bridge inspection results in ArcGIS and provides additional evidence to suggest that thermal infrared imagery provides similar results to chain dragging for bridge inspection. --- paper_title: Strain and Displacement Controls by Fibre Bragg Grating and Digital Image Correlation paper_content: Test control is traditionally performed by a feedback signal from a displacement transducer or force gauge positioned inside the actuator of a test machine. For highly compliant test rigs, this is a problem since the response of the rig influences the results. It is therefore beneficial to control the test based on measurements performed directly on the test specimen. In this paper, fibre Bragg grating (FBG) and Digital Image Correlation (DIC) are used to control a test. The FBG sensors offer the possibility of measuring strains inside the specimen, while the DIC system measures strains and displacement on the surface of the specimen. In this paper, a three-point bending test is used to demonstrate the functionality of a control loop, where the FBG and DIC signals are used as control channels. The FBG strain control was capable of controlling the test within an error tolerance of 20 µm m−1. However, the measurement uncertainty offered by the FBG system allowed a tolerance of 8.3 µm m−1. The DIC displacement control proved capable of controlling the displacement within an accuracy of 0.01 mm. --- paper_title: Integration of computer imaging and sensor data for structural health monitoring of bridges paper_content: The condition of civil infrastructure systems (CIS) changes over their life cycle for different reasons such as damage, overloading, severe environmental inputs, and ageing due normal continued use. The structural performance often decreases as a result of the change in condition. Objective condition assessment and performance evaluation are challenging activities since they require some type of monitoring to track the response over a period of time. In this paper, integrated use of video images and sensor data in the context of structural health monitoring is demonstrated as promising technologies for the safety of civil structures in general and bridges in particular. First, the challenges and possible solutions to using video images and computer vision techniques for structural health monitoring are presented. Then, the synchronized image and sensing data are analyzed to obtain unit influence line (UIL) as an index for monitoring bridge behavior under identified loading conditions. Subsequently, the UCF 4-span bridge model is used to demonstrate the integration and implementation of imaging devices and traditional sensing technology with UIL for evaluating and tracking the bridge behavior. It is shown that video images and computer vision techniques can be used to detect, classify and track different vehicles with synchronized sensor measurements to establish an input–output relationship to determine the normalized response of the bridge. --- paper_title: Camera-based monitoring of the rigid-body displacement of a mandrel in superconducting cable production paper_content: We describe a machine vision measurement head that is used to monitor the mandrel position in production of superconducting cables. Two cameras are orthogonally aligned, viewing different sections of the cylindrical part of the mandrel. The use of telecentric lenses obviates the need for re-calibration after the replacement of mandrel. All parameters of rigid-body motion are obtained in linear theory by using a multivariate least-squares fitting procedure on dynamically corresponded sets of target points that vary due to partial blocking by rotating wires. A rigorous analysis of measurement uncertainty is given. --- paper_title: Vision-based estimation of vertical dynamic loading induced by jumping and bobbing crowds on civil structures paper_content: Abstract People's motion on civil structures induces dynamic loading that may lead to excessive vibrations. The complete characterization of this force distribution over a wide area due to a large number of people is still an unsolved issue. This work presents a measuring technique for the vertical load estimation in case of jumping and bobbing crowd, based on the evaluation of the vertical inertia of the human body. Laboratory experiments verify the proposed model on a single volunteer through standard inertial sensors and then extend it introducing the non-contact measuring technique. The method validation is carried out in a real environment: a stand of the G. Meazza stadium in Milan, dynamically characterized in terms of frequency response function. The load induced by groups of jumping people is estimated with the proposed method and the resulting structure accelerations are computed: the comparison between measured and estimated vibrations shows a very high correspondence in both time domain and main spectral components and, above all, the performances do not get worse as the number of volunteer increases. --- paper_title: Study on Three-Dimensional Displacement Measurement Method of EAST Cold Magnet Based on Computer Vision paper_content: The location of superconducting tokamak magnets decides the position and shape of plasma, it is significant of real-time acquiring the location of tokamak magnets to stably operate the tokamak. At present, magnet displacement measurement is taken in displacement sensor, but the factors affecting the measurement results have much because of tokamak structural features, this paper proposes an improved monocular laser triangle measuring method, it can effectively reduce the distractions, using the image processing software specially developed, we can quickly get magnet operating data, experimental results show that this method precision is high, this method can meet the requirements of the measure. --- paper_title: Application of computer vision and laser interferometer to the inspection of line scale paper_content: In the paper machine vision, laser interferometer and coordinate measuring machine (CMM) are combined to develop a vision inspection system. The measurement capability of the developed system is investigated by measuring the distances between the lines on a standard line scale. The vision camera is used to replace the probe of the CMM to take the images of the interested lines on a line scale at two different positions. Meanwhile, the displacement of the CCD camera is measured using laser interferometer. Using subpixel edge localization and outlier-excluding least-squares regression, the distance between two interested lines is computed under an image plane coordinate system. By adding the displacement of the CCD camera measured using laser interferometer, the line space can be determined. Experiments have been performed repeatedly to measure the line space on the 1.00 and 300.0 mm line scales. Results indicate that the measured data only have a little fluctuation and are close to those obtained by the NML (National Measurement Laboratory, Taiwan). --- paper_title: A new joint application of non-invasive remote sensing techniques for structural health monitoring paper_content: This paper aims at analysing the potentialities of a new technological approach for the dynamic monitoring of civil infrastructures. The proposed approach is based on the joint use of a high-frequency thermal camera and a microwave radar interferometer to measure the oscillations due to traffic excitations of the Sihlhochstrasse Bridge, Switzerland, which was selected as test bed site in the ISTIMES project (EU—Seventh Framework Programme). The good quality of the results encourages the use of the proposed approach for the static and dynamic investigation of structures and infrastructures. Moreover, the remote sensing character of the two applied techniques makes them particularly suitable to study structures located in areas affected by natural hazard phenomena, and also to monitor cultural heritage buildings for which some conventional techniques are considered invasive. Obviously, their reliability needs further experiments and comparisons with standard contact sensors. ---	Title: A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications Section 1: Introduction Description 1: This section introduces the concept of structural health monitoring (SHM), and discusses the evolution and significance of machine vision-based SHM technologies. Section 2: Machine Vision Methods Description 2: This section covers the basic components and procedures involved in machine vision methods for structural monitoring. Section 3: Image Processing Algorithms Description 3: This section details various image processing algorithms employed in structural monitoring, explaining their functionalities and applications. Section 4: Systematic Errors Assessment and Reduction Description 4: This section addresses different kinds of errors associated with vision-based measurement systems, and describes methodologies for error assessment and reduction. Section 5: Two-Dimensional (2D) Structural Displacement Monitoring Description 5: This section focuses on techniques and systems for monitoring 2D structural displacements, including case studies and examples. Section 6: Three-Dimensional (3D) Structural Displacement Monitoring Description 6: This section explores methods for 3D structural displacement monitoring using multiple cameras and vision reconstruction techniques. Section 7: Structural Strain and Stress Monitoring Description 7: This section discusses vision-based methods to measure structural strain and stress, with examples from various materials and structural elements. Section 8: Vibration Monitoring and Dynamic Characteristics Identification Description 8: This section outlines the use of high-speed cameras for vibration monitoring and identification of dynamic characteristics, like natural frequency and modal damping ratio. Section 9: Crack Inspection and Characterization Description 9: This section highlights advanced image processing techniques for detecting and characterizing cracks and other surface features on structures. Section 10: Integration Technology Description 10: This section describes the integration of machine vision technology with other sensing methods to enhance structural health monitoring capabilities. Section 11: Conclusions Description 11: This section provides a summary of the state-of-the-art in machine vision-based SHM, outlining key achievements, limitations, and future prospects in the field.
A SURVEY OF MODEL-BASED SENSOR DATA ACQUISITION AND MANAGEMENT	19	--- paper_title: Efficient gathering of correlated data in sensor networks paper_content: In this article, we design techniques that exploit data correlations in sensor data to minimize communication costs (and hence, energy costs) incurred during data gathering in a sensor network. Our proposed approach is to select a small subset of sensor nodes that may be sufficient to reconstruct data for the entire sensor network. Then, during data gathering only the selected sensors need to be involved in communication. The selected set of sensors must also be connected, since they need to relay data to the data-gathering node. We define the problem of selecting such a set of sensors as the connected correlation-dominating set problem, and formulate it in terms of an appropriately defined correlation structure that captures general data correlations in a sensor network. We develop a set of energy-efficient distributed algorithms and competitive centralized heuristics to select a connected correlation-dominating set of small size. The designed distributed algorithms can be implemented in an asynchronous communication model, and can tolerate message losses. We also design an exponential (but nonexhaustive) centralized approximation algorithm that returns a solution within O(log n) of the optimal size. Based on the approximation algorithm, we design a class of centralized heuristics that are empirically shown to return near-optimal solutions. Simulation results over randomly generated sensor networks with both artificially and naturally generated data sets demonstrate the efficiency of the designed algorithms and the viability of our technique—even in dynamic conditions. --- paper_title: Exploiting correlated attributes in acquisitional query processing paper_content: Sensor networks and other distributed information systems (such as the Web) must frequently access data that has a high per-attribute acquisition cost, in terms of energy, latency, or computational resources. When executing queries that contain several predicates over such expensive attributes, we observe that it can be beneficial to use correlations to automatically introduce low-cost attributes whose observation will allow the query processor to better estimate die selectivity of these expensive predicates. In particular, we show how to build conditional plans that branch into one or more sub-plans, each with a different ordering for the expensive query predicates, based on the runtime observation of low-cost attributes. We frame the problem of constructing the optimal conditional plan for a given user query and set of candidate low-cost attributes as an optimization problem. We describe an exponential time algorithm for finding such optimal plans, and describe a polynomial-time heuristic for identifying conditional plans that perform well in practice. We also show how to compactly model conditional probability distributions needed to identify correlations and build these plans. We evaluate our algorithms against several real-world sensor-network data sets, showing several-times performance increases for a variety of queries versus traditional optimization techniques. --- paper_title: MauveDB: supporting model-based user views in database systems paper_content: Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system. --- paper_title: PRESTO: feedback-driven data management in sensor networks paper_content: This paper presents PRESTO, a novel two-tier sensor data management architecture comprising proxies and sensors that cooperate with one another for acquiring data and processing queries. PRESTO proxies construct time-series models of observed trends in the sensor data and transmit the parameters of the model to sensors. Sensors check sensed data with model-predicted values and transmit only deviations from the predictions back to the proxy. Such a model-driven push approach is energy-efficient, while ensuring that anomalous data trends are never missed. In addition to supporting queries on current data, PRESTO also supports queries on historical data using interpolation and local archival at sensors. PRESTO can adapt model and system parameters to data and query dynamics to further extract energy savings. We have implemented PRESTO on a sensor testbed comprising Intel Stargates and Telos Motes. Our experiments show that in a temperature monitoring application, PRESTO yields one to two orders of magnitude reduction in energy requirements over on-demand, proactive or model-driven pull approaches. PRESTO also results in an order of magnitude reduction in query latency in a 1% duty-cycled five hop sensor network over a system that forwards all queries to remote sensor nodes. --- paper_title: Energy conservation in wireless sensor networks: A survey paper_content: In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs. --- paper_title: PAQ: Time Series Forecasting for Approximate Query Answering in Sensor Networks paper_content: In this paper, we present a method for approximating the values of sensors in a wireless sensor network based on time series forecasting. More specifically, our approach relies on autoregressive models built at each sensor to predict local readings. Nodes transmit these local models to a sink node, which uses them to predict sensor values without directly communicating with sensors. When needed, nodes send information about outlier readings and model updates to the sink. We show that this approach can dramatically reduce the amount of communication required to monitor the readings of all sensors in a network, and demonstrate that our approach provides provably-correct, user-controllable error bounds on the predicted values of each sensor. --- paper_title: Model-Driven Data Acquisition in Sensor Networks paper_content: Declarative queries are proving to be an attractive paradigm for ineracting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this paper, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques. --- paper_title: A Weighted Moving Average-based Approach for Cleaning Sensor Data paper_content: Nowadays, wireless sensor networks have been widely used in many monitoring applications. Due to the low quality of sensors and random effects of the environments, however, it is well known that the collected sensor data are noisy. Therefore, it is very critical to clean the sensor data before using them to answer queries or conduct data analysis. Popular data cleaning approaches, such as the moving average, cannot meet the requirements of both energy efficiency and quick response time in many sensor related applications. In this paper, we propose a hybrid sensor data cleaning approach with confidence. Specifically, we propose a smart weighted moving average (WMA) algorithm that collects confidence data from sensors and computes the weighted moving average. The rationale behind the WMA algorithm is to draw more samples for a particular value that is of great importance to the moving average, and provide higher confidence weight for this value, such that this important value can be quickly reflected in the moving average. Based on our extensive simulation results, we demonstrate that, compared to the simple moving average (SMA), our WMA approach can effectively clean data and offer quick response time. --- paper_title: A neuro-fuzzy approach for sensor network data cleaning paper_content: Sensor networks have become an important source of data with numerous applications in monitoring various real-life phenomena as well as industrial applications and traffic control. However, sensor data are subject to several sources of errors as the data captured from the physical world through these sensor devices tend to be incomplete, noisy, and unreliable, thus yielding imprecise or even incorrect and misleading answers which can be very significative if they result in immediate critical decisions or activation of actuators. Traditional data cleaning techniques cannot be applied in this context as they do not take into account the strong spatial and temporal correlations typically present in sensor data, so machine learning techniques could greatly be of aid. In this paper, we propose a neuro-fuzzy regression approach to clean sensor network data: the well known ANFIS model is employed for reducing the uncertainty associated with the data thus obtaining a more accurate estimate of sensor readings. The obtained cleaning results show good ANFIS performance compared to other common used model such as kernel methods, and we demonstrate its effectiveness if the cleaning model has to be implemented at sensor level rather than at base-station level. --- paper_title: Declarative Support for Sensor Data Cleaning paper_content: Pervasive applications rely on data captured from the physical world through sensor devices. Data provided by these devices, however, tend to be unreliable. The data must, therefore, be cleaned before an application can make use of them, leading to additional complexity for application development and deployment. Here we present Extensible Sensor stream Processing (ESP), a framework for building sensor data cleaning infrastructures for use in pervasive applications. ESP is designed as a pipeline using declarative cleaning mechanisms based on spatial and temporal characteristics of sensor data. We demonstrate ESP's effectiveness and ease of use through three real-world scenarios. --- paper_title: MauveDB: supporting model-based user views in database systems paper_content: Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system. --- paper_title: ERACER: a database approach for statistical inference and data cleaning paper_content: Real-world databases often contain syntactic and semantic errors, in spite of integrity constraints and other safety measures incorporated into modern DBMSs. We present ERACER, an iterative statistical framework for inferring missing information and correcting such errors automatically. Our approach is based on belief propagation and relational dependency networks, and includes an efficient approximate inference algorithm that is easily implemented in standard DBMSs using SQL and user defined functions. The system performs the inference and cleansing tasks in an integrated manner, using shrinkage techniques to infer correct values accurately even in the presence of dirty data. We evaluate the proposed methods empirically on multiple synthetic and real-world data sets. The results show that our framework achieves accuracy comparable to a baseline statistical method using Bayesian networks with exact inference. However, our framework has wider applicability than the Bayesian network baseline, due to its ability to reason with complex, cyclic relational dependencies. --- paper_title: A Deferred Cleansing Method for RFID Data Analytics paper_content: Radio Frequency Identification is gaining broader adoption in many areas. One of the challenges in implementing an RFID-based system is dealing with anomalies in RFID reads. A small number of anomalies can translate into large errors in analytical results. Conventional "eager" approaches cleanse all data upfront and then apply queries on cleaned data. However, this approach is not feasible when several applications define anomalies and corrections on the same data set differently and not all anomalies can be defined beforehand. This necessitates anomaly handling at query time. We introduce a deferred approach for detecting and correcting RFID data anomalies. Each application specifies the detection and the correction of relevant anomalies using declarative sequence-based rules. An application query is then automatically rewritten based on the cleansing rules that the application has specified, to provide answers over cleaned data. We show that a naive approach to deferred cleansing that applies rules without leveraging query information can be prohibitive. We develop two novel rewrite methods, both of which reduce the amount of data to be cleaned, by exploiting predicates in application queries while guaranteeing correct answers. We leverage standardized SQL/OLAP functionality to implement rules specified in a declarative sequence-based language. This allows efficient evaluation of cleansing rules using existing query processing capabilities of a DBMS. Our experimental results show that deferred cleansing is affordable for typical analytic queries over RFID data. --- paper_title: MIST: Distributed Indexing and Querying in Sensor Networks using Statistical Models paper_content: The modeling of high level semantic events from low level sensor signals is important in order to understand distributed phenomena. For such content-modeling purposes, transformation of numeric data into symbols and the modeling of resulting symbolic sequences can be achieved using statistical models---Markov Chains (MCs) and Hidden Markov Models (HMMs). We consider the problem of distributed indexing and semantic querying over such sensor models. Specifically, we are interested in efficiently answering (i) range queries: return all sensors that have observed an unusual sequence of symbols with a high likelihood, (ii) top-1 queries: return the sensor that has the maximum probability of observing a given sequence, and (iii) 1-NN queries: return the sensor (model) which is most similar to a query model. All the above queries can be answered at the centralized base station, if each sensor transmits its model to the base station. However, this is communication-intensive. We present a much more efficient alternative---a distributed index structure, MIST (Model-based Index STructure), and accompanying algorithms for answering the above queries. MIST aggregates two or more constituent models into a single composite model, and constructs an in-network hierarchy over such composite models. We develop two kinds of composite models: the first kind captures the average behavior of the underlying models and the second kind captures the extreme behaviors of the underlying models. Using the index parameters maintained at the root of a subtree, we bound the probability of observation of a query sequence from a sensor in the subtree. We also bound the distance of a query model to a sensor model using these parameters. Extensive experimental evaluation on both real-world and synthetic data sets show that the MIST schemes scale well in terms of network size and number of model states. We also show its superior performance over the centralized schemes in terms of update, query, and total communication costs. --- paper_title: Querying continuous functions in a database system paper_content: Many scientific, financial, data mining and sensor network applications need to work with continuous, rather than discrete data e.g., temperature as a function of location, or stock prices or vehicle trajectories as a function of time. Querying raw or discrete data is unsatisfactory for these applications -- e.g., in a sensor network, it is necessary to interpolate sensor readings to predict values at locations where sensors are not deployed. In other situations, raw data can be inaccurate owing to measurement errors, and it is useful to fit continuous functions to raw data and query the functions, rather than raw data itself -- e.g., fitting a smooth curve to noisy sensor readings, or a smooth trajectory to GPS data containing gaps or outliers. Existing databases do not support storing or querying continuous functions, short of brute-force discretization of functions into a collection of tuples. We present FunctionDB, a novel database system that treats mathematical functions as first-class citizens that can be queried like traditional relations. The key contribution of FunctionDB is an efficient and accurate algebraic query processor - for the broad class of multi-variable polynomial functions, FunctionDB executes queries directly on the algebraic representation of functions without materializing them into discrete points, using symbolic operations: zero finding, variable substitution, and integration. Even when closed form solutions are intractable, FunctionDB leverages symbolic approximation operations to improve performance. We evaluate FunctionDB on real data sets from a temperature sensor network, and on traffic traces from Boston roads. We show that operating in the functional domain has substantial advantages in terms of accuracy (15-30%) and up to order of magnitude (10x-100x) performance wins over existing approaches that represent models as discrete collections of points. --- paper_title: Probabilistic Inference over RFID Streams in Mobile Environments paper_content: Recent innovations in RFID technology are enabling large-scale cost-effective deployments in retail, healthcare, pharmaceuticals and supply chain management. The advent of mobile or handheld readers adds significant new challenges to RFID stream processing due to the inherent reader mobility, increased noise, and incomplete data. In this paper, we address the problem of translating noisy, incomplete raw streams from mobile RFID readers into clean, precise event streams with location information. Specifically we propose a probabilistic model to capture the mobility of the reader, object dynamics, and noisy readings. Our model can self-calibrate by automatically estimating key parameters from observed data. Based on this model, we employ a sampling-based technique called particle filtering to infer clean, precise information about object locations from raw streams from mobile RFID readers. Since inference based on standard particle filtering is neither scalable nor efficient in our settings, we propose three enhancements---particle factorization, spatial indexing, and belief compression---for scalable inference over large numbers of objects and high-volume streams. Our experiments show that our approach can offer 49\% error reduction over a state-of-the-art data cleaning approach such as SMURF while also being scalable and efficient. --- paper_title: Cleaning and querying noisy sensors paper_content: Sensor networks have become an important source of data with numerous applications in monitoring various real-life phenomena as well as industrial applications and traffic control. Unfortunately, sensor data is subject to several sources of errors such as noise from external sources, hardware noise, inaccuracies and imprecision, and various environmental effects. Such errors may seriously impact the answer to any query posed to the sensors. In particular, they may yield imprecise or even incorrect and misleading answers which can be very significant if they result in immediate critical decisions or activation of actuators. In this paper, we present a framework for cleaning and querying noisy sensors. Specifically, we present a Bayesian approach for reducing the uncertainty associated with the data, that arise due to random noise, in an online fashion. Our approach combines prior knowledge of the true sensor reading, the noise characteristics of this sensor, and the observed noisy reading in order to obtain a more accurate estimate of the reading. This cleaning step can be performed either at the sensor level or at the base-station. Based on our proposed uncertainty models and using a statistical approach, we introduce several algorithms for answering traditional database queries over uncertain sensor readings. Finally, we present a preliminary evaluation of our proposed approach using synthetic data and highlight some exciting research directions in this area. --- paper_title: Adaptive Cleaning for RFID Data Streams paper_content: To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications. --- paper_title: ORDEN: outlier region detection and exploration in sensor networks paper_content: Sensor networks play a central role in applications that monitor variables in geographic areas such as the traffic volume on roads or the temperature in the environment. A key feature users are often interested in when employing such systems is the detection of unusual phenomena, that is, anomalous values measured by the sensors. In this demonstration, we present a system, called ORDEN, that allows for the detection and (visual) exploration of outliers and anomalous events in sensor networks in real-time. In particular, the system constructs outlier regions from anomalous sensor measurements to provide for a comprehensive description of the spatial extent of phenomena of interest. With our system, users can interactively explore displayed outlier regions and investigate the heterogeneity within individual regions using different parameter and threshold settings. Using real-world sensor data streams from different application domains, we demonstrate the effectiveness and utility of our system. --- paper_title: Online Filtering, Smoothing and Probabilistic Modeling of Streaming data paper_content: In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new data arrives. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over several synthetic and real datasets, that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of tight integration between dynamic probabilistic models and databases. --- paper_title: Predictive Modeling-Based Data Collection in Wireless Sensor Networks paper_content: We address the problem of designing practical, energy-efficient protocols for data collection in wireless sensor networks using predictive modeling. Prior work has suggested several approaches to capture and exploit the rich spatio-temporal correlations prevalent in WSNs during data collection. Although shown to be effective in reducing the data collection cost, those approaches use simplistic corelation models and further, ignore many idiosyncrasies of WSNs, in particular the broadcast nature of communication. Our proposed approach is based on approximating the joint probability distribution over the sensors using undirected graphical models, ideally suited to exploit both the spatial correlations and the broadcast nature of communication. We present algorithms for optimally using such a model for data collection under different communication models, and for identifying an appropriate model to use for a given sensor network. Experiments over synthetic and real-world datasets show that our approach significantly reduces the data collection cost. --- paper_title: Efficiently Maintaining Distributed Model-Based Views on Real-Time Data Streams paper_content: Minimizing communication cost is a fundamental problem in large-scale federated sensor networks. Maintaining model-based views of data streams has been highlighted because it permits efficient data communication by transmitting parameter values of models, instead of original data streams. We propose a framework that employs the advantages of using model-based views for communication-efficient stream data processing over federated sensor networks, yet it significantly improves state-of-the-art approaches. The framework is generic and any time-parameterized models can be plugged, while accuracy guarantees for query results are ensured throughout the large-scale networks. In addition, we boost the performance of the framework by the coded model update that enables efficient model update from one node to another. It predetermines parameter values for the model, updates only identifiers of the parameter values, and compresses the identifiers by utilizing bitmaps. Moreover, we propose a correlation model, named coded inter-variable model, that merges the efficiency of the coded model update with that of correlation models. Empirical studies with real data demonstrate that our proposal achieves substantial amounts of communication reduction, outperforming state-of-the art methods. --- paper_title: Efficient time series matching by wavelets paper_content: Time series stored as feature vectors can be indexed by multidimensional index trees like R-Trees for fast retrieval. Due to the dimensionality curse problem, transformations are applied to time series to reduce the number of dimensions of the feature vectors. Different transformations like Discrete Fourier Transform (DFT) Discrete Wavelet Transform (DWT), Karhunen-Loeve (KL) transform or Singular Value Decomposition (SVD) can be applied. While the use of DFT and K-L transform or SVD have been studied on the literature, to our knowledge, there is no in-depth study on the application of DWT. In this paper we propose to use Haar Wavelet Transform for time series indexing. The major contributions are: (1) we show that Euclidean distance is preserved in the Haar transformed domain and no false dismissal will occur, (2) we show that Haar transform can outperform DFT through experiments, (3) a new similarity model is suggested to accommodate vertical shift of time series, and (4) a two-phase method is proposed for efficient n-nearest neighbor query in time series databases. --- paper_title: Streaming Pattern Discovery in Multiple Time-Series paper_content: In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Time-series). Given n numerical data streams, all of whose values we observe at each time tick t, SPIRIT can incrementally find correlations and hidden variables, which summarise the key trends in the entire stream collection. It can do this quickly, with no buffering of stream values and without comparing pairs of streams. Moreover, it is any-time, single pass, and it dynamically detects changes. The discovered trends can also be used to immediately spot potential anomalies, to do efficient forecasting and, more generally, to dramatically simplify further data processing. Our experimental evaluation and case studies show that SPIRIT can incrementally capture correlations and discover trends, efficiently and effectively. --- paper_title: GAMPS: compressing multi sensor data by grouping and amplitude scaling paper_content: We consider the problem of collectively approximating a set of sensor signals using the least amount of space so that any individual signal can be efficiently reconstructed within a given maximum (L∞) error e. The problem arises naturally in applications that need to collect large amounts of data from multiple concurrent sources, such as sensors, servers and network routers, and archive them over a long period of time for offline data mining. We present GAMPS, a general framework that addresses this problem by combining several novel techniques. First, it dynamically groups multiple signals together so that signals within each group are correlated and can be maximally compressed jointly. Second, it appropriately scales the amplitudes of different signals within a group and compresses them within the maximum allowed reconstruction error bound. Our schemes are polynomial time O(α, β approximation schemes, meaning that the maximum (L∞) error is at most α e and it uses at most β times the optimal memory. Finally, GAMPS maintains an index so that various queries can be issued directly on compressed data. Our experiments on several real-world sensor datasets show that GAMPS significantly reduces space without compromising the quality of search and query. --- paper_title: StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time paper_content: Consider the problem of monitoring tens of thousands of time series data streams in an online fashion and making decisions based on them. In addition to single stream statistics such as average and standard deviation, we also want to find high correlations among all pairs of streams. A stock market trader might use such a tool to spot arbitrage opportunities. This paper proposes efficient methods for solving this problem based on Discrete Fourier Transforms and a three level time interval hierarchy. Extensive experiments on synthetic data and real world financial trading data show that our algorithm beats the direct computation approach by several orders of magnitude. It also improves on previous Fourier Transform approaches by allowing the efficient computation of time-delayed correlation over any size sliding window and any time delay. Correlation also lends itself to an efficient grid-based data structure. The result is the first algorithm that we know of to compute correlations over thousands of data streams in real time. The algorithm is incremental, has fixed response time, and can monitor the pairwise correlations of 10,000 streams on a single PC. The algorithm is embarrassingly parallelizable. --- paper_title: Similarity search over time-series data using wavelets paper_content: Considers the use of wavelet transformations as a dimensionality reduction technique to permit efficient similarity searching over high-dimensional time-series data. While numerous transformations have been proposed and studied, the only wavelet that has been shown to be effective for this application is the Haar wavelet. In this work, we observe that a large class of wavelet transformations (not only orthonormal wavelets but also bi-orthonormal wavelets) can be used to support similarity searching. This class includes the most popular and most effective wavelets being used in image compression. We present a detailed performance study of the effects of using different wavelets on the performance of similarity searching for time-series data. We include several wavelets that outperform both the Haar wavelet and the best-known non-wavelet transformations for this application. To ensure our results are usable by an application engineer, we also show how to configure an indexing strategy for the best-performing transformations. Finally, we identify classes of data that can be indexed efficiently using these wavelet transformations. --- paper_title: Efficient gathering of correlated data in sensor networks paper_content: In this article, we design techniques that exploit data correlations in sensor data to minimize communication costs (and hence, energy costs) incurred during data gathering in a sensor network. Our proposed approach is to select a small subset of sensor nodes that may be sufficient to reconstruct data for the entire sensor network. Then, during data gathering only the selected sensors need to be involved in communication. The selected set of sensors must also be connected, since they need to relay data to the data-gathering node. We define the problem of selecting such a set of sensors as the connected correlation-dominating set problem, and formulate it in terms of an appropriately defined correlation structure that captures general data correlations in a sensor network. We develop a set of energy-efficient distributed algorithms and competitive centralized heuristics to select a connected correlation-dominating set of small size. The designed distributed algorithms can be implemented in an asynchronous communication model, and can tolerate message losses. We also design an exponential (but nonexhaustive) centralized approximation algorithm that returns a solution within O(log n) of the optimal size. Based on the approximation algorithm, we design a class of centralized heuristics that are empirically shown to return near-optimal solutions. Simulation results over randomly generated sensor networks with both artificially and naturally generated data sets demonstrate the efficiency of the designed algorithms and the viability of our technique—even in dynamic conditions. --- paper_title: Exploiting correlated attributes in acquisitional query processing paper_content: Sensor networks and other distributed information systems (such as the Web) must frequently access data that has a high per-attribute acquisition cost, in terms of energy, latency, or computational resources. When executing queries that contain several predicates over such expensive attributes, we observe that it can be beneficial to use correlations to automatically introduce low-cost attributes whose observation will allow the query processor to better estimate die selectivity of these expensive predicates. In particular, we show how to build conditional plans that branch into one or more sub-plans, each with a different ordering for the expensive query predicates, based on the runtime observation of low-cost attributes. We frame the problem of constructing the optimal conditional plan for a given user query and set of candidate low-cost attributes as an optimization problem. We describe an exponential time algorithm for finding such optimal plans, and describe a polynomial-time heuristic for identifying conditional plans that perform well in practice. We also show how to compactly model conditional probability distributions needed to identify correlations and build these plans. We evaluate our algorithms against several real-world sensor-network data sets, showing several-times performance increases for a variety of queries versus traditional optimization techniques. --- paper_title: MauveDB: supporting model-based user views in database systems paper_content: Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system. --- paper_title: PRESTO: feedback-driven data management in sensor networks paper_content: This paper presents PRESTO, a novel two-tier sensor data management architecture comprising proxies and sensors that cooperate with one another for acquiring data and processing queries. PRESTO proxies construct time-series models of observed trends in the sensor data and transmit the parameters of the model to sensors. Sensors check sensed data with model-predicted values and transmit only deviations from the predictions back to the proxy. Such a model-driven push approach is energy-efficient, while ensuring that anomalous data trends are never missed. In addition to supporting queries on current data, PRESTO also supports queries on historical data using interpolation and local archival at sensors. PRESTO can adapt model and system parameters to data and query dynamics to further extract energy savings. We have implemented PRESTO on a sensor testbed comprising Intel Stargates and Telos Motes. Our experiments show that in a temperature monitoring application, PRESTO yields one to two orders of magnitude reduction in energy requirements over on-demand, proactive or model-driven pull approaches. PRESTO also results in an order of magnitude reduction in query latency in a 1% duty-cycled five hop sensor network over a system that forwards all queries to remote sensor nodes. --- paper_title: Energy conservation in wireless sensor networks: A survey paper_content: In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs. --- paper_title: PAQ: Time Series Forecasting for Approximate Query Answering in Sensor Networks paper_content: In this paper, we present a method for approximating the values of sensors in a wireless sensor network based on time series forecasting. More specifically, our approach relies on autoregressive models built at each sensor to predict local readings. Nodes transmit these local models to a sink node, which uses them to predict sensor values without directly communicating with sensors. When needed, nodes send information about outlier readings and model updates to the sink. We show that this approach can dramatically reduce the amount of communication required to monitor the readings of all sensors in a network, and demonstrate that our approach provides provably-correct, user-controllable error bounds on the predicted values of each sensor. --- paper_title: Model-Driven Data Acquisition in Sensor Networks paper_content: Declarative queries are proving to be an attractive paradigm for ineracting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this paper, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques. --- paper_title: TinyDB: an acquisitional query processing system for sensor networks paper_content: We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices. --- paper_title: The design of an acquisitional query processor for sensor networks paper_content: We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices. --- paper_title: TiNA: a scheme for temporal coherency-aware in-network aggregation paper_content: This paper presents TiNA, a scheme for minimizing energy consumption in sensor networks by exploiting end-user tolerance to temporal coherency. TiNA utilizes temporal coherency tolerances to both reduce the amount of information transmitted by individual nodes (communication cost dominates power usage in sensor networks), and to improve quality of data when not all sensor readings can be propagated up the network within a given time constraint. TiNA was evaluated against a traditional in-network aggregation scheme with respect to power savings as well as the quality of data for aggregate queries. Preliminary results show that TiNA can reduce power consumption by up to 50% without any loss in the quality of data. --- paper_title: TAG: a Tiny AGgregation service for ad-hoc sensor networks paper_content: We present the Tiny AGgregation (TAG) service for aggregation in low-power, distributed, wireless environments. TAG allows users to express simple, declarative queries and have them distributed and executed efficiently in networks of low-power, wireless sensors. We discuss various generic properties of aggregates, and show how those properties affect the performance of our in network approach. We include a performance study demonstrating the advantages of our approach over traditional centralized, out-of-network methods, and discuss a variety of optimizations for improving the performance and fault tolerance of the basic solution. --- paper_title: Query processing in sensor networks paper_content: Hardware for sensor nodes that combine physical sensors, actuators, embedded processors, and communication components has advanced significantly over the last decade, and made the large-scale deployment of such sensors a reality. Applications range from monitoring applications such as inventory maintenance over health care to military applications. In this paper, we evaluate the design of a query layer for sensor networks. The query layer accepts queries in a declarative language that are then optimized to generate ecient query execution plans with in-network processing which can significantly reduce resource requirements. We examine the main architectural components of such a query layer, concentrating on in-network aggregation, interaction of in-network aggregation with the wireless routing protocol, and distributed query processing. Initial simulation experiments with the ns-2 network simulator show the tradeos of our system. --- paper_title: Exploiting correlated attributes in acquisitional query processing paper_content: Sensor networks and other distributed information systems (such as the Web) must frequently access data that has a high per-attribute acquisition cost, in terms of energy, latency, or computational resources. When executing queries that contain several predicates over such expensive attributes, we observe that it can be beneficial to use correlations to automatically introduce low-cost attributes whose observation will allow the query processor to better estimate die selectivity of these expensive predicates. In particular, we show how to build conditional plans that branch into one or more sub-plans, each with a different ordering for the expensive query predicates, based on the runtime observation of low-cost attributes. We frame the problem of constructing the optimal conditional plan for a given user query and set of candidate low-cost attributes as an optimization problem. We describe an exponential time algorithm for finding such optimal plans, and describe a polynomial-time heuristic for identifying conditional plans that perform well in practice. We also show how to compactly model conditional probability distributions needed to identify correlations and build these plans. We evaluate our algorithms against several real-world sensor-network data sets, showing several-times performance increases for a variety of queries versus traditional optimization techniques. --- paper_title: Model-Driven Data Acquisition in Sensor Networks paper_content: Declarative queries are proving to be an attractive paradigm for ineracting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this paper, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques. --- paper_title: PRESTO: feedback-driven data management in sensor networks paper_content: This paper presents PRESTO, a novel two-tier sensor data management architecture comprising proxies and sensors that cooperate with one another for acquiring data and processing queries. PRESTO proxies construct time-series models of observed trends in the sensor data and transmit the parameters of the model to sensors. Sensors check sensed data with model-predicted values and transmit only deviations from the predictions back to the proxy. Such a model-driven push approach is energy-efficient, while ensuring that anomalous data trends are never missed. In addition to supporting queries on current data, PRESTO also supports queries on historical data using interpolation and local archival at sensors. PRESTO can adapt model and system parameters to data and query dynamics to further extract energy savings. We have implemented PRESTO on a sensor testbed comprising Intel Stargates and Telos Motes. Our experiments show that in a temperature monitoring application, PRESTO yields one to two orders of magnitude reduction in energy requirements over on-demand, proactive or model-driven pull approaches. PRESTO also results in an order of magnitude reduction in query latency in a 1% duty-cycled five hop sensor network over a system that forwards all queries to remote sensor nodes. --- paper_title: Time Series Analysis and Its Applications paper_content: Characteristics of time series.- Time series regression and exploratory data analysis.- ARIMA models.- Spectral analysis and filtering.- Additional time domain topics.- State-space models.- Statistical methods in the frequency domain. --- paper_title: Data-Driven Processing in Sensor Networks paper_content: Wireless sensor networks are poised to enable continuous data collection on unprecedented scales, in terms of area location and size, and frequency. This is a great boon to elds such as ecological modeling. We are collaborating with researchers to build sophisticated temporal and spatial models of forest growth, utilizing a variety of measurements. There exists a crucial challenge in supporting this activity: network nodes have limited battery life, and radio communication is the dominant energy consumer. The straightforward solution of instructing all nodes to report their measurements as they are taken to a base station will quickly consume the network’s energy. On the other hand, the solution of building models for node behavior and substituting these in place of the actual measurements is in conict with the end goal of constructing models. To address this dilemma, we propose data-driven processing, the goal of which is to provide continuous data without continuous reporting, but with checks against the actual data. Our primary strategy for this is suppression, which uses in-network monitoring to limit the amount of communication to the base station. Suppression employs models for optimization of data collection, but not at the risk of correctness. We discuss techniques for designing data-driven collection, such as building suppression schemes and incorporating models into them. We then present and address some of the major challenges to making this approach practical, such as handling failure and avoiding the need to co-design the network application and communication layers. --- paper_title: Snapshot queries: towards data-centric sensor networks paper_content: In this paper we introduce the idea of snapshot queries for energy efficient data acquisition in sensor networks. Network nodes generate models of their surrounding environment that are used for electing, using a localized algorithm, a small set of representative nodes in the network. These representative nodes constitute a network snapshot and can be used to provide quick approximate answers to user queries while reducing substantially the energy consumption in the network. We present a detailed experimental study of our framework and algorithms, varying multiple parameters like the available memory of the sensor nodes, their transmission range, the network message loss etc. Depending on the configuration, snapshot queries provide a reduction of up to 90% in the number of nodes that need to participate in a user query. --- paper_title: A Weighted Moving Average-based Approach for Cleaning Sensor Data paper_content: Nowadays, wireless sensor networks have been widely used in many monitoring applications. Due to the low quality of sensors and random effects of the environments, however, it is well known that the collected sensor data are noisy. Therefore, it is very critical to clean the sensor data before using them to answer queries or conduct data analysis. Popular data cleaning approaches, such as the moving average, cannot meet the requirements of both energy efficiency and quick response time in many sensor related applications. In this paper, we propose a hybrid sensor data cleaning approach with confidence. Specifically, we propose a smart weighted moving average (WMA) algorithm that collects confidence data from sensors and computes the weighted moving average. The rationale behind the WMA algorithm is to draw more samples for a particular value that is of great importance to the moving average, and provide higher confidence weight for this value, such that this important value can be quickly reflected in the moving average. Based on our extensive simulation results, we demonstrate that, compared to the simple moving average (SMA), our WMA approach can effectively clean data and offer quick response time. --- paper_title: Declarative Support for Sensor Data Cleaning paper_content: Pervasive applications rely on data captured from the physical world through sensor devices. Data provided by these devices, however, tend to be unreliable. The data must, therefore, be cleaned before an application can make use of them, leading to additional complexity for application development and deployment. Here we present Extensible Sensor stream Processing (ESP), a framework for building sensor data cleaning infrastructures for use in pervasive applications. ESP is designed as a pipeline using declarative cleaning mechanisms based on spatial and temporal characteristics of sensor data. We demonstrate ESP's effectiveness and ease of use through three real-world scenarios. --- paper_title: A Pipelined Framework for Online Cleaning of Sensor Data Streams paper_content: Data captured from the physical world through sensor devices tends to be noisy and unreliable. The data cleaning process for such data is not easily handled by standard data warehouse-oriented techniques, which do not take into account the strong temporal and spatial components of receptor data. We present Extensible receptor Stream Processing (ESP), a declarative query-based framework designed to clean the data streams produced by sensor devices. --- paper_title: Outlier Detection Techniques for Wireless Sensor Networks: A Survey paper_content: In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree. --- paper_title: ORDEN: outlier region detection and exploration in sensor networks paper_content: Sensor networks play a central role in applications that monitor variables in geographic areas such as the traffic volume on roads or the temperature in the environment. A key feature users are often interested in when employing such systems is the detection of unusual phenomena, that is, anomalous values measured by the sensors. In this demonstration, we present a system, called ORDEN, that allows for the detection and (visual) exploration of outliers and anomalous events in sensor networks in real-time. In particular, the system constructs outlier regions from anomalous sensor measurements to provide for a comprehensive description of the spatial extent of phenomena of interest. With our system, users can interactively explore displayed outlier regions and investigate the heterogeneity within individual regions using different parameter and threshold settings. Using real-world sensor data streams from different application domains, we demonstrate the effectiveness and utility of our system. --- paper_title: A Weighted Moving Average-based Approach for Cleaning Sensor Data paper_content: Nowadays, wireless sensor networks have been widely used in many monitoring applications. Due to the low quality of sensors and random effects of the environments, however, it is well known that the collected sensor data are noisy. Therefore, it is very critical to clean the sensor data before using them to answer queries or conduct data analysis. Popular data cleaning approaches, such as the moving average, cannot meet the requirements of both energy efficiency and quick response time in many sensor related applications. In this paper, we propose a hybrid sensor data cleaning approach with confidence. Specifically, we propose a smart weighted moving average (WMA) algorithm that collects confidence data from sensors and computes the weighted moving average. The rationale behind the WMA algorithm is to draw more samples for a particular value that is of great importance to the moving average, and provide higher confidence weight for this value, such that this important value can be quickly reflected in the moving average. Based on our extensive simulation results, we demonstrate that, compared to the simple moving average (SMA), our WMA approach can effectively clean data and offer quick response time. --- paper_title: Creating probabilistic databases from imprecise time-series data paper_content: Although efficient processing of probabilistic databases is a well-established field, a wide range of applications are still unable to benefit from these techniques due to the lack of means for creating probabilistic databases. In fact, it is a challenging problem to associate concrete probability values with given time-series data for forming a probabilistic database, since the probability distributions used for deriving such probability values vary over time. In this paper, we propose a novel approach to create tuple-level probabilistic databases from (imprecise) time-series data. To the best of our knowledge, this is the first work that introduces a generic solution for creating probabilistic databases from arbitrary time series, which can work in online as well as offline fashion. Our approach consists of two key components. First, the dynamic density metrics that infer time-dependent probability distributions for time series, based on various mathematical models. Our main metric, called the GARCH metric, can robustly capture such evolving probability distributions regardless of the presence of erroneous values in a given time series. Second, the Ω-View builder that creates probabilistic databases from the probability distributions inferred by the dynamic density metrics. For efficient processing, we introduce the σ-cache that reuses the information derived from probability values generated at previous times. Extensive experiments over real datasets demonstrate the effectiveness of our approach. --- paper_title: A neuro-fuzzy approach for sensor network data cleaning paper_content: Sensor networks have become an important source of data with numerous applications in monitoring various real-life phenomena as well as industrial applications and traffic control. However, sensor data are subject to several sources of errors as the data captured from the physical world through these sensor devices tend to be incomplete, noisy, and unreliable, thus yielding imprecise or even incorrect and misleading answers which can be very significative if they result in immediate critical decisions or activation of actuators. Traditional data cleaning techniques cannot be applied in this context as they do not take into account the strong spatial and temporal correlations typically present in sensor data, so machine learning techniques could greatly be of aid. In this paper, we propose a neuro-fuzzy regression approach to clean sensor network data: the well known ANFIS model is employed for reducing the uncertainty associated with the data thus obtaining a more accurate estimate of sensor readings. The obtained cleaning results show good ANFIS performance compared to other common used model such as kernel methods, and we demonstrate its effectiveness if the cleaning model has to be implemented at sensor level rather than at base-station level. --- paper_title: Incorporating quality aspects in sensor data streams paper_content: Sensors are increasingly embedded into physical products in order to capture data about their conditions and usage for decision making in business applications. However, a major issue for such applications is the limited quality of the captured data due to inherently restricted precision and performance of the sensors. Moreover, the data quality is further decreased by data processing to meet resource constraints in streaming environments and ultimately influences business decisions. The issue of how to efficiently provide applications with information about data quality (DQ) is still an open research problem. In my Ph.D. thesis, I address this problem by developing a system to provide business applications with accurate information on data quality. Furthermore, the system will be able to incorporate and guarantee user-defined data quality levels. In this paper, I will present the major results from my research so far. This includes a novel jumping-window-based approach for the efficient transfer of data quality information as well as a flexible metamodel for storage and propagation of data quality. The comprehensive analysis of common data processing operators w.r.t. their impact on data quality allows a fruitful knowledge evaluation and thus diminishes incorrect business decisions. --- paper_title: Representing Data Quality in Sensor Data Streaming Environments paper_content: Sensors in smart-item environments capture data about product conditions and usage to support business decisions as well as production automation processes. A challenging issue in this application area is the restricted quality of sensor data due to limited sensor precision and sensor failures. Moreover, data stream processing to meet resource constraints in streaming environments introduces additional noise and decreases the data quality. In order to avoid wrong business decisions due to dirty data, quality characteristics have to be captured, processed, and provided to the respective business task. However, the issue of how to efficiently provide applications with information about data quality is still an open research problem. In this article, we address this problem by presenting a flexible model for the propagation and processing of data quality. The comprehensive analysis of common data stream processing operators and their impact on data quality allows a fruitful data evaluation and diminishes incorrect business decisions. Further, we propose the data quality model control to adapt the data quality granularity to the data stream interestingness. --- paper_title: Probabilistic Inference over RFID Streams in Mobile Environments paper_content: Recent innovations in RFID technology are enabling large-scale cost-effective deployments in retail, healthcare, pharmaceuticals and supply chain management. The advent of mobile or handheld readers adds significant new challenges to RFID stream processing due to the inherent reader mobility, increased noise, and incomplete data. In this paper, we address the problem of translating noisy, incomplete raw streams from mobile RFID readers into clean, precise event streams with location information. Specifically we propose a probabilistic model to capture the mobility of the reader, object dynamics, and noisy readings. Our model can self-calibrate by automatically estimating key parameters from observed data. Based on this model, we employ a sampling-based technique called particle filtering to infer clean, precise information about object locations from raw streams from mobile RFID readers. Since inference based on standard particle filtering is neither scalable nor efficient in our settings, we propose three enhancements---particle factorization, spatial indexing, and belief compression---for scalable inference over large numbers of objects and high-volume streams. Our experiments show that our approach can offer 49\% error reduction over a state-of-the-art data cleaning approach such as SMURF while also being scalable and efficient. --- paper_title: Cleaning and querying noisy sensors paper_content: Sensor networks have become an important source of data with numerous applications in monitoring various real-life phenomena as well as industrial applications and traffic control. Unfortunately, sensor data is subject to several sources of errors such as noise from external sources, hardware noise, inaccuracies and imprecision, and various environmental effects. Such errors may seriously impact the answer to any query posed to the sensors. In particular, they may yield imprecise or even incorrect and misleading answers which can be very significant if they result in immediate critical decisions or activation of actuators. In this paper, we present a framework for cleaning and querying noisy sensors. Specifically, we present a Bayesian approach for reducing the uncertainty associated with the data, that arise due to random noise, in an online fashion. Our approach combines prior knowledge of the true sensor reading, the noise characteristics of this sensor, and the observed noisy reading in order to obtain a more accurate estimate of the reading. This cleaning step can be performed either at the sensor level or at the base-station. Based on our proposed uncertainty models and using a statistical approach, we introduce several algorithms for answering traditional database queries over uncertain sensor readings. Finally, we present a preliminary evaluation of our proposed approach using synthetic data and highlight some exciting research directions in this area. --- paper_title: Data cleaning using belief propagation paper_content: Effective data cleaning is critical in many applications where the quality of data is poor due to missing values or inaccurate values. Fortunately, a wide spectrum of applications exhibit strong dependencies between data samples, and such dependencies can be used very effectively for cleaning the data. For example, the readings of nearby sensors are generally correlated, and proteins interact with each other when performing crucial functions. We propose a data cleaning approach, based on modeling data dependencies with Markov networks. Belief propagation is used to efficiently compute the marginal or maximum posterior probabilities, so as to infer missing values or to correct errors. To illustrate the benefits and generality of the technique, we discuss its use in several applications and report on the data quality and improvements so obtained. --- paper_title: Anomaly detection: A survey paper_content: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with. --- paper_title: Online Outlier Detection in Sensor Data Using Non-Parametric Models paper_content: Sensor networks have recently found many popular applications in a number of different settings. Sensors at different locations can generate streaming data, which can be analyzed in real-time to identify events of interest. In this paper, we propose a framework that computes in a distributed fashion an approximation of multi-dimensional data distributions in order to enable complex applications in resource-constrained sensor networks.We motivate our technique in the context of the problem of outlier detection. We demonstrate how our framework can be extended in order to identify either distance- or density-based outliers in a single pass over the data, and with limited memory requirements. Experiments with synthetic and real data show that our method is efficient and accurate, and compares favorably to other proposed techniques. We also demonstrate the applicability of our technique to other related problems in sensor networks. --- paper_title: Outlier-Aware Data Aggregation in Sensor Networks paper_content: In this paper we discuss a robust aggregation framework that can detect spurious measurements and refrain from incorporating them in the computed aggregate values. Our framework can consider different definitions of an outlier node, based on a specified minimum support. Our experimental evaluation demonstrates the benefits of our approach. --- paper_title: Outlier Detection Techniques for Wireless Sensor Networks: A Survey paper_content: In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree. --- paper_title: Outlier detection in sensor networks paper_content: Outlier detection has many important applications in sensor networks, e.g., abnormal event detection, animal behavior change, etc. It is a difficult problem since global information about data distributions must be known to identify outliers. In this paper, we use a histogram-based method for outlier detection to reduce communication cost. Rather than collecting all the data in one location for centralized processing, we propose collecting hints (in the form of a histogram) about the data distribution, and using the hints to filter out unnecessary data and identify potential outliers. We show that this method can be used for detecting outliers in terms of two different definitions. Our simulation results show that the histogram method can dramatically reduce the communication cost. --- paper_title: An overview of anomaly detection techniques: Existing solutions and latest technological trends paper_content: As advances in networking technology help to connect the distant corners of the globe and as the Internet continues to expand its influence as a medium for communications and commerce, the threat from spammers, attackers and criminal enterprises has also grown accordingly. It is the prevalence of such threats that has made intrusion detection systems-the cyberspace's equivalent to the burglar alarm-join ranks with firewalls as one of the fundamental technologies for network security. However, today's commercially available intrusion detection systems are predominantly signature-based intrusion detection systems that are designed to detect known attacks by utilizing the signatures of those attacks. Such systems require frequent rule-base updates and signature updates, and are not capable of detecting unknown attacks. In contrast, anomaly detection systems, a subset of intrusion detection systems, model the normal system/network behavior which enables them to be extremely effective in finding and foiling both known as well as unknown or ''zero day'' attacks. While anomaly detection systems are attractive conceptually, a host of technological problems need to be overcome before they can be widely adopted. These problems include: high false alarm rate, failure to scale to gigabit speeds, etc. In this paper, we provide a comprehensive survey of anomaly detection systems and hybrid intrusion detection systems of the recent past and present. We also discuss recent technological trends in anomaly detection and identify open problems and challenges in this area. --- paper_title: ORDEN: outlier region detection and exploration in sensor networks paper_content: Sensor networks play a central role in applications that monitor variables in geographic areas such as the traffic volume on roads or the temperature in the environment. A key feature users are often interested in when employing such systems is the detection of unusual phenomena, that is, anomalous values measured by the sensors. In this demonstration, we present a system, called ORDEN, that allows for the detection and (visual) exploration of outliers and anomalous events in sensor networks in real-time. In particular, the system constructs outlier regions from anomalous sensor measurements to provide for a comprehensive description of the spatial extent of phenomena of interest. With our system, users can interactively explore displayed outlier regions and investigate the heterogeneity within individual regions using different parameter and threshold settings. Using real-world sensor data streams from different application domains, we demonstrate the effectiveness and utility of our system. --- paper_title: Declarative Support for Sensor Data Cleaning paper_content: Pervasive applications rely on data captured from the physical world through sensor devices. Data provided by these devices, however, tend to be unreliable. The data must, therefore, be cleaned before an application can make use of them, leading to additional complexity for application development and deployment. Here we present Extensible Sensor stream Processing (ESP), a framework for building sensor data cleaning infrastructures for use in pervasive applications. ESP is designed as a pipeline using declarative cleaning mechanisms based on spatial and temporal characteristics of sensor data. We demonstrate ESP's effectiveness and ease of use through three real-world scenarios. --- paper_title: ERACER: a database approach for statistical inference and data cleaning paper_content: Real-world databases often contain syntactic and semantic errors, in spite of integrity constraints and other safety measures incorporated into modern DBMSs. We present ERACER, an iterative statistical framework for inferring missing information and correcting such errors automatically. Our approach is based on belief propagation and relational dependency networks, and includes an efficient approximate inference algorithm that is easily implemented in standard DBMSs using SQL and user defined functions. The system performs the inference and cleansing tasks in an integrated manner, using shrinkage techniques to infer correct values accurately even in the presence of dirty data. We evaluate the proposed methods empirically on multiple synthetic and real-world data sets. The results show that our framework achieves accuracy comparable to a baseline statistical method using Bayesian networks with exact inference. However, our framework has wider applicability than the Bayesian network baseline, due to its ability to reason with complex, cyclic relational dependencies. --- paper_title: A Deferred Cleansing Method for RFID Data Analytics paper_content: Radio Frequency Identification is gaining broader adoption in many areas. One of the challenges in implementing an RFID-based system is dealing with anomalies in RFID reads. A small number of anomalies can translate into large errors in analytical results. Conventional "eager" approaches cleanse all data upfront and then apply queries on cleaned data. However, this approach is not feasible when several applications define anomalies and corrections on the same data set differently and not all anomalies can be defined beforehand. This necessitates anomaly handling at query time. We introduce a deferred approach for detecting and correcting RFID data anomalies. Each application specifies the detection and the correction of relevant anomalies using declarative sequence-based rules. An application query is then automatically rewritten based on the cleansing rules that the application has specified, to provide answers over cleaned data. We show that a naive approach to deferred cleansing that applies rules without leveraging query information can be prohibitive. We develop two novel rewrite methods, both of which reduce the amount of data to be cleaned, by exploiting predicates in application queries while guaranteeing correct answers. We leverage standardized SQL/OLAP functionality to implement rules specified in a declarative sequence-based language. This allows efficient evaluation of cleansing rules using existing query processing capabilities of a DBMS. Our experimental results show that deferred cleansing is affordable for typical analytic queries over RFID data. --- paper_title: Adaptive Cleaning for RFID Data Streams paper_content: To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications. --- paper_title: MauveDB: supporting model-based user views in database systems paper_content: Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system. --- paper_title: Fundamentals of Database Systems paper_content: From the Publisher: ::: Fundamentals of Database Systems combines clear explanations of theory and design, broad coverage of models and real systems, and excellent examples with up-to-date introductions to modern database technologies. This edition is completely revised and updated, and reflects the latest trends in technological and application development. Professors Elmasri and Navathe focus on the relational model and include coverage of recent object-oriented developments. They also address advanced modeling and system enhancements in the areas of active databases, temporal and spatial databases, and multimedia information systems. This edition also surveys the latest application areas of data warehousing, data mining, web databases, digital libraries, GIS, and genome databases. New to the Third Edition ::: Reorganized material on data modeling to clearly separate entity relationship modeling, extended entity relationship modeling, and object-oriented modeling Expanded coverage of the object-oriented and object/relational approach to data management, including ODMG and SQL3 Uses examples from real database systems including OracleTM and Microsoft AccessAE Includes discussion of decision support applications of data warehousing and data mining, as well as emerging technologies of web databases, multimedia, and mobile databases Covers advanced modeling in the areas of active, temporal, and spatial databases Provides coverage of issues of physical database tuning Discusses current database application areas of GIS, genome, and digital libraries --- paper_title: Fundamentals of Database Systems paper_content: From the Publisher: ::: Fundamentals of Database Systems combines clear explanations of theory and design, broad coverage of models and real systems, and excellent examples with up-to-date introductions to modern database technologies. This edition is completely revised and updated, and reflects the latest trends in technological and application development. Professors Elmasri and Navathe focus on the relational model and include coverage of recent object-oriented developments. They also address advanced modeling and system enhancements in the areas of active databases, temporal and spatial databases, and multimedia information systems. This edition also surveys the latest application areas of data warehousing, data mining, web databases, digital libraries, GIS, and genome databases. New to the Third Edition ::: Reorganized material on data modeling to clearly separate entity relationship modeling, extended entity relationship modeling, and object-oriented modeling Expanded coverage of the object-oriented and object/relational approach to data management, including ODMG and SQL3 Uses examples from real database systems including OracleTM and Microsoft AccessAE Includes discussion of decision support applications of data warehousing and data mining, as well as emerging technologies of web databases, multimedia, and mobile databases Covers advanced modeling in the areas of active, temporal, and spatial databases Provides coverage of issues of physical database tuning Discusses current database application areas of GIS, genome, and digital libraries --- paper_title: Querying continuous functions in a database system paper_content: Many scientific, financial, data mining and sensor network applications need to work with continuous, rather than discrete data e.g., temperature as a function of location, or stock prices or vehicle trajectories as a function of time. Querying raw or discrete data is unsatisfactory for these applications -- e.g., in a sensor network, it is necessary to interpolate sensor readings to predict values at locations where sensors are not deployed. In other situations, raw data can be inaccurate owing to measurement errors, and it is useful to fit continuous functions to raw data and query the functions, rather than raw data itself -- e.g., fitting a smooth curve to noisy sensor readings, or a smooth trajectory to GPS data containing gaps or outliers. Existing databases do not support storing or querying continuous functions, short of brute-force discretization of functions into a collection of tuples. We present FunctionDB, a novel database system that treats mathematical functions as first-class citizens that can be queried like traditional relations. The key contribution of FunctionDB is an efficient and accurate algebraic query processor - for the broad class of multi-variable polynomial functions, FunctionDB executes queries directly on the algebraic representation of functions without materializing them into discrete points, using symbolic operations: zero finding, variable substitution, and integration. Even when closed form solutions are intractable, FunctionDB leverages symbolic approximation operations to improve performance. We evaluate FunctionDB on real data sets from a temperature sensor network, and on traffic traces from Boston roads. We show that operating in the functional domain has substantial advantages in terms of accuracy (15-30%) and up to order of magnitude (10x-100x) performance wins over existing approaches that represent models as discrete collections of points. --- paper_title: Online Filtering, Smoothing and Probabilistic Modeling of Streaming data paper_content: In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new data arrives. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over several synthetic and real datasets, that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of tight integration between dynamic probabilistic models and databases. --- paper_title: Evaluating Probabilistic Queries over Imprecise Data paper_content: Sensors are often employed to monitor continuously changing entities like locations of moving objects and temperature. The sensor readings are reported to a database system, and are subsequently used to answer queries. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), the database may not be able to keep track of the actual values of the entities. Queries that use these old values may produce incorrect answers. However, if the degree of uncertainty between the actual data value and the database value is limited, one can place more confidence in the answers to the queries. More generally, query answers can be augmented with probabilistic guarantees of the validity of the answers. In this paper, we study probabilistic query evaluation based on uncertain data. A classification of queries is made based upon the nature of the result set. For each class, we develop algorithms for computing probabilistic answers, and provide efficient indexing and numeric solutions. We address the important issue of measuring the quality of the answers to these queries, and provide algorithms for efficiently pulling data from relevant sensors or moving objects in order to improve the quality of the executing queries. Extensive experiments are performed to examine the effectiveness of several data update policies. --- paper_title: U-DBMS: A Database System for Managing Constantly-Evolving Data paper_content: In many systems, sensors are used to acquire information from external environments such as temperature, pressure and locations. Due to continuous changes in these values, and limited resources (e.g., network bandwidth and battery power), it is often infeasible for the database to store the exact values at all times. Queries that uses these old values can produce invalid results. In order to manage the uncertainty between the actual sensor value and the database value, we propose a system called U-DBMS. U-DBMS extends the database system with uncertainty management functionalities. In particular, each data value is represented as an interval and a probability distribution function, and it can be processed with probabilistic query operators to produce imprecise (but correct) answers. This demonstration presents a PostgreSQL-based system that handles uncertainty and probabilistic queries for constantly-evolving data. --- paper_title: MIST: Distributed Indexing and Querying in Sensor Networks using Statistical Models paper_content: The modeling of high level semantic events from low level sensor signals is important in order to understand distributed phenomena. For such content-modeling purposes, transformation of numeric data into symbols and the modeling of resulting symbolic sequences can be achieved using statistical models---Markov Chains (MCs) and Hidden Markov Models (HMMs). We consider the problem of distributed indexing and semantic querying over such sensor models. Specifically, we are interested in efficiently answering (i) range queries: return all sensors that have observed an unusual sequence of symbols with a high likelihood, (ii) top-1 queries: return the sensor that has the maximum probability of observing a given sequence, and (iii) 1-NN queries: return the sensor (model) which is most similar to a query model. All the above queries can be answered at the centralized base station, if each sensor transmits its model to the base station. However, this is communication-intensive. We present a much more efficient alternative---a distributed index structure, MIST (Model-based Index STructure), and accompanying algorithms for answering the above queries. MIST aggregates two or more constituent models into a single composite model, and constructs an in-network hierarchy over such composite models. We develop two kinds of composite models: the first kind captures the average behavior of the underlying models and the second kind captures the extreme behaviors of the underlying models. Using the index parameters maintained at the root of a subtree, we bound the probability of observation of a query sequence from a sensor in the subtree. We also bound the distance of a query model to a sensor model using these parameters. Extensive experimental evaluation on both real-world and synthetic data sets show that the MIST schemes scale well in terms of network size and number of model states. We also show its superior performance over the centralized schemes in terms of update, query, and total communication costs. --- paper_title: TinyDB: an acquisitional query processing system for sensor networks paper_content: We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices. --- paper_title: High-performance complex event processing over streams paper_content: In this paper, we present the design, implementation, and evaluation of a system that executes complex event queries over real-time streams of RFID readings encoded as events. These complex event queries filter and correlate events to match specific patterns, and transform the relevant events into new composite events for the use of external monitoring applications. Stream-based execution of these queries enables time-critical actions to be taken in environments such as supply chain management, surveillance and facility management, healthcare, etc. We first propose a complex event language that significantly extends existing event languages to meet the needs of a range of RFID-enabled monitoring applications. We then describe a query plan-based approach to efficiently implementing this language. Our approach uses native operators to efficiently handle query-defined sequences, which are a key component of complex event processing, and pipeline such sequences to subsequent operators that are built by leveraging relational techniques. We also develop a large suite of optimization techniques to address challenges such as large sliding windows and intermediate result sizes. We demonstrate the effectiveness of our approach through a detailed performance analysis of our prototype implementation under a range of data and query workloads as well as through a comparison to a state-of-the-art stream processor. --- paper_title: Event queries on correlated probabilistic streams paper_content: A major problem in detecting events in streams of data is that the data can be imprecise (e.g. RFID data). However, current state-ofthe-art event detection systems such as Cayuga [14], SASE [46] or SnoopIB[1], assume the data is precise. Noise in the data can be captured using techniques such as hidden Markov models. Inference on these models creates streams of probabilistic events which cannot be directly queried by existing systems. To address this challenge we propose Lahar1, an event processing system for probabilistic event streams. By exploiting the probabilistic nature of the data, Lahar yields a much higher recall and precision than deterministic techniques operating over only the most probable tuples. By using a novel static analysis and novel algorithms, Lahar processes data orders of magnitude more efficiently than a naive approach based on sampling. In this paper, we present Lahar's static analysis and core algorithms. We demonstrate the quality and performance of our approach through experiments with our prototype implementation and comparisons with alternate methods. --- paper_title: Probabilistic Inference over RFID Streams in Mobile Environments paper_content: Recent innovations in RFID technology are enabling large-scale cost-effective deployments in retail, healthcare, pharmaceuticals and supply chain management. The advent of mobile or handheld readers adds significant new challenges to RFID stream processing due to the inherent reader mobility, increased noise, and incomplete data. In this paper, we address the problem of translating noisy, incomplete raw streams from mobile RFID readers into clean, precise event streams with location information. Specifically we propose a probabilistic model to capture the mobility of the reader, object dynamics, and noisy readings. Our model can self-calibrate by automatically estimating key parameters from observed data. Based on this model, we employ a sampling-based technique called particle filtering to infer clean, precise information about object locations from raw streams from mobile RFID readers. Since inference based on standard particle filtering is neither scalable nor efficient in our settings, we propose three enhancements---particle factorization, spatial indexing, and belief compression---for scalable inference over large numbers of objects and high-volume streams. Our experiments show that our approach can offer 49\% error reduction over a state-of-the-art data cleaning approach such as SMURF while also being scalable and efficient. --- paper_title: An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback paper_content: We introduce an extended representation of time series that allows fast, accurate classification and clustering in addition to the ability to explore time series data in a relevance feedback framework. The representation consists of piece-wise linear segments to represent shape and a weight vector that contains the relative importance of each individual linear segment. In the classification context, the weights are learned automatically as part of the training cycle. In the relevance feedback context, the weights are determined by an interactive and iterative process in which users rate various choices presented to them. Our representation allows a user to define a variety of similarity measures that can be tailored to specific domains. We demonstrate our approach on space telemetry, medical and synthetic data. --- paper_title: Efficiently Maintaining Distributed Model-Based Views on Real-Time Data Streams paper_content: Minimizing communication cost is a fundamental problem in large-scale federated sensor networks. Maintaining model-based views of data streams has been highlighted because it permits efficient data communication by transmitting parameter values of models, instead of original data streams. We propose a framework that employs the advantages of using model-based views for communication-efficient stream data processing over federated sensor networks, yet it significantly improves state-of-the-art approaches. The framework is generic and any time-parameterized models can be plugged, while accuracy guarantees for query results are ensured throughout the large-scale networks. In addition, we boost the performance of the framework by the coded model update that enables efficient model update from one node to another. It predetermines parameter values for the model, updates only identifiers of the parameter values, and compresses the identifiers by utilizing bitmaps. Moreover, we propose a correlation model, named coded inter-variable model, that merges the efficiency of the coded model update with that of correlation models. Empirical studies with real data demonstrate that our proposal achieves substantial amounts of communication reduction, outperforming state-of-the art methods. --- paper_title: Towards Online Multi-model Approximation of Time Series paper_content: The increasing use of sensor technology for various monitoring applications (e.g. air-pollution, traffic, climate-change, etc.) has led to an unprecedented volume of streaming data that has to be efficiently aggregated, stored and retrieved. Real-time model-based data approximation and filtering is a common solution for reducing the storage (and communication) overhead. However, the selection of the most efficient model depends on the characteristics of the data stream, namely rate, burstiness, data range, etc., which cannot be always known a priori for (mobile) sensors and they can even dynamically change. In this paper, we investigate the innovative concept of efficiently combining multiple approximation models in real-time. Our approach dynamically adapts to the properties of the data stream and approximates each data segment with the most suitable model. As experimentally proved, our multi-model approximation approach always produces fewer or equal data segments than those of the best individual model, and thus provably achieves higher data compression ratio than individual linear models. --- paper_title: Querying continuous functions in a database system paper_content: Many scientific, financial, data mining and sensor network applications need to work with continuous, rather than discrete data e.g., temperature as a function of location, or stock prices or vehicle trajectories as a function of time. Querying raw or discrete data is unsatisfactory for these applications -- e.g., in a sensor network, it is necessary to interpolate sensor readings to predict values at locations where sensors are not deployed. In other situations, raw data can be inaccurate owing to measurement errors, and it is useful to fit continuous functions to raw data and query the functions, rather than raw data itself -- e.g., fitting a smooth curve to noisy sensor readings, or a smooth trajectory to GPS data containing gaps or outliers. Existing databases do not support storing or querying continuous functions, short of brute-force discretization of functions into a collection of tuples. We present FunctionDB, a novel database system that treats mathematical functions as first-class citizens that can be queried like traditional relations. The key contribution of FunctionDB is an efficient and accurate algebraic query processor - for the broad class of multi-variable polynomial functions, FunctionDB executes queries directly on the algebraic representation of functions without materializing them into discrete points, using symbolic operations: zero finding, variable substitution, and integration. Even when closed form solutions are intractable, FunctionDB leverages symbolic approximation operations to improve performance. We evaluate FunctionDB on real data sets from a temperature sensor network, and on traffic traces from Boston roads. We show that operating in the functional domain has substantial advantages in terms of accuracy (15-30%) and up to order of magnitude (10x-100x) performance wins over existing approaches that represent models as discrete collections of points. --- paper_title: Online Piece-wise Linear Approximation of Numerical Streams with Precision Guarantees paper_content: Continuous "always-on" monitoring is beneficial for a number of applications, but potentially imposes a high load in terms of communication, storage and power consumption when a large number of variables need to be monitored. We introduce two new filtering techniques, swing filters and slide filters, that represent within a prescribed precision a time-varying numerical signal by a piecewise linear function, consisting of connected line segments for swing filters and (mostly) disconnected line segments for slide filters. We demonstrate the effectiveness of swing and slide filters in terms of their compression power by applying them to a real-life data set plus a variety of synthetic data sets. For nearly all combinations of signal behavior and precision requirements, the proposed techniques outperform the earlier approaches for online filtering in terms of data reduction. The slide filter, in particular, consistently dominates all other filters, with up to twofold improvement over the best of the previous techniques. --- paper_title: An online algorithm for segmenting time series paper_content: In recent years, there has been an explosion of interest in mining time-series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time-series data. A variety of algorithms have been proposed to obtain this representation, with several algorithms having been independently rediscovered several times. In this paper, we undertake the first extensive review and empirical comparison of all proposed techniques. We show that all these algorithms have fatal flaws from a data-mining perspective. We introduce a novel algorithm that we empirically show to be superior to all others in the literature. --- paper_title: Capturing sensor-generated time series with quality guarantees paper_content: We are interested in capturing time series generated by small wireless electronic sensors. Battery-operated sensors must avoid heavy use of their wireless radio which is a key cause of energy dissipation. When many sensors transmit, the resources of the recipient of the data are taxed; hence, limiting communication will benefit the recipient as well. We show how time series generated by sensors can be captured and stored in a database system (archive). Sensors compress time series instead of sending them in raw form. We propose an optimal online algorithm for constructing a piecewise constant approximation (PCA) of a time series which guarantees that the compressed representation satisfies an error bound on the L/sub /spl infin// distance. In addition to the capture task, we often want to estimate the values of a time series ahead of time, e.g., to answer real-time queries. To achieve this, sensors may fit predictive models on observed data, sending parameters of these models to the archive. We exploit the interplay between prediction and compression in a unified framework that avoids duplicating effort and leads to reduced communication. --- paper_title: An online algorithm for segmenting time series paper_content: In recent years, there has been an explosion of interest in mining time-series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time-series data. A variety of algorithms have been proposed to obtain this representation, with several algorithms having been independently rediscovered several times. In this paper, we undertake the first extensive review and empirical comparison of all proposed techniques. We show that all these algorithms have fatal flaws from a data-mining perspective. We introduce a novel algorithm that we empirically show to be superior to all others in the literature. --- paper_title: Online Piece-wise Linear Approximation of Numerical Streams with Precision Guarantees paper_content: Continuous "always-on" monitoring is beneficial for a number of applications, but potentially imposes a high load in terms of communication, storage and power consumption when a large number of variables need to be monitored. We introduce two new filtering techniques, swing filters and slide filters, that represent within a prescribed precision a time-varying numerical signal by a piecewise linear function, consisting of connected line segments for swing filters and (mostly) disconnected line segments for slide filters. We demonstrate the effectiveness of swing and slide filters in terms of their compression power by applying them to a real-life data set plus a variety of synthetic data sets. For nearly all combinations of signal behavior and precision requirements, the proposed techniques outperform the earlier approaches for online filtering in terms of data reduction. The slide filter, in particular, consistently dominates all other filters, with up to twofold improvement over the best of the previous techniques. --- paper_title: Adaptive filters for continuous queries over distributed data streams paper_content: We consider an environment where distributed data sources continuously stream updates to a centralized processor that monitors continuous queries over the distributed data. Significant communication overhead is incurred in the presence of rapid update streams, and we propose a new technique for reducing the overhead. Users register continuous queries with precision requirements at the central stream processor, which installs filters at remote data sources. The filters adapt to changing conditions to minimize stream rates while guaranteeing that all continuous queries still receive the updates necessary to provide answers of adequate precision at all times. Our approach enables applications to trade precision for communication overhead at a fine granularity by individually adjusting the precision constraints of continuous queries over streams in a multi-query workload. Through experiments performed on synthetic data simulations and a real network monitoring implementation, we demonstrate the effectiveness of our approach in achieving low communication overhead compared with alternate approaches. --- paper_title: Online amnesic approximation of streaming time series paper_content: The past decade has seen a wealth of research on time series representations, because the manipulation, storage, and indexing of large volumes of raw time series data is impractical. The vast majority of research has concentrated on representations that are calculated in batch mode and represent each value with approximately equal fidelity. However, the increasing deployment of mobile devices and real time sensors has brought home the need for representations that can be incrementally updated, and can approximate the data with fidelity proportional to its age. The latter property allows us to answer queries about the recent past with greater precision, since in many domains recent information is more useful than older information. We call such representations amnesic. While there has been previous work on amnesic representations, the class of amnesic functions possible was dictated by the representation itself. We introduce a novel representation of time series that can represent arbitrary, user-specified amnesic functions. For example, a meteorologist may decide that data that is twice as old can tolerate twice as much error, and thus, specify a linear amnesic function. In contrast, an econometrist might opt for an exponential amnesic function. We propose online algorithms for our representation, and discuss their properties. Finally, we perform an extensive empirical evaluation on 40 datasets, and show that our approach can efficiently maintain a high quality amnesic approximation. --- paper_title: Energy conservation in wireless sensor networks: A survey paper_content: In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs. --- paper_title: Locally adaptive dimensionality reduction for indexing large time series databases paper_content: Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because of the typically high dimensionality of the data. The most promising solutions involve performing dimensionality reduction on the data, then indexing the reduced data with a multidimensional index structure. Many dimensionality reduction techniques have been proposed, including Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and the Discrete Wavelet Transform (DWT). In this article, we introduce a new dimensionality reduction technique, which we call Adaptive Piecewise Constant Approximation (APCA). While previous techniques (e.g., SVD, DFT and DWT) choose a common representation for all the items in the database that minimizes the global reconstruction error, APCA approximates each time series by a set of constant value segments of varying lengths such that their individual reconstruction errors are minimal. We show how APCA can be indexed using a multidimensional index structure. We propose two distance measures in the indexed space that exploit the high fidelity of APCA for fast searching: a lower bounding Euclidean distance approximation, and a non-lower-bounding, but very tight, Euclidean distance approximation, and show how they can support fast exact searching and even faster approximate searching on the same index structure. We theoretically and empirically compare APCA to all the other techniques and demonstrate its superiority. --- paper_title: Efficient retrieval of similar time sequences under time warping paper_content: Fast similarity searching in large time sequence databases has typically used Euclidean distance as a dissimilarity metric. However, for several applications, including matching of voice, audio and medical signals (e.g., electrocardiograms), one is required to permit local accelerations and decelerations in the rate of sequences, leading to a popular, field tested dissimilarity metric called the "time warping" distance. From the indexing viewpoint, this metric presents two major challenges: (a) it does not lead to any natural indexable "features", and (b) comparing two sequences requires time quadratic in the sequence length. To address each problem, we propose to use: (a) a modification of the so called "FastMap", to map sequences into points, with little compromise of "recall" (typically zero); and (b) a fast linear test, to help us discard quickly many of the false alarms that FastMap will typically introduce. Using both ideas in cascade, our proposed method achieved up to an order of magnitude speed-up over sequential scanning on both real and synthetic datasets. --- paper_title: Online Information Compression in Sensor Networks paper_content: In the emerging area of wireless sensor networks, one of the most typical challenges is to retrieve historical information from the sensor nodes. Due to the resource limitation of sensor nodes (processing, memory, bandwidth, and energy), the collected information of sensor nodes has to be compressed quickly and precisely for transmission. In this paper, we propose a new technique -- the ALVQ (Adoptive Learning Vector Quantization) algorithm to compress this historical information. The ALVQ algorithm constructs a codebook to capture the prominent features of the data and with these features all the other data can be piece-wise encoded for compression. In addition, with two-level regression of the codebook's update, ALVQ algorithm saves the data transfer bandwidth and improves the compression precision further. Finally, we consider the problem of transmitting data in a sensor network while maximizing the precision. We show how we apply our algorithm so that a set of sensors can dynamically share a wireless communication channel. --- paper_title: Compressing historical information in sensor networks paper_content: We are inevitably moving into a realm where small and inexpensive wireless devices would be seamlessly embedded in the physical world and form a wireless sensor network in order to perform complex monitoring and computational tasks. Such networks pose new challenges in data processing and dissemination because of the limited resources (processing, bandwidth, energy) that such devices possess. In this paper we propose a new technique for compressing multiple streams containing historical data from each sensor. Our method exploits correlation and redundancy among multiple measurements on the same sensor and achieves high degree of data reduction while managing to capture even the smallest details of the recorded measurements. The key to our technique is the base signal, a series of values extracted from the real measurements, used for encoding piece-wise linear correlations among the collected data values. We provide efficient algorithms for extracting the base signal features from the data and for encoding the measurements using these features. Our experiments demonstrate that our method by far outperforms standard approximation techniques like Wavelets. Histograms and the Discrete Cosine Transform, on a variety of error metrics and for real datasets from different domains. --- paper_title: GAMPS: compressing multi sensor data by grouping and amplitude scaling paper_content: We consider the problem of collectively approximating a set of sensor signals using the least amount of space so that any individual signal can be efficiently reconstructed within a given maximum (L∞) error e. The problem arises naturally in applications that need to collect large amounts of data from multiple concurrent sources, such as sensors, servers and network routers, and archive them over a long period of time for offline data mining. We present GAMPS, a general framework that addresses this problem by combining several novel techniques. First, it dynamically groups multiple signals together so that signals within each group are correlated and can be maximally compressed jointly. Second, it appropriately scales the amplitudes of different signals within a group and compresses them within the maximum allowed reconstruction error bound. Our schemes are polynomial time O(α, β approximation schemes, meaning that the maximum (L∞) error is at most α e and it uses at most β times the optimal memory. Finally, GAMPS maintains an index so that various queries can be issued directly on compressed data. Our experiments on several real-world sensor datasets show that GAMPS significantly reduces space without compromising the quality of search and query. --- paper_title: Towards Online Multi-model Approximation of Time Series paper_content: The increasing use of sensor technology for various monitoring applications (e.g. air-pollution, traffic, climate-change, etc.) has led to an unprecedented volume of streaming data that has to be efficiently aggregated, stored and retrieved. Real-time model-based data approximation and filtering is a common solution for reducing the storage (and communication) overhead. However, the selection of the most efficient model depends on the characteristics of the data stream, namely rate, burstiness, data range, etc., which cannot be always known a priori for (mobile) sensors and they can even dynamically change. In this paper, we investigate the innovative concept of efficiently combining multiple approximation models in real-time. Our approach dynamically adapts to the properties of the data stream and approximates each data segment with the most suitable model. As experimentally proved, our multi-model approximation approach always produces fewer or equal data segments than those of the best individual model, and thus provably achieves higher data compression ratio than individual linear models. --- paper_title: Capturing sensor-generated time series with quality guarantees paper_content: We are interested in capturing time series generated by small wireless electronic sensors. Battery-operated sensors must avoid heavy use of their wireless radio which is a key cause of energy dissipation. When many sensors transmit, the resources of the recipient of the data are taxed; hence, limiting communication will benefit the recipient as well. We show how time series generated by sensors can be captured and stored in a database system (archive). Sensors compress time series instead of sending them in raw form. We propose an optimal online algorithm for constructing a piecewise constant approximation (PCA) of a time series which guarantees that the compressed representation satisfies an error bound on the L/sub /spl infin// distance. In addition to the capture task, we often want to estimate the values of a time series ahead of time, e.g., to answer real-time queries. To achieve this, sensors may fit predictive models on observed data, sending parameters of these models to the archive. We exploit the interplay between prediction and compression in a unified framework that avoids duplicating effort and leads to reduced communication. --- paper_title: An evaluation of multi-resolution storage for sensor networks paper_content: Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. Centralized data collection and analysis adversely impact sensor node lifetime. Previous sensor network research has, therefore, focused on in network aggregation and query processing, but has done so for applications where the features of interest are known a priori. When features are not known a priori, as is the case with many scientific applications in dense sensor arrays, efficient support for multi-resolution storage and iterative, drill-down queries is essential.Our system demonstrates the use of in-network wavelet-based summarization and progressive aging of summaries in support of long-term querying in storage and communication-constrained networks. We evaluate the performance of our linux implementation and show that it achieves: (a) low communication overhead for multi-resolution summarization, (b) highly efficient drill-down search over such summaries, and (c) efficient use of network storage capacity through load-balancing and progressive aging of summaries. --- paper_title: Efficient time series matching by wavelets paper_content: Time series stored as feature vectors can be indexed by multidimensional index trees like R-Trees for fast retrieval. Due to the dimensionality curse problem, transformations are applied to time series to reduce the number of dimensions of the feature vectors. Different transformations like Discrete Fourier Transform (DFT) Discrete Wavelet Transform (DWT), Karhunen-Loeve (KL) transform or Singular Value Decomposition (SVD) can be applied. While the use of DFT and K-L transform or SVD have been studied on the literature, to our knowledge, there is no in-depth study on the application of DWT. In this paper we propose to use Haar Wavelet Transform for time series indexing. The major contributions are: (1) we show that Euclidean distance is preserved in the Haar transformed domain and no false dismissal will occur, (2) we show that Haar transform can outperform DFT through experiments, (3) a new similarity model is suggested to accommodate vertical shift of time series, and (4) a two-phase method is proposed for efficient n-nearest neighbor query in time series databases. --- paper_title: Dimensions: why do we need a new data handling architecture for sensor networks? paper_content: An important class of networked systems is emerging that involve very large numbers of small, low-power, wireless devices. These systems offer the ability to sense the environment densely, offering unprecedented opportunities for many scientific disciplines to observe the physical world. In this paper, we argue that a data handling architecture for these devices should incorporate their extreme resource constraints - energy, storage and processing - and spatio-temporal interpretation of the physical world in the design, cost model, and metrics of evaluation. We describe DIMENSIONS, a system that provides a unified view of data handling in sensor networks, incorporating long-term storage, multi-resolution data access and spatio-temporal pattern mining. --- paper_title: Similarity search over time-series data using wavelets paper_content: Considers the use of wavelet transformations as a dimensionality reduction technique to permit efficient similarity searching over high-dimensional time-series data. While numerous transformations have been proposed and studied, the only wavelet that has been shown to be effective for this application is the Haar wavelet. In this work, we observe that a large class of wavelet transformations (not only orthonormal wavelets but also bi-orthonormal wavelets) can be used to support similarity searching. This class includes the most popular and most effective wavelets being used in image compression. We present a detailed performance study of the effects of using different wavelets on the performance of similarity searching for time-series data. We include several wavelets that outperform both the Haar wavelet and the best-known non-wavelet transformations for this application. To ensure our results are usable by an application engineer, we also show how to configure an indexing strategy for the best-performing transformations. Finally, we identify classes of data that can be indexed efficiently using these wavelet transformations. --- paper_title: Online Piece-wise Linear Approximation of Numerical Streams with Precision Guarantees paper_content: Continuous "always-on" monitoring is beneficial for a number of applications, but potentially imposes a high load in terms of communication, storage and power consumption when a large number of variables need to be monitored. We introduce two new filtering techniques, swing filters and slide filters, that represent within a prescribed precision a time-varying numerical signal by a piecewise linear function, consisting of connected line segments for swing filters and (mostly) disconnected line segments for slide filters. We demonstrate the effectiveness of swing and slide filters in terms of their compression power by applying them to a real-life data set plus a variety of synthetic data sets. For nearly all combinations of signal behavior and precision requirements, the proposed techniques outperform the earlier approaches for online filtering in terms of data reduction. The slide filter, in particular, consistently dominates all other filters, with up to twofold improvement over the best of the previous techniques. --- paper_title: StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time paper_content: Consider the problem of monitoring tens of thousands of time series data streams in an online fashion and making decisions based on them. In addition to single stream statistics such as average and standard deviation, we also want to find high correlations among all pairs of streams. A stock market trader might use such a tool to spot arbitrage opportunities. This paper proposes efficient methods for solving this problem based on Discrete Fourier Transforms and a three level time interval hierarchy. Extensive experiments on synthetic data and real world financial trading data show that our algorithm beats the direct computation approach by several orders of magnitude. It also improves on previous Fourier Transform approaches by allowing the efficient computation of time-delayed correlation over any size sliding window and any time delay. Correlation also lends itself to an efficient grid-based data structure. The result is the first algorithm that we know of to compute correlations over thousands of data streams in real time. The algorithm is incremental, has fixed response time, and can monitor the pairwise correlations of 10,000 streams on a single PC. The algorithm is embarrassingly parallelizable. --- paper_title: Capturing sensor-generated time series with quality guarantees paper_content: We are interested in capturing time series generated by small wireless electronic sensors. Battery-operated sensors must avoid heavy use of their wireless radio which is a key cause of energy dissipation. When many sensors transmit, the resources of the recipient of the data are taxed; hence, limiting communication will benefit the recipient as well. We show how time series generated by sensors can be captured and stored in a database system (archive). Sensors compress time series instead of sending them in raw form. We propose an optimal online algorithm for constructing a piecewise constant approximation (PCA) of a time series which guarantees that the compressed representation satisfies an error bound on the L/sub /spl infin// distance. In addition to the capture task, we often want to estimate the values of a time series ahead of time, e.g., to answer real-time queries. To achieve this, sensors may fit predictive models on observed data, sending parameters of these models to the archive. We exploit the interplay between prediction and compression in a unified framework that avoids duplicating effort and leads to reduced communication. --- paper_title: A Weighted Moving Average-based Approach for Cleaning Sensor Data paper_content: Nowadays, wireless sensor networks have been widely used in many monitoring applications. Due to the low quality of sensors and random effects of the environments, however, it is well known that the collected sensor data are noisy. Therefore, it is very critical to clean the sensor data before using them to answer queries or conduct data analysis. Popular data cleaning approaches, such as the moving average, cannot meet the requirements of both energy efficiency and quick response time in many sensor related applications. In this paper, we propose a hybrid sensor data cleaning approach with confidence. Specifically, we propose a smart weighted moving average (WMA) algorithm that collects confidence data from sensors and computes the weighted moving average. The rationale behind the WMA algorithm is to draw more samples for a particular value that is of great importance to the moving average, and provide higher confidence weight for this value, such that this important value can be quickly reflected in the moving average. Based on our extensive simulation results, we demonstrate that, compared to the simple moving average (SMA), our WMA approach can effectively clean data and offer quick response time. --- paper_title: A neuro-fuzzy approach for sensor network data cleaning paper_content: Sensor networks have become an important source of data with numerous applications in monitoring various real-life phenomena as well as industrial applications and traffic control. However, sensor data are subject to several sources of errors as the data captured from the physical world through these sensor devices tend to be incomplete, noisy, and unreliable, thus yielding imprecise or even incorrect and misleading answers which can be very significative if they result in immediate critical decisions or activation of actuators. Traditional data cleaning techniques cannot be applied in this context as they do not take into account the strong spatial and temporal correlations typically present in sensor data, so machine learning techniques could greatly be of aid. In this paper, we propose a neuro-fuzzy regression approach to clean sensor network data: the well known ANFIS model is employed for reducing the uncertainty associated with the data thus obtaining a more accurate estimate of sensor readings. The obtained cleaning results show good ANFIS performance compared to other common used model such as kernel methods, and we demonstrate its effectiveness if the cleaning model has to be implemented at sensor level rather than at base-station level. --- paper_title: Declarative Support for Sensor Data Cleaning paper_content: Pervasive applications rely on data captured from the physical world through sensor devices. Data provided by these devices, however, tend to be unreliable. The data must, therefore, be cleaned before an application can make use of them, leading to additional complexity for application development and deployment. Here we present Extensible Sensor stream Processing (ESP), a framework for building sensor data cleaning infrastructures for use in pervasive applications. ESP is designed as a pipeline using declarative cleaning mechanisms based on spatial and temporal characteristics of sensor data. We demonstrate ESP's effectiveness and ease of use through three real-world scenarios. --- paper_title: MauveDB: supporting model-based user views in database systems paper_content: Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system. --- paper_title: PRESTO: feedback-driven data management in sensor networks paper_content: This paper presents PRESTO, a novel two-tier sensor data management architecture comprising proxies and sensors that cooperate with one another for acquiring data and processing queries. PRESTO proxies construct time-series models of observed trends in the sensor data and transmit the parameters of the model to sensors. Sensors check sensed data with model-predicted values and transmit only deviations from the predictions back to the proxy. Such a model-driven push approach is energy-efficient, while ensuring that anomalous data trends are never missed. In addition to supporting queries on current data, PRESTO also supports queries on historical data using interpolation and local archival at sensors. PRESTO can adapt model and system parameters to data and query dynamics to further extract energy savings. We have implemented PRESTO on a sensor testbed comprising Intel Stargates and Telos Motes. Our experiments show that in a temperature monitoring application, PRESTO yields one to two orders of magnitude reduction in energy requirements over on-demand, proactive or model-driven pull approaches. PRESTO also results in an order of magnitude reduction in query latency in a 1% duty-cycled five hop sensor network over a system that forwards all queries to remote sensor nodes. --- paper_title: TinyDB: an acquisitional query processing system for sensor networks paper_content: We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices. --- paper_title: Efficient time series matching by wavelets paper_content: Time series stored as feature vectors can be indexed by multidimensional index trees like R-Trees for fast retrieval. Due to the dimensionality curse problem, transformations are applied to time series to reduce the number of dimensions of the feature vectors. Different transformations like Discrete Fourier Transform (DFT) Discrete Wavelet Transform (DWT), Karhunen-Loeve (KL) transform or Singular Value Decomposition (SVD) can be applied. While the use of DFT and K-L transform or SVD have been studied on the literature, to our knowledge, there is no in-depth study on the application of DWT. In this paper we propose to use Haar Wavelet Transform for time series indexing. The major contributions are: (1) we show that Euclidean distance is preserved in the Haar transformed domain and no false dismissal will occur, (2) we show that Haar transform can outperform DFT through experiments, (3) a new similarity model is suggested to accommodate vertical shift of time series, and (4) a two-phase method is proposed for efficient n-nearest neighbor query in time series databases. --- paper_title: The design of an acquisitional query processor for sensor networks paper_content: We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices. --- paper_title: Online amnesic approximation of streaming time series paper_content: The past decade has seen a wealth of research on time series representations, because the manipulation, storage, and indexing of large volumes of raw time series data is impractical. The vast majority of research has concentrated on representations that are calculated in batch mode and represent each value with approximately equal fidelity. However, the increasing deployment of mobile devices and real time sensors has brought home the need for representations that can be incrementally updated, and can approximate the data with fidelity proportional to its age. The latter property allows us to answer queries about the recent past with greater precision, since in many domains recent information is more useful than older information. We call such representations amnesic. While there has been previous work on amnesic representations, the class of amnesic functions possible was dictated by the representation itself. We introduce a novel representation of time series that can represent arbitrary, user-specified amnesic functions. For example, a meteorologist may decide that data that is twice as old can tolerate twice as much error, and thus, specify a linear amnesic function. In contrast, an econometrist might opt for an exponential amnesic function. We propose online algorithms for our representation, and discuss their properties. Finally, we perform an extensive empirical evaluation on 40 datasets, and show that our approach can efficiently maintain a high quality amnesic approximation. --- paper_title: ERACER: a database approach for statistical inference and data cleaning paper_content: Real-world databases often contain syntactic and semantic errors, in spite of integrity constraints and other safety measures incorporated into modern DBMSs. We present ERACER, an iterative statistical framework for inferring missing information and correcting such errors automatically. Our approach is based on belief propagation and relational dependency networks, and includes an efficient approximate inference algorithm that is easily implemented in standard DBMSs using SQL and user defined functions. The system performs the inference and cleansing tasks in an integrated manner, using shrinkage techniques to infer correct values accurately even in the presence of dirty data. We evaluate the proposed methods empirically on multiple synthetic and real-world data sets. The results show that our framework achieves accuracy comparable to a baseline statistical method using Bayesian networks with exact inference. However, our framework has wider applicability than the Bayesian network baseline, due to its ability to reason with complex, cyclic relational dependencies. --- paper_title: Towards Online Multi-model Approximation of Time Series paper_content: The increasing use of sensor technology for various monitoring applications (e.g. air-pollution, traffic, climate-change, etc.) has led to an unprecedented volume of streaming data that has to be efficiently aggregated, stored and retrieved. Real-time model-based data approximation and filtering is a common solution for reducing the storage (and communication) overhead. However, the selection of the most efficient model depends on the characteristics of the data stream, namely rate, burstiness, data range, etc., which cannot be always known a priori for (mobile) sensors and they can even dynamically change. In this paper, we investigate the innovative concept of efficiently combining multiple approximation models in real-time. Our approach dynamically adapts to the properties of the data stream and approximates each data segment with the most suitable model. As experimentally proved, our multi-model approximation approach always produces fewer or equal data segments than those of the best individual model, and thus provably achieves higher data compression ratio than individual linear models. --- paper_title: Capturing sensor-generated time series with quality guarantees paper_content: We are interested in capturing time series generated by small wireless electronic sensors. Battery-operated sensors must avoid heavy use of their wireless radio which is a key cause of energy dissipation. When many sensors transmit, the resources of the recipient of the data are taxed; hence, limiting communication will benefit the recipient as well. We show how time series generated by sensors can be captured and stored in a database system (archive). Sensors compress time series instead of sending them in raw form. We propose an optimal online algorithm for constructing a piecewise constant approximation (PCA) of a time series which guarantees that the compressed representation satisfies an error bound on the L/sub /spl infin// distance. In addition to the capture task, we often want to estimate the values of a time series ahead of time, e.g., to answer real-time queries. To achieve this, sensors may fit predictive models on observed data, sending parameters of these models to the archive. We exploit the interplay between prediction and compression in a unified framework that avoids duplicating effort and leads to reduced communication. --- paper_title: MIST: Distributed Indexing and Querying in Sensor Networks using Statistical Models paper_content: The modeling of high level semantic events from low level sensor signals is important in order to understand distributed phenomena. For such content-modeling purposes, transformation of numeric data into symbols and the modeling of resulting symbolic sequences can be achieved using statistical models---Markov Chains (MCs) and Hidden Markov Models (HMMs). We consider the problem of distributed indexing and semantic querying over such sensor models. Specifically, we are interested in efficiently answering (i) range queries: return all sensors that have observed an unusual sequence of symbols with a high likelihood, (ii) top-1 queries: return the sensor that has the maximum probability of observing a given sequence, and (iii) 1-NN queries: return the sensor (model) which is most similar to a query model. All the above queries can be answered at the centralized base station, if each sensor transmits its model to the base station. However, this is communication-intensive. We present a much more efficient alternative---a distributed index structure, MIST (Model-based Index STructure), and accompanying algorithms for answering the above queries. MIST aggregates two or more constituent models into a single composite model, and constructs an in-network hierarchy over such composite models. We develop two kinds of composite models: the first kind captures the average behavior of the underlying models and the second kind captures the extreme behaviors of the underlying models. Using the index parameters maintained at the root of a subtree, we bound the probability of observation of a query sequence from a sensor in the subtree. We also bound the distance of a query model to a sensor model using these parameters. Extensive experimental evaluation on both real-world and synthetic data sets show that the MIST schemes scale well in terms of network size and number of model states. We also show its superior performance over the centralized schemes in terms of update, query, and total communication costs. --- paper_title: An online algorithm for segmenting time series paper_content: In recent years, there has been an explosion of interest in mining time-series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time-series data. A variety of algorithms have been proposed to obtain this representation, with several algorithms having been independently rediscovered several times. In this paper, we undertake the first extensive review and empirical comparison of all proposed techniques. We show that all these algorithms have fatal flaws from a data-mining perspective. We introduce a novel algorithm that we empirically show to be superior to all others in the literature. --- paper_title: Querying continuous functions in a database system paper_content: Many scientific, financial, data mining and sensor network applications need to work with continuous, rather than discrete data e.g., temperature as a function of location, or stock prices or vehicle trajectories as a function of time. Querying raw or discrete data is unsatisfactory for these applications -- e.g., in a sensor network, it is necessary to interpolate sensor readings to predict values at locations where sensors are not deployed. In other situations, raw data can be inaccurate owing to measurement errors, and it is useful to fit continuous functions to raw data and query the functions, rather than raw data itself -- e.g., fitting a smooth curve to noisy sensor readings, or a smooth trajectory to GPS data containing gaps or outliers. Existing databases do not support storing or querying continuous functions, short of brute-force discretization of functions into a collection of tuples. We present FunctionDB, a novel database system that treats mathematical functions as first-class citizens that can be queried like traditional relations. The key contribution of FunctionDB is an efficient and accurate algebraic query processor - for the broad class of multi-variable polynomial functions, FunctionDB executes queries directly on the algebraic representation of functions without materializing them into discrete points, using symbolic operations: zero finding, variable substitution, and integration. Even when closed form solutions are intractable, FunctionDB leverages symbolic approximation operations to improve performance. We evaluate FunctionDB on real data sets from a temperature sensor network, and on traffic traces from Boston roads. We show that operating in the functional domain has substantial advantages in terms of accuracy (15-30%) and up to order of magnitude (10x-100x) performance wins over existing approaches that represent models as discrete collections of points. --- paper_title: Probabilistic Inference over RFID Streams in Mobile Environments paper_content: Recent innovations in RFID technology are enabling large-scale cost-effective deployments in retail, healthcare, pharmaceuticals and supply chain management. The advent of mobile or handheld readers adds significant new challenges to RFID stream processing due to the inherent reader mobility, increased noise, and incomplete data. In this paper, we address the problem of translating noisy, incomplete raw streams from mobile RFID readers into clean, precise event streams with location information. Specifically we propose a probabilistic model to capture the mobility of the reader, object dynamics, and noisy readings. Our model can self-calibrate by automatically estimating key parameters from observed data. Based on this model, we employ a sampling-based technique called particle filtering to infer clean, precise information about object locations from raw streams from mobile RFID readers. Since inference based on standard particle filtering is neither scalable nor efficient in our settings, we propose three enhancements---particle factorization, spatial indexing, and belief compression---for scalable inference over large numbers of objects and high-volume streams. Our experiments show that our approach can offer 49\% error reduction over a state-of-the-art data cleaning approach such as SMURF while also being scalable and efficient. --- paper_title: Cleaning and querying noisy sensors paper_content: Sensor networks have become an important source of data with numerous applications in monitoring various real-life phenomena as well as industrial applications and traffic control. Unfortunately, sensor data is subject to several sources of errors such as noise from external sources, hardware noise, inaccuracies and imprecision, and various environmental effects. Such errors may seriously impact the answer to any query posed to the sensors. In particular, they may yield imprecise or even incorrect and misleading answers which can be very significant if they result in immediate critical decisions or activation of actuators. In this paper, we present a framework for cleaning and querying noisy sensors. Specifically, we present a Bayesian approach for reducing the uncertainty associated with the data, that arise due to random noise, in an online fashion. Our approach combines prior knowledge of the true sensor reading, the noise characteristics of this sensor, and the observed noisy reading in order to obtain a more accurate estimate of the reading. This cleaning step can be performed either at the sensor level or at the base-station. Based on our proposed uncertainty models and using a statistical approach, we introduce several algorithms for answering traditional database queries over uncertain sensor readings. Finally, we present a preliminary evaluation of our proposed approach using synthetic data and highlight some exciting research directions in this area. --- paper_title: Similarity search over time-series data using wavelets paper_content: Considers the use of wavelet transformations as a dimensionality reduction technique to permit efficient similarity searching over high-dimensional time-series data. While numerous transformations have been proposed and studied, the only wavelet that has been shown to be effective for this application is the Haar wavelet. In this work, we observe that a large class of wavelet transformations (not only orthonormal wavelets but also bi-orthonormal wavelets) can be used to support similarity searching. This class includes the most popular and most effective wavelets being used in image compression. We present a detailed performance study of the effects of using different wavelets on the performance of similarity searching for time-series data. We include several wavelets that outperform both the Haar wavelet and the best-known non-wavelet transformations for this application. To ensure our results are usable by an application engineer, we also show how to configure an indexing strategy for the best-performing transformations. Finally, we identify classes of data that can be indexed efficiently using these wavelet transformations. --- paper_title: Online Filtering, Smoothing and Probabilistic Modeling of Streaming data paper_content: In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new data arrives. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over several synthetic and real datasets, that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of tight integration between dynamic probabilistic models and databases. ---	Title: A SURVEY OF MODEL-BASED SENSOR DATA ACQUISITION AND MANAGEMENT Section 1: Introduction Description 1: Introduce the growth in sensor data and the significance of model-based techniques for data acquisition and management. Summarize the categories of tasks covered, such as data acquisition, cleaning, query processing, and compression. Section 2: Model-Based Sensor Data Acquisition Description 2: Discuss various techniques for model-based sensor data acquisition, focusing on energy consumption and communication costs. Cover pull-based and push-based approaches along with specific methods like TinyDB, BBQ, PRESTO, and Ken. Section 3: Preliminaries Description 3: Define the basic model of a sensor network and establish the notation utilized in the chapter. Explain the scenario of sensors used for environmental monitoring. Section 4: The Sensor Data Acquisition Query Description 4: Describe the process of creating and maintaining the sensor values table. Present an example of a sensor data acquisition query and its execution. Section 5: Pull-Based Data Acquisition Description 5: Elaborate on pull-based acquisition methods such as TinyDB (with ACQP and Semantic Routing Trees) and BBQ (using multi-dimensional Gaussian distributions). Section 6: Push-Based Data Acquisition Description 6: Explain push-based techniques like PRESTO and Ken, focusing on predictive models and the communication protocols between sensor nodes and base stations. Section 7: Model-Based Sensor Data Cleaning Description 7: Describe the methods for detecting and correcting erroneous sensor values using regression, probabilistic, and outlier detection models. Mention declarative data cleaning approaches. Section 8: Models for Sensor Data Cleaning Description 8: Review popular models used in data cleaning, such as regression models (polynomial and Chebyshev), probabilistic models (Kalman filter and Bayes' theorem), and outlier detection techniques. Section 9: Model-Based Query Processing Description 9: Discuss approaches like in-network query processing and centralized query processing using model-based views, symbolic query evaluation, and processing over uncertain data. Cover dynamic probabilistic models and the use of HMMs. Section 10: Processing Event Queries Description 10: Introduce event queries and their importance in continuously monitoring particular events in sensor data. Provide references to related works. Section 11: Model-Based Sensor Data Compression Description 11: Categorize and review important model-based approaches for compressing sensor data. Cover methods like piecewise approximation, compressing correlated data streams, multi-model data compression, and orthogonal transformations. Section 12: Overview of Sensor Data Compression System Description 12: Present the goal of sensor data compression systems and describe general approaches to segmentation and approximation of data streams. Section 13: Methods for Data Segmentation Description 13: Discuss categorization of piecewise linear approximation algorithms into sliding window, top-down, and bottom-up approaches. Section 14: Piecewise Approximation Description 14: Explain techniques such as piecewise linear approximation and piecewise constant approximation, including filters like swing and slide. Section 15: Compressing Correlated Data Streams Description 15: Review methods for exploiting spatial and temporal correlations among different data streams to achieve compression. Section 16: Multi-Model Data Compression Description 16: Discuss the effectiveness of using multiple models for approximating data streams and selection strategies for the best models. Section 17: Orthogonal Transformations Description 17: Introduce orthogonal transformation approaches such as DFT and discrete wavelet transform for dimensionality reduction and data compression. Section 18: Lossless vs. Lossy Compression Description 18: Compare lossless and lossy compression techniques, discussing their use cases and performance implications. Section 19: Summary Description 19: Provide a comprehensive overview of the various model-based techniques for sensor data acquisition, cleaning, query processing, and compression. Summarize key methods and their contributions to efficient data management.
XML Query Processing and Query Languges: A Survey	8	--- paper_title: A Query Processing Approach for XML Database Systems paper_content: Besides the storage engine, the query processor of a database system is the most critical component when it comes to performance and scalability. Research on query processing for relational database systems developed an approach which we believe should also be adopted for the newly proposed XML database systems. It includes a syntactic and semantic analyzation phase, the mapping onto an internal query representation, algebraic and cost-based optimization, and finally the execution on a record-oriented interface. Each step hides its own challenges and will therefore be discussed throughout this paper. Our contribution can be understood as a roadmap that reveals a desirable set of functionalities for an XML query processor. --- paper_title: The Niagara Internet Query System paper_content: Many projections envision a future in which the Internet is populated with a vast number of Web-accessible XML files—a “World-Wide Database”. Recently, there has been a great deal of research into XML query languages to enable the execution of database-style queries over these XML files. However, merely being an XML query-processing engine does not render a system suitable for querying the Internet. A truly useful system must provide mechanisms to (a) find the XML files that are relevant to a given query, and (b) deal with remote data sources that either provide unpredictable data access and transfer rates, or are infinite streams, or both. The Niagara Internet Query System was designed from the bottom-up to provide these mechanisms. It finds relevant XML documents by using a novel collaboration between the Niagara XML-QL query processor and the Niagara “text-in-context” XML search engine. To handle infinite streams and data sources with unpredictable rates, it supports a “get partial” operation on blocking operators in order to produce partial query results, and inserts synchronization packets at critical points in the operator tree to guarantee the consistency of (partial) results. The Niagara Internet Query System is public domain software that can be found at http://www-db.cs.wisc.edu/niagara/. Category: Research. --- paper_title: Navigation- vs. index-based XML multi-query processing paper_content: XML path queries form the basis of complex filtering of XML data. Most current XML path query processing techniques can be divided in two groups. Navigation-based algorithms compute results by analyzing an input document one tag at a time. In contrast, index-based algorithms take advantage of precomputed numbering schemes over the input XML document. We introduce a new index-based technique, index-filter, to answer multiple XML path queries. Index-filter uses indexes built over the document tags to avoid processing large portions of the input document that are guaranteed not to be part of any match. We analyze index-filter and compare it against Y-filter, a state-of-the-art navigation-based technique. We show that both techniques have their advantages, and we discuss the scenarios under which each technique is superior to the other one. In particular, we show that while most XML path query processing techniques work off SAX events, in some cases it pays off to preprocess the input document, augmenting it with auxiliary information that can be used to evaluate the queries faster. We present experimental results over real and synthetic XML documents that validate our claims. --- paper_title: Efficient Recursive XML Query Processing Using Relational Database Systems paper_content: Recursive queries are quite important in the context of XML databases. In addition, several recent papers have investigated a relational approach to store XML data and there is growing evidence that schema-conscious approaches are a better option than schema-oblivious techniques as far as query performance is concerned. However, the issue of recursive XML queries for such approaches has not been dealt with satisfactorily. In this paper we argue that it is possible to design a schema-oblivious approach that outperforms schema-conscious approaches for certain types of recursive queries. To that end, we propose a novel schema-oblivious approach, called SUCXENT++ (Schema Unconcious XML Enabled System), that outperforms existing schema-oblivious approaches such as XParent by up to 15 times and schema-conscious approaches (Shared-Inlining) by up to eight times for recursive query execution. Our approach has up to two times smaller storage requirements compared to existing schema-oblivious approaches and 10% less than schema-conscious techniques. In addition SUCXENT++ performs marginally better than Shared-Inlining and is 5.7-47 times faster than XParent as far as insertion time is concerned. --- paper_title: MonetDB/XQuery: a fast XQuery processor powered by a relational engine paper_content: Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met. --- paper_title: The BEA/XQRL Streaming XQuery Processor paper_content: In this paper, we describe the design, implementation, and performance characteristics of a complete, industrial-strength XQuery engine, the BEA streaming XQuery processor. The engine was designed to provide very high performance for message processing applications, i.e., for transforming XML data streams, and it is a central component of the 8.1 release of BEA's WebLogic Integration (WLI) product. This XQuery engine is fully compliant with the August 2002 draft of the W3C XML Query Language specification. A goal of this paper is to describe how an efficient, fully compliant XQuery engine can be built from a few relatively simple components and well-understood technologies. --- paper_title: NaXDB - Realizing Pipelined XQuery Processing in a Native XML Database System paper_content: Supporting queries and modifications on XML documents is a challenging task, and several related approaches exist. When implementing query and modification languages efficiently, the actual persistent storage of the XML data is of particular importance. Generally speaking, the structure of XML data significantly differs from the well-known relational data-model. This paper presents the prototypical implementation of NaXDB, a native XML database management system (DBMS). A native approach means the XML data is stored as a hierarchical tree of linked objects. NaXDB is implemented as a MaxDB by MySQL kernel module and thus inherits several DBMS functionality. Furthermore, NaXDB uses objectoriented extensions of of MaxDB by MySQL to store the tree of linked objects. NaXDB implements a large subset of the query language specification XQuery 1.0 as well as XUpdate for modification. The design and architecture of the prototypical implementation is presented, and concepts for query processing within the database server are discussed. --- paper_title: Holistic twig joins: optimal XML pattern matching paper_content: XML employs a tree-structured data model, and, naturally, XML queries specify patterns of selection predicates on multiple elements related by a tree structure. Finding all occurrences of such a twig pattern in an XML database is a core operation for XML query processing. Prior work has typically decomposed the twig pattern into binary structural (parent-child and ancestor-descendant) relationships, and twig matching is achieved by: (i) using structural join algorithms to match the binary relationships against the XML database, and (ii) stitching together these basic matches. A limitation of this approach for matching twig patterns is that intermediate result sizes can get large, even when the input and output sizes are more manageable.In this paper, we propose a novel holistic twig join algorithm, TwigStack, for matching an XML query twig pattern. Our technique uses a chain of linked stacks to compactly represent partial results to root-to-leaf query paths, which are then composed to obtain matches for the twig pattern. When the twig pattern uses only ancestor-descendant relationships between elements, TwigStack is I/O and CPU optimal among all sequential algorithms that read the entire input: it is linear in the sum of sizes of the input lists and the final result list, but independent of the sizes of intermediate results. We then show how to use (a modification of) B-trees, along with TwigStack, to match query twig patterns in sub-linear time. Finally, we complement our analysis with experimental results on a range of real and synthetic data, and query twig patterns. --- paper_title: Structural joins: a primitive for efficient XML query pattern matching paper_content: XML queries typically specify patterns of selection predicates on multiple elements that have some specified tree structured relationships. The primitive tree structured relationships are parent-child and ancestor-descendant, and finding all occurrences of these relationships in an XML database is a core operation for XML query processing. We develop two families of structural join algorithms for this task: tree-merge and stack-tree. The tree-merge algorithms are a natural extension of traditional merge joins and the multi-predicate merge joins, while the stack-tree algorithms have no counterpart in traditional relational join processing. We present experimental results on a range of data and queries using the TIMBER native XML query engine built on top of SHORE. We show that while, in some cases, tree-merge algorithms can have performance comparable to stack-tree algorithms, in many cases they are considerably worse. This behavior is explained by analytical results that demonstrate that, on sorted inputs, the stack-tree algorithms have worst-case I/O and CPU complexities linear in the sum of the sizes of inputs and output, while the tree-merge algorithms do not have the same guarantee. --- paper_title: An Efficient XPath Query Processor for XML Streams paper_content: Streaming XPath evaluation algorithms must record a potentially exponential number of pattern matches when both predicates and descendant axes are present in queries, and the XML data is recursive. In this paper, we use a compact data structure to encode these pattern matches rather than storing them explicitly. We then propose a polynomial time streaming algorithm to evaluate XPath queries by probing the data structure in a lazy fashion. Extensive experiments show that our approach not only has a good theoretical complexity bound but is also efficient in practice. --- paper_title: Mixed Mode XML Query Processing paper_content: Querying XML documents typically involves both tree-based navigation and pattern matching similar to that used in structured information retrieval domains. In this paper, we show that for good performance, a native XML query processing system should support query plans that mix these two processing paradigms. We describe our prototype native XML system, and report on experiments demonstrating that even for simple queries, there are a number of options for how to combine tree-based navigation and structural joins based on information retrieval-style inverted lists, and that these options can have widely varying performance. We present ways of transparently using both techniques in a single system, and provide a cost model for identifying efficient combinations of the techniques. Our preliminary experimental results prove the viability of our approach. ---	Title: XML Query Processing and Query Languages: A Survey Section 1: INTRODUCTION Description 1: Introduce the functionalities of XML, its structure, and the importance of XML query languages and processors. Section 2: XML QUERY LANGUAGES Description 2: Review the evolution of XML query languages, with a focus on XQuery and other historical proposals, detailing their important characteristics and criteria. Section 3: APPROACHES FOR XML QUERY PROCESSING Description 3: Discuss various approaches for XML query processing, including both high-level abstractions and procedural evaluations. Section 4: Query Processing on Flat File Scheme Description 4: Analyze the flat file processing approach, explain different processing techniques such as yfilter, index-filter, and pathstack, and outline their performance considerations. Section 5: Processing on Relational Structure Description 5: Explain the relational structure approach for storing and querying XML data, detailing different relational storage schemes and their implementation. Section 6: Query Processing on Native Storage Scheme Description 6: Describe the native storage scheme approach, including labeling schemes for XML elements, stack-based algorithms, and holistic twig joins for efficient query processing. Section 7: TOWARD FUTURE XML DATABASE MANAGEMENT SYSTEMS Description 7: Discuss the future directions for XML database management systems, including trends, potential research areas, and application interoperability. Section 8: CONCLUSION Description 8: Summarize the current state of XML query processing, highlight the current trends and challenges, and suggest areas for future research.
A Review of Energy Efficient Dynamic Source Routing Protocol for Mobile Ad Hoc Networks	15	--- paper_title: Energy Efficient Routing Protocols for Mobile Ad Hoc Networks paper_content: Nodes in a mobile ad hoc network have limited battery power. If a node is used frequently for transmission or overhearing of data packets, more energy is consumed by that node and after certain amount of time the energy level may not be sufficient for data transmission resulting in link failure. In this paper, we have considered two routing protocols-Dynamic Source Routing (DSR) & Minimum Maximum Battery cost Routing (MMBCR) and studied their performances in terms of network lifetime for the same network scenario. Simulations are carried out using NS2. Finally from the simulation results we have concluded that MMBCR gives more network lifetime by selecting route with maximum battery capacity thereby outperforming DSR. General Terms Energy efficiency, MANETS, Routing Protocols. --- paper_title: Ad Hoc Wireless Networks: Architectures and Protocols paper_content: Practical design and performance solutions for every ad hoc wireless networkAd Hoc Wireless Networks comprise mobile devices that use wireless transmission for communication. They can be set up anywhere and any time because they eliminate the complexities of infrastructure setup and central administration-and they have enormous commercial and military potential. Now, there's a book that addresses every major issue related to their design and performance. Ad Hoc Wireless Networks: Architectures and Protocols presents state-of-the-art techniques and solutions, and supports them with easy-to-understand examples. The book starts off with the fundamentals of wireless networking (wireless PANs, LANs, MANs, WANs, and wireless Internet) and goes on to address such current topics as Wi-Fi networks, optical wireless networks, and hybrid wireless architectures. Coverage includes: Medium access control, routing, multicasting, and transport protocols QoS provisioning, energy management, security, multihop pricing, and much more In-depth discussion of wireless sensor networks and ultra wideband technology More than 200 examples and end-of-chapter problemsAd Hoc Wireless Networks is an invaluable resource for every network engineer, technical manager, and researcher designing or building ad hoc wireless networks. --- paper_title: Energy Efficient Routing Protocols for Mobile Ad Hoc Networks paper_content: Nodes in a mobile ad hoc network have limited battery power. If a node is used frequently for transmission or overhearing of data packets, more energy is consumed by that node and after certain amount of time the energy level may not be sufficient for data transmission resulting in link failure. In this paper, we have considered two routing protocols-Dynamic Source Routing (DSR) & Minimum Maximum Battery cost Routing (MMBCR) and studied their performances in terms of network lifetime for the same network scenario. Simulations are carried out using NS2. Finally from the simulation results we have concluded that MMBCR gives more network lifetime by selecting route with maximum battery capacity thereby outperforming DSR. General Terms Energy efficiency, MANETS, Routing Protocols. --- paper_title: Energy conserving routing in wireless ad-hoc networks paper_content: An ad-hoc network of wireless static nodes is considered as it arises in a rapidly deployed, sensor-based, monitoring system. Information is generated in certain nodes and needs to reach a set of designated gateway nodes. Each node may adjust its power within a certain range that determines the set of possible one hop away neighbors. Traffic forwarding through multiple hops is employed when the intended destination is not within immediate reach. The nodes have limited initial amounts of energy that is consumed at different rates depending on the power level and the intended receiver. We propose algorithms to select the routes and the corresponding power levels such that the time until the batteries of the nodes drain-out is maximized. The algorithms are local and amenable to distributed implementation. When there is a single power level, the problem is reduced to a maximum flow problem with node capacities and the algorithms converge to the optimal solution. When there are multiple power levels then the achievable lifetime is close to the optimal (that is computed by linear programming) most of the time. It turns out that in order to maximize the lifetime, the traffic should be routed such that the energy consumption is balanced among the nodes in proportion to their energy reserves, instead of routing to minimize the absolute consumed power. --- paper_title: Ad Hoc Wireless Networks: Architectures and Protocols paper_content: Practical design and performance solutions for every ad hoc wireless networkAd Hoc Wireless Networks comprise mobile devices that use wireless transmission for communication. They can be set up anywhere and any time because they eliminate the complexities of infrastructure setup and central administration-and they have enormous commercial and military potential. Now, there's a book that addresses every major issue related to their design and performance. Ad Hoc Wireless Networks: Architectures and Protocols presents state-of-the-art techniques and solutions, and supports them with easy-to-understand examples. The book starts off with the fundamentals of wireless networking (wireless PANs, LANs, MANs, WANs, and wireless Internet) and goes on to address such current topics as Wi-Fi networks, optical wireless networks, and hybrid wireless architectures. Coverage includes: Medium access control, routing, multicasting, and transport protocols QoS provisioning, energy management, security, multihop pricing, and much more In-depth discussion of wireless sensor networks and ultra wideband technology More than 200 examples and end-of-chapter problemsAd Hoc Wireless Networks is an invaluable resource for every network engineer, technical manager, and researcher designing or building ad hoc wireless networks. --- paper_title: Energy conserving routing in wireless ad-hoc networks paper_content: An ad-hoc network of wireless static nodes is considered as it arises in a rapidly deployed, sensor-based, monitoring system. Information is generated in certain nodes and needs to reach a set of designated gateway nodes. Each node may adjust its power within a certain range that determines the set of possible one hop away neighbors. Traffic forwarding through multiple hops is employed when the intended destination is not within immediate reach. The nodes have limited initial amounts of energy that is consumed at different rates depending on the power level and the intended receiver. We propose algorithms to select the routes and the corresponding power levels such that the time until the batteries of the nodes drain-out is maximized. The algorithms are local and amenable to distributed implementation. When there is a single power level, the problem is reduced to a maximum flow problem with node capacities and the algorithms converge to the optimal solution. When there are multiple power levels then the achievable lifetime is close to the optimal (that is computed by linear programming) most of the time. It turns out that in order to maximize the lifetime, the traffic should be routed such that the energy consumption is balanced among the nodes in proportion to their energy reserves, instead of routing to minimize the absolute consumed power. --- paper_title: Power-aware localized routing in wireless networks paper_content: A cost aware metric for wireless networks based on remaining battery power at nodes was proposed for shortest-cost routing algorithms, assuming constant transmission power. Power-aware metrics, where transmission power depends on distance between nodes and corresponding shortest power algorithms were also proposed. We define a power-cost metric based on the combination of both node's lifetime and distance-based power metrics. We investigate some properties of power adjusted transmissions and show that, if additional nodes can be placed at desired locations between two nodes at distance d, the transmission power can be made linear in d as opposed to d/sup /spl alpha// dependence for /spl alpha/ /spl ges/ 2. This provides basis for power, cost, and power-cost localized routing algorithms where nodes make routing decisions solely on the basis, of location of their neighbors and destination. The power-aware routing algorithm attempts to minimize the total power needed to route a message between a source and a destination. The cost-aware routing algorithm is aimed at extending the battery's worst-case lifetime at each node. The combined power-cost localized routing algorithm attempts to minimize the total power needed and to avoid nodes with a short battery's remaining lifetime. We prove that the proposed localized power, cost, and power-cost efficient routing algorithms are loop-free and show their efficiency by experiments. --- paper_title: Power-aware localized routing in wireless networks paper_content: A cost aware metric for wireless networks based on remaining battery power at nodes was proposed for shortest-cost routing algorithms, assuming constant transmission power. Power-aware metrics, where transmission power depends on distance between nodes and corresponding shortest power algorithms were also proposed. We define a power-cost metric based on the combination of both node's lifetime and distance-based power metrics. We investigate some properties of power adjusted transmissions and show that, if additional nodes can be placed at desired locations between two nodes at distance d, the transmission power can be made linear in d as opposed to d/sup /spl alpha// dependence for /spl alpha/ /spl ges/ 2. This provides basis for power, cost, and power-cost localized routing algorithms where nodes make routing decisions solely on the basis, of location of their neighbors and destination. The power-aware routing algorithm attempts to minimize the total power needed to route a message between a source and a destination. The cost-aware routing algorithm is aimed at extending the battery's worst-case lifetime at each node. The combined power-cost localized routing algorithm attempts to minimize the total power needed and to avoid nodes with a short battery's remaining lifetime. We prove that the proposed localized power, cost, and power-cost efficient routing algorithms are loop-free and show their efficiency by experiments. --- paper_title: Energy saving dynamic source routing for ad hoc wireless networks paper_content: In this paper, energy saving dynamic source routing (ESDSR) protocol is introduced to maximize the life-span of a mobile ad hoc network (MANET). Many theoretical studies show that energy consumption in MANET can be significantly reduced using energy-aware routing protocols compared to fixed-power minimum-hop routing protocols. Two approaches are broadly suggested for energy-aware routing protocols - transmission power control approach and load sharing approach. ESDSR integrates the advantages of those two approaches. In ESDSR, the routing decision is based on a load balancing approach. Once a routing decision is made, link by link transmit power adjustment per packet is done based on a transmit power control approach. We modified dynamic source routing (DSR) protocol to make it energy aware by a network simulator (network simulator-2 of University of California). The simulation results show that the proposed ESDSR can save energy up to 40% per packet and it can send 20 % more packets to destinations by spending the same battery power in compare to DSR. --- paper_title: Overhead reduction and energy management in DSR for MANETs paper_content: Dynamic Source Routing (DSR) Protocol is a commonly applied reactive protocol for mobile ad hoc networks (MANETs). When the network size is increased, overhead is increased due to the source routing nature of DSR. Moreover, energy consumption of the nodes increases, as the nodes act as intermediate nodes for many source destination pairs. In order to improve the scalability of DSR, in this paper, a modification is proposed, and is referred to as Modified DSR (MDSR). Secondly, in order to conserve the battery power, MDSR with energy management is proposed, in which the packets are transmitted with minimum required energy. Simulation results show that both MDSR and MDSR with Energy management generate less overhead and delay compared to standard DSR. It is observed that in MDSR with Energy management, irrespective of network size, the average energy consumption per node is 37.9% less compared to that of standard DSR. ---	Title: A Review of Energy Efficient Dynamic Source Routing Protocol for Mobile Ad Hoc Networks Section 1: INTRODUCTION Description 1: This section provides an overview of Ad Hoc Networks, characterizes the nature of MANETs, discusses the challenges faced particularly regarding power constraint and the motivation for energy efficient routing protocols based on DSR. Section 2: ROUTING PROCESS IN AD HOC NETWORKS Description 2: This section explains the basic routing process in MANETs, covering the route discovery and route selection mechanisms, and highlights existing ad hoc routing protocols with specific characteristics needed. Section 3: Dynamic source routing protocol Description 3: This section provides a detailed explanation of the DSR protocol, including the route discovery and route maintenance processes, along with its benefits and limitations. Section 4: ENERGY EFFICIENT ROUTING PROTOCOLS Description 4: This section discusses different energy efficient routing protocols built on DSR, elaborates on various approaches such as transmission power control, load distribution, and sleep/power-down mechanisms to minimize energy consumption. Section 5: RELATED WORK Description 5: This section reviews various modifications and enhancements made on the traditional DSR to develop energy-efficient routing protocols over the past years. Section 6: Global Energy Aware Routing (GEAR) Protocol Description 6: This section describes the GEAR protocol and its approach to selecting routes based on residual battery power, along with associated problems and limitations. Section 7: Local Energy Aware Routing (LEAR) Protocol Description 7: This section outlines the LEAR protocol mechanism which considers nodes' willingness based on remaining battery power to participate in routing. Section 8: Energy Saving Dynamic Source Routing (ESDSR) Protocol Description 8: This section details ESDSR, focusing on the load balancing and transmission power control strategies designed to prolong network lifetime. Section 9: Energy Dependent DSR Routing (EEDSR) Protocol Description 9: This section explains EEDSR's method using residual battery power and drain rate to determine node participation in forwarding data packets. Section 10: Energy Efficient DSR Protocol (E2DSR) Description 10: This section discusses E2DSR's new control packet structures and route selection algorithms for energy efficiency. Section 11: Topology Control Based Power-Aware and Battery Life-aware DSR (TPBDSR) Protocol Description 11: This section describes TPBDSR's mechanism to adjust transmitting power based on network topology changes and its impact on node lifetime. Section 12: Minimum Energy Dynamic Source Routing Protocol (MEDSR) Description 12: This section covers MEDSR's dual mechanism approach, utilizing low and high power levels for route discovery to ensure network connectivity and energy savings. Section 13: Modified DSR Routing (MDSR) Protocol Description 13: This section explains MDSR's aim to reduce overhead by minimizing routing reply packets and implementing fixed header sizes. Section 14: Multi-path Energy Aware DSR Routing (MEADSR) protocol Description 14: This section elucidates MEADSR's integration of multipath and energy-aware techniques for efficient routing and balanced energy consumption among nodes. Section 15: Conclusion Description 15: This section synthesizes the discussion on energy consumption issues in MANETs, compares various energy-efficient protocols based on DSR, and emphasizes the need for integrating multiple techniques to enhance network performance.
Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey	10	--- paper_title: Using Ontologies to Strengthen Folksonomies and Enrich Information Retrieval in Weblogs paper_content: While free-tagging classification is widely used in social software implementations and especially in weblogs, it raises various issues regarding information retrieval. In this paper, we describe an approach that mixes folksonomies and semantic web technologies in order to solve some of these problems, and to enrich information retrieval capabilities among blog posts. We first introduce the corporate context of the study and the issues we have faced that motivated our approach. Then, we argue how the use of domain ontologies combined with the SIOC vocabulary on the top of an existing folksonomy and weblogging platform offers a way to get rid of free-tagging classification flaws, and enhances information retrieval by suggesting related blog posts. Aside of the theoretical background, this paper also focuses on implementation. We present experimental results of this approach through the example of add-ons to a corporate blogging platform and the associated semantic web search engine, that extensively uses RDF and other semantic web technologies to find appropriate information and suggest related posts. --- paper_title: The complex dynamics of collaborative tagging paper_content: The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags. --- paper_title: Ontologies are us: a unified model of social networks and semantics paper_content: In our work we extend the traditional bipartite model of ontologies with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages. --- paper_title: GroupMe! - Where Semantic Web meets Web 2.0 paper_content: Grouping is an attractive interaction metaphor for users to create reference collections of Web resources they are interested in. Each grouping activity has a certain semantics: things which were previously unrelated are now connected with others via the group. We present the GroupMe! application which allows users to group and arrange multimedia Web resources they are interested in. GroupMe! has an easy-touse interface for gathering and grouping of resources, and allows users to tag everything they like. The semantics of any user interaction is captured, transformed and stored as adequate RDF descriptions. As an example application of this automatically derived RDF content, we show the enhancement of search for tagged Web resources, which evaluates the grouping information to deduce additional contextual information about the resources. GroupMe! is available via http://www.groupme.org. --- paper_title: Bridging the Gap Between Folksonomies and the Semantic Web: An Experience Report paper_content: While folksonomies allow tagging of similar resources with a variety of tags, their content retrieval mechanisms are severely hampered by being agnostic to the relations that exist between these tags. To overcome this limitation, several methods have been proposed to find groups of implicitly inter-related tags. We believe that content retrieval can be further improved by making the relations between tags explicit. In this paper we propose the semantic enrichment of folksonomy tags with explicit relations by harvesting the Semantic Web, i.e., dynamically selecting and combining relevant bits of knowledge from online ontologies. Our experimental results show that, while semantic enrichment needs to be aware of the particular characteristics of folksonomies and the Semantic Web, it is beneficial for both. --- paper_title: Integrating Folksonomies with the Semantic Web paper_content: While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified. --- paper_title: Using the Semantic Web as Background Knowledge for Ontology Mapping paper_content: While current approaches to ontology mapping produce good results by mainly relying on label and structure based similarity measures, there are several cases in which they fail to discover important mappings. In this paper we describe a novel approach to ontology mapping, which is able to avoid this limitation by using background knowledge. Existing approaches relying on background knowledge typically have one or both of two key limitations: 1) they rely on a manually selected reference ontology; 2) they suffer from the noise introduced by the use of semi-structured sources, such as text corpora. Our technique circumvents these limitations by exploiting the increasing amount of semantic resources available online. As a result, there is no need either for a manually selected reference ontology (the relevant ontologies are dynamically selected from an online ontology repository), or for transforming background knowledge in an ontological form. The promising results from experiments on two real life thesauri indicate both that our approach has a high precision and also that it can find mappings, which are typically missed by existing approaches. --- paper_title: Discovering Shared Conceptualizations in Folksonomies paper_content: Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples. --- paper_title: Ontology of Folksonomy: A Mash-Up of Apples and Oranges paper_content: Ontologies are enabling technology for the Semantic Web. They are a means for people to state what they mean by the terms used in data that they might generate, share, or consume. Folksonomies are an emergent phenomenon of the social Web. They arise from data about how people associate terms with content that they generate, share, or consume. Recently the two ideas have been put into opposition, as if they were right and left poles of a political spectrum. This is a false dichotomy; they are more like apples and oranges. In fact, as the Semantic Web matures and the social Web grows, there is increasing value in applying Semantic Web technologies to the data of the social Web. This article is an attempt to clarify the distinct roles for ontologies and folksonomies, and preview some new work that applies the two ideas togetherâ€”an ontology of folk-sonomy. --- paper_title: Using Ontologies to Strengthen Folksonomies and Enrich Information Retrieval in Weblogs paper_content: While free-tagging classification is widely used in social software implementations and especially in weblogs, it raises various issues regarding information retrieval. In this paper, we describe an approach that mixes folksonomies and semantic web technologies in order to solve some of these problems, and to enrich information retrieval capabilities among blog posts. We first introduce the corporate context of the study and the issues we have faced that motivated our approach. Then, we argue how the use of domain ontologies combined with the SIOC vocabulary on the top of an existing folksonomy and weblogging platform offers a way to get rid of free-tagging classification flaws, and enhances information retrieval by suggesting related blog posts. Aside of the theoretical background, this paper also focuses on implementation. We present experimental results of this approach through the example of add-ons to a corporate blogging platform and the associated semantic web search engine, that extensively uses RDF and other semantic web technologies to find appropriate information and suggest related posts. --- paper_title: Extreme Tagging: Emergent Semantics through the Tagging of Tags paper_content: While the Semantic Web requires a large amount of structured knowledge (triples) to allow machine reasoning, the acquisition of this knowledge still represents an open issue. Indeed, expressing expert knowledge in a given formalism is a tedious process. Less structured annotations such as tagging have, however, proved immensely popular, whilst existing unstructured or semi-structured collaborative knowledge bases such as Wikipedia have proven to be useful and scalable. Both processes are often regulated through social mechanisms such as wiki-like operations, recommendations, ratings, and collaborative games. To promote collaborative tagging as a means to acquire unstructured as well as structured knowledge we introduce the notion of Extreme Tagging, which describes systems which allow the tagging of resources, as well as of tags themselves and their relations. We provide a formal description of extreme tagging followed by examples and highlight the necessity of regulatory processes which can be applied to it. We also present a prototype implementation. --- paper_title: Using Ontologies to Strengthen Folksonomies and Enrich Information Retrieval in Weblogs paper_content: While free-tagging classification is widely used in social software implementations and especially in weblogs, it raises various issues regarding information retrieval. In this paper, we describe an approach that mixes folksonomies and semantic web technologies in order to solve some of these problems, and to enrich information retrieval capabilities among blog posts. We first introduce the corporate context of the study and the issues we have faced that motivated our approach. Then, we argue how the use of domain ontologies combined with the SIOC vocabulary on the top of an existing folksonomy and weblogging platform offers a way to get rid of free-tagging classification flaws, and enhances information retrieval by suggesting related blog posts. Aside of the theoretical background, this paper also focuses on implementation. We present experimental results of this approach through the example of add-ons to a corporate blogging platform and the associated semantic web search engine, that extensively uses RDF and other semantic web technologies to find appropriate information and suggest related posts. --- paper_title: Ontology of Folksonomy: A Mash-Up of Apples and Oranges paper_content: Ontologies are enabling technology for the Semantic Web. They are a means for people to state what they mean by the terms used in data that they might generate, share, or consume. Folksonomies are an emergent phenomenon of the social Web. They arise from data about how people associate terms with content that they generate, share, or consume. Recently the two ideas have been put into opposition, as if they were right and left poles of a political spectrum. This is a false dichotomy; they are more like apples and oranges. In fact, as the Semantic Web matures and the social Web grows, there is increasing value in applying Semantic Web technologies to the data of the social Web. This article is an attempt to clarify the distinct roles for ontologies and folksonomies, and preview some new work that applies the two ideas togetherâ€”an ontology of folk-sonomy. --- paper_title: Meaning Of A Tag: A collaborative approach to bridge the gap between tagging and Linked Data paper_content: This paper introduces MOAT, a lightweight Semantic Web framework that provides a collaborative way to let Web 2.0 content producers give meanings to their tags in a machine- readable way. To achieve this goal, this approach relies on Linked Data principles, using URIs from existing resources to define these meanings. That way, users can create inter- linked RDF data and let their content enter the Semantic Web, while solving some limits of free-tagging at the same time. --- paper_title: Ontogame: Weaving the semantic web by online gaming paper_content: Most of the challenges faced when building the Semantic Web require a substantial amount of human labor and intelligence. Despite significant advancement in ontology learning and human language technology, the tasks of ontology construction, semantic annotation, and establishing alignments between multiple ontologies remain highly dependent on human intelligence. This means that individuals need to contribute time and sometimes other resources. Unfortunately, we observe a serious lack of user involvement in the aforementioned tasks, which may be due to the absence of motivations for people who contribute. As a novel solution, we (1) propose to masquerade the core tasks of weaving the Semantic Web behind online, multi-player game scenarios, in order to create proper incentives for human users to get involved. Doing so, we adopt the findings from the already famous "games with a purpose" by von Ahn, who has shown that presenting a useful task, which requires human intelligence, in the form of an online game can motivate a large amount of people to work heavily on this task, and this for free. Then, we (2) describe our generic OntoGame platform, and (3) several gaming scenarios for various tasks plus our respective prototypes. Based on the analysis of user data and interviews with players, we provide preliminary evidence that users (4) enjoy the games and are willing to dedicate their time to those games, (5) are able to produce high-quality conceptual choices. Eventually we show how users entertaining themselves by online games can unknowingly help weave and maintain the Semantic Web. --- paper_title: Extreme Tagging: Emergent Semantics through the Tagging of Tags paper_content: While the Semantic Web requires a large amount of structured knowledge (triples) to allow machine reasoning, the acquisition of this knowledge still represents an open issue. Indeed, expressing expert knowledge in a given formalism is a tedious process. Less structured annotations such as tagging have, however, proved immensely popular, whilst existing unstructured or semi-structured collaborative knowledge bases such as Wikipedia have proven to be useful and scalable. Both processes are often regulated through social mechanisms such as wiki-like operations, recommendations, ratings, and collaborative games. To promote collaborative tagging as a means to acquire unstructured as well as structured knowledge we introduce the notion of Extreme Tagging, which describes systems which allow the tagging of resources, as well as of tags themselves and their relations. We provide a formal description of extreme tagging followed by examples and highlight the necessity of regulatory processes which can be applied to it. We also present a prototype implementation. --- paper_title: Towards Semantically-Interlinked Online Communities paper_content: Online community sites have replaced the traditional means of keeping a community informed via libraries and publishing. At present, online communities are islands that are not interlinked. We describe different types of online communities and tools that are currently used to build and support such communities. Ontologies and Semantic Web technologies offer an upgrade path to providing more complex services. Fusing information and inferring links between the various applications and types of information provides relevant insights that make the available information on the Internet more valuable. We present the SIOC ontology which combines terms from vocabularies that already exist with new terms needed to describe the relationships between concepts in the realm of online community sites. --- paper_title: GroupMe! - Where Semantic Web meets Web 2.0 paper_content: Grouping is an attractive interaction metaphor for users to create reference collections of Web resources they are interested in. Each grouping activity has a certain semantics: things which were previously unrelated are now connected with others via the group. We present the GroupMe! application which allows users to group and arrange multimedia Web resources they are interested in. GroupMe! has an easy-touse interface for gathering and grouping of resources, and allows users to tag everything they like. The semantics of any user interaction is captured, transformed and stored as adequate RDF descriptions. As an example application of this automatically derived RDF content, we show the enhancement of search for tagged Web resources, which evaluates the grouping information to deduce additional contextual information about the resources. GroupMe! is available via http://www.groupme.org. --- paper_title: Tag Mediated Society with SCOT Ontology paper_content: In this paper we give an overview of the int.ere.st for a social tagging, bookmarking, and sharing service. It is based on the SCOT ontology. The SCOT ontology can represent the structure and semantics for social tagging data and provide methods for sharing and reusing them. We describe how it enables users to participate in a semantic social tagging from functional point of view and show how int.ere.st allows users to save, tag, and search SCOT ontologies. All kinds of user contributions in the system will be exposed as RDF vocabularies that connect them. We believe it is a good starting point to build Semantic Web based society using tagging data. --- paper_title: mle: Enhancing the Exploration of Mailing List Archives Through Making Semantics Explicit paper_content: Following and understanding discussions on mailing lists is a prevalent task for executives and policy makers in order to get an impression of one's company image. However, existing solutions providing a Web-based archive require substantial manual effort to search for or filter certain information. With mle we propose a new way to automatically process mailing list archives. The tool is realised based on two Semantic Web technologies: Firstly, SIOC is utilised as the primary vocabulary for describing posts, people, and topics; secondly the RDF metadata is deployed by means of embedding it in the Web page encoded in RDFa. --- paper_title: Integrating Folksonomies with the Semantic Web paper_content: While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified. --- paper_title: Ontology of Folksonomy: A Mash-Up of Apples and Oranges paper_content: Ontologies are enabling technology for the Semantic Web. They are a means for people to state what they mean by the terms used in data that they might generate, share, or consume. Folksonomies are an emergent phenomenon of the social Web. They arise from data about how people associate terms with content that they generate, share, or consume. Recently the two ideas have been put into opposition, as if they were right and left poles of a political spectrum. This is a false dichotomy; they are more like apples and oranges. In fact, as the Semantic Web matures and the social Web grows, there is increasing value in applying Semantic Web technologies to the data of the social Web. This article is an attempt to clarify the distinct roles for ontologies and folksonomies, and preview some new work that applies the two ideas togetherâ€”an ontology of folk-sonomy. --- paper_title: The complex dynamics of collaborative tagging paper_content: The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags. --- paper_title: Using Ontologies to Strengthen Folksonomies and Enrich Information Retrieval in Weblogs paper_content: While free-tagging classification is widely used in social software implementations and especially in weblogs, it raises various issues regarding information retrieval. In this paper, we describe an approach that mixes folksonomies and semantic web technologies in order to solve some of these problems, and to enrich information retrieval capabilities among blog posts. We first introduce the corporate context of the study and the issues we have faced that motivated our approach. Then, we argue how the use of domain ontologies combined with the SIOC vocabulary on the top of an existing folksonomy and weblogging platform offers a way to get rid of free-tagging classification flaws, and enhances information retrieval by suggesting related blog posts. Aside of the theoretical background, this paper also focuses on implementation. We present experimental results of this approach through the example of add-ons to a corporate blogging platform and the associated semantic web search engine, that extensively uses RDF and other semantic web technologies to find appropriate information and suggest related posts. --- paper_title: Ontologies are us: a unified model of social networks and semantics paper_content: In our work we extend the traditional bipartite model of ontologies with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages. --- paper_title: Towards Semantically-Interlinked Online Communities paper_content: Online community sites have replaced the traditional means of keeping a community informed via libraries and publishing. At present, online communities are islands that are not interlinked. We describe different types of online communities and tools that are currently used to build and support such communities. Ontologies and Semantic Web technologies offer an upgrade path to providing more complex services. Fusing information and inferring links between the various applications and types of information provides relevant insights that make the available information on the Internet more valuable. We present the SIOC ontology which combines terms from vocabularies that already exist with new terms needed to describe the relationships between concepts in the realm of online community sites. --- paper_title: Integrating Folksonomies with the Semantic Web paper_content: While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified. --- paper_title: Using Ontologies to Strengthen Folksonomies and Enrich Information Retrieval in Weblogs paper_content: While free-tagging classification is widely used in social software implementations and especially in weblogs, it raises various issues regarding information retrieval. In this paper, we describe an approach that mixes folksonomies and semantic web technologies in order to solve some of these problems, and to enrich information retrieval capabilities among blog posts. We first introduce the corporate context of the study and the issues we have faced that motivated our approach. Then, we argue how the use of domain ontologies combined with the SIOC vocabulary on the top of an existing folksonomy and weblogging platform offers a way to get rid of free-tagging classification flaws, and enhances information retrieval by suggesting related blog posts. Aside of the theoretical background, this paper also focuses on implementation. We present experimental results of this approach through the example of add-ons to a corporate blogging platform and the associated semantic web search engine, that extensively uses RDF and other semantic web technologies to find appropriate information and suggest related posts. --- paper_title: Ontologies are us: a unified model of social networks and semantics paper_content: In our work we extend the traditional bipartite model of ontologies with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages. --- paper_title: Extreme Tagging: Emergent Semantics through the Tagging of Tags paper_content: While the Semantic Web requires a large amount of structured knowledge (triples) to allow machine reasoning, the acquisition of this knowledge still represents an open issue. Indeed, expressing expert knowledge in a given formalism is a tedious process. Less structured annotations such as tagging have, however, proved immensely popular, whilst existing unstructured or semi-structured collaborative knowledge bases such as Wikipedia have proven to be useful and scalable. Both processes are often regulated through social mechanisms such as wiki-like operations, recommendations, ratings, and collaborative games. To promote collaborative tagging as a means to acquire unstructured as well as structured knowledge we introduce the notion of Extreme Tagging, which describes systems which allow the tagging of resources, as well as of tags themselves and their relations. We provide a formal description of extreme tagging followed by examples and highlight the necessity of regulatory processes which can be applied to it. We also present a prototype implementation. ---	Title: Bridging Ontologies and Folksonomies to Leverage Knowledge Sharing on the Social Web: A Brief Survey Section 1: Introduction Description 1: Introduce the paper's focus on leveraging knowledge sharing through bridging ontologies and folksonomies on the social web. Section 2: Structure in Folksonomies Description 2: Discuss the analysis of semantic information within folksonomies and methods to extract semantic relationships. Section 3: Clustering and Mapping with Ontologies Description 3: Explain methods for clustering tags and mapping them to ontological concepts. Section 4: Data Mining and Folksonomies Description 4: Describe the application of data mining techniques to extract meaningful information from folksonomies. Section 5: Enriching Folksonomies Description 5: Present works that support folksonomies with semantic web technologies to enrich social tagging platforms. Section 6: Guiding Tagging with Ontologies Description 6: Discuss solutions to integrate ontologies minimally intrusively with social tagging interfaces. Section 7: Building an Ontology of Folksonomy Description 7: Describe the construction and benefits of an ontology dedicated to formalizing the act of tagging. Section 8: Ontologies to Interlink Social Softwares Description 8: Present contributions aiming to interconnect online communities using semantic web formalisms. Section 9: Social Aspects Description 9: Address the social aspects and design models fitting the actual usage of knowledge sharing platforms. Section 10: Perspectives Description 10: Provide perspectives on the integration of folksonomies with semantic web technologies, including remaining challenges and future opportunities.
Coordinated contour following control for machining operations-a survey	5	--- paper_title: Passive control of bilateral teleoperated manipulators paper_content: The control of a bilateral teleoperated manipulator system is considered. The goals of the control are to (i) coordinate the motions of the two manipulators according to a predefined kinematic scaling, (ii) render the dynamics of a locked system, and its response to forces from the human operator and environment to approximate that of predefined natural dynamics, (iii) to provide for possible scaling of power. In addition, for safety reasons, the closed loop system need to remain passive. For linear dynamically similar systems, dynamics of the system can be block diagonalized into two decoupled mechanical systems: the shape system that deals with the coordination error, and the locked system that describe the average motion of the two manipulators. The passive velocity field control methodology is then applied to the shape system to regulate the coordination error at 0 and at the same time preserves the passivity of the overall system. ---	Title: Coordinated Contour Following Control for Machining Operations - A Survey Section 1: Introduction Description 1: This section introduces the fundamental concepts of contour following in machining operations and the objectives of the paper. Section 2: Non-coordination based approach Description 2: This section reviews traditional control approaches focusing on improving trajectory tracking for each individual machining axis without coordination. Section 3: Cross-coupled Control Approach Description 3: This section examines approaches that use cross-coupled or synchronizing control strategies to utilize contour error in the feedback structure. Section 4: Velocity Field Control Approach Description 4: This section reviews the velocity field control approach that abandons timed trajectory requirements and instead uses a time-invariant velocity field for contour following. Section 5: Conclusions Description 5: This section summarizes the various approaches to contour following control, their benefits, and limitations.
A State-of-the-Art Survey on Semantic Web Mining	6	--- paper_title: Finding association rules in semantic web data paper_content: The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach. --- paper_title: “Hello world”, web mining for e-learning paper_content: Abstract As the internet and mobile applications are getting an important role in our lives, usage of mobile services also took place in educational field since the internet is widespread, which is usually called by the terms “e-learning” or “distance learning”. A known issue on e-learning is all the content’s being online and less face-to-face communication than traditional learning; this brings the problem of chasing student’s success, and advising and managing student’s way of studying. Hence, a recent hot topic, data mining, can be applied on student’s data left on e-learning portals to guide the instructor and advisors to help students’ being more successful. Recent researches done on this topic showed that e-learning combined with data mining can decrease the gap between itself and traditional learning — referred as semantic web mining in general. --- paper_title: Using Ontologies in Semantic Data Mining with SEGS and g-SEGS paper_content: With the expanding of the SemanticWeb and the availability of numerous ontologies which provide domain background knowledge and semantic descriptors to the data, the amount of semantic data is rapidly growing. The data mining community is faced with a paradigm shift: instead of mining the abundance of empirical data supported by the background knowledge, the new challenge is to mine the abundance of knowledge encoded in domain ontologies, constrained by the heuristics computed from the empirical data collection. We address this challenge by an approach, named semantic data mining, where domain ontologies define the hypothesis search space, and the data is used as means of constraining and guiding the process of hypothesis search and evaluation. The use of prototype semantic data mining systems SEGS and g-SEGS is demonstrated in a simple semantic data mining scenario and in two reallife functional genomics scenarios of mining biological ontologies with the support of experimental microarray data. --- paper_title: Development of semantic decision tree paper_content: The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data. --- paper_title: “Hello world”, web mining for e-learning paper_content: Abstract As the internet and mobile applications are getting an important role in our lives, usage of mobile services also took place in educational field since the internet is widespread, which is usually called by the terms “e-learning” or “distance learning”. A known issue on e-learning is all the content’s being online and less face-to-face communication than traditional learning; this brings the problem of chasing student’s success, and advising and managing student’s way of studying. Hence, a recent hot topic, data mining, can be applied on student’s data left on e-learning portals to guide the instructor and advisors to help students’ being more successful. Recent researches done on this topic showed that e-learning combined with data mining can decrease the gap between itself and traditional learning — referred as semantic web mining in general. --- paper_title: Handbook of Semantic Web Technologies paper_content: After years of mostly theoretical research, Semantic Web Technologies are now reaching out into application areas like bioinformatics, eCommerce, eGovernment, or Social Webs. Applications like genomic ontologies, semantic web services, automated catalogue alignment, ontology matching, or blogs and social networks are constantly increasing, often driven or at least backed up by companies like Google, Amazon, YouTube, Facebook, LinkedIn and others. The need to leverage the potential of combining information in a meaningful way in order to be able to benefit from the Web will create further demand for and interest in Semantic Web research. This movement, based on the growing maturity of related research results, necessitates a reliable reference source from which beginners to the field can draw a first basic knowledge of the main underlying technologies as well as state-of-the-art application areas. This handbook, put together by three leading authorities in the field, and supported by an advisory board of highly reputed researchers, fulfils exactly this need. It is the first dedicated reference work in this field, collecting contributions about both the technical foundations of the Semantic Web as well as their main usage in other scientific fields like life sciences, engineering, business, or education. --- paper_title: Secure and Intelligent Decision Making in Semantic Web Mining paper_content: With the huge amount of information available online the World Wide Web is a fertile area for data mining research. The Web has become a major vehicle in performing research and education related activities for researches and students. Web mining is the use of data mining technologies to automatically interact and discover information from web documents, which can be in structured, unstructured or semistructured form. We present an enterprise framework regarding semantic web mining in distance learning, which can be used to not only improve the quality of web mining results but also enhances the functions and services and the interoperability of long distance educational information systems and standards in the educational field. For on line distance education system we propose an Ontology-based approach to share online data and retrieve all relevant data about students and their courses. Thus semantic web ontology help build better web mining analysis in educational institute and web mining in-turns helps contract basis more powerful ontology in distance learning. Since the majority of the online data considered as private data we need various mechanism for privacy preservation and control over the online presence data. We propose privacy protection in semantic web mining using role back access control. --- paper_title: Development of semantic decision tree paper_content: The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data. --- paper_title: Handbook of Semantic Web Technologies paper_content: After years of mostly theoretical research, Semantic Web Technologies are now reaching out into application areas like bioinformatics, eCommerce, eGovernment, or Social Webs. Applications like genomic ontologies, semantic web services, automated catalogue alignment, ontology matching, or blogs and social networks are constantly increasing, often driven or at least backed up by companies like Google, Amazon, YouTube, Facebook, LinkedIn and others. The need to leverage the potential of combining information in a meaningful way in order to be able to benefit from the Web will create further demand for and interest in Semantic Web research. This movement, based on the growing maturity of related research results, necessitates a reliable reference source from which beginners to the field can draw a first basic knowledge of the main underlying technologies as well as state-of-the-art application areas. This handbook, put together by three leading authorities in the field, and supported by an advisory board of highly reputed researchers, fulfils exactly this need. It is the first dedicated reference work in this field, collecting contributions about both the technical foundations of the Semantic Web as well as their main usage in other scientific fields like life sciences, engineering, business, or education. --- paper_title: Development of semantic decision tree paper_content: The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data. --- paper_title: Finding association rules in semantic web data paper_content: The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach. --- paper_title: Secure and Intelligent Decision Making in Semantic Web Mining paper_content: With the huge amount of information available online the World Wide Web is a fertile area for data mining research. The Web has become a major vehicle in performing research and education related activities for researches and students. Web mining is the use of data mining technologies to automatically interact and discover information from web documents, which can be in structured, unstructured or semistructured form. We present an enterprise framework regarding semantic web mining in distance learning, which can be used to not only improve the quality of web mining results but also enhances the functions and services and the interoperability of long distance educational information systems and standards in the educational field. For on line distance education system we propose an Ontology-based approach to share online data and retrieve all relevant data about students and their courses. Thus semantic web ontology help build better web mining analysis in educational institute and web mining in-turns helps contract basis more powerful ontology in distance learning. Since the majority of the online data considered as private data we need various mechanism for privacy preservation and control over the online presence data. We propose privacy protection in semantic web mining using role back access control. --- paper_title: Research on semantic Web mining paper_content: A semantic-based Web mining is mentioned by many people in order to improve Web service levels and address the existing Web services which is supported by the lack of semantic problem. Semantic-based Web data mining is a combination of the semantic Web and Web mining. Web mining results help to build the semantic Web. The knowledge of Semantic Web makes Web mining easier to achieve, but also can improve the effectiveness of Web mining. This paper firstly introduces the related knowledge of Semantic Web and Web mining, and then discusses the semantic-based Web mining, finally proposes to build a semantic-based Web mining model under the framework of the Agent. --- paper_title: Mining association rules from semantic web data paper_content: The amount of ontologies and semantic annotations available on the Web is constantly increasing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/S and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive just the appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of a query pattern. Initial experiments performed on real world semantic data enjoy promising results and show the usefulness of the approach. --- paper_title: Handbook of Semantic Web Technologies paper_content: After years of mostly theoretical research, Semantic Web Technologies are now reaching out into application areas like bioinformatics, eCommerce, eGovernment, or Social Webs. Applications like genomic ontologies, semantic web services, automated catalogue alignment, ontology matching, or blogs and social networks are constantly increasing, often driven or at least backed up by companies like Google, Amazon, YouTube, Facebook, LinkedIn and others. The need to leverage the potential of combining information in a meaningful way in order to be able to benefit from the Web will create further demand for and interest in Semantic Web research. This movement, based on the growing maturity of related research results, necessitates a reliable reference source from which beginners to the field can draw a first basic knowledge of the main underlying technologies as well as state-of-the-art application areas. This handbook, put together by three leading authorities in the field, and supported by an advisory board of highly reputed researchers, fulfils exactly this need. It is the first dedicated reference work in this field, collecting contributions about both the technical foundations of the Semantic Web as well as their main usage in other scientific fields like life sciences, engineering, business, or education. --- paper_title: Secure and Intelligent Decision Making in Semantic Web Mining paper_content: With the huge amount of information available online the World Wide Web is a fertile area for data mining research. The Web has become a major vehicle in performing research and education related activities for researches and students. Web mining is the use of data mining technologies to automatically interact and discover information from web documents, which can be in structured, unstructured or semistructured form. We present an enterprise framework regarding semantic web mining in distance learning, which can be used to not only improve the quality of web mining results but also enhances the functions and services and the interoperability of long distance educational information systems and standards in the educational field. For on line distance education system we propose an Ontology-based approach to share online data and retrieve all relevant data about students and their courses. Thus semantic web ontology help build better web mining analysis in educational institute and web mining in-turns helps contract basis more powerful ontology in distance learning. Since the majority of the online data considered as private data we need various mechanism for privacy preservation and control over the online presence data. We propose privacy protection in semantic web mining using role back access control. --- paper_title: Finding association rules in semantic web data paper_content: The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach. --- paper_title: “Hello world”, web mining for e-learning paper_content: Abstract As the internet and mobile applications are getting an important role in our lives, usage of mobile services also took place in educational field since the internet is widespread, which is usually called by the terms “e-learning” or “distance learning”. A known issue on e-learning is all the content’s being online and less face-to-face communication than traditional learning; this brings the problem of chasing student’s success, and advising and managing student’s way of studying. Hence, a recent hot topic, data mining, can be applied on student’s data left on e-learning portals to guide the instructor and advisors to help students’ being more successful. Recent researches done on this topic showed that e-learning combined with data mining can decrease the gap between itself and traditional learning — referred as semantic web mining in general. --- paper_title: Research on semantic Web mining paper_content: A semantic-based Web mining is mentioned by many people in order to improve Web service levels and address the existing Web services which is supported by the lack of semantic problem. Semantic-based Web data mining is a combination of the semantic Web and Web mining. Web mining results help to build the semantic Web. The knowledge of Semantic Web makes Web mining easier to achieve, but also can improve the effectiveness of Web mining. This paper firstly introduces the related knowledge of Semantic Web and Web mining, and then discusses the semantic-based Web mining, finally proposes to build a semantic-based Web mining model under the framework of the Agent. --- paper_title: Mining association rules from semantic web data paper_content: The amount of ontologies and semantic annotations available on the Web is constantly increasing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/S and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive just the appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of a query pattern. Initial experiments performed on real world semantic data enjoy promising results and show the usefulness of the approach. --- paper_title: Development of semantic decision tree paper_content: The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data. --- paper_title: Finding association rules in semantic web data paper_content: The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach. --- paper_title: “Hello world”, web mining for e-learning paper_content: Abstract As the internet and mobile applications are getting an important role in our lives, usage of mobile services also took place in educational field since the internet is widespread, which is usually called by the terms “e-learning” or “distance learning”. A known issue on e-learning is all the content’s being online and less face-to-face communication than traditional learning; this brings the problem of chasing student’s success, and advising and managing student’s way of studying. Hence, a recent hot topic, data mining, can be applied on student’s data left on e-learning portals to guide the instructor and advisors to help students’ being more successful. Recent researches done on this topic showed that e-learning combined with data mining can decrease the gap between itself and traditional learning — referred as semantic web mining in general. --- paper_title: Research on semantic Web mining paper_content: A semantic-based Web mining is mentioned by many people in order to improve Web service levels and address the existing Web services which is supported by the lack of semantic problem. Semantic-based Web data mining is a combination of the semantic Web and Web mining. Web mining results help to build the semantic Web. The knowledge of Semantic Web makes Web mining easier to achieve, but also can improve the effectiveness of Web mining. This paper firstly introduces the related knowledge of Semantic Web and Web mining, and then discusses the semantic-based Web mining, finally proposes to build a semantic-based Web mining model under the framework of the Agent. --- paper_title: Using data mining techniques for exploring learning object repositories paper_content: Purpose – This paper aims to show the results obtained from the data mining techniques application to learning objects (LO) metadata.Design/methodology/approach – A general review of the literature was carried out. The authors gathered and pre‐processed the data, and then analyzed the results of data mining techniques applied upon the LO metadata.Findings – It is possible to extract new knowledge based on learning objects stored in repositories. For example it is possible to identify distinctive features and group learning objects according to them. Semantic relationships can also be found among the attributes that describe learning objects.Research limitations/implications – In the first section, four test repositories are included for case study. In the second section, the analysis is focused on the most complete repository from the pedagogical point of view.Originality/value – Many publications report results of analysis on repositories mainly focused on the number, evolution and growth of the learning... --- paper_title: Mining association rules from semantic web data paper_content: The amount of ontologies and semantic annotations available on the Web is constantly increasing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/S and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive just the appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of a query pattern. Initial experiments performed on real world semantic data enjoy promising results and show the usefulness of the approach. --- paper_title: Context and target configurations for mining RDF data paper_content: Association rule mining has been widely studied in the context of basket analysis and sale recommendations [1]. In fact, the concept can be applied to any domain with many items or events in which interesting relationships can be inferred from co-occurrence of those items or events in existing subsets (transactions). The increasing amount of Linked Open Data (LOD) in the World Wide Web raises new opportunities and challenges for the data mining community [5]. LOD is often represented in the Resource Description Framework (RDF) data model. In RDF, data is represented by a triple structure consisting of subject, predicate, and object (SPO). Each triple represents a statement/fact. We propose an approach that applies association rule mining at statement level by introducing the concept of mining configurations. --- paper_title: Extracting significant Website Key Objects: A Semantic Web mining approach paper_content: Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users. --- paper_title: Development of semantic decision tree paper_content: The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data. --- paper_title: Finding association rules in semantic web data paper_content: The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach. --- paper_title: Context and target configurations for mining RDF data paper_content: Association rule mining has been widely studied in the context of basket analysis and sale recommendations [1]. In fact, the concept can be applied to any domain with many items or events in which interesting relationships can be inferred from co-occurrence of those items or events in existing subsets (transactions). The increasing amount of Linked Open Data (LOD) in the World Wide Web raises new opportunities and challenges for the data mining community [5]. LOD is often represented in the Resource Description Framework (RDF) data model. In RDF, data is represented by a triple structure consisting of subject, predicate, and object (SPO). Each triple represents a statement/fact. We propose an approach that applies association rule mining at statement level by introducing the concept of mining configurations. --- paper_title: Secure and Intelligent Decision Making in Semantic Web Mining paper_content: With the huge amount of information available online the World Wide Web is a fertile area for data mining research. The Web has become a major vehicle in performing research and education related activities for researches and students. Web mining is the use of data mining technologies to automatically interact and discover information from web documents, which can be in structured, unstructured or semistructured form. We present an enterprise framework regarding semantic web mining in distance learning, which can be used to not only improve the quality of web mining results but also enhances the functions and services and the interoperability of long distance educational information systems and standards in the educational field. For on line distance education system we propose an Ontology-based approach to share online data and retrieve all relevant data about students and their courses. Thus semantic web ontology help build better web mining analysis in educational institute and web mining in-turns helps contract basis more powerful ontology in distance learning. Since the majority of the online data considered as private data we need various mechanism for privacy preservation and control over the online presence data. We propose privacy protection in semantic web mining using role back access control. --- paper_title: Using Ontologies in Semantic Data Mining with SEGS and g-SEGS paper_content: With the expanding of the SemanticWeb and the availability of numerous ontologies which provide domain background knowledge and semantic descriptors to the data, the amount of semantic data is rapidly growing. The data mining community is faced with a paradigm shift: instead of mining the abundance of empirical data supported by the background knowledge, the new challenge is to mine the abundance of knowledge encoded in domain ontologies, constrained by the heuristics computed from the empirical data collection. We address this challenge by an approach, named semantic data mining, where domain ontologies define the hypothesis search space, and the data is used as means of constraining and guiding the process of hypothesis search and evaluation. The use of prototype semantic data mining systems SEGS and g-SEGS is demonstrated in a simple semantic data mining scenario and in two reallife functional genomics scenarios of mining biological ontologies with the support of experimental microarray data. --- paper_title: “Hello world”, web mining for e-learning paper_content: Abstract As the internet and mobile applications are getting an important role in our lives, usage of mobile services also took place in educational field since the internet is widespread, which is usually called by the terms “e-learning” or “distance learning”. A known issue on e-learning is all the content’s being online and less face-to-face communication than traditional learning; this brings the problem of chasing student’s success, and advising and managing student’s way of studying. Hence, a recent hot topic, data mining, can be applied on student’s data left on e-learning portals to guide the instructor and advisors to help students’ being more successful. Recent researches done on this topic showed that e-learning combined with data mining can decrease the gap between itself and traditional learning — referred as semantic web mining in general. --- paper_title: Mining association rules from semantic web data paper_content: The amount of ontologies and semantic annotations available on the Web is constantly increasing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/S and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive just the appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of a query pattern. Initial experiments performed on real world semantic data enjoy promising results and show the usefulness of the approach. --- paper_title: Extracting significant Website Key Objects: A Semantic Web mining approach paper_content: Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users. --- paper_title: Development of semantic decision tree paper_content: The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data. ---	Title: A State-of-the-Art Survey on Semantic Web Mining Section 1: Introduction Description 1: Introduce the concept of Semantic Web Mining by integrating Semantic Web and Data Mining, emphasizing the growing importance and potential applications of this field. Section 2: Semantic Web Description 2: Provide an overview of the Semantic Web, including its definition, reasons for development, representation techniques, and examples from the commercial domain. Section 3: Data Mining Description 3: Introduce the concept of data mining, describe popular data mining techniques, and provide a brief overview of Web Mining and its types. Section 4: Semantic Web Mining Description 4: Offer a detailed introduction to Semantic Web Mining, discuss challenges faced, propose solutions, and present case studies supporting its usefulness across various domains. Section 5: Conclusion Description 5: Summarize the survey findings, highlighting the synergy between Web Mining and the Semantic Web, and conclude with insights into the future potential of Semantic Web Mining. Section 6: Acknowledgements Description 6: Acknowledge the support and contributions of individuals and organizations that facilitated the research.
Social Engineering Attacks: A Survey	9	--- paper_title: Social engineering and digital technologies for the security of the social capital' development paper_content: Social capital is transformed due to the digitalized environment built with the ICT expansion. The evolution of the social capital' forming and development reflects the new forms and models of communication, the extended possibilities of choice and of presence into the Web as a unique space of exchange. This article presents the theoretical approach to the analysis of behavior' regulation within the framework of social technologies implemented to the digital space. Social engineering represents an important factor of the violation of information security' rules, the IT-specialists attribute the majority of the crime in the sector to the insiders of private business companies and to the imprudence of individuals. The social capital can play the both positive and negative roles to assure information security issues, the social engineering can be in the contradiction with the tendencies of the social capital development, but the social technologies used are based on the social capital development. --- paper_title: Security Evaluation of Wireless Network Access Points paper_content: Abstract The paper focuses on the real-world usage of IEEE 802.11 wireless network encryption and Wi-Fi Protected Setup (WPS) function. A brief history on the development of encryption methods and WPS is given. Wireless scanning of 802.11 networks in a capital city has been performed, and the results of it have been analysed. To ascertain the knowledge about the security of wireless networks of the average user, an online survey has been conducted. To test the security of encryption methods and WPS function, practical attacks against private test wireless networks have been made. The authors conclude that the safest way to set up 802.11 network with a pre-shared key is to use Wi-Fi Protected Access 2 (WPA2) encryption without support for WPS function. Statistics in Riga shows that networks are often configured otherwise and thus vulnerable to attacks. Survey results prove that respondents are not well informed regarding the security of wireless networks. --- paper_title: Trust and Social Engineering in Human Robot Interaction: Will a Robot Make You Disclose Sensitive Information, Conform to Its Recommendations or Gamble? paper_content: Robots such as information security and overtrust in them are gaining increasing relevance. This research aims at giving an insight into how trust toward robots could be exploited for the purpose of social engineering. Drawing on Mitnick's model, a well-known social engineering framework, an interactive scenario with the humanoid robot iCub was designed to emulate a social engineering attack. At first, iCub attempted to collect the kind of personal information usually gathered by social engineers by asking a series of private questions. Then, the robot tried to develop trust and rapport with participants by offering reliable clues during a treasure hunt game. At the end of the treasure hunt, the robot tried to exploit the gained trust in order to make participants gamble the money they won. The results show that people tend to build rapport with and trust toward the robot, resulting in the disclosure of sensitive information, conformation to its suggestions and gambling. --- paper_title: CANDY: A Social Engineering Attack to Leak Information from Infotainment System paper_content: The introduction of Information and Communications Technologies (ICT) systems into vehicles make them more prone to cyber-security attacks that may impact of vehicles capability and, consequently, on the safety of drivers, passengers. In this paper, we focus on how to exploit security vulnerabilities affecting user-to-vehicle and intra- vehicle communications to hack the infotainment system to retrieve information about both vehicle and driver. Indeed, we designed and developed CANDY, a set of malicious APP injecting in a genuine Android APP, acting as a Trojan-horse on the Android In-Vehicle infotainment system. It opens a back-door that allows an attacker to remotely access to the infotainment system. We use this back-door to hit the privacy of the driver by recording her voice and collect information circulating on the CAN bus about the vehicle. CANDY is distributed by using social engineering techniques. --- paper_title: Social engineering as an attack vector for ransomware paper_content: Constant ransomware attacks achieve to evade multiple security perimeters, because its strategy is based on the exploitation of users-the weakest link in the security chain-getting high levels of effectiveness. In this paper, an in-depth analysis of the anatomy of the ransomware is develop, and how they combine technology with manipulation, through social engineering, to meet their goals and compromise thousands of computers. Finally, simulations of the phases that it follows are carried out, leaving the necessary guidelines to evaluate the security of the information, and thus, generate policies that minimize the risks of loss of information. --- paper_title: Social Engineering Toolkit — A systematic approach to social engineering paper_content: Social engineering techniques, exploiting humans as information systems' security weakest link, are mostly the initial attack vectors within larger intrusions and information system compromises. In order to practically evaluate the risks of information leakage trough the target organizations' employees, when performing a penetration test, an ethical hacker must consider social engineering as a very important aspect of the performed test. Social Engineering Toolkit (SET) is an integrated set of tools designed specifically to perform advanced attacks against the human element, and is the most advanced, if not the only toolkit of such kind that is publicly available as open source software. Incorporating many social engineering attack vectors, it heavily depends on Metasploit, an integrated penetration testing framework. This paper gives a brief introduction to the Social Engineering Toolkit software architecture, and provides an overview of supported attack vectors. --- paper_title: Social engineering and digital technologies for the security of the social capital' development paper_content: Social capital is transformed due to the digitalized environment built with the ICT expansion. The evolution of the social capital' forming and development reflects the new forms and models of communication, the extended possibilities of choice and of presence into the Web as a unique space of exchange. This article presents the theoretical approach to the analysis of behavior' regulation within the framework of social technologies implemented to the digital space. Social engineering represents an important factor of the violation of information security' rules, the IT-specialists attribute the majority of the crime in the sector to the insiders of private business companies and to the imprudence of individuals. The social capital can play the both positive and negative roles to assure information security issues, the social engineering can be in the contradiction with the tendencies of the social capital development, but the social technologies used are based on the social capital development. --- paper_title: Security Analytics: Big Data Analytics for cybersecurity: A review of trends, techniques and tools paper_content: The rapid growth of the Internet has brought with it an exponential increase in the type and frequency of cyber attacks. Many well-known cybersecurity solutions are in place to counteract these attacks. However, the generation of Big Data over computer networks is rapidly rendering these traditional solutions obsolete. To cater for this problem, corporate research is now focusing on Security Analytics, i.e., the application of Big Data Analytics techniques to cybersecurity. Analytics can assist network managers particularly in the monitoring and surveillance of real-time network streams and real-time detection of both malicious and suspicious (outlying) patterns. Such a behavior is envisioned to encompass and enhance all traditional security techniques. This paper presents a comprehensive survey on the state of the art of Security Analytics, i.e., its description, technology, trends, and tools. It hence aims to convince the reader of the imminent application of analytics as an unparalleled cybersecurity solution in the near future. --- paper_title: Security Evaluation of Wireless Network Access Points paper_content: Abstract The paper focuses on the real-world usage of IEEE 802.11 wireless network encryption and Wi-Fi Protected Setup (WPS) function. A brief history on the development of encryption methods and WPS is given. Wireless scanning of 802.11 networks in a capital city has been performed, and the results of it have been analysed. To ascertain the knowledge about the security of wireless networks of the average user, an online survey has been conducted. To test the security of encryption methods and WPS function, practical attacks against private test wireless networks have been made. The authors conclude that the safest way to set up 802.11 network with a pre-shared key is to use Wi-Fi Protected Access 2 (WPA2) encryption without support for WPS function. Statistics in Riga shows that networks are often configured otherwise and thus vulnerable to attacks. Survey results prove that respondents are not well informed regarding the security of wireless networks. --- paper_title: On tackling social engineering web phishing attacks utilizing software defined networks (SDN) approach paper_content: Web phishing attacks are one of the challenging security threats. Phishing depends on humans’ behavior but not protocols and devices vulnerabilities. In this work, software defined networking (SDN) will be tailored to tackle phishing attacks. In SDN, network devices forward received packets to a central point ‘controller’ that makes decision on behalf of them. This approach allows more control and management over network devices and protocol. In this work, we propose a neural network based phishing prevention algorithm (PPA) that is implemented utilizing Ryu, an open source, SDN controller. The PPA algorithm has been tested in a home network that is constructed with HP2920-24G switch. Moreover, a phished version of Facebook, Yahoo and Hotmail login pages have been written and hosted on three different free hosting domains. PPA has detected all of the phished versions and allowed the access to real version of these services. --- paper_title: Trust and Social Engineering in Human Robot Interaction: Will a Robot Make You Disclose Sensitive Information, Conform to Its Recommendations or Gamble? paper_content: Robots such as information security and overtrust in them are gaining increasing relevance. This research aims at giving an insight into how trust toward robots could be exploited for the purpose of social engineering. Drawing on Mitnick's model, a well-known social engineering framework, an interactive scenario with the humanoid robot iCub was designed to emulate a social engineering attack. At first, iCub attempted to collect the kind of personal information usually gathered by social engineers by asking a series of private questions. Then, the robot tried to develop trust and rapport with participants by offering reliable clues during a treasure hunt game. At the end of the treasure hunt, the robot tried to exploit the gained trust in order to make participants gamble the money they won. The results show that people tend to build rapport with and trust toward the robot, resulting in the disclosure of sensitive information, conformation to its suggestions and gambling. --- paper_title: A Serious Game for Eliciting Social Engineering Security Requirements paper_content: Social engineering is the acquisition of informationabout computer systems by methods that deeply include non-technical means. While technical security of most critical systemsis high, the systems remain vulnerable to attacks from socialengineers. Social engineering is a technique that: (i) does notrequire any (advanced) technical tools, (ii) can be used by anyone,(iii) is cheap. Traditional security requirements elicitation approaches oftenfocus on vulnerabilities in network or software systems. Fewapproaches even consider the exploitation of humans via socialengineering and none of them elicits personal behaviours of indi-vidual employees. While the amount of social engineering attacksand the damage they cause rise every year, the security awarenessof these attacks and their consideration during requirementselicitation remains negligible. We propose to use a card game to elicit these requirements, which all employees of a company can play to understand thethreat and document security requirements. The game considersthe individual context of a company and presents underlyingprinciples of human behaviour that social engineers exploit, aswell as concrete attack patterns. We evaluated our approachwith several groups of researchers, IT administrators, andprofessionals from industry. --- paper_title: Social Engineering and Insider Threats paper_content: This paper describes our research on the insider threats of Social engineering. Social engineering is a method using interaction between humans to get the access of a system in an illegal way. Due to staff's lack of confidentiality, the confidentiality of records is compromised, data is stolen or financial damage is done. This is insider threat. Social engineering and insider threat are two of the most relevant subjects in cyber security today. This research summarizes and seeks solution for the drawback of Social engineering through analyzing the Insider Threat cases. The first stage is to introduce the importance of using social engineering to reduce internet crime by analyzing the past loss created by insider threats. The second test illustrates insider threats' hazards to network security are ongoing. The third part covers the situation of insider threats with the emphasis on the security side. The topic of security aspect is extended to the rest of internal control of system, data exchange, and management of employees and their communication content. Actually, by the time of this abstract, insider threats are still not being taken as seriously as it should be. Many companies and organizations have given little thought to the insider threat but have concentrated on keeping attackers outside the network. This research will directly focus on the insider threats of organizations and the ways hackers use social engineering with the latest analysis of technology involved and examples that are close to common cybercrime. We aim to reveal the importance of reducing insider threats in organizations. The further research will be focused on a group consisted of managers and engineers within a company and the communication means of staff to the outside world. The analysis of the related crime cases will help prevent similar tragedy and seek possible approaches. --- paper_title: Social engineering attack examples, templates and scenarios paper_content: The field of information security is a fast-growing discipline. Even though the effectiveness of security measures to protect sensitive information is increasing, people remain susceptible to manipulation and thus the human element remains a weak link. A social engineering attack targets this weakness by using various manipulation techniques to elicit sensitive information. The field of social engineering is still in its early stages with regard to formal definitions, attack frameworks and templates of attacks. This paper proposes detailed social engineering attack templates that are derived from real-world social engineering examples. Current documented examples of social engineering attacks do not include all the attack steps and phases. The proposed social engineering attack templates attempt to alleviate the problem of limited documented literature on social engineering attacks by mapping the real-world examples to the social engineering attack framework. Mapping several similar real-world examples to the social engineering attack framework allows one to establish a detailed flow of the attack whilst abstracting subjects and objects. This mapping is then utilised to propose the generalised social engineering attack templates that are representative of real-world examples, whilst still being general enough to encompass several different real-world examples. The proposed social engineering attack templates cover all three types of communication, namely bidirectional communication, unidirectional communication and indirect communication. In order to perform comparative studies of different social engineering models, processes and frameworks, it is necessary to have a formalised set of social engineering attack scenarios that are fully detailed in every phase and step of the process. The social engineering attack templates are converted to social engineering attack scenarios by populating the template with both subjects and objects from real-world examples whilst still maintaining the detailed flow of the attack as provided in the template. Furthermore, this paper illustrates how the social engineering attack scenarios are applied to verify a social engineering attack detection model. These templates and scenarios can be used by other researchers to either expand on, use for comparative measures, create additional examples or evaluate models for completeness. Additionally, the proposed social engineering attack templates can also be used to develop social engineering awareness material. --- paper_title: CANDY: A Social Engineering Attack to Leak Information from Infotainment System paper_content: The introduction of Information and Communications Technologies (ICT) systems into vehicles make them more prone to cyber-security attacks that may impact of vehicles capability and, consequently, on the safety of drivers, passengers. In this paper, we focus on how to exploit security vulnerabilities affecting user-to-vehicle and intra- vehicle communications to hack the infotainment system to retrieve information about both vehicle and driver. Indeed, we designed and developed CANDY, a set of malicious APP injecting in a genuine Android APP, acting as a Trojan-horse on the Android In-Vehicle infotainment system. It opens a back-door that allows an attacker to remotely access to the infotainment system. We use this back-door to hit the privacy of the driver by recording her voice and collect information circulating on the CAN bus about the vehicle. CANDY is distributed by using social engineering techniques. --- paper_title: Reverse TCP and Social Engineering Attacks in the Era of Big Data paper_content: TCP is a connection-oriented protocol used for the transport of information across the Internet. The very nature of this powerful medium attracts cyber criminals who are continuously searching for new vulnerabilities within the TCP protocol to exploit for nefarious means. Reverse TCP attacks are a relatively new approach to exploit this connection process. The attacker is able to seize remote access to the victim end user's network. Success in this attack largely depends on skillful social engineering techniques to target specific end users in order to open the connection. In the era of Big Data, social engineering are expected to be more feasible. This paper examines various methods that adversaries may use to implement their attacks. Our work implements a reverse TCP attack via a virtualized environment, detailing the process used to gain unauthorized access to victim's machine. This paper also discusses the key threats that the reverse TCP attack may pose to end users and will provide a testbed to determine how effective computer systems are able to defend against this attack. --- paper_title: Social engineering as an attack vector for ransomware paper_content: Constant ransomware attacks achieve to evade multiple security perimeters, because its strategy is based on the exploitation of users-the weakest link in the security chain-getting high levels of effectiveness. In this paper, an in-depth analysis of the anatomy of the ransomware is develop, and how they combine technology with manipulation, through social engineering, to meet their goals and compromise thousands of computers. Finally, simulations of the phases that it follows are carried out, leaving the necessary guidelines to evaluate the security of the information, and thus, generate policies that minimize the risks of loss of information. --- paper_title: Social Engineering Toolkit — A systematic approach to social engineering paper_content: Social engineering techniques, exploiting humans as information systems' security weakest link, are mostly the initial attack vectors within larger intrusions and information system compromises. In order to practically evaluate the risks of information leakage trough the target organizations' employees, when performing a penetration test, an ethical hacker must consider social engineering as a very important aspect of the performed test. Social Engineering Toolkit (SET) is an integrated set of tools designed specifically to perform advanced attacks against the human element, and is the most advanced, if not the only toolkit of such kind that is publicly available as open source software. Incorporating many social engineering attack vectors, it heavily depends on Metasploit, an integrated penetration testing framework. This paper gives a brief introduction to the Social Engineering Toolkit software architecture, and provides an overview of supported attack vectors. --- paper_title: Social engineering and digital technologies for the security of the social capital' development paper_content: Social capital is transformed due to the digitalized environment built with the ICT expansion. The evolution of the social capital' forming and development reflects the new forms and models of communication, the extended possibilities of choice and of presence into the Web as a unique space of exchange. This article presents the theoretical approach to the analysis of behavior' regulation within the framework of social technologies implemented to the digital space. Social engineering represents an important factor of the violation of information security' rules, the IT-specialists attribute the majority of the crime in the sector to the insiders of private business companies and to the imprudence of individuals. The social capital can play the both positive and negative roles to assure information security issues, the social engineering can be in the contradiction with the tendencies of the social capital development, but the social technologies used are based on the social capital development. --- paper_title: Security Analytics: Big Data Analytics for cybersecurity: A review of trends, techniques and tools paper_content: The rapid growth of the Internet has brought with it an exponential increase in the type and frequency of cyber attacks. Many well-known cybersecurity solutions are in place to counteract these attacks. However, the generation of Big Data over computer networks is rapidly rendering these traditional solutions obsolete. To cater for this problem, corporate research is now focusing on Security Analytics, i.e., the application of Big Data Analytics techniques to cybersecurity. Analytics can assist network managers particularly in the monitoring and surveillance of real-time network streams and real-time detection of both malicious and suspicious (outlying) patterns. Such a behavior is envisioned to encompass and enhance all traditional security techniques. This paper presents a comprehensive survey on the state of the art of Security Analytics, i.e., its description, technology, trends, and tools. It hence aims to convince the reader of the imminent application of analytics as an unparalleled cybersecurity solution in the near future. --- paper_title: Security Evaluation of Wireless Network Access Points paper_content: Abstract The paper focuses on the real-world usage of IEEE 802.11 wireless network encryption and Wi-Fi Protected Setup (WPS) function. A brief history on the development of encryption methods and WPS is given. Wireless scanning of 802.11 networks in a capital city has been performed, and the results of it have been analysed. To ascertain the knowledge about the security of wireless networks of the average user, an online survey has been conducted. To test the security of encryption methods and WPS function, practical attacks against private test wireless networks have been made. The authors conclude that the safest way to set up 802.11 network with a pre-shared key is to use Wi-Fi Protected Access 2 (WPA2) encryption without support for WPS function. Statistics in Riga shows that networks are often configured otherwise and thus vulnerable to attacks. Survey results prove that respondents are not well informed regarding the security of wireless networks. --- paper_title: On tackling social engineering web phishing attacks utilizing software defined networks (SDN) approach paper_content: Web phishing attacks are one of the challenging security threats. Phishing depends on humans’ behavior but not protocols and devices vulnerabilities. In this work, software defined networking (SDN) will be tailored to tackle phishing attacks. In SDN, network devices forward received packets to a central point ‘controller’ that makes decision on behalf of them. This approach allows more control and management over network devices and protocol. In this work, we propose a neural network based phishing prevention algorithm (PPA) that is implemented utilizing Ryu, an open source, SDN controller. The PPA algorithm has been tested in a home network that is constructed with HP2920-24G switch. Moreover, a phished version of Facebook, Yahoo and Hotmail login pages have been written and hosted on three different free hosting domains. PPA has detected all of the phished versions and allowed the access to real version of these services. --- paper_title: Trust and Social Engineering in Human Robot Interaction: Will a Robot Make You Disclose Sensitive Information, Conform to Its Recommendations or Gamble? paper_content: Robots such as information security and overtrust in them are gaining increasing relevance. This research aims at giving an insight into how trust toward robots could be exploited for the purpose of social engineering. Drawing on Mitnick's model, a well-known social engineering framework, an interactive scenario with the humanoid robot iCub was designed to emulate a social engineering attack. At first, iCub attempted to collect the kind of personal information usually gathered by social engineers by asking a series of private questions. Then, the robot tried to develop trust and rapport with participants by offering reliable clues during a treasure hunt game. At the end of the treasure hunt, the robot tried to exploit the gained trust in order to make participants gamble the money they won. The results show that people tend to build rapport with and trust toward the robot, resulting in the disclosure of sensitive information, conformation to its suggestions and gambling. --- paper_title: A Serious Game for Eliciting Social Engineering Security Requirements paper_content: Social engineering is the acquisition of informationabout computer systems by methods that deeply include non-technical means. While technical security of most critical systemsis high, the systems remain vulnerable to attacks from socialengineers. Social engineering is a technique that: (i) does notrequire any (advanced) technical tools, (ii) can be used by anyone,(iii) is cheap. Traditional security requirements elicitation approaches oftenfocus on vulnerabilities in network or software systems. Fewapproaches even consider the exploitation of humans via socialengineering and none of them elicits personal behaviours of indi-vidual employees. While the amount of social engineering attacksand the damage they cause rise every year, the security awarenessof these attacks and their consideration during requirementselicitation remains negligible. We propose to use a card game to elicit these requirements, which all employees of a company can play to understand thethreat and document security requirements. The game considersthe individual context of a company and presents underlyingprinciples of human behaviour that social engineers exploit, aswell as concrete attack patterns. We evaluated our approachwith several groups of researchers, IT administrators, andprofessionals from industry. --- paper_title: Social Engineering and Insider Threats paper_content: This paper describes our research on the insider threats of Social engineering. Social engineering is a method using interaction between humans to get the access of a system in an illegal way. Due to staff's lack of confidentiality, the confidentiality of records is compromised, data is stolen or financial damage is done. This is insider threat. Social engineering and insider threat are two of the most relevant subjects in cyber security today. This research summarizes and seeks solution for the drawback of Social engineering through analyzing the Insider Threat cases. The first stage is to introduce the importance of using social engineering to reduce internet crime by analyzing the past loss created by insider threats. The second test illustrates insider threats' hazards to network security are ongoing. The third part covers the situation of insider threats with the emphasis on the security side. The topic of security aspect is extended to the rest of internal control of system, data exchange, and management of employees and their communication content. Actually, by the time of this abstract, insider threats are still not being taken as seriously as it should be. Many companies and organizations have given little thought to the insider threat but have concentrated on keeping attackers outside the network. This research will directly focus on the insider threats of organizations and the ways hackers use social engineering with the latest analysis of technology involved and examples that are close to common cybercrime. We aim to reveal the importance of reducing insider threats in organizations. The further research will be focused on a group consisted of managers and engineers within a company and the communication means of staff to the outside world. The analysis of the related crime cases will help prevent similar tragedy and seek possible approaches. --- paper_title: Social engineering attack examples, templates and scenarios paper_content: The field of information security is a fast-growing discipline. Even though the effectiveness of security measures to protect sensitive information is increasing, people remain susceptible to manipulation and thus the human element remains a weak link. A social engineering attack targets this weakness by using various manipulation techniques to elicit sensitive information. The field of social engineering is still in its early stages with regard to formal definitions, attack frameworks and templates of attacks. This paper proposes detailed social engineering attack templates that are derived from real-world social engineering examples. Current documented examples of social engineering attacks do not include all the attack steps and phases. The proposed social engineering attack templates attempt to alleviate the problem of limited documented literature on social engineering attacks by mapping the real-world examples to the social engineering attack framework. Mapping several similar real-world examples to the social engineering attack framework allows one to establish a detailed flow of the attack whilst abstracting subjects and objects. This mapping is then utilised to propose the generalised social engineering attack templates that are representative of real-world examples, whilst still being general enough to encompass several different real-world examples. The proposed social engineering attack templates cover all three types of communication, namely bidirectional communication, unidirectional communication and indirect communication. In order to perform comparative studies of different social engineering models, processes and frameworks, it is necessary to have a formalised set of social engineering attack scenarios that are fully detailed in every phase and step of the process. The social engineering attack templates are converted to social engineering attack scenarios by populating the template with both subjects and objects from real-world examples whilst still maintaining the detailed flow of the attack as provided in the template. Furthermore, this paper illustrates how the social engineering attack scenarios are applied to verify a social engineering attack detection model. These templates and scenarios can be used by other researchers to either expand on, use for comparative measures, create additional examples or evaluate models for completeness. Additionally, the proposed social engineering attack templates can also be used to develop social engineering awareness material. --- paper_title: CANDY: A Social Engineering Attack to Leak Information from Infotainment System paper_content: The introduction of Information and Communications Technologies (ICT) systems into vehicles make them more prone to cyber-security attacks that may impact of vehicles capability and, consequently, on the safety of drivers, passengers. In this paper, we focus on how to exploit security vulnerabilities affecting user-to-vehicle and intra- vehicle communications to hack the infotainment system to retrieve information about both vehicle and driver. Indeed, we designed and developed CANDY, a set of malicious APP injecting in a genuine Android APP, acting as a Trojan-horse on the Android In-Vehicle infotainment system. It opens a back-door that allows an attacker to remotely access to the infotainment system. We use this back-door to hit the privacy of the driver by recording her voice and collect information circulating on the CAN bus about the vehicle. CANDY is distributed by using social engineering techniques. --- paper_title: Reverse TCP and Social Engineering Attacks in the Era of Big Data paper_content: TCP is a connection-oriented protocol used for the transport of information across the Internet. The very nature of this powerful medium attracts cyber criminals who are continuously searching for new vulnerabilities within the TCP protocol to exploit for nefarious means. Reverse TCP attacks are a relatively new approach to exploit this connection process. The attacker is able to seize remote access to the victim end user's network. Success in this attack largely depends on skillful social engineering techniques to target specific end users in order to open the connection. In the era of Big Data, social engineering are expected to be more feasible. This paper examines various methods that adversaries may use to implement their attacks. Our work implements a reverse TCP attack via a virtualized environment, detailing the process used to gain unauthorized access to victim's machine. This paper also discusses the key threats that the reverse TCP attack may pose to end users and will provide a testbed to determine how effective computer systems are able to defend against this attack. --- paper_title: Security and privacy challenges in smart cities paper_content: Abstract The construction of smart cities will bring about a higher quality of life to the masses through digital interconnectivity, leading to increased efficiency and accessibility in cities. Smart cities must ensure individual privacy and security in order to ensure that its citizens will participate. If citizens are reluctant to participate, the core advantages of a smart city will dissolve. This article will identify and offer possible solutions to five smart city challenges, in hopes of anticipating destabilizing and costly disruptions. The challenges include privacy preservation with high dimensional data, securing a network with a large attack surface, establishing trustworthy data sharing practices, properly utilizing artificial intelligence, and mitigating failures cascading through the smart network. Finally, further research directions are provided to encourage further exploration of smart city challenges before their construction. --- paper_title: A literature survey on social engineering attacks: Phishing attack paper_content: Phishing is a network type attack where the attacker creates the fake of an existing webpage to fool an online user into elicit personal Information. The prime objective of this review is to do literature survey on social engineering attack: Phishing attack and techniques to detect attack. Phishing is the combination of social engineering and technical methods to convince the user to reveal their personal data. The paper discusses about the Phishing social engineering attack theoretically and their issues in the life of human Beings. Phishing is typically carried out by Email spoofing or instant messaging. It targets the user who has no knowledge about social engineering attacks, and internet security, like persons who do not take care of privacy of their accounts details such as Facebook, Gmail, credit banks accounts and other financial accounts. The paper discusses various types of Phishing attacks such as Tab-napping, spoofing emails, Trojan horse, hacking and how to prevent them. At the same time this paper also provides different techniques to detect these attacks so that they can be easily dealt with in case one of them occurs. The paper gives a thorough analysis of various Phishing attacks along with their advantages and disadvantages. --- paper_title: A FORMAL CLASSIFICATION OF INTERNET BANKING ATTACKS AND VULNERABILITIES paper_content: A formal classification of attacks and vulnerabilities that affect current internet banking systems is presented along with two attacks which demonstrate the insecurity of such systems. Based ona thorough analysis of current security models, we propose a guidelines for designing secure internet banking systems which are not affected by the presented attacks and vulnerabilities . --- paper_title: A framework to mitigate social engineering through social media within the enterprise paper_content: The influx of employees using social media throughout the working environment has presented information security professionals with an extensive array of challenges facing people, process and technology. Social engineering through social media is a formidable enterprise concern due to the proclivity of social engineers targeting employees through these mediums to attack information assets residing within the organization. This research was motivated by a perceived gap in academic literature of organizational controls and guidance for employees concerning social engineering threats through social media adoption. The purpose of this paper is to propose a social media policy framework focusing on the practical reduction of social engineering risk through ICT security policy control. The framework's development involved an analysis of the primary social engineering through social media challenges alongside current information security standard recommendations. The resulting proposal for the SESM (Social Engineering through Social Media) framework addresses these challenges by conceptualizing enterprise implementation through the interconnection of relevant existing IT security standards in juxtaposition with our own social media policy development framework. --- paper_title: Email trouble: Secrets of spoofing, the dangers of social engineering, and how we can help paper_content: Email spoofing is a method of scamming individuals by impersonating a trusted correspondent via email. Incidences of successful Business Email Compromise (BEC) implemented by email spoofing are rising astronomically. Existing security systems are not widely implemented and cannot provide perfect protection against a technological threat that relies on social engineering for success. When existing security systems are implemented the settings are generally not restrictive enough to catch the more sophisticated email attacks. Businesses are not comfortable with legitimate emails being lost due to security false positives. Our idea for a solution would add a layer to existing precautions that would permit looser server-side security settings but would warn the user when discrepancies occur in the header source code that could result from a spoofed email. We suggest a client-side sentinel to vet email header source code and alert the user to potential problems. This software could log alerts, notify company officials, remind users of company policies to be followed in the event of suspicious email, and could increase user accountability by logging incidents. Users could have the option of white-listing frequently flagged trusted correspondents which would decrease the annoyance of false positives. --- paper_title: Social Engineering Attack Strategies and Defence Approaches paper_content: This paper examines the role and value of information security awareness efforts in defending against social engineering attacks. It categories the different social engineering threats and tactics used in targeting employees and the approaches to defend against such attacks. While we review these techniques, we attempt to develop a thorough understanding of human security threats, with a suitable balance between structured improvements to defend human weaknesses, and efficiently focused security training and awareness building. Finally, the paper shows that a multi-layered shield can mitigate various security risks and minimize the damage to systems and data. --- paper_title: Security and privacy challenges in smart cities paper_content: Abstract The construction of smart cities will bring about a higher quality of life to the masses through digital interconnectivity, leading to increased efficiency and accessibility in cities. Smart cities must ensure individual privacy and security in order to ensure that its citizens will participate. If citizens are reluctant to participate, the core advantages of a smart city will dissolve. This article will identify and offer possible solutions to five smart city challenges, in hopes of anticipating destabilizing and costly disruptions. The challenges include privacy preservation with high dimensional data, securing a network with a large attack surface, establishing trustworthy data sharing practices, properly utilizing artificial intelligence, and mitigating failures cascading through the smart network. Finally, further research directions are provided to encourage further exploration of smart city challenges before their construction. --- paper_title: A framework to mitigate social engineering through social media within the enterprise paper_content: The influx of employees using social media throughout the working environment has presented information security professionals with an extensive array of challenges facing people, process and technology. Social engineering through social media is a formidable enterprise concern due to the proclivity of social engineers targeting employees through these mediums to attack information assets residing within the organization. This research was motivated by a perceived gap in academic literature of organizational controls and guidance for employees concerning social engineering threats through social media adoption. The purpose of this paper is to propose a social media policy framework focusing on the practical reduction of social engineering risk through ICT security policy control. The framework's development involved an analysis of the primary social engineering through social media challenges alongside current information security standard recommendations. The resulting proposal for the SESM (Social Engineering through Social Media) framework addresses these challenges by conceptualizing enterprise implementation through the interconnection of relevant existing IT security standards in juxtaposition with our own social media policy development framework. --- paper_title: Email trouble: Secrets of spoofing, the dangers of social engineering, and how we can help paper_content: Email spoofing is a method of scamming individuals by impersonating a trusted correspondent via email. Incidences of successful Business Email Compromise (BEC) implemented by email spoofing are rising astronomically. Existing security systems are not widely implemented and cannot provide perfect protection against a technological threat that relies on social engineering for success. When existing security systems are implemented the settings are generally not restrictive enough to catch the more sophisticated email attacks. Businesses are not comfortable with legitimate emails being lost due to security false positives. Our idea for a solution would add a layer to existing precautions that would permit looser server-side security settings but would warn the user when discrepancies occur in the header source code that could result from a spoofed email. We suggest a client-side sentinel to vet email header source code and alert the user to potential problems. This software could log alerts, notify company officials, remind users of company policies to be followed in the event of suspicious email, and could increase user accountability by logging incidents. Users could have the option of white-listing frequently flagged trusted correspondents which would decrease the annoyance of false positives. --- paper_title: CANDY: A Social Engineering Attack to Leak Information from Infotainment System paper_content: The introduction of Information and Communications Technologies (ICT) systems into vehicles make them more prone to cyber-security attacks that may impact of vehicles capability and, consequently, on the safety of drivers, passengers. In this paper, we focus on how to exploit security vulnerabilities affecting user-to-vehicle and intra- vehicle communications to hack the infotainment system to retrieve information about both vehicle and driver. Indeed, we designed and developed CANDY, a set of malicious APP injecting in a genuine Android APP, acting as a Trojan-horse on the Android In-Vehicle infotainment system. It opens a back-door that allows an attacker to remotely access to the infotainment system. We use this back-door to hit the privacy of the driver by recording her voice and collect information circulating on the CAN bus about the vehicle. CANDY is distributed by using social engineering techniques. --- paper_title: Social Engineering and Insider Threats paper_content: This paper describes our research on the insider threats of Social engineering. Social engineering is a method using interaction between humans to get the access of a system in an illegal way. Due to staff's lack of confidentiality, the confidentiality of records is compromised, data is stolen or financial damage is done. This is insider threat. Social engineering and insider threat are two of the most relevant subjects in cyber security today. This research summarizes and seeks solution for the drawback of Social engineering through analyzing the Insider Threat cases. The first stage is to introduce the importance of using social engineering to reduce internet crime by analyzing the past loss created by insider threats. The second test illustrates insider threats' hazards to network security are ongoing. The third part covers the situation of insider threats with the emphasis on the security side. The topic of security aspect is extended to the rest of internal control of system, data exchange, and management of employees and their communication content. Actually, by the time of this abstract, insider threats are still not being taken as seriously as it should be. Many companies and organizations have given little thought to the insider threat but have concentrated on keeping attackers outside the network. This research will directly focus on the insider threats of organizations and the ways hackers use social engineering with the latest analysis of technology involved and examples that are close to common cybercrime. We aim to reveal the importance of reducing insider threats in organizations. The further research will be focused on a group consisted of managers and engineers within a company and the communication means of staff to the outside world. The analysis of the related crime cases will help prevent similar tragedy and seek possible approaches. --- paper_title: Blockchain-based Mutual Authentication Security Protocol for Distributed RFID Systems paper_content: Since radio frequency identification (RFID) technology has been used in various scenarios such as supply chain, access control system and credit card, tremendous efforts have been made to improve the authentication between tags and readers to prevent potential attacks. Though effective in certain circumstances, these existing methods usually require a server to maintain a database of identity related information for every tag, which makes the system vulnerable to the SQL injection attack and not suitable for distributed environment. To address these problems, we now propose a novel blockchain-based mutual authentication security protocol. In this new scheme, there is no need for the trusted third parties to provide security and privacy for the system. Authentication is executed as an unmodifiable transaction based on blockchain rather than database, which applies to distributed RFID systems with high security demand and relatively low real-time requirement. Analysis shows that our protocol is logically correct and can prevent multiple attacks. --- paper_title: Social engineering as an attack vector for ransomware paper_content: Constant ransomware attacks achieve to evade multiple security perimeters, because its strategy is based on the exploitation of users-the weakest link in the security chain-getting high levels of effectiveness. In this paper, an in-depth analysis of the anatomy of the ransomware is develop, and how they combine technology with manipulation, through social engineering, to meet their goals and compromise thousands of computers. Finally, simulations of the phases that it follows are carried out, leaving the necessary guidelines to evaluate the security of the information, and thus, generate policies that minimize the risks of loss of information. --- paper_title: Dynamic ransomware protection using deterministic random bit generator paper_content: Ransomware has become a very significant cyber threat. The basic idea of ransomware was presented in the form of a cryptovirus in 1995. However, it was considered as merely a conceptual topic since then for over a decade. In 2017, ransomware has become a reality, with several famous cases of ransomware having compromised important computer systems worldwide. For example, the damage caused by CryptoLocker and WannaCry is huge, as well as global. They encrypt victims' files and require user's payment to decrypt them. Because they utilize public key cryptography, the key for recovery cannot be found in the footprint of the ransomware on the victim's system. Therefore, once infected, the system cannot be recovered without paying for restoration. Various methods to deal this threat have been developed by antivirus researchers and experts in network security. However, it is believed that cryptographic defense is infeasible because recovering a victim's files is computationally as difficult as breaking a public key cryptosystem. Quite recently, various approaches to protect the crypto-API of an OS from malicious codes have been proposed. Most ransomware generate encryption keys using the random number generation service provided by the victim's OS. Thus, if a user can control all random numbers generated by the system, then he/she can recover the random numbers used by the ransomware for the encryption key. In this paper, we propose a dynamic ransomware protection method that replaces the random number generator of the OS with a user-defined generator. As the proposed method causes the virus program to generate keys based on the output from the user-defined generator, it is possible to recover an infected file system by reproducing the keys the attacker used to perform the encryption. --- paper_title: Blockchain-based Mutual Authentication Security Protocol for Distributed RFID Systems paper_content: Since radio frequency identification (RFID) technology has been used in various scenarios such as supply chain, access control system and credit card, tremendous efforts have been made to improve the authentication between tags and readers to prevent potential attacks. Though effective in certain circumstances, these existing methods usually require a server to maintain a database of identity related information for every tag, which makes the system vulnerable to the SQL injection attack and not suitable for distributed environment. To address these problems, we now propose a novel blockchain-based mutual authentication security protocol. In this new scheme, there is no need for the trusted third parties to provide security and privacy for the system. Authentication is executed as an unmodifiable transaction based on blockchain rather than database, which applies to distributed RFID systems with high security demand and relatively low real-time requirement. Analysis shows that our protocol is logically correct and can prevent multiple attacks. --- paper_title: Cutting the Gordian Knot: A Look Under the Hood of Ransomware Attacks paper_content: In this paper, we present the results of a long-term study of ransomware attacks that have been observed in the wild between 2006 and 2014. We also provide a holistic view on how ransomware attacks have evolved during this period by analyzing 1,359 samples that belong to 15 different ransomware families. Our results show that, despite a continuous improvement in the encryption, deletion, and communication techniques in the main ransomware families, the number of families with sophisticated destructive capabilities remains quite small. In fact, our analysis reveals that in a large number of samples, the malware simply locks the victim's computer desktop or attempts to encrypt or delete the victim's files using only superficial techniques.i?źOur analysis also suggests that stopping advanced ransomware attacks is not as complex as it has been previously reported. For example, we show that by monitoring abnormal file system activity, it is possible to design a practical defense system that could stop a large number of ransomware attacks, even those using sophisticated encryption capabilities. A close examination on the file system activities of multiple ransomware samples suggests that by looking at I/O requests and protecting Master File Table MFT in the NTFS file system, it is possible to detect and prevent a significant number of zero-day ransomware attacks. --- paper_title: An Approach To Perceive Tabnabbing Attack paper_content: The growth of Internet has many pros and cons to mankind, which is easily visible in day to day activities. The growth of Internet has also manifested into the other domain of cyber crimes. Phishing, web defacement, money laundering, tax evasion, etc. are some of the examples of cyber crimes that have been reported in literature. It has become vital to make technology reliable, so that it can record and intelligently apprise the user of illegal activity. The objective of this work is twofold. Firstly, in this paper tabnabbing which is a type of phishing attack is explored by developing an attack scenario. Secondly, the signature based detection mechanism is proposed to handle tabnabbing attack. --- paper_title: TabShots: client-side detection of tabnabbing attacks paper_content: As the web grows larger and larger and as the browser becomes the vehicle-of-choice for delivering many applications of daily use, the security and privacy of web users is under constant attack. Phishing is as prevalent as ever, with anti-phishing communities reporting thousands of new phishing campaigns each month. In 2010, tabnabbing, a variation of phishing, was introduced. In a tabnabbing attack, an innocuous-looking page, opened in a browser tab, disguises itself as the login page of a popular web application, when the user's focus is on a different tab. The attack exploits the trust of users for already opened pages and the user habit of long-lived browser tabs. To combat this recent attack, we propose TabShots. TabShots is a browser extension that helps browsers and users to remember what each tab looked like, before the user changed tabs. Our system compares the appearance of each tab and highlights the parts that were changed, allowing the user to distinguish between legitimate changes and malicious masquerading. Using an experimental evaluation on the most popular sites of the Internet, we show that TabShots has no impact on 78% of these sites, and very little on another 19%. Thereby, TabShots effectively protects users against tabnabbing attacks without affecting their browsing habits and without breaking legitimate popular sites. --- paper_title: A Serious Game for Eliciting Social Engineering Security Requirements paper_content: Social engineering is the acquisition of informationabout computer systems by methods that deeply include non-technical means. While technical security of most critical systemsis high, the systems remain vulnerable to attacks from socialengineers. Social engineering is a technique that: (i) does notrequire any (advanced) technical tools, (ii) can be used by anyone,(iii) is cheap. Traditional security requirements elicitation approaches oftenfocus on vulnerabilities in network or software systems. Fewapproaches even consider the exploitation of humans via socialengineering and none of them elicits personal behaviours of indi-vidual employees. While the amount of social engineering attacksand the damage they cause rise every year, the security awarenessof these attacks and their consideration during requirementselicitation remains negligible. We propose to use a card game to elicit these requirements, which all employees of a company can play to understand thethreat and document security requirements. The game considersthe individual context of a company and presents underlyingprinciples of human behaviour that social engineers exploit, aswell as concrete attack patterns. We evaluated our approachwith several groups of researchers, IT administrators, andprofessionals from industry. --- paper_title: A Taxonomy for Social Engineering attacks paper_content: As the technology to secure information improves, hackers will employ less technical means to get access to unauthorized data. The use of Social Engineering as a non tech method of hacking has been increasingly used during the past few years. There are different types of social engineering methods reported but what is lacking is a unifying effort to understand these methods in the aggregate. This paper aims to classify these methods through taxonomy so that organizations can gain a better understanding of these attack methods and accordingly be vigilant against them. --- paper_title: A Taxonomy for Social Engineering attacks paper_content: As the technology to secure information improves, hackers will employ less technical means to get access to unauthorized data. The use of Social Engineering as a non tech method of hacking has been increasingly used during the past few years. There are different types of social engineering methods reported but what is lacking is a unifying effort to understand these methods in the aggregate. This paper aims to classify these methods through taxonomy so that organizations can gain a better understanding of these attack methods and accordingly be vigilant against them. --- paper_title: SoK: Everyone Hates Robocalls: A Survey of Techniques Against Telephone Spam paper_content: Telephone spam costs United States consumers $8.6 billion annually. In 2014, the Federal Trade Commission has received over 22 million complaints of illegal and wanted calls. Telephone spammers today are leveraging recent technical advances in the telephony ecosystem to distribute massive automated spam calls known as robocalls. Given that anti-spam techniques and approaches are effective in the email domain, the question we address is: what are the effective defenses against spam calls? In this paper, we first describe the telephone spam ecosystem, specifically focusing on the differences between email and telephone spam. Then, we survey the existing telephone spam solutions and, by analyzing the failings of the current techniques, derive evaluation criteria that are critical to an acceptable solution. We believe that this work will help guide the development of effective telephone spam defenses, as well as provide a framework to evaluate future defenses. --- paper_title: Advanced social engineering attacks paper_content: Social engineering has emerged as a serious threat in virtual communities and is an effective means to attack information systems. The services used by today's knowledge workers prepare the ground for sophisticated social engineering attacks. The growing trend towards BYOD (bring your own device) policies and the use of online communication and collaboration tools in private and business environments aggravate the problem. In globally acting companies, teams are no longer geographically co-located, but staffed just-in-time. The decrease in personal interaction combined with a plethora of tools used for communication (e-mail, IM, Skype, Dropbox, LinkedIn, Lync, etc.) create new attack vectors for social engineering attacks. Recent attacks on companies such as the New York Times and RSA have shown that targeted spear-phishing attacks are an effective, evolutionary step of social engineering attacks. Combined with zero-day-exploits, they become a dangerous weapon that is often used by advanced persistent threats. This paper provides a taxonomy of well-known social engineering attacks as well as a comprehensive overview of advanced social engineering attacks on the knowledge worker. --- paper_title: A client-side anti-pharming (CSAP) approach paper_content: Pharming is a type of social engineering attack wherein an attacker wants to steal sensitive information of internet users. Pharming is advanced technique than Phishing attack. In phishing, attacker's URL is different from targeted legitimate website URL but in case of pharming, attacker's URL is same to legitimate URL. Pharming attacks can be conducted by exploiting vulnerabilities in DNS server, which is more advanced type of attack than phishing. It's a special attack because the attacker doesn't have to target individual user. When attacker performs pharming on a Domain Name System (DNS) server, all users who are using DNS service through that server will fall victim of pharming attack. Various techniques are proposed for avoiding pharming attack. We present an approach which uses multiple DNS servers. --- paper_title: Social engineering: Revisiting end-user awareness and susceptibility to classic attack vectors paper_content: Social engineering relies on human vulnerability to exploit system security. Social engineering attacks are relatively harder to protect against as they mainly target the user, and not just hardware or software system defenses. End user awareness can be considered as one of the simplest yet most effective ways to protect the end user against social engineering vectors. The present study ascertains the level of user susceptibility to social engineering attacks in a cooperating corporate organization. Two attack scenarios, a spear-phishing campaign and a physical intrusion vector were designed targeting the organization's user population (employees) based on publicly available information from the Internet. Clues relating to social engineering techniques were included in the attacks to alert suspicious users. Despite the revealing signs of a social engineering campaign, the results indicated that a significantly high proportion (46–60%) of the users fell prey and failed to identify the attacks. It was observed that lack of user awareness remained the primary cause of the success of the attacks, requiring corrective action through post-incident training and regular IT security drills. --- paper_title: Mitigating social engineering for improved cybersecurity paper_content: In this paper, Social Engineering threats and trends are investigated and mitigation strategies recommended. The national and economic security of countries now relies on the Cyberspace because virtually all businesses processes are using the Internet. Unfortunately, Cyber-criminality is increasing and rated the fastest growing crime worldwide. Social engineering, a technique whereby cybercriminals trick their victims into disclosing log-in information without using any technical gadget has been identified as one of the most dangerous threats of our time. Due to the fact that any identity theft or breach in an organization's information system resulting in disclosure of sensitive information could have far-reaching consequences such as financial losses, disruption of services, damage to public image, or even bringing the organization or a nation to a complete standstill, this paper x-rayed most prevalent social engineering attacks, their typical tools and recommended mitigation strategies. --- paper_title: A Taxonomy for Social Engineering attacks paper_content: As the technology to secure information improves, hackers will employ less technical means to get access to unauthorized data. The use of Social Engineering as a non tech method of hacking has been increasingly used during the past few years. There are different types of social engineering methods reported but what is lacking is a unifying effort to understand these methods in the aggregate. This paper aims to classify these methods through taxonomy so that organizations can gain a better understanding of these attack methods and accordingly be vigilant against them. --- paper_title: An Approach To Perceive Tabnabbing Attack paper_content: The growth of Internet has many pros and cons to mankind, which is easily visible in day to day activities. The growth of Internet has also manifested into the other domain of cyber crimes. Phishing, web defacement, money laundering, tax evasion, etc. are some of the examples of cyber crimes that have been reported in literature. It has become vital to make technology reliable, so that it can record and intelligently apprise the user of illegal activity. The objective of this work is twofold. Firstly, in this paper tabnabbing which is a type of phishing attack is explored by developing an attack scenario. Secondly, the signature based detection mechanism is proposed to handle tabnabbing attack. --- paper_title: A layered defense mechanism for a social engineering aware perimeter paper_content: While many cyber security organizations urge the corporate world to use defence-in-depth to create vigilant network perimeters, the human factor is often overlooked. Security evaluation frameworks focus mostly on critical assets of an organization and technical aspects of prevailing risks. There is consequently no specific framework to identify, categorize, analyse and mitigate social engineering related risks. This paper identifies the requirement for such a framework through an in-depth investigation of an actual organization and extensive analysis of existing methodologies. On the basis of this a layered defence strategy SERA is developed, starting with the basic building blocks for social-engineering aware risk analysis. A chronological attack classification framework is presented as an enhancement of existing frameworks on social engineering. --- paper_title: An Overview of Social Engineering in the Context of Information Security paper_content: Social engineering in the context of information security is the exploitation of human psychology to gain access into secure data. Human emotion can act as both a strength and a weakness. When it comes to the world booming with technology, human emotions which are completely unrelated to the matter is made to relate through social engineering. Social engineering employs ‘traps’ to pry on human emotion and its vulnerability, taking advantage of the flaws of human psychology. Information security breaches utilising social engineering techniques are vast, so that social engineering in this context is a topic which could not be neglected. This research paper presents an overview of social engineering attacks and suggested defence mechanisms. An introduction to social engineering attacks are given, with context to the current trends and related vulnerabilities. Main reasons for the spread of social engineering attacks in the current context are also presented. Attack frameworks are presented and defence approaches are proposed at the end. --- paper_title: Vulnerability to social engineering in social networks: a proposed user-centric framework paper_content: Social networking sites have billions of users who communicate and share their personal information every day. Social engineering is considered one of the biggest threats to information security nowadays. Social engineering is an attacker technique to manipulate and deceive users in order to access or gain privileged information. Such attacks are continuously developed to deceive a high number of potential victims. The number of social engineering attacks has risen dramatically in the past few years, causing unpleasant damage both to organizations and individuals. Yet little research has discussed social engineering in the virtual environments of social networks. One approach to counter these exploits is through research that aims to understand why people fall victim to such attacks. Previous social engineering and deception research have not satisfactory identified the factors that influence the users’ ability to detect attacks. Characteristics that influence users’ vulnerability must be investigated to address this issue and help to build a profile for vulnerable users in order to focus on increasing the training programs and education for those users. In this context, the present study proposes a user-centric framework to understand the user's susceptibility, relevant factors and dimensions. --- paper_title: Malicious PDF detection using metadata and structural features paper_content: Owed to their versatile functionality and widespread adoption, PDF documents have become a popular avenue for user exploitation ranging from large-scale phishing attacks to targeted attacks. In this paper, we present a framework for robust detection of malicious documents through machine learning. Our approach is based on features extracted from document metadata and structure. Using real-world datasets, we demonstrate the the adequacy of these document properties for malware detection and the durability of these features across new malware variants. Our analysis shows that the Random Forests classification method, an ensemble classifier that randomly selects features for each individual classification tree, yields the best detection rates, even on previously unseen malware. Indeed, using multiple datasets containing an aggregate of over 5,000 unique malicious documents and over 100,000 benign ones, our classification rates remain well above 99% while maintaining low false positives of 0.2% or less for different classification parameters and experimental scenarios. Moreover, the classifier has the ability to detect documents crafted for targeted attacks and separate them from broadly distributed malicious PDF documents. Remarkably, we also discovered that by artificially reducing the influence of the top features in the classifier, we can still achieve a high rate of detection in an adversarial setting where the attacker is aware of both the top features utilized in the classifier and our normality model. Thus, the classifier is resilient against mimicry attacks even with knowledge of the document features, classification method, and training set. --- paper_title: Measuring Source Credibility of Social Engineering Attackers on Facebook paper_content: Past research has suggested that social networking sites are the most common source for social engineering-based attacks. Persuasion research shows that people are more likely to obey and accept a message when the source’s presentation appears to be credible. However, many factors can impact the perceived credibility of a source, depending on its type and the characteristics of the environment. Our previous research showed that there are four dimensions of source credibility in terms of social engineering on Facebook: perceived sincerity, perceived competence, perceived attraction, and perceived worthiness. Because the dimensionalities of source credibility as well as their measurement scales can fluctuate from one type of source to another and from one type of context to another, our aim in this study includes validating the existence of those four dimensions toward the credibility of social engineering attackers on Facebook and developing a valid measurement scale for every dimension of them. --- paper_title: Flow whitelisting in SCADA networks paper_content: Supervisory Control And Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities. Modern SCADA networks are becoming more vulnerable to network attacks, due to the now common use of standard communication protocols and increased interconnection to corporate networks and the Internet. In this work, we propose an approach to improve the security of these networks based on flow whitelisting. A flow whitelist describes the legitimate traffic solely using four properties of network packets: the client address, the server address, the server-side port, and the transport protocol. The proposed approach consists in learning a flow whitelist by capturing network traffic and aggregating it into flows for a given period of time. After this learning phase is complete, any non-whitelisted connection observed generates an alarm. The evaluation of the approach focuses on two important whitelist characteristics: size and stability. We demonstrate the applicability of the approach using real-world traffic traces, captured in two water treatment plants and a gas and electric utility. --- paper_title: A model for social engineering awareness program for schools paper_content: Advancements in security has over the years of technological growth been mainly focused on providing secured technological infrastructure. The developed security measures and counter-measures have played a major role in reducing the surge of cyber-attacks. However, hackers have continued to exploit vulnerabilities due to the human element to gain access into otherwise secured systems. Risks and potential for exploits are more so in schools where the human vulnerability is enhanced by young impressionable pupils. Social engineering, the art of manipulating people so they give up confidential information, is increasingly the approach of choice for hackers who exploit the human element. Social engineers bypass secured systems in schools by directing targeting and exploiting the human vulnerabilities of school's students and staff. Education through awareness campaigns are typically used in countering the threat from social engineering. Such awareness campaigns tend to however be too holistic in focus to lead to the significant and sustainable change in behaviour required to counter social engineering. This paper presents a model for designing and implementing social engineering awareness programmes aimed at fostering behaviour change in schools. It demonstrates the process of designing a social engineering awareness program to meet all types of learning styles by using different multiple communication methods. Evaluation and continuous reinforcement approaches are also presented. A pilot implementation of our proposed model for social engineering awareness programme shows a significant change in behaviour of school's teaching staff. --- paper_title: Social Engineering: Hacking into Humans paper_content: In this world of digitalization, the need for data privacy and data security is quite important. The IT companies today prefer their data over everything. Not only for companies, the data privacy important for any individual. But no matter how secure is the company, how advanced is the technology used or how much up to date their software is, there's still a vulnerability in every sector known as ‘Human'. The art of gathering sensitive information from a human being is known as Social Engineering. Technology has increased drastically in the past few years but the threat of Social engineering is still a problem. Social engineering attacks are ince=reasing day by day due to lack of awareness and knowledge. Social engineering is a really common practice to gather information and sensitive data through the use of mobile numbers, emails, SMS or direct approach. Social engineering can be really useful for the attacker if done in a proper manner.'Kevin Mitnik' is the most renowned social engineers of all time. In this paper, we are going to discuss Social Engineering, its types, how it affects us and how to prevent these attacks. Also, many proofs of Concepts are also presented in this paper. --- paper_title: Social engineering attack modeling with the use of Bayesian networks paper_content: The problem of modeling socio-engineering attacks on the user's machine using a Bayesian belief networks. The system models the complex “information system - personnel - critical documents” presented in the form of Bayesian belief networks, containing a set of psychological characteristics. This approach can significantly improve the performance and productivity of software for the analysis of security of user information system. --- paper_title: Protocols for mitigating blackhole attacks in delay tolerant networks paper_content: High node mobility and infrequent connectivity in delay tolerant networks (DTNs) makes it challenging to implement traditional security algorithms for detecting malicious nodes. In DTN, most of the routing algorithms are based on the announcement of routing metrics like probability of delivery, contact strength or social group strength by the nodes in contact. Blackhole in DTN exploits these characteristics of routing protocols and either announces a high value of these metrics or tries to attain a high value for them by following fast, repeated movement patterns. Dynamic social grouping (DSG) based routing algorithm shows that social behavior of nodes helps to make better forwarding decisions and to achieve highest message delivery ratio amongst other existing routing algorithms. We examine the impact of blackholes, intermittent blackholes and tailgating attack on DSG. We propose a suit of three solutions. Our first solution detects blackholes and tailgating malicious nodes in the network, however, is not suitable for intermittent blackholes. Second solution handles intermittent blackholes and performs well when the nodes are well connected. The third and final solution handles intermittent blackholes in sparsely connected as well as in well-connected networks. In all proposed solutions, blackholes are not able to degrade the performance of the protocols by changing their geographical locations. We demonstrate through simulation that our protocols improve upon the message delivery ratio over the existing solutions. An appropriate protocol from the suit may be used depending upon an application. --- paper_title: A New Method for Detection of Phishing Websites: URL Detection paper_content: Phishing is an unlawful activity wherein people are misled into the wrong sites by using various fraudulent methods. The aim of these phishing websites is to confiscate personal information or other financial details for personal benefits or misuse. As technology advances, the phishing approaches used need to get progressed and there is a dire need for better security and better mechanisms to prevent as well as detect these phishing approaches. The primary focus of this paper is to put forth a model as a solution to detect phishing websites by using the URL detection method using Random Forest algorithm. There are 3 major phases such as Parsing, Heuristic Classification of data, Performance Analysis in this model and each phase makes use of a different technique or algorithm for processing of data to give better results. --- paper_title: CryptoLock (and Drop It): Stopping Ransomware Attacks on User Data paper_content: Ransomware is a growing threat that encrypts auser's files and holds the decryption key until a ransom ispaid by the victim. This type of malware is responsible fortens of millions of dollars in extortion annually. Worse still, developing new variants is trivial, facilitating the evasion of manyantivirus and intrusion detection systems. In this work, we presentCryptoDrop, an early-warning detection system that alerts a userduring suspicious file activity. Using a set of behavior indicators, CryptoDrop can halt a process that appears to be tampering witha large amount of the user's data. Furthermore, by combininga set of indicators common to ransomware, the system can beparameterized for rapid detection with low false positives. Ourexperimental analysis of CryptoDrop stops ransomware fromexecuting with a median loss of only 10 files (out of nearly5,100 available files). Our results show that careful analysis ofransomware behavior can produce an effective detection systemthat significantly mitigates the amount of victim data loss. --- paper_title: Social engineering in social networking sites: Affect-based model paper_content: While social engineering represents a real and ominous threat to many organizations, companies, governments, and individuals, social networking sites (SNSs), have been identified as among the most common means of social engineering attacks. Owing to factors that reduce the ability of users to detect social engineering tricks and increase the ability of attackers to launch them, SNSs seem to be perfect breeding ground for exploiting the vulnerabilities of people, and the weakest link in security. This work will contribute to the knowledge of social engineering by identifying different entities and subentities that affect social engineering based attacks in SNSs. Moreover, this paper includes an intensive and comprehensive overview of different aspects of social engineering threats in SNSs. --- paper_title: Improving Awareness of Social Engineering Attacks paper_content: Social engineering is a method of attack involving the exploitation of human weakness, gullibility and ignorance. Although related techniques have existed for some time, current awareness of social engineering and its many guises is relatively low and efforts are therefore required to improve the protection of the user community. This paper begins by examining the problems posed by social engineering, and outlining some of the previous efforts that have been made to address the threat. This leads toward the discussion of a new awareness-raising website that has been specifically designed to aid users in understanding and avoiding the risks. Findings from an experimental trial involving 46 participants are used to illustrate that the system served to increase users’ understanding of threat concepts, as well as providing an engaging environment in which they would be likely to persevere with their learning. --- paper_title: Solutions for counteracting human deception in social engineering attacks paper_content: The purpose of this paper is to investigate the top three cybersecurity issues in organizations related to social engineering and aggregate solutions for counteracting human deception in social engineering attacks.,A total of 20 experts within Information System Security Association participated in a three-round Delphi study for aggregating and condensing expert opinions. Three rounds moved participants toward consensus for solutions to counteract social engineering attacks in organizations.,Three significant issues: compromised data; ineffective practices; and lack of ongoing education produced three target areas for implementing best practices in countering social engineering attacks. The findings offer counteractions by including education, policies, processes and continuous training in security practices.,Study limitations include lack of prior data on effective social engineering defense. Research implications stem from the psychology of human deception and trust with the ability to detect deception.,Practical implications relate to human judgment in complying with effective security policies and programs and consistent education and training. Future research may include exploring financial, operational and educational costs of implementing social engineering solutions.,Social implications apply across all knowledge workers who benefit from technology and are trusted to protect organizational assets and intellectual property.,This study contributes to the field of cybersecurity with a focus on trust and human deception to investigate solutions to counter social engineering attacks. This paper adds to under-represented cybersecurity research regarding effective implementation for social engineering defense. --- paper_title: Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes paper_content: The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or “things” to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes. --- paper_title: Cutting the Gordian Knot: A Look Under the Hood of Ransomware Attacks paper_content: In this paper, we present the results of a long-term study of ransomware attacks that have been observed in the wild between 2006 and 2014. We also provide a holistic view on how ransomware attacks have evolved during this period by analyzing 1,359 samples that belong to 15 different ransomware families. Our results show that, despite a continuous improvement in the encryption, deletion, and communication techniques in the main ransomware families, the number of families with sophisticated destructive capabilities remains quite small. In fact, our analysis reveals that in a large number of samples, the malware simply locks the victim's computer desktop or attempts to encrypt or delete the victim's files using only superficial techniques.i?źOur analysis also suggests that stopping advanced ransomware attacks is not as complex as it has been previously reported. For example, we show that by monitoring abnormal file system activity, it is possible to design a practical defense system that could stop a large number of ransomware attacks, even those using sophisticated encryption capabilities. A close examination on the file system activities of multiple ransomware samples suggests that by looking at I/O requests and protecting Master File Table MFT in the NTFS file system, it is possible to detect and prevent a significant number of zero-day ransomware attacks. --- paper_title: From Intrusion Detection to an Intrusion Response System: Fundamentals, Requirements, and Future Directions paper_content: In the past few decades, the rise in attacks on communication devices in networks has resulted in a reduction of network functionality, throughput, and performance. To detect and mitigate these network attacks, researchers, academicians, and practitioners developed Intrusion Detection Systems (IDSs) with automatic response systems. The response system is considered an important component of IDS, since without a timely response IDSs may not function properly in countering various attacks, especially on a real-time basis. To respond appropriately, IDSs should select the optimal response option according to the type of network attack. This research study provides a complete survey of IDSs and Intrusion Response Systems (IRSs) on the basis of our in-depth understanding of the response option for different types of network attacks. Knowledge of the path from IDS to IRS can assist network administrators and network staffs in understanding how to tackle different attacks with state-of-the-art technologies. --- paper_title: Scoping the Cyber Security Body of Knowledge paper_content: Cybersecurity is becoming an important element in curricula at all education levels. However, the foundational knowledge on which the field of cybersecurity is being developed is fragmented, and as a result, it can be difficult for both students and educators to map coherent paths of progression through the subject. The Cyber Security Body of Knowledge (CyBOK) project (www.cybok.org) aims to codify the foundational and generally recognized knowledge on cybersecurity. --- paper_title: Online Social Networks Security: Threats, Attacks, and Future Directions paper_content: A list of well-known Online Social Networks extend to hundreds of available sites with hundreds of thousands, millions, and even billions of registered accounts; for instance, Facebook as of April 2016 has around two billion active users. Online Social Networks made a difference in many people’s lives and helped in opening avenues that were not possible before. However, as in any success story there is a downside. Cyber-attacks that used to have a small or limited effect can now have a huge distributed effect through utilizing those social network sites. Some attacks are more apparent than others in this context; hence this chapter discusses how serious attacks are possible in online social networks and what has been done to encounter them. It will discuss privacy, Sybil attacks, social engineering, spam, malware, botnet attacks, and the trade-off between services, security, and users’ rights. --- paper_title: A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks paper_content: Social engineering is used as an umbrella term for a broad spectrum of computer exploitations that employ a variety of attack vectors and strategies to psychologically manipulate a user. Semantic attacks are the specific type of social engineering attacks that bypass technical defences by actively manipulating object characteristics, such as platform or system applications, to deceive rather than directly attack the user. Commonly observed examples include obfuscated URLs, phishing emails, drive-by downloads, spoofed websites and scareware to name a few. This article presents a taxonomy of semantic attacks, as well as a survey of applicable defences. By contrasting the threat landscape and the associated mitigation techniques in a single comparative matrix, we identify the areas where further research can be particularly beneficial. --- paper_title: The social engineering attack spiral (SEAS) paper_content: Cybercrime is on the increase and attacks are becoming ever more sophisticated. Organisations are investing huge sums of money and vast resources in trying to establish effective and timely countermeasures. This is still a game of catch up, where hackers have the upper hand and potential victims are trying to produce secure systems hardened against what feels like are inevitable future attacks. The focus so far has been on technology and not people and the amount of resource allocated to countermeasures and research into cyber security attacks follows the same trend. This paper adds to the growing body of work looking at social engineering attacks and therefore seeks to redress this imbalance to some extent. The objective is to produce a model for social engineering that provides a better understanding of the attack process such that improved and timely countermeasures can be applied and early interventions implemented. ---	Title: Social Engineering Attacks: A Survey Section 1: Introduction Description 1: Provide an introduction about social engineering attacks, their impact on cybersecurity, and the importance of the survey. Section 2: Social Engineering Attacks Description 2: Discuss the significance and different phases of social engineering attacks. Section 3: Attacks Classification Description 3: Classify social engineering attacks into human-based, computer-based, social, technical, and physical-based categories. Section 4: Attacks Description Description 4: Describe various types of social engineering attacks such as phishing, pretexting, baiting, tailgating, ransomware, fake software attacks, reverse social engineering, pop-up windows, phone/email scams, robocalls, and others. Section 5: Prevention Techniques Description 5: Discuss various techniques and strategies to prevent social engineering attacks including training, policies, anti-phishing tools, and other security measures. Section 6: Mitigation Techniques Description 6: Outline techniques to mitigate and handle social engineering attacks after they occur, including human-based and technology-based strategies. Section 7: Comparison Description 7: Compare different countermeasures and mitigation techniques for social engineering attacks. Section 8: Challenges and Future Directions Description 8: Identify key challenges and suggest future directions for improving the detection, prevention, and mitigation of social engineering attacks. Section 9: Conclusions Description 9: Summarize the findings of the survey, discuss the importance of novel detection techniques and programs, and emphasize the need for cybersecurity education.
Datasets on object manipulation and interaction: a survey	6	--- paper_title: Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data paper_content: Activity recognition has shown impressive progress in recent years. However, the challenges of detecting fine-grained activities and understanding how they are combined into composite activities have been largely overlooked. In this work we approach both tasks and present a dataset which provides detailed annotations to address them. The first challenge is to detect fine-grained activities, which are defined by low inter-class variability and are typically characterized by fine-grained body motions. We explore how human pose and hands can help to approach this challenge by comparing two pose-based and two hand-centric features with state-of-the-art holistic features. To attack the second challenge, recognizing composite activities, we leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts. We show the benefits of our hand-centric approach for fine-grained activity classification and detection. For composite activity recognition we find that decomposition into attributes allows sharing information across composites and is essential to attack this hard task. Using script data we can recognize novel composites without having training data for them. --- paper_title: Script data for attribute-based recognition of composite activities paper_content: State-of-the-art human activity recognition methods build on discriminative learning which requires a representative training set for good performance. This leads to scalability issues for the recognition of large sets of highly diverse activities. In this paper we leverage the fact that many human activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts. To share and transfer knowledge between composite activities we model them by a common set of attributes corresponding to basic actions and object participants. This attribute representation allows to incorporate script data that delivers new variations of a composite activity or even to unseen composite activities. In our experiments on 41 composite cooking tasks, we found that script data to successfully capture the high variability of composite activities. We show improvements in a supervised case where training data for all composite cooking tasks is available, but we are also able to recognize unseen composites by just using script data and without any manual video annotation. --- paper_title: Learning to Recognize Daily Actions using Gaze paper_content: We present a probabilistic generative model for simultaneously recognizing daily actions and predicting gaze locations in videos recorded from an egocentric camera. We focus on activities requiring eye-hand coordination and model the spatio-temporal relationship between the gaze point, the scene objects, and the action label. Our model captures the fact that the distribution of both visual features and object occurrences in the vicinity of the gaze point is correlated with the verb-object pair describing the action. It explicitly incorporates known properties of gaze behavior from the psychology literature, such as the temporal delay between fixation and manipulation events. We present an inference method that can predict the best sequence of gaze locations and the associated action label from an input sequence of images. We demonstrate improvements in action recognition rates and gaze prediction accuracy relative to state-of-the-art methods, on two new datasets that contain egocentric videos of daily activities and gaze. --- paper_title: Human Activity Detection from RGBD Images paper_content: Being able to detect and recognize human activities is important for making personal assistant robots useful in performing assistive tasks. The challenge is to develop a system that is low-cost, reliable in unstructured home settings, and also straightforward to use. In this paper, we use a RGBD sensor (Microsoft Kinect) as the input sensor, and present learning algorithms to infer the activities. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM). It considers a person's activity as composed of a set of sub-activities, and infers the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve an average performance of 84.3% when the person was seen before in the training set (and 64.2% when the person was not seen before). --- paper_title: Learning human activities and object affordances from RGB-D videos paper_content: Understanding human activities and object affordances are two very important skills, especially for personal robots which operate in human environments. In this work, we consider the problem of extracting a descriptive labeling of the sequence of sub-activities being performed by a human, and more importantly, of their interactions with the objects in the form of associated affordances. Given a RGB-D video, we jointly model the human activities and object affordances as a Markov random field where the nodes represent objects and sub-activities, and the edges represent the relationships between object affordances, their relations with sub-activities, and their evolution over time. We formulate the learning problem using a structural support vector machine (SSVM) approach, where labelings over various alternate temporal segmentations are considered as latent variables. We tested our method on a challenging dataset comprising 120 activity videos collected from 4 subjects, and obtained an accuracy of 79.4% for affordance, 63.4% for sub-activity and 75.0% for high-level activity labeling. We then demonstrate the use of such descriptive labeling in performing assistive tasks by a PR2 robot. --- paper_title: Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers paper_content: Within the context of an endeavor to provide situated support for people with cognitive impairments in the kitchen, we developed and evaluated classifiers for recognizing 11 actions involved in food preparation. Data was collected from 20 lay subjects using four specially designed kitchen utensils incorporating embedded 3-axis accelerometers. Subjects were asked to prepare a mixed salad in our laboratory-based instrumented kitchen environment. Video of each subject's food preparation activities were independently annotated by three different coders. Several classifiers were trained and tested using these features. With an overall accuracy of 82.9% our investigation demonstrated that a broad set of food preparation actions can be reliably recognized using sensors embedded in kitchen utensils. --- paper_title: Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers paper_content: Within the context of an endeavor to provide situated support for people with cognitive impairments in the kitchen, we developed and evaluated classifiers for recognizing 11 actions involved in food preparation. Data was collected from 20 lay subjects using four specially designed kitchen utensils incorporating embedded 3-axis accelerometers. Subjects were asked to prepare a mixed salad in our laboratory-based instrumented kitchen environment. Video of each subject's food preparation activities were independently annotated by three different coders. Several classifiers were trained and tested using these features. With an overall accuracy of 82.9% our investigation demonstrated that a broad set of food preparation actions can be reliably recognized using sensors embedded in kitchen utensils. --- paper_title: Learning to Recognize Daily Actions using Gaze paper_content: We present a probabilistic generative model for simultaneously recognizing daily actions and predicting gaze locations in videos recorded from an egocentric camera. We focus on activities requiring eye-hand coordination and model the spatio-temporal relationship between the gaze point, the scene objects, and the action label. Our model captures the fact that the distribution of both visual features and object occurrences in the vicinity of the gaze point is correlated with the verb-object pair describing the action. It explicitly incorporates known properties of gaze behavior from the psychology literature, such as the temporal delay between fixation and manipulation events. We present an inference method that can predict the best sequence of gaze locations and the associated action label from an input sequence of images. We demonstrate improvements in action recognition rates and gaze prediction accuracy relative to state-of-the-art methods, on two new datasets that contain egocentric videos of daily activities and gaze. --- paper_title: Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data paper_content: Activity recognition has shown impressive progress in recent years. However, the challenges of detecting fine-grained activities and understanding how they are combined into composite activities have been largely overlooked. In this work we approach both tasks and present a dataset which provides detailed annotations to address them. The first challenge is to detect fine-grained activities, which are defined by low inter-class variability and are typically characterized by fine-grained body motions. We explore how human pose and hands can help to approach this challenge by comparing two pose-based and two hand-centric features with state-of-the-art holistic features. To attack the second challenge, recognizing composite activities, we leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts. We show the benefits of our hand-centric approach for fine-grained activity classification and detection. For composite activity recognition we find that decomposition into attributes allows sharing information across composites and is essential to attack this hard task. Using script data we can recognize novel composites without having training data for them. --- paper_title: Script data for attribute-based recognition of composite activities paper_content: State-of-the-art human activity recognition methods build on discriminative learning which requires a representative training set for good performance. This leads to scalability issues for the recognition of large sets of highly diverse activities. In this paper we leverage the fact that many human activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts. To share and transfer knowledge between composite activities we model them by a common set of attributes corresponding to basic actions and object participants. This attribute representation allows to incorporate script data that delivers new variations of a composite activity or even to unseen composite activities. In our experiments on 41 composite cooking tasks, we found that script data to successfully capture the high variability of composite activities. We show improvements in a supervised case where training data for all composite cooking tasks is available, but we are also able to recognize unseen composites by just using script data and without any manual video annotation. --- paper_title: The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities paper_content: This paper describes a framework for modeling human activities as temporally structured processes. Our approach is motivated by the inherently hierarchical nature of human activities and the close correspondence between human actions and speech: We model action units using Hidden Markov Models, much like words in speech. These action units then form the building blocks to model complex human activities as sentences using an action grammar. To evaluate our approach, we collected a large dataset of daily cooking activities: The dataset includes a total of 52 participants, each performing a total of 10 cooking activities in multiple real-life kitchens, resulting in over 77 hours of video footage. We evaluate the HTK toolkit, a state-of-the-art speech recognition engine, in combination with multiple video feature descriptors, for both the recognition of cooking activities (e.g., making pancakes) as well as the semantic parsing of videos into action units (e.g., cracking eggs). Our results demonstrate the benefits of structured temporal generative approaches over existing discriminative approaches in coping with the complexity of human daily life activities. --- paper_title: A database for fine grained activity detection of cooking activities paper_content: While activity recognition is a current focus of research the challenging problem of fine-grained activity recognition is largely overlooked. We thus propose a novel database of 65 cooking activities, continuously recorded in a realistic setting. Activities are distinguished by fine-grained body motions that have low inter-class variability and high intra-class variability due to diverse subjects and ingredients. We benchmark two approaches on our dataset, one based on articulated pose tracks and the second using holistic video features. While the holistic approach outperforms the pose-based approach, our evaluation suggests that fine-grained activities are more difficult to detect and the body model can help in those cases. Providing high-resolution videos as well as an intermediate pose representation we hope to foster research in fine-grained activity recognition. --- paper_title: Combining embedded accelerometers with computer vision for recognizing food preparation activities paper_content: This paper introduces a publicly available dataset of complex activities that involve manipulative gestures. The dataset captures people preparing mixed salads and contains more than 4.5 hours of accelerometer and RGB-D video data, detailed annotations, and an evaluation protocol for comparison of activity recognition algorithms. Providing baseline results for one possible activity recognition task, this paper further investigates modality fusion methods at different stages of the recognition pipeline: (i) prior to feature extraction through accelerometer localization, (ii) at feature level via feature concatenation, and (iii) at classification level by combining classifier outputs. Empirical evaluation shows that fusing information captured by these sensor types can considerably improve recognition performance. --- paper_title: Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers paper_content: Within the context of an endeavor to provide situated support for people with cognitive impairments in the kitchen, we developed and evaluated classifiers for recognizing 11 actions involved in food preparation. Data was collected from 20 lay subjects using four specially designed kitchen utensils incorporating embedded 3-axis accelerometers. Subjects were asked to prepare a mixed salad in our laboratory-based instrumented kitchen environment. Video of each subject's food preparation activities were independently annotated by three different coders. Several classifiers were trained and tested using these features. With an overall accuracy of 82.9% our investigation demonstrated that a broad set of food preparation actions can be reliably recognized using sensors embedded in kitchen utensils. --- paper_title: Kitchen Scene Context Based Gesture Recognition: A Contest in ICPR2012 paper_content: This paper introduces a new open dataset "Actions for Cooking Eggs ACE Dataset" and summarizes results of the contest on "Kitchen Scene Context based Gesture Recognition", in conjunction with ICPR2012. The dataset consists of naturally performed actions in a kitchen environment. Five kinds of cooking menus were actually performed by five different actors, and the cooking actions were recorded by a Kinect Sensor. Color image sequences and depth image sequences are both available. Besides, action label was given to each frame. To estimate the action label, action recognition method has to analyze not only actor's action, but also scene contexts such as ingredients and cooking utensils. We compare the submitted algorithms and the results in this paper. --- paper_title: Audio-visual classification and detection of human manipulation actions paper_content: This thesis builds on the observation that robots cannot be programmed to handle any possible situation in the world. Like humans, they need mechanisms to deal with previously unseen situations and unknown objects. One of the skills humans rely on to deal with the unknown is the ability to learn by observing others. This thesis addresses the challenge of enabling a robot to learn from a human instructor. In particular, it is focused on objects. How can a robot find previously unseen objects? How can it track the object with its gaze? How can the object be employed in activities? Throughout this thesis, these questions are addressed with the end goal of allowing a robot to observe a human instructor and learn how to perform an activity. The robot is assumed to know very little about the world and it is supposed to discover objects autonomously. Given a visual input, object hypotheses are formulated by leveraging on common contextual knowledge often used by humans (e.g. gravity, compactness, convexity). Moreover, unknown objects are tracked and their appearance is updated over time since only a small fraction of the object is visible from the robot initially. Finally, object functionality is inferred by looking how the human instructor is manipulating objects and how objects are used in relation to others. All the methods included in this thesis have been evaluated on datasets that are publicly available or that we collected, showing the importance of these learning abilities. --- paper_title: The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities paper_content: This paper describes a framework for modeling human activities as temporally structured processes. Our approach is motivated by the inherently hierarchical nature of human activities and the close correspondence between human actions and speech: We model action units using Hidden Markov Models, much like words in speech. These action units then form the building blocks to model complex human activities as sentences using an action grammar. To evaluate our approach, we collected a large dataset of daily cooking activities: The dataset includes a total of 52 participants, each performing a total of 10 cooking activities in multiple real-life kitchens, resulting in over 77 hours of video footage. We evaluate the HTK toolkit, a state-of-the-art speech recognition engine, in combination with multiple video feature descriptors, for both the recognition of cooking activities (e.g., making pancakes) as well as the semantic parsing of videos into action units (e.g., cracking eggs). Our results demonstrate the benefits of structured temporal generative approaches over existing discriminative approaches in coping with the complexity of human daily life activities. --- paper_title: The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition paper_content: We introduce the publicly available TUM Kitchen Data Set as a comprehensive collection of activity sequences recorded in a kitchen environment equipped with multiple complementary sensors. The recorded data consists of observations of naturally performed manipulation tasks as encountered in everyday activities of human life. Several instances of a table-setting task were performed by different subjects, involving the manipulation of objects and the environment. We provide the original video sequences, full-body motion capture data recorded by a markerless motion tracker, RFID tag readings and magnetic sensor readings from objects and the environment, as well as corresponding action labels. In this paper, we both describe how the data was computed, in particular the motion tracker and the labeling, and give examples what it can be used for. We present first results of an automatic method for segmenting the observed motions into semantic classes, and describe how the data can be integrated in a knowledge-based framework for reasoning about the observations. --- paper_title: Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data paper_content: Activity recognition has shown impressive progress in recent years. However, the challenges of detecting fine-grained activities and understanding how they are combined into composite activities have been largely overlooked. In this work we approach both tasks and present a dataset which provides detailed annotations to address them. The first challenge is to detect fine-grained activities, which are defined by low inter-class variability and are typically characterized by fine-grained body motions. We explore how human pose and hands can help to approach this challenge by comparing two pose-based and two hand-centric features with state-of-the-art holistic features. To attack the second challenge, recognizing composite activities, we leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts. We show the benefits of our hand-centric approach for fine-grained activity classification and detection. For composite activity recognition we find that decomposition into attributes allows sharing information across composites and is essential to attack this hard task. Using script data we can recognize novel composites without having training data for them. --- paper_title: The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities paper_content: This paper describes a framework for modeling human activities as temporally structured processes. Our approach is motivated by the inherently hierarchical nature of human activities and the close correspondence between human actions and speech: We model action units using Hidden Markov Models, much like words in speech. These action units then form the building blocks to model complex human activities as sentences using an action grammar. To evaluate our approach, we collected a large dataset of daily cooking activities: The dataset includes a total of 52 participants, each performing a total of 10 cooking activities in multiple real-life kitchens, resulting in over 77 hours of video footage. We evaluate the HTK toolkit, a state-of-the-art speech recognition engine, in combination with multiple video feature descriptors, for both the recognition of cooking activities (e.g., making pancakes) as well as the semantic parsing of videos into action units (e.g., cracking eggs). Our results demonstrate the benefits of structured temporal generative approaches over existing discriminative approaches in coping with the complexity of human daily life activities. --- paper_title: A database for fine grained activity detection of cooking activities paper_content: While activity recognition is a current focus of research the challenging problem of fine-grained activity recognition is largely overlooked. We thus propose a novel database of 65 cooking activities, continuously recorded in a realistic setting. Activities are distinguished by fine-grained body motions that have low inter-class variability and high intra-class variability due to diverse subjects and ingredients. We benchmark two approaches on our dataset, one based on articulated pose tracks and the second using holistic video features. While the holistic approach outperforms the pose-based approach, our evaluation suggests that fine-grained activities are more difficult to detect and the body model can help in those cases. Providing high-resolution videos as well as an intermediate pose representation we hope to foster research in fine-grained activity recognition. --- paper_title: Activity recognition using the velocity histories of tracked keypoints paper_content: We present an activity recognition feature inspired by human psychophysical performance. This feature is based on the velocity history of tracked keypoints. We present a generative mixture model for video sequences using this feature, and show that it performs comparably to local spatio-temporal features on the KTH activity recognition dataset. In addition, we contribute a new activity recognition dataset, focusing on activities of daily living, with high resolution video sequences of complex actions. We demonstrate the superiority of our velocity history feature on high resolution video sequences of complicated activities. Further, we show how the velocity history feature can be extended, both with a more sophisticated latent velocity model, and by combining the velocity history feature with other useful information, like appearance, position, and high level semantic information. Our approach performs comparably to established and state of the art methods on the KTH dataset, and significantly outperforms all other methods on our challenging new dataset. --- paper_title: The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition paper_content: We introduce the publicly available TUM Kitchen Data Set as a comprehensive collection of activity sequences recorded in a kitchen environment equipped with multiple complementary sensors. The recorded data consists of observations of naturally performed manipulation tasks as encountered in everyday activities of human life. Several instances of a table-setting task were performed by different subjects, involving the manipulation of objects and the environment. We provide the original video sequences, full-body motion capture data recorded by a markerless motion tracker, RFID tag readings and magnetic sensor readings from objects and the environment, as well as corresponding action labels. In this paper, we both describe how the data was computed, in particular the motion tracker and the labeling, and give examples what it can be used for. We present first results of an automatic method for segmenting the observed motions into semantic classes, and describe how the data can be integrated in a knowledge-based framework for reasoning about the observations. --- paper_title: Combining embedded accelerometers with computer vision for recognizing food preparation activities paper_content: This paper introduces a publicly available dataset of complex activities that involve manipulative gestures. The dataset captures people preparing mixed salads and contains more than 4.5 hours of accelerometer and RGB-D video data, detailed annotations, and an evaluation protocol for comparison of activity recognition algorithms. Providing baseline results for one possible activity recognition task, this paper further investigates modality fusion methods at different stages of the recognition pipeline: (i) prior to feature extraction through accelerometer localization, (ii) at feature level via feature concatenation, and (iii) at classification level by combining classifier outputs. Empirical evaluation shows that fusing information captured by these sensor types can considerably improve recognition performance. --- paper_title: Human Activity Detection from RGBD Images paper_content: Being able to detect and recognize human activities is important for making personal assistant robots useful in performing assistive tasks. The challenge is to develop a system that is low-cost, reliable in unstructured home settings, and also straightforward to use. In this paper, we use a RGBD sensor (Microsoft Kinect) as the input sensor, and present learning algorithms to infer the activities. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM). It considers a person's activity as composed of a set of sub-activities, and infers the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve an average performance of 84.3% when the person was seen before in the training set (and 64.2% when the person was not seen before). --- paper_title: Learning human activities and object affordances from RGB-D videos paper_content: Understanding human activities and object affordances are two very important skills, especially for personal robots which operate in human environments. In this work, we consider the problem of extracting a descriptive labeling of the sequence of sub-activities being performed by a human, and more importantly, of their interactions with the objects in the form of associated affordances. Given a RGB-D video, we jointly model the human activities and object affordances as a Markov random field where the nodes represent objects and sub-activities, and the edges represent the relationships between object affordances, their relations with sub-activities, and their evolution over time. We formulate the learning problem using a structural support vector machine (SSVM) approach, where labelings over various alternate temporal segmentations are considered as latent variables. We tested our method on a challenging dataset comprising 120 activity videos collected from 4 subjects, and obtained an accuracy of 79.4% for affordance, 63.4% for sub-activity and 75.0% for high-level activity labeling. We then demonstrate the use of such descriptive labeling in performing assistive tasks by a PR2 robot. --- paper_title: Learning to Recognize Daily Actions using Gaze paper_content: We present a probabilistic generative model for simultaneously recognizing daily actions and predicting gaze locations in videos recorded from an egocentric camera. We focus on activities requiring eye-hand coordination and model the spatio-temporal relationship between the gaze point, the scene objects, and the action label. Our model captures the fact that the distribution of both visual features and object occurrences in the vicinity of the gaze point is correlated with the verb-object pair describing the action. It explicitly incorporates known properties of gaze behavior from the psychology literature, such as the temporal delay between fixation and manipulation events. We present an inference method that can predict the best sequence of gaze locations and the associated action label from an input sequence of images. We demonstrate improvements in action recognition rates and gaze prediction accuracy relative to state-of-the-art methods, on two new datasets that contain egocentric videos of daily activities and gaze. --- paper_title: Detecting activities of daily living in first-person camera views paper_content: We present a novel dataset and novel algorithms for the problem of detecting activities of daily living (ADL) in firstperson camera views. We have collected a dataset of 1 million frames of dozens of people performing unscripted, everyday activities. The dataset is annotated with activities, object tracks, hand positions, and interaction events. ADLs differ from typical actions in that they can involve long-scale temporal structure (making tea can take a few minutes) and complex object interactions (a fridge looks different when its door is open). We develop novel representations including (1) temporal pyramids, which generalize the well-known spatial pyramid to approximate temporal correspondence when scoring a model and (2) composite object models that exploit the fact that objects look different when being interacted with. We perform an extensive empirical evaluation and demonstrate that our novel representations produce a two-fold improvement over traditional approaches. Our analysis suggests that real-world ADL recognition is “all about the objects,” and in particular, “all about the objects being interacted with.” --- paper_title: A public domain dataset for ADL recognition using wrist-placed accelerometers paper_content: The automatic monitoring of specific Activities of Daily Living (ADL) can be a useful tool for Human-Robot Interaction in smart environments and Assistive Robotics applications. The qualitative definition that is given for most ADL and the lack of well-defined benchmarks, however, are obstacles toward the identification of the most effective monitoring approaches for different tasks. The contribution of the article is two-fold: (i) we propose a taxonomy of ADL allowing for their categorization with respect to the most suitable monitoring approach; (ii) we present a freely available dataset of acceleration data, coming from a wrist-worn wearable device, targeting the recognition of 14 different human activities. --- paper_title: The Yale human grasping dataset: Grasp, object, and task data in household and machine shop environments paper_content: This paper presents a dataset of human grasping behavior in unstructured environments. Wide-angle head-mounted camera video was recorded from two housekeepers and two machinists during their regular work activities, and the grasp types, objects, and tasks were analyzed and coded by study staff. The full dataset contains 27.7 hours of tagged video and represents a wide range of manipulative behaviors spanning much of the typical human hand usage. We provide the original videos, a spreadsheet including the tagged grasp type, object, and task parameters, time information for each successive grasp, and video screenshots for each instance. Example code is provided for MATLAB and R, demonstrating how to load in the dataset and produce simple plots. --- paper_title: The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition paper_content: We introduce the publicly available TUM Kitchen Data Set as a comprehensive collection of activity sequences recorded in a kitchen environment equipped with multiple complementary sensors. The recorded data consists of observations of naturally performed manipulation tasks as encountered in everyday activities of human life. Several instances of a table-setting task were performed by different subjects, involving the manipulation of objects and the environment. We provide the original video sequences, full-body motion capture data recorded by a markerless motion tracker, RFID tag readings and magnetic sensor readings from objects and the environment, as well as corresponding action labels. In this paper, we both describe how the data was computed, in particular the motion tracker and the labeling, and give examples what it can be used for. We present first results of an automatic method for segmenting the observed motions into semantic classes, and describe how the data can be integrated in a knowledge-based framework for reasoning about the observations. --- paper_title: Human Activity Detection from RGBD Images paper_content: Being able to detect and recognize human activities is important for making personal assistant robots useful in performing assistive tasks. The challenge is to develop a system that is low-cost, reliable in unstructured home settings, and also straightforward to use. In this paper, we use a RGBD sensor (Microsoft Kinect) as the input sensor, and present learning algorithms to infer the activities. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM). It considers a person's activity as composed of a set of sub-activities, and infers the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve an average performance of 84.3% when the person was seen before in the training set (and 64.2% when the person was not seen before). --- paper_title: Learning human activities and object affordances from RGB-D videos paper_content: Understanding human activities and object affordances are two very important skills, especially for personal robots which operate in human environments. In this work, we consider the problem of extracting a descriptive labeling of the sequence of sub-activities being performed by a human, and more importantly, of their interactions with the objects in the form of associated affordances. Given a RGB-D video, we jointly model the human activities and object affordances as a Markov random field where the nodes represent objects and sub-activities, and the edges represent the relationships between object affordances, their relations with sub-activities, and their evolution over time. We formulate the learning problem using a structural support vector machine (SSVM) approach, where labelings over various alternate temporal segmentations are considered as latent variables. We tested our method on a challenging dataset comprising 120 activity videos collected from 4 subjects, and obtained an accuracy of 79.4% for affordance, 63.4% for sub-activity and 75.0% for high-level activity labeling. We then demonstrate the use of such descriptive labeling in performing assistive tasks by a PR2 robot. --- paper_title: A public domain dataset for ADL recognition using wrist-placed accelerometers paper_content: The automatic monitoring of specific Activities of Daily Living (ADL) can be a useful tool for Human-Robot Interaction in smart environments and Assistive Robotics applications. The qualitative definition that is given for most ADL and the lack of well-defined benchmarks, however, are obstacles toward the identification of the most effective monitoring approaches for different tasks. The contribution of the article is two-fold: (i) we propose a taxonomy of ADL allowing for their categorization with respect to the most suitable monitoring approach; (ii) we present a freely available dataset of acceleration data, coming from a wrist-worn wearable device, targeting the recognition of 14 different human activities. --- paper_title: Kitchen Scene Context Based Gesture Recognition: A Contest in ICPR2012 paper_content: This paper introduces a new open dataset "Actions for Cooking Eggs ACE Dataset" and summarizes results of the contest on "Kitchen Scene Context based Gesture Recognition", in conjunction with ICPR2012. The dataset consists of naturally performed actions in a kitchen environment. Five kinds of cooking menus were actually performed by five different actors, and the cooking actions were recorded by a Kinect Sensor. Color image sequences and depth image sequences are both available. Besides, action label was given to each frame. To estimate the action label, action recognition method has to analyze not only actor's action, but also scene contexts such as ingredients and cooking utensils. We compare the submitted algorithms and the results in this paper. --- paper_title: UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild paper_content: We introduce UCF101 which is currently the largest dataset of human actions. It consists of 101 action classes, over 13k clips and 27 hours of video data. The database consists of realistic user uploaded videos containing camera motion and cluttered background. Additionally, we provide baseline action recognition results on this new dataset using standard bag of words approach with overall performance of 44.5%. To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large number of classes, large number of clips and also unconstrained nature of such clips. --- paper_title: Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data paper_content: Activity recognition has shown impressive progress in recent years. However, the challenges of detecting fine-grained activities and understanding how they are combined into composite activities have been largely overlooked. In this work we approach both tasks and present a dataset which provides detailed annotations to address them. The first challenge is to detect fine-grained activities, which are defined by low inter-class variability and are typically characterized by fine-grained body motions. We explore how human pose and hands can help to approach this challenge by comparing two pose-based and two hand-centric features with state-of-the-art holistic features. To attack the second challenge, recognizing composite activities, we leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts. We show the benefits of our hand-centric approach for fine-grained activity classification and detection. For composite activity recognition we find that decomposition into attributes allows sharing information across composites and is essential to attack this hard task. Using script data we can recognize novel composites without having training data for them. --- paper_title: The Yale human grasping dataset: Grasp, object, and task data in household and machine shop environments paper_content: This paper presents a dataset of human grasping behavior in unstructured environments. Wide-angle head-mounted camera video was recorded from two housekeepers and two machinists during their regular work activities, and the grasp types, objects, and tasks were analyzed and coded by study staff. The full dataset contains 27.7 hours of tagged video and represents a wide range of manipulative behaviors spanning much of the typical human hand usage. We provide the original videos, a spreadsheet including the tagged grasp type, object, and task parameters, time information for each successive grasp, and video screenshots for each instance. Example code is provided for MATLAB and R, demonstrating how to load in the dataset and produce simple plots. --- paper_title: The YCB object and Model set: Towards common benchmarks for manipulation research paper_content: In this paper we present the Yale-CMU-Berkeley (YCB) Object and Model set, intended to be used for benchmarking in robotic grasping and manipulation research. The objects in the set are designed to cover various aspects of the manipulation problem; it includes objects of daily life with different shapes, sizes, textures, weight and rigidity, as well as some widely used manipulation tests. The associated database provides high-resolution RGBD scans, physical properties and geometric models of the objects for easy incorporation into manipulation and planning software platforms. A comprehensive literature survey on existing benchmarks and object datasets is also presented and their scope and limitations are discussed. The set will be freely distributed to research groups worldwide at a series of tutorials at robotics conferences, and will be otherwise available at a reasonable purchase cost. --- paper_title: Robot programming by demonstration paper_content: The presented paper shows a pilot development of a robot ‘task programming method’. In this method, the user programs the robot task by demonstrating it (Programming by Demonstration, PbD). PbD is applied on a robotic arm with two degrees-of-freedom for programming a constrained motion task. --- paper_title: The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition paper_content: We introduce the publicly available TUM Kitchen Data Set as a comprehensive collection of activity sequences recorded in a kitchen environment equipped with multiple complementary sensors. The recorded data consists of observations of naturally performed manipulation tasks as encountered in everyday activities of human life. Several instances of a table-setting task were performed by different subjects, involving the manipulation of objects and the environment. We provide the original video sequences, full-body motion capture data recorded by a markerless motion tracker, RFID tag readings and magnetic sensor readings from objects and the environment, as well as corresponding action labels. In this paper, we both describe how the data was computed, in particular the motion tracker and the labeling, and give examples what it can be used for. We present first results of an automatic method for segmenting the observed motions into semantic classes, and describe how the data can be integrated in a knowledge-based framework for reasoning about the observations. --- paper_title: Script data for attribute-based recognition of composite activities paper_content: State-of-the-art human activity recognition methods build on discriminative learning which requires a representative training set for good performance. This leads to scalability issues for the recognition of large sets of highly diverse activities. In this paper we leverage the fact that many human activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts. To share and transfer knowledge between composite activities we model them by a common set of attributes corresponding to basic actions and object participants. This attribute representation allows to incorporate script data that delivers new variations of a composite activity or even to unseen composite activities. In our experiments on 41 composite cooking tasks, we found that script data to successfully capture the high variability of composite activities. We show improvements in a supervised case where training data for all composite cooking tasks is available, but we are also able to recognize unseen composites by just using script data and without any manual video annotation. --- paper_title: Learning to Recognize Daily Actions using Gaze paper_content: We present a probabilistic generative model for simultaneously recognizing daily actions and predicting gaze locations in videos recorded from an egocentric camera. We focus on activities requiring eye-hand coordination and model the spatio-temporal relationship between the gaze point, the scene objects, and the action label. Our model captures the fact that the distribution of both visual features and object occurrences in the vicinity of the gaze point is correlated with the verb-object pair describing the action. It explicitly incorporates known properties of gaze behavior from the psychology literature, such as the temporal delay between fixation and manipulation events. We present an inference method that can predict the best sequence of gaze locations and the associated action label from an input sequence of images. We demonstrate improvements in action recognition rates and gaze prediction accuracy relative to state-of-the-art methods, on two new datasets that contain egocentric videos of daily activities and gaze. --- paper_title: The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities paper_content: This paper describes a framework for modeling human activities as temporally structured processes. Our approach is motivated by the inherently hierarchical nature of human activities and the close correspondence between human actions and speech: We model action units using Hidden Markov Models, much like words in speech. These action units then form the building blocks to model complex human activities as sentences using an action grammar. To evaluate our approach, we collected a large dataset of daily cooking activities: The dataset includes a total of 52 participants, each performing a total of 10 cooking activities in multiple real-life kitchens, resulting in over 77 hours of video footage. We evaluate the HTK toolkit, a state-of-the-art speech recognition engine, in combination with multiple video feature descriptors, for both the recognition of cooking activities (e.g., making pancakes) as well as the semantic parsing of videos into action units (e.g., cracking eggs). Our results demonstrate the benefits of structured temporal generative approaches over existing discriminative approaches in coping with the complexity of human daily life activities. --- paper_title: A database for fine grained activity detection of cooking activities paper_content: While activity recognition is a current focus of research the challenging problem of fine-grained activity recognition is largely overlooked. We thus propose a novel database of 65 cooking activities, continuously recorded in a realistic setting. Activities are distinguished by fine-grained body motions that have low inter-class variability and high intra-class variability due to diverse subjects and ingredients. We benchmark two approaches on our dataset, one based on articulated pose tracks and the second using holistic video features. While the holistic approach outperforms the pose-based approach, our evaluation suggests that fine-grained activities are more difficult to detect and the body model can help in those cases. Providing high-resolution videos as well as an intermediate pose representation we hope to foster research in fine-grained activity recognition. --- paper_title: Detecting activities of daily living in first-person camera views paper_content: We present a novel dataset and novel algorithms for the problem of detecting activities of daily living (ADL) in firstperson camera views. We have collected a dataset of 1 million frames of dozens of people performing unscripted, everyday activities. The dataset is annotated with activities, object tracks, hand positions, and interaction events. ADLs differ from typical actions in that they can involve long-scale temporal structure (making tea can take a few minutes) and complex object interactions (a fridge looks different when its door is open). We develop novel representations including (1) temporal pyramids, which generalize the well-known spatial pyramid to approximate temporal correspondence when scoring a model and (2) composite object models that exploit the fact that objects look different when being interacted with. We perform an extensive empirical evaluation and demonstrate that our novel representations produce a two-fold improvement over traditional approaches. Our analysis suggests that real-world ADL recognition is “all about the objects,” and in particular, “all about the objects being interacted with.” --- paper_title: Discovery of activity patterns using topic models paper_content: In this work we propose a novel method to recognize daily routines as a probabilistic combination of activity patterns. The use of topic models enables the automatic discovery of such patterns in a user's daily routine. We report experimental results that show the ability of the approach to model and recognize daily routines without user annotation. --- paper_title: BigBIRD: A large-scale 3D database of object instances paper_content: The state of the art in computer vision has rapidly advanced over the past decade largely aided by shared image datasets. However, most of these datasets tend to consist of assorted collections of images from the web that do not include 3D information or pose information. Furthermore, they target the problem of object category recognition—whereas solving the problem of object instance recognition might be sufficient for many robotic tasks. To address these issues, we present a highquality, large-scale dataset of 3D object instances, with accurate calibration information for every image. We anticipate that “solving” this dataset will effectively remove many perceptionrelated problems for mobile, sensing-based robots. The contributions of this work consist of: (1) BigBIRD, a dataset of 100 objects (and growing), composed of, for each object, 600 3D point clouds and 600 high-resolution (12 MP) images spanning all views, (2) a method for jointly calibrating a multi-camera system, (3) details of our data collection system, which collects all required data for a single object in under 6 minutes with minimal human effort, and (4) multiple software components (made available in open source), used to automate multi-sensor calibration and the data collection process. All code and data are available at http://rll.eecs.berkeley.edu/bigbird. --- paper_title: Human Activity Detection from RGBD Images paper_content: Being able to detect and recognize human activities is important for making personal assistant robots useful in performing assistive tasks. The challenge is to develop a system that is low-cost, reliable in unstructured home settings, and also straightforward to use. In this paper, we use a RGBD sensor (Microsoft Kinect) as the input sensor, and present learning algorithms to infer the activities. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM). It considers a person's activity as composed of a set of sub-activities, and infers the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve an average performance of 84.3% when the person was seen before in the training set (and 64.2% when the person was not seen before). --- paper_title: Activity recognition using the velocity histories of tracked keypoints paper_content: We present an activity recognition feature inspired by human psychophysical performance. This feature is based on the velocity history of tracked keypoints. We present a generative mixture model for video sequences using this feature, and show that it performs comparably to local spatio-temporal features on the KTH activity recognition dataset. In addition, we contribute a new activity recognition dataset, focusing on activities of daily living, with high resolution video sequences of complex actions. We demonstrate the superiority of our velocity history feature on high resolution video sequences of complicated activities. Further, we show how the velocity history feature can be extended, both with a more sophisticated latent velocity model, and by combining the velocity history feature with other useful information, like appearance, position, and high level semantic information. Our approach performs comparably to established and state of the art methods on the KTH dataset, and significantly outperforms all other methods on our challenging new dataset. --- paper_title: Learning human activities and object affordances from RGB-D videos paper_content: Understanding human activities and object affordances are two very important skills, especially for personal robots which operate in human environments. In this work, we consider the problem of extracting a descriptive labeling of the sequence of sub-activities being performed by a human, and more importantly, of their interactions with the objects in the form of associated affordances. Given a RGB-D video, we jointly model the human activities and object affordances as a Markov random field where the nodes represent objects and sub-activities, and the edges represent the relationships between object affordances, their relations with sub-activities, and their evolution over time. We formulate the learning problem using a structural support vector machine (SSVM) approach, where labelings over various alternate temporal segmentations are considered as latent variables. We tested our method on a challenging dataset comprising 120 activity videos collected from 4 subjects, and obtained an accuracy of 79.4% for affordance, 63.4% for sub-activity and 75.0% for high-level activity labeling. We then demonstrate the use of such descriptive labeling in performing assistive tasks by a PR2 robot. --- paper_title: Combining embedded accelerometers with computer vision for recognizing food preparation activities paper_content: This paper introduces a publicly available dataset of complex activities that involve manipulative gestures. The dataset captures people preparing mixed salads and contains more than 4.5 hours of accelerometer and RGB-D video data, detailed annotations, and an evaluation protocol for comparison of activity recognition algorithms. Providing baseline results for one possible activity recognition task, this paper further investigates modality fusion methods at different stages of the recognition pipeline: (i) prior to feature extraction through accelerometer localization, (ii) at feature level via feature concatenation, and (iii) at classification level by combining classifier outputs. Empirical evaluation shows that fusing information captured by these sensor types can considerably improve recognition performance. --- paper_title: Audio-visual classification and detection of human manipulation actions paper_content: This thesis builds on the observation that robots cannot be programmed to handle any possible situation in the world. Like humans, they need mechanisms to deal with previously unseen situations and unknown objects. One of the skills humans rely on to deal with the unknown is the ability to learn by observing others. This thesis addresses the challenge of enabling a robot to learn from a human instructor. In particular, it is focused on objects. How can a robot find previously unseen objects? How can it track the object with its gaze? How can the object be employed in activities? Throughout this thesis, these questions are addressed with the end goal of allowing a robot to observe a human instructor and learn how to perform an activity. The robot is assumed to know very little about the world and it is supposed to discover objects autonomously. Given a visual input, object hypotheses are formulated by leveraging on common contextual knowledge often used by humans (e.g. gravity, compactness, convexity). Moreover, unknown objects are tracked and their appearance is updated over time since only a small fraction of the object is visible from the robot initially. Finally, object functionality is inferred by looking how the human instructor is manipulating objects and how objects are used in relation to others. All the methods included in this thesis have been evaluated on datasets that are publicly available or that we collected, showing the importance of these learning abilities. --- paper_title: Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers paper_content: Within the context of an endeavor to provide situated support for people with cognitive impairments in the kitchen, we developed and evaluated classifiers for recognizing 11 actions involved in food preparation. Data was collected from 20 lay subjects using four specially designed kitchen utensils incorporating embedded 3-axis accelerometers. Subjects were asked to prepare a mixed salad in our laboratory-based instrumented kitchen environment. Video of each subject's food preparation activities were independently annotated by three different coders. Several classifiers were trained and tested using these features. With an overall accuracy of 82.9% our investigation demonstrated that a broad set of food preparation actions can be reliably recognized using sensors embedded in kitchen utensils. --- paper_title: A public domain dataset for ADL recognition using wrist-placed accelerometers paper_content: The automatic monitoring of specific Activities of Daily Living (ADL) can be a useful tool for Human-Robot Interaction in smart environments and Assistive Robotics applications. The qualitative definition that is given for most ADL and the lack of well-defined benchmarks, however, are obstacles toward the identification of the most effective monitoring approaches for different tasks. The contribution of the article is two-fold: (i) we propose a taxonomy of ADL allowing for their categorization with respect to the most suitable monitoring approach; (ii) we present a freely available dataset of acceleration data, coming from a wrist-worn wearable device, targeting the recognition of 14 different human activities. --- paper_title: Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data paper_content: Activity recognition has shown impressive progress in recent years. However, the challenges of detecting fine-grained activities and understanding how they are combined into composite activities have been largely overlooked. In this work we approach both tasks and present a dataset which provides detailed annotations to address them. The first challenge is to detect fine-grained activities, which are defined by low inter-class variability and are typically characterized by fine-grained body motions. We explore how human pose and hands can help to approach this challenge by comparing two pose-based and two hand-centric features with state-of-the-art holistic features. To attack the second challenge, recognizing composite activities, we leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts. We show the benefits of our hand-centric approach for fine-grained activity classification and detection. For composite activity recognition we find that decomposition into attributes allows sharing information across composites and is essential to attack this hard task. Using script data we can recognize novel composites without having training data for them. --- paper_title: Script data for attribute-based recognition of composite activities paper_content: State-of-the-art human activity recognition methods build on discriminative learning which requires a representative training set for good performance. This leads to scalability issues for the recognition of large sets of highly diverse activities. In this paper we leverage the fact that many human activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts. To share and transfer knowledge between composite activities we model them by a common set of attributes corresponding to basic actions and object participants. This attribute representation allows to incorporate script data that delivers new variations of a composite activity or even to unseen composite activities. In our experiments on 41 composite cooking tasks, we found that script data to successfully capture the high variability of composite activities. We show improvements in a supervised case where training data for all composite cooking tasks is available, but we are also able to recognize unseen composites by just using script data and without any manual video annotation. --- paper_title: A database for fine grained activity detection of cooking activities paper_content: While activity recognition is a current focus of research the challenging problem of fine-grained activity recognition is largely overlooked. We thus propose a novel database of 65 cooking activities, continuously recorded in a realistic setting. Activities are distinguished by fine-grained body motions that have low inter-class variability and high intra-class variability due to diverse subjects and ingredients. We benchmark two approaches on our dataset, one based on articulated pose tracks and the second using holistic video features. While the holistic approach outperforms the pose-based approach, our evaluation suggests that fine-grained activities are more difficult to detect and the body model can help in those cases. Providing high-resolution videos as well as an intermediate pose representation we hope to foster research in fine-grained activity recognition. ---	Title: Datasets on Object Manipulation and Interaction: A Survey Section 1: INTRODUCTION Description 1: Write about the significance and the role of datasets in scientific fields, especially in object manipulation research, and introduce the purpose of the survey. Section 2: DATASETS OF COOKING ACTIVITY Description 2: Present datasets related to cooking activities, providing details on the modalities, activities performed, and annotations, along with their relevance to object manipulation. Section 3: DATASETS OF ACTIVITIES OF DAILY LIVING (ADL) Description 3: Discuss datasets encompassing general daily activities, specifying the included modalities, types of activities, annotations, and their application to object manipulation research. Section 4: DATASET SUMMARY Description 4: Summarize the findings from the reviewed datasets, highlighting shared annotated activities, object identifiability, and forms of temporal segmentation, and provide a complete list of annotated activities. Section 5: CONCLUSION Description 5: Provide a summary of the review, reiterate the significance of the surveyed datasets for object manipulation research, and offer suggestions for future datasets. Section 6: ACKNOWLEDGEMENT Description 6: Acknowledge the financial and academic support received for conducting the survey and compiling the review.
Survey on Prediction Algorithms in Smart Homes	16	--- paper_title: Bio-signal based control in assistive Robots: A survey paper_content: Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized. --- paper_title: The role of prediction algorithms in the MavHome smart home architecture paper_content: The goal of the MavHome project is to create a home that acts as a rational agent. The agent seeks to maximize inhabitant comfort and minimize operation cost. To achieve these goals, the agent must be able to predict the mobility patterns and device usages of the inhabitants. We introduce the MavHome project and its underlying architecture. The role of prediction algorithms within the architecture is discussed, and three prediction algorithms that are central to home operations are presented. We demonstrate the effectiveness of these algorithms on synthetic and/or actual smart home data. --- paper_title: A Review of Smart Homes—Past, Present, and Future paper_content: A smart home is an application of ubiquitous computing in which the home environment is monitored by ambient intelligence to provide context-aware services and facilitate remote home control. This paper presents an overview of previous smart home research as well as the associated technologies. A brief discussion on the building blocks of smart homes and their interrelationships is presented. It describes collective information about sensors, multimedia devices, communication protocols, and systems, which are widely used in smart home implementation. Special algorithms from different fields and their significance are explained according to their scope of use in smart homes. This paper also presents a concrete guideline for future researchers to follow in developing a practical and sustainable smart home. --- paper_title: MavHome: an agent-based smart home paper_content: The goal of the MavHome (Managing An Intelligent Versatile Home) project is to create a home that acts as an intelligent agent. In this paper we introduce the MavHome architecture. The role of prediction algorithms within the architecture is discussed, and a meta-predictor is presented which combines the strengths of multiple approaches to inhabitant action prediction. We demonstrate the effectiveness of these algorithms on smart home data. --- paper_title: Activity Recognition in the Home Using Simple and Ubiquitous Sensors paper_content: In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used. --- paper_title: Behavioural pattern identification and prediction in intelligent environments paper_content: In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments. --- paper_title: Predicting inhabitant action using action and task models with application to smart homes paper_content: An intelligent home is likely in the near future. An important ingredient in an intelligent environment such as a home is prediction – of the next low-level action, the next location, and the next high-level task that an inhabitant is likely to perform. In this paper we model inhabitant actions as states in a simple Markov model. We introduce an enhancement to this basic approach, the Task-based Markov model (TMM) method. TMM discovers high-level inhabitant tasks using the supplied unlabeled data. We investigate clustering of actions to identify tasks, and integrate clusters into a hidden Markov model that predicts the next inhabitant action. We validate our approach and observe that for simulated data we achieve good accuracy using both the simple Markov model and the TMM, whereas on real data we see that simple Markov models outperform the TMM. We also perform an analysis of the performance of the HMM in the framework of the TMM when diverse patterns are introduced into the data. --- paper_title: Bayesian Networks Structure Learning for Activity Prediction in Smart Homes paper_content: This paper presents a sequence-based activity prediction approach which uses Bayesian networks in a novel two-step process to predict both activities and their corresponding features. In addition to the proposed model, we also present the results of several search and score (SaS) and constraint-based (CB) Bayesian structure learning algorithms. The activity prediction performance of the proposed model is compared with the naive Bayes and the other aforementionedSaS and CB algorithms. The experimental results are performed on real data collected from a smart home over the period of five months. The results suggest the superior activity prediction accuracy of the proposed network over the resulting networks of the mentioned Bayesian network structure learning algorithms. --- paper_title: Discovering models of software processes from event-based data paper_content: Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be difficult, costly, and error prone. This presents a practical barrier to the adoption of process technologies, which would be lowered by automated assistance in creating formal models. To this end, we have developed a data analysis technique that we term process discovery. Under this technique, data describing process events are first captured from an on-going process and then used to generate a formal model of the behavior of that process. In this article we describe a Markov method that we developed specifically for process discovery, as well as describe two additional methods that we adopted from other domains and augmented for our purposes. The three methods range from the purely algorithmic to the purely statistical. We compare the methods and discuss their application in an industrial case study. --- paper_title: Modeling human behavior from simple sensors in the home paper_content: Pervasive sensors in the home have a variety of applications including energy minimization, activity monitoring for elders, and tutors for household tasks such as cooking. Many of the common sensors today are binary, e.g. IR motion sensors, door close sensors, and floor pressure pads. Predicting user behavior is one of the key enablers for applications. While we consider smart home data here, the general problem is one of predicting discrete human actions. Drawing on Activity Theory, the language as action principle, and speech understanding research, we argue that smoothed n-grams are very appropriate for this task. We built such a model and applied it to data gathered from 3 smart home installations. The data showed a classic Zipf or power-law distribution, similar to speech and language. We found that the predictive accuracy of the n-gram model ranges from 51% to 39%, which is significantly above the baseline for the deployments of 16, 76 and 70 sensors. While we cannot directly compare this result with other work (lack of shared data), by examination of high entropy zones in the datasets (e.g. the kitchen triangle) we argue that accuracies around 50% are best possible for this task. --- paper_title: A tutorial on hidden Markov models and selected applications in speech recognition paper_content: This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. > --- paper_title: AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS paper_content: We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective make sit possible to consider novel generalizations to hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs. --- paper_title: Location aware resource management in smart homes paper_content: The rapid advances in a wide range of wireless access technologies along with the efficient use of smart spaces have already set the stage for the development of smart homes. Context-awareness is perhaps the most salient feature in these intelligent computing platforms. The "location" information of the users plays a vital role in defining this context. To extract the best performance and efficacy of such smart computing environments, one needs a scalable, technology-independent location service. We have developed a predictive framework for location-aware resource optimization in smart homes. The underlying compression mechanism helps in efficient learning of an inhabitant's movement (location) profiles in the symbolic domain. The concept of Asymptotic Equipartition Property (AEP) in information theory helps to predict the inhabitant's future location as well as most likely path-segments with good accuracy. Successful prediction helps in pro-active resource management and on-demand operations of automated devices along the inhabitant's future paths and locations - thus providing the necessary comfort at a near-optimal cost. Simulation results on a typical smart home floor plans corroborate this high prediction success and demonstrate sufficient reduction in daily energy-consumption, manual operations and time spent by the inhabitant which are considered as a fair measure of his/her comfort. --- paper_title: Energy storage management in smart homes based on resident activity of daily life recognition paper_content: Recently, home energy storage system is emerging as one of the main driving forces to prompt the development of the future smart grid. By leveraging time-based electricity pricing, the home energy storage system can store energy during off-peak periods and supply energy to residential customers during on-peak periods, such that the stress on main power system can be relieved. Yet, an efficient use of home energy storage system is still a challenging issue, due to the nonlinear properties of the battery in terms of energy conversion loss and shortened battery life, and the randomness in residential energy demand. In this paper, we investigate the utilization of smart home monitoring and communication technologies to recognize the resident activity of daily life (ADL) and propose a non-homogeneous hidden Markov model (NHMM) to characterize the residential energy demand. An optimal energy storage management problem is formulated by taking into account the NHMM and nonlinear battery properties. This problem belongs to a class of adaptive stochastic control problems in smart grid with nonlinear value functions. In order to solve this problem efficiently, piecewise linear approximation is applied to the energy conversion function, and a state-dependent multi-threshold policy is proposed and proved to be optimal. The performance of the proposed energy management scheme is evaluated via a case study based on CASAS smart home dataset collected in real life by Washington State University. Numerical results indicate that our proposed energy storage management scheme can achieve energy cost savings, in comparison with existing schemes with uniform and non-uniform discharging profiles. --- paper_title: Using duration to learn activities of daily living in a smart home environment paper_content: Recognition of inhabitants' activities of daily living (ADLs) is an important task in smart homes to support assisted living for elderly people aging in place. However, uncertain information brings challenge to activity recognition which can be categorised into environmental uncertainties from sensor readings and user uncertainties of variations in the ways to carry out activities in different contexts, or by different users within the same environment. To address the challenges of these two types of uncertainty, in this paper, we introduce the innovative idea of incorporating activity duration into the framework of learning inhabitants' behaviour patterns on carrying out ADLs in smart home environment. A probabilistic learning algorithm is proposed with duration information in the context of multi-inhabitants in a single home environment. The prediction is for both inhabitant and ADL using the learned model representing what activity is carried out and who performed it. Experiments are designed for the evaluation of duration information in identifying activities and inhabitants. Real data have been collected in a smart kitchen laboratory, and realistic synthetic data are generated for evaluation. Evaluations show encouraging results for higher-level activity identification and improvement on inhabitant and activity prediction in the challenging situation of incomplete observation due to unreliable sensors compared to models that are derived with no duration information. The approach also provides a potential opportunity to identify inhabitants' concept drift in long-term monitoring and respond to a deteriorating situation at as early stage as possible. --- paper_title: A Knowledge-Driven Approach to Activity Recognition in Smart Homes paper_content: This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes. The approach goes beyond the traditional data-centric methods for activity recognition in three ways. First, it makes extensive use of domain knowledge in the life cycle of activity recognition. Second, it uses ontologies for explicit context and activity modeling and representation. Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition. In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies. We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process. Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition. The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory. We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios. An average activity recognition rate of 94.44 percent was achieved and the average recognition runtime per recognition operation was measured as 2.5 seconds. --- paper_title: Intervention of non-inhabitant activities detection in smart home environment paper_content: Inhabitants daily activity form a pattern in their daily life which has important things in smart home. These patterns can be used to recognize the inhabitant activity that is useful to enhance the smart home services like energy efficiency service, where these patterns can be used as inhabitant behavior to reduce an unnecessary appliances or lightings usage based on the activities their conduct. Recognition accuracy is important things for providing particular service needs on automation process in smart home, but activity recognition faces many challenges in real world cause of diversity and complexity of the activities. Inter-subject variability activities often appear in real world situation that accuracy of recognition process can be affected. For instance, there is a possible situation where family or colleague visits to inhabitant's home in long term. Non-inhabitant activities may conduct with a different way or different behavior than inhabitant does. This situation is producing activities where is not carried from legitimate inhabitant. In this paper, we propose a method to overcome the activity recognition issue that commonly occurred. Our proposed method using temporal relation approach, which can detect a non-inhabitant activity. This approach is separating detected activities from inhabitant's observed activities, so the activity recognition will perform effectively. We assess the effectiveness of our approach using Activity Daily Living (ADL) provided by WSU Smart Home Project dataset. --- paper_title: Monitoring Activities of Daily Living in Smart Homes: Understanding human behavior paper_content: Monitoring the activities of daily living (ADLs) and detection of deviations from previous patterns is crucial to assessing the ability of an elderly person to live independently in their community and in early detection of upcoming critical situations. "Aging in place" for an elderly person is one key element in ambient assisted living (AAL) technologies. --- paper_title: Use of Prediction Algorithms in Smart Homes paper_content: Smart Homes' or 'Intelligent Homes' are capable in making smart or rational decisions and increase home automation. This is done to maximize inhabitant comfort and minimize operation cost. Tracing and predicting the mobility patterns and usages of devices by the inhabitant, sets a step towards the objective. The paper discusses in detail, the role of certain Prediction algorithms to bring about next event recognition. Further, an Episode Discovery helps in finding the frequency of occurrence of these events and targeting the particular events for automation. The effectiveness of the Prediction algorithms used is demonstrated ;making it clear how they prove to be a key component in the efficient implementation of a Smart Home architecture. --- paper_title: Learning to Control a Smart Home Environment paper_content: The goal of the MavHome (Managing An Intelligent Versatile Home) project is to create a home that acts as an intelligent agent. In this paper, we introduce the MavHome architecture and two learning algorithms that play central roles in the smart home. The first algorithm predicts actions the inhabitant will take in the home. The second algorithm learns a policy to control the home. Effectiveness of the algorithms on smart home data is presented and we document the application of the technology in a working smart home environment. --- paper_title: LeZi-update: an information-theoretic approach to track mobile users in PCS networks paper_content: The complexity of the mobility tracking problem in a cellular environment has been characterized under an informationtheoretic framework. Shannon’s entropy measure is identified as a basis for comparing user mobility models. By building and maintaining a dictionary of individual user’s path updates (as opposed to the widely used location updates), the proposed adaptive on-line algorithm can learn subscribers’ profiles. This technique evolves out of the concepts of lossless compression. The compressibility of the variable-to-fixed length encoding of the acclaimed LempelZiv family of algorithms reduces the update cost, whereas their built-in predictive power can be effectively used to reduce paging cost. --- paper_title: Active Lezi: An incremental parsing algorithm for sequential prediction paper_content: Prediction is an important component in a variety of domains in Artificial Intelligence and Machine Learning, in order that Intelligent Systems may make more informed and reliable decisions. Certain domains require that prediction be performed on sequences of events that can typically be modeled as stochastic processes. This work presents Active LeZi (ALZ), a sequential prediction algorithm that is founded on an Information Theoretic approach, and is based on the acclaimed LZ78 family of data compression algorithms. The efficacy of this algorithm in a typical Smart Environment – the Smart Home, is demonstrated by employing this algorithm to predict device usage in the home. The performance of this algorithm is tested on synthetic data sets that are representative of typical interactions between a Smart Home and the inhabitant. In addition, for the Smart Home environment, we introduce a method of learning a measure of the relative time between actions using ALZ, and demonstrate the efficacy of this approach on synthetic Smart Home data. --- paper_title: An Improved Position Prediction Algorithm Based on Active LeZi in Smart Home paper_content: In smart home environments, each inhabitant has their own habits, these habits have certain periodic and time-varying characteristics. According to these characteristics, an improved Time-varying LeZi algorithm (TALZ) is proposed, which is based on Active LeZi algorithm (ALZ). Compared to other models, the results show that TALZ has smaller time complexity, higher prediction accuracy of laboratory data acquisition, and it is suitable for real-time, accurate position prediction in smart home environments. --- paper_title: Compression of Individual Sequences via Variable-Rate Coding paper_content: Compressibility of individual sequences by the class of generalized finite-state information-lossless encoders is investigated. These encoders can operate in a variable-rate mode as well as a fixed-rate one, and they allow for any finite-state scheme of variable-length-to-variable-length coding. For every individual infinite sequence x a quantity \rho(x) is defined, called the compressibility of x , which is shown to be the asymptotically attainable lower bound on the compression ratio that can be achieved for x by any finite-state encoder. This is demonstrated by means of a constructive coding theorem and its converse that, apart from their asymptotic significance, also provide useful performance criteria for finite and practical data-compression tasks. The proposed concept of compressibility is also shown to play a role analogous to that of entropy in classical information theory where one deals with probabilistic ensembles of sequences rather than with individual sequences. While the definition of \rho(x) allows a different machine for each different sequence to be compressed, the constructive coding theorem leads to a universal algorithm that is asymptotically optimal for all sequences. --- paper_title: A customized flocking algorithm for swarms of sensors tracking a swarm of targets paper_content: Wireless mobile sensor networks (WMSNs) are groups of mobile sensing agents with multi-modal sensing capabilities that communicate over wireless networks. WMSNs have more flexibility in terms of deployment and exploration abilities over static sensor networks. Sensor networks have a wide range of applications in security and surveillance systems, environmental monitoring, data gathering for network-centric healthcare systems, monitoring seismic activities and atmospheric events, tracking traffic congestion and air pollution levels, localization of autonomous vehicles in intelligent transportation systems, and detecting failures of sensing, storage, and switching components of smart grids. The above applications require target tracking for processes and events of interest occurring in an environment. Various methods and approaches have been proposed in order to track one or more targets in a pre-defined area. Usually, this turns out to be a complicated job involving higher order mathematics coupled with artificial intelligence due to the dynamic nature of the targets. To optimize the resources we need to have an approach that works in a more straightforward manner while resulting in fairly satisfactory data. In this paper we have discussed the various cases that might arise while flocking a group of sensors to track targets in a given environment. The approach has been developed from scratch although some basic assumptions have been made keeping in mind some previous theories. This paper outlines a customized approach for feasibly tracking swarms of targets in a specific area so as to minimize the resources and optimize tracking efficiency. --- paper_title: A Knowledge-Driven Approach to Activity Recognition in Smart Homes paper_content: This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes. The approach goes beyond the traditional data-centric methods for activity recognition in three ways. First, it makes extensive use of domain knowledge in the life cycle of activity recognition. Second, it uses ontologies for explicit context and activity modeling and representation. Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition. In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies. We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process. Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition. The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory. We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios. An average activity recognition rate of 94.44 percent was achieved and the average recognition runtime per recognition operation was measured as 2.5 seconds. --- paper_title: A Minimalist Flocking Algorithm for Swarm Robots paper_content: In this paper we describe a low-end and easy to implement flocking algorithm which was developed for very simple swarm robots and which works without communication, memory or global information. By adapting traditional flocking algorithms and eliminating the need for communication, we created an algorithm with emergent flocking properties. We analyse its potential of aggregating an initially scattered robot swarm, which is not a trivial task for robots that only have local information. --- paper_title: SPEED: An Inhabitant Activity Prediction Algorithm for Smart Homes paper_content: This paper proposes an algorithm, called sequence prediction via enhanced episode discovery (SPEED), to predict inhabitant activity in smart homes. SPEED is a variant of the sequence prediction algorithm. It works with the episodes of smart home events that have been extracted based on the on -off states of home appliances. An episode is a set of sequential user activities that periodically occur in smart homes. The extracted episodes are processed and arranged in a finite-order Markov model. A method based on prediction by partial matching (PPM) algorithm is applied to predict the next activity from the previous history. The result shows that SPEED achieves an 88.3% prediction accuracy, which is better than LeZi Update, Active LeZi, IPAM, and C4.5. --- paper_title: Nash Q-Learning for General-Sum Stochastic Games paper_content: We extend Q-learning to a noncooperative multiagent context, using the framework of general-sum stochastic games. A learning agent maintains Q-functions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Q-values. This learning protocol provably converges given certain restrictions on the stage games (defined by Q-values) that arise during learning. Experiments with a pair of two-player grid games suggest that such restrictions on the game structure are not necessarily required. Stage games encountered during learning in both grid environments violate the conditions. However, learning consistently converges in the first grid game, which has a unique equilibrium Q-function, but sometimes fails to converge in the second, which has three different equilibrium Q-functions. In a comparison of offline learning performance in both games, we find agents are more likely to reach a joint optimal path with Nash Q-learning than with a single-agent Q-learning method. When at least one agent adopts Nash Q-learning, the performance of both agents is better than using single-agent Q-learning. We have also implemented an online version of Nash Q-learning that balances exploration with exploitation, yielding improved performance. --- paper_title: Context-aware resource management in multi-inhabitant smart homes a Nash H-learning based approach paper_content: A smart home aims at building intelligence automation with a goal to provide its inhabitants with maximum possible comfort, minimize the resource consumption and thus overall cost of maintaining the home. 'Context awareness' is perhaps the most salient feature of such an intelligent environment. Clearly, an inhabitant's mobility and activities play a significant role in defining his contexts in and around the home. Although there exists an optimal algorithm for location and activity tracking of a single inhabitant, the correlation and dependence between multiple inhabitants' contexts within the same environment make the location and activity tracking more challenging. In this paper, we first prove that the optimal location prediction across multiple inhabitants in smart homes is an NP-hard problem. Next, to capture the correlation and interactions of different inhabitants' movements (and hence activities), we develop a novel framework based on a game theoretic, Nash H-learning approach that attempts to minimize the joint location uncertainty. The framework achieves a Nash equilibrium such that no inhabitant is given preference over others. This results in more accurate prediction of contexts and better adaptive control of automated devices, leading to a mobility-aware resource (say, energy) management scheme in multi-inhabitant smart homes. Experimental results demonstrate that the proposed framework is capable of adaptively controlling a smart environment, thus reducing energy consumption and enhancing the comfort of the inhabitants. --- paper_title: Research on Mining Association Behavior of Smart Home Users Based on Apriori Algorithm paper_content: This paper mainly studies the improvement of Apriori algorithm based on smart home system. Association rule algorithm has the promotion effect to the analysis of the smart home user behavior. Apriori algorithm is one of the classical algorithms of association rules. However, the efficiency of the Apriori algorithm is not high, there is stillroom for improvement in smart home. Based on the characteristics of smart home system and terminal operation platform, this paper optimizes the Apriori algorithm. By introducing the auxiliary matrix, the number of scanning database is reduced, and the speed of the support solving is accelerated. Simulation experiment results show that the Apriori algorithm optimized is more efficient than the optimal algorithm. In rapid advocacy of the Internet of things,with the improvement of people's living standards and housing conditions, digital intelligent have walk into people's life. Smart home products have already entered the vision of ordinary families and greatly simplified people's life. Which make it possible to develop a new lifestyle. Smart home products can not just stay in the original remote control mode. Packing multiple behaviors ofusers and concurrent operation will further streamline operations that improve the intelligence level. Custom profile is a manifestation. Technology product is continually improving the experience of users and to simplify user operations, which is the next target of smart products. According to the user's behavior,mining high degree of user behavior associatedthrough data mining analysis and then recommended as a personalized user profile. Which replacing the custom profiles of trouble, adding intelligence andmeet user preferences in operation. In data mining, association ruleis an importantissue. Association rules can find the existence of things that may exist or contactfrom the data behind. Which could be applied to the association study of user behavior in the smart home. The most classical association rule algorithm is Apriori algorithm. Apriori algorithm is an iterative manner based mining method by searching frequent item set. The mining process of Apriori algorithm is complex because of the iterative. Based on the characteristics of smart home system, this paper proposes an improved method of Apriori algorithm, which makes it more suitable for the implementation and application of the terminal platform. --- paper_title: Keeping the Resident in the Loop: Adapting the Smart Home to the User paper_content: Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data. --- paper_title: A unified view of the apriori-based algorithms for frequent episode discovery paper_content: Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discovery methods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders. --- paper_title: Improving home automation by discovering regularly occurring device usage patterns paper_content: The data stream captured by recording inhabitant-device interactions in an environment can be mined to discover significant patterns, which an intelligent agent could use to automate device interactions. However, this knowledge discovery problem is complicated by several challenges, such as excessive noise in the data, data that does not naturally exist as transactions, a need to operate in real time, and a domain where frequency may not be the best discriminator. We propose a novel data mining technique that addresses these challenges and discovers regularly-occurring interactions with a smart home. We also discuss a case study that shows the data mining technique can improve the accuracy of two prediction algorithms, thus demonstrating multiple uses for a home automation system. Finally, we present an analysis of the algorithm and results obtained using inhabitant interactions. --- paper_title: A Time Series Based Sequence Prediction Algorithm to Detect Activities of Daily Living in Smart Home paper_content: OBJECTIVES ::: The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included. ::: ::: ::: METHODS ::: The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge. ::: ::: ::: RESULTS ::: The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED. ::: ::: ::: CONCLUSIONS ::: Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people. --- paper_title: TEMPORAL MODELING OF HUMAN ACTIVITY IN SMART HOMES paper_content: Recognition of human activity is a potential challenge to design an effective smart home. The paper proposed a novel algorithm to recognize activities of daily living (ADL) of the resident. It provides analysis and mathematical modeling of temporal intervals of the event. The opposite entity states are used to extract the pattern of event sequence. Each extracted episode represents a distinct task of the resident. Result shows that, the algorithm can successfully identify 135 unique tasks of different lengths with temporal characteristics. The analysis confirms that temporal pattern follows normal distribution which can be modeled by Gaussian function. --- paper_title: A Novel Deployment Scheme for Green Internet of Things paper_content: The Internet of Things (IoT) has been realized as one of the most promising networking paradigms that bridge the gap between the cyber and physical world. Developing green deployment schemes for IoT is a challenging issue since IoT achieves a larger scale and becomes more complex so that most of the current schemes for deploying wireless sensor networks (WSNs) cannot be transplanted directly in IoT. This paper addresses this challenging issue by proposing a deployment scheme to achieve green networked IoT. The contributions made in this paper include: 1) a hierarchical system framework for a general IoT deployment, 2) an optimization model on the basis of proposed system framework to realize green IoT, and 3) a minimal energy consumption algorithm for solving the presented optimization model. The numerical results on minimal energy consumption and network lifetime of the system indicate that the deployment scheme proposed in this paper is more flexible and energy efficient compared to typical WSN deployment scheme; thus is applicable to the green IoT deployment. --- paper_title: Toward Efficient Multi-Keyword Fuzzy Search Over Encrypted Outsourced Data With Accuracy Improvement paper_content: Keyword-based search over encrypted outsourced data has become an important tool in the current cloud computing scenario. The majority of the existing techniques are focusing on multi-keyword exact match or single keyword fuzzy search. However, those existing techniques find less practical significance in real-world applications compared with the multi-keyword fuzzy search technique over encrypted data. The first attempt to construct such a multi-keyword fuzzy search scheme was reported by Wang et al. , who used locality-sensitive hashing functions and Bloom filtering to meet the goal of multi-keyword fuzzy search. Nevertheless, Wang’s scheme was only effective for a one letter mistake in keyword but was not effective for other common spelling mistakes. Moreover, Wang’s scheme was vulnerable to server out-of-order problems during the ranking process and did not consider the keyword weight. In this paper, based on Wang et al. ’s scheme, we propose an efficient multi-keyword fuzzy ranked search scheme based on Wang et al. ’s scheme that is able to address the aforementioned problems. First, we develop a new method of keyword transformation based on the uni-gram, which will simultaneously improve the accuracy and creates the ability to handle other spelling mistakes. In addition, keywords with the same root can be queried using the stemming algorithm. Furthermore, we consider the keyword weight when selecting an adequate matching file set. Experiments using real-world data show that our scheme is practically efficient and achieve high accuracy. --- paper_title: Incremental learning for ν -Support Vector Regression paper_content: The ? -Support Vector Regression ( ? -SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ? on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ? -Support Vector Classification ( ? -SVC) (Scholkopf et?al., 2000), ? -SVR introduces an additional linear term into its objective function. Thus, directly applying the accurate on-line ? -SVC algorithm (AONSVM) to ? -SVR will not generate an effective initial solution. It is the main challenge to design an incremental ? -SVR learning algorithm. To overcome this challenge, we propose a special procedure called initial adjustments in this paper. This procedure adjusts the weights of ? -SVC based on the Karush-Kuhn-Tucker (KKT) conditions to prepare an initial solution for the incremental learning. Combining the initial adjustments with the two steps of AONSVM produces an exact and effective incremental ? -SVR learning algorithm (INSVR). Theoretical analysis has proven the existence of the three key inverse matrices, which are the cornerstones of the three steps of INSVR (including the initial adjustments), respectively. The experiments on benchmark datasets demonstrate that INSVR can avoid the infeasible updating paths as far as possible, and successfully converges to the optimal solution. The results also show that INSVR is faster than batch ? -SVR algorithms with both cold and warm starts. ---	Title: Survey on Prediction Algorithms in Smart Homes Section 1: Introduction Description 1: Introduce the concept of smart homes and the importance of prediction algorithms in enabling intelligent control and automation. Section 2: Background of Prediction Algorithms Description 2: Provide background information on prediction algorithms, including system models and data used in the context of smart homes. Section 3: System Models Description 3: Discuss various system models that are pertinent to prediction algorithms in smart homes, such as Markov models and Bayesian networks. Section 4: Activities of Daily Living (ADLs) Description 4: Explain how prediction algorithms utilize the concept of activities of daily living (ADLs) to forecast future events based on historical data in smart home environments. Section 5: Location Prediction Description 5: Discuss the importance of predicting the location of inhabitants within a smart environment and various methodologies used for this task. Section 6: Review of Prediction Algorithms Description 6: Extensively review several prediction algorithms used in smart homes, highlighting their features, strengths, and weaknesses. Section 7: Active LeZi Description 7: Detail the technical aspects and application of the Active LeZi algorithm in smart environments. Section 8: Flocking Description 8: Describe the flocking algorithm, its basis in clustering techniques, and its applications in smart homes. Section 9: SPEED Description 9: Examine the SPEED algorithm and how it uses periodic tendencies to predict user activities in smart homes. Section 10: Nash H-Learning Description 10: Analyze the Nash H-learning algorithm, focusing on its applications in predicting the location of multiple users within a smart home. Section 11: Apriori Algorithm Description 11: Explain the Apriori algorithm and its enhancements for use in smart home environments. Section 12: Summary of Algorithms Description 12: Summarize and compare the reviewed prediction algorithms, emphasizing their similarities and differences in approach and data handling. Section 13: Algorithm Enhancements Description 13: Discuss various enhancements and extensions to prediction algorithms that aim to improve performance in smart home applications. Section 14: Episode Discovery Description 14: Describe the episode discovery method and its role in enhancing prediction algorithms by identifying significant patterns in the data. Section 15: Temporal Relations Description 15: Explain the importance of temporal relations in improving prediction accuracy and anomaly detection within smart home environments. Section 16: Conclusion Description 16: Summarize the key points discussed in the paper and highlight the significance of prediction algorithms in the development of intelligent smart home systems.
Graphical Model-based Approaches to Target Tracking in Sensor Networks: An Overview of Some Recent Work and Challenges	9	--- paper_title: Probabilistic Networks and Expert Systems paper_content: From the Publisher: ::: Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors over a number of years, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms, emphasizing those cases in which exact answers are obtainable. The book will be of interest to researchers and graduate students in artificial intelligence who desire an understanding of the mathematical and statistical basis of probabilistic expert systems, and to students and research workers in statistics wanting an introduction to this fascinating and rapidly developing field. The careful attention to detail will also make this work an important reference source for all those involved in the theory and applications of probabilistic expert systems. --- paper_title: Distributed fusion in sensor networks: a graphical models perspective paper_content: This paper presents an overview of research conducted to bridge the rich field of graphical models with the emerging field of data fusion for sensor networks. Both theoretical issues and prototyping applications are discussed in addition to suggesting new lines of reasoning. --- paper_title: Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues paper_content: Preface * 1 Probability Review * 2 Discrete Time Markov Models * 3 Recurrence and Ergodicity * 4 Long Run Behavior * 5 Lyapunov Functions and Martingales * 6 Eigenvalues and Nonhomogeneous Markov Chains * 7 Gibbs Fields and Monte Carlo Simulation * 8 Continuous-Time Markov Models 9 Poisson Calculus and Queues * Appendix * Bibliography * Author Index * Subject Index --- paper_title: Tree consistency and bounds on the performance of the max-product algorithm and its generalizations paper_content: Finding the maximum a posteriori (MAP) assignment of a discrete-state distribution specified by a graphical model requires solving an integer program. The max-product algorithm, also known as the max-plus or min-sum algorithm, is an iterative method for (approximately) solving such a problem on graphs with cycles. We provide a novel perspective on the algorithm, which is based on the idea of reparameterizing the distribution in terms of so-called pseudo-max-marginals on nodes and edges of the graph. This viewpoint provides conceptual insight into the max-product algorithm in application to graphs with cycles. First, we prove the existence of max-product fixed points for positive distributions on arbitrary graphs. Next, we show that the approximate max-marginals computed by max-product are guaranteed to be consistent, in a suitable sense to be defined, over every tree of the graph. We then turn to characterizing the nature of the approximation to the MAP assignment computed by max-product. We generalize previous work by showing that for any graph, the max-product assignment satisfies a particular optimality condition with respect to any subgraph containing at most one cycle per connected component. We use this optimality condition to derive upper bounds on the difference between the log probability of the true MAP assignment, and the log probability of a max-product assignment. Finally, we consider extensions of the max-product algorithm that operate over higher-order cliques, and show how our reparameterization analysis extends in a natural manner. --- paper_title: Distributed fusion in sensor networks: a graphical models perspective paper_content: This paper presents an overview of research conducted to bridge the rich field of graphical models with the emerging field of data fusion for sensor networks. Both theoretical issues and prototyping applications are discussed in addition to suggesting new lines of reasoning. --- paper_title: Constructing free-energy approximations and generalized belief propagation algorithms paper_content: Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems that is exact when the factor graph is a tree, but only approximate when the factor graph has cycles. We show that BP fixed points correspond to the stationary points of the Bethe approximation of the free energy for a factor graph. We explain how to obtain region-based free energy approximations that improve the Bethe approximation, and corresponding generalized belief propagation (GBP) algorithms. We emphasize the conditions a free energy approximation must satisfy in order to be a "valid" or "maxent-normal" approximation. We describe the relationship between four different methods that can be used to generate valid approximations: the "Bethe method", the "junction graph method", the "cluster variation method", and the "region graph method". Finally, we explain how to tell whether a region-based approximation, and its corresponding GBP algorithm, is likely to be accurate, and describe empirical results showing that GBP can significantly outperform BP. --- paper_title: Distributed data association for multi-target tracking in sensor networks paper_content: Associating sensor measurements with target tracks is a fundamental and challenging problem in multi-target tracking. The problem is even more ::: challenging in the context of sensor networks, since association is coupled ::: across the network, yet centralized data processing is in general ::: infeasible due to power and bandwidth limitations. Hence efficient, distributed solutions are needed. We propose techniques based on graphical models to efficiently solve such data association problems in sensor networks. Our approach scales well with the number of sensor nodes in the network, and it is well--suited for distributed implementation. Distributed inference is realized by a message--passing algorithm which requires iterative, parallel exchange of information among neighboring nodes on the graph. So as to address trade--offs between inference performance and communication costs, we also propose a communication--sensitive form of message--passing that is capable of achieving near--optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data. --- paper_title: A Distributed Algorithm for Managing Multi-target Identities in Wireless Ad-hoc Sensor Networks paper_content: This paper presents a scalable distributed algorithm for computing and maintaining multi-target identity information. The algorithm builds on a novel representational framework, Identity-Mass Flow, to overcome the problem of exponential computational complexity in managing multi-target identity explicitly. The algorithm uses local information to efficiently update the global multi-target identity information represented as a doubly stochastic matrix, and can be efficiently mapped to nodes in a wireless ad hoc sensor network. The paper describes a distributed implementation of the algorithm in sensor networks. Simulation results have validated the Identity-Mass Flow framework and demonstrated the feasibility of the algorithm. --- paper_title: Data association based on optimization in graphical models with application to sensor networks paper_content: We propose techniques based on graphical models for efficiently solving data association problems arising in multiple target tracking with distributed sensor networks. Graphical models provide a powerful framework for representing the statistical dependencies among a collection of random variables, and are widely used in many applications (e.g., computer vision, error-correcting codes). We consider two different types of data association problems, corresponding to whether or not it is known a priori which targets are within the surveillance range of each sensor. We first demonstrate how to transform these two problems to inference problems on graphical models. With this transformation, both problems can be solved efficiently by local message-passing algorithms for graphical models, which solve optimization problems in a distributed manner by exchange of information among neighboring nodes on the graph. Moreover, a suitably reweighted version of the max-product algorithm yields provably optimal data associations. These approaches scale well with the number of sensors in the network, and moreover are well suited to being realized in a distributed fashion. So as to address trade-offs between performance and communication costs, we propose a communication-sensitive form of message-passing that is capable of achieving near-optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data. --- paper_title: Distributed data association for multi-target tracking in sensor networks paper_content: Associating sensor measurements with target tracks is a fundamental and challenging problem in multi-target tracking. The problem is even more ::: challenging in the context of sensor networks, since association is coupled ::: across the network, yet centralized data processing is in general ::: infeasible due to power and bandwidth limitations. Hence efficient, distributed solutions are needed. We propose techniques based on graphical models to efficiently solve such data association problems in sensor networks. Our approach scales well with the number of sensor nodes in the network, and it is well--suited for distributed implementation. Distributed inference is realized by a message--passing algorithm which requires iterative, parallel exchange of information among neighboring nodes on the graph. So as to address trade--offs between inference performance and communication costs, we also propose a communication--sensitive form of message--passing that is capable of achieving near--optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data. --- paper_title: Distributed Group Management in Sensor Networks: Algorithms and Applications to Localization and Tracking paper_content: The tradeoff between performance and scalability is a fundamental issue in distributed sensor networks. In this paper, we propose a novel scheme to efficiently organize and utilize network resources for target localization. Motivated by the essential role of geographic proximity in sensing, sensors are organized into geographically local collaborative groups. In a target tracking context, we present a dynamic group management method to initiate and maintain multiple tracks in a distributed manner. Collaborative groups are formed, each responsible for tracking a single target. The sensor nodes within a group coordinate their behavior using geographically-limited message passing. Mechanisms such as these for managing local collaborations are essential building blocks for scalable sensor network applications. --- paper_title: Information-driven dynamic sensor collaboration paper_content: This article overviews the information-driven approach to sensor collaboration in ad hoc sensor networks. The main idea is for a network to determine participants in a "sensor collaboration" by dynamically optimizing the information utility of data for a given cost of communication and computation. A definition of information utility is introduced, and several approximate measures of the information utility are developed for reasons of computational tractability. We illustrate the use of this approach using examples drawn from tracking applications. --- paper_title: Distributed data association for multi-target tracking in sensor networks paper_content: Associating sensor measurements with target tracks is a fundamental and challenging problem in multi-target tracking. The problem is even more ::: challenging in the context of sensor networks, since association is coupled ::: across the network, yet centralized data processing is in general ::: infeasible due to power and bandwidth limitations. Hence efficient, distributed solutions are needed. We propose techniques based on graphical models to efficiently solve such data association problems in sensor networks. Our approach scales well with the number of sensor nodes in the network, and it is well--suited for distributed implementation. Distributed inference is realized by a message--passing algorithm which requires iterative, parallel exchange of information among neighboring nodes on the graph. So as to address trade--offs between inference performance and communication costs, we also propose a communication--sensitive form of message--passing that is capable of achieving near--optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data. --- paper_title: An Efficient Message-Passing Algorithm for Optimizing Decentralized Detection Networks paper_content: A promising feature of emerging wireless sensor networks is the opportunity for each spatially-distributed node to measure its local state and transmit only information relevant to effective global decision-making. An equally important design objective, as a result of each node's finite power, is for measurement processing to satisfy explicit constraints on, or perhaps make selective use of, the distributed algorithmic resources. We formulate this multi-objective design problem within the Bayesian decentralized detection paradigm, modeling resource constraints by a directed acyclic network with low-rate, unreliable communication links. Existing team theory establishes when necessary optimality conditions reduce to a convergent iterative algorithm to be executed offline (i.e., before measurements are processed). Even so, this offline algorithm has exponential complexity in the number of nodes, and its distributed implementation assumes a fully-connected communication network. We state conditions under which the offline algorithm admits an efficient message-passing interpretation, featuring linear complexity and a natural distributed implementation. We experiment with a simulated network of binary detectors, applying the message-passing algorithm to optimize the achievable tradeoff between global detection performance and network-wide online communication. The empirical analysis also exposes a design tradeoff between constraining in-network processing to preserve resources (per online measurement) and then having to consume resources (per offline reorganization) to maintain detection performance. --- paper_title: Loopy belief propagation: Convergence and effects of message errors paper_content: Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether due to quantization of the messages or model parameters, from other simplified message or model representations, or from stochastic approximation methods. The introduction of such errors into the BP message computations has the potential to affect the solution obtained adversely. We analyze the effect resulting from message approximation under two particular measures of error, and show bounds on the accumulation of errors in the system. This analysis leads to convergence conditions for traditional BP message passing, and both strict bounds and estimates of the resulting error in systems of approximate BP message passing. --- paper_title: A robust architecture for distributed inference in sensor networks paper_content: Many inference problems that arise in sensor networks require the computation of a global conclusion that is consistent with local information known to each node. A large class of these problems---including probabilistic inference, regression, and control problems---can be solved by message passing on a data structure called a junction tree. In this paper, we present a distributed architecture for solving these problems that is robust to unreliable communication and node failures. In this architecture, the nodes of the sensor network assemble themselves into a junction tree and exchange messages between neighbors to solve the inference problem efficiently and exactly. A key part of the architecture is an efficient distributed algorithm for optimizing the choice of junction tree to minimize the communication and computation required by inference. We present experimental results from a prototype implementation on a 97-node Mica2 mote network, as well as simulation results for three applications: distributed sensor calibration, optimal control, and sensor field modeling. These experiments demonstrate that our distributed architecture can solve many important inference problems exactly, efficiently, and robustly. --- paper_title: Distributed data association for multi-target tracking in sensor networks paper_content: Associating sensor measurements with target tracks is a fundamental and challenging problem in multi-target tracking. The problem is even more ::: challenging in the context of sensor networks, since association is coupled ::: across the network, yet centralized data processing is in general ::: infeasible due to power and bandwidth limitations. Hence efficient, distributed solutions are needed. We propose techniques based on graphical models to efficiently solve such data association problems in sensor networks. Our approach scales well with the number of sensor nodes in the network, and it is well--suited for distributed implementation. Distributed inference is realized by a message--passing algorithm which requires iterative, parallel exchange of information among neighboring nodes on the graph. So as to address trade--offs between inference performance and communication costs, we also propose a communication--sensitive form of message--passing that is capable of achieving near--optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data. ---	Title: Graphical Model-based Approaches to Target Tracking in Sensor Networks: An Overview of Some Recent Work and Challenges Section 1: Introduction Description 1: This section introduces sensor networks, the role of graphical models, and the context of target tracking in sensor networks. Section 2: Graphical Models Description 2: This section provides an overview of graphical models, focusing on Markov Random Fields and their relevance to sensor networks. Section 3: Inference in Sensor Networks Description 3: This section discusses how inference tasks are structured in sensor networks using graphical models and the importance of information structure. Section 4: Inference Tasks in Target Tracking Description 4: This section introduces key tasks in target tracking such as source localization and data association, and their graphical model representations. Section 5: Sensor Characterization Description 5: This section describes sensor measurement models and the general process of sensor characterization in the context of target tracking. Section 6: Source Localization Description 6: This section explores methodologies for localizing targets using sensor networks, including graphical models for multi-target scenarios. Section 7: Data Association Description 7: This section delves into the data association problem, particularly in multi-sensor, multi-target tracking and the use of graphical models for resolution. Section 8: Track Maintenance Description 8: This section covers the maintenance of tracks including initiation, updating, and finalization in a distributed sensor network environment. Section 9: Discussion Description 9: This section discusses the challenges and considerations in applying graphical models to sensor networks, especially under communication and energy constraints.
Overview of Electrochemical DNA Biosensors: New Approaches to Detect the Expression of Life	10	--- paper_title: Whole genome analysis: experimental access to all genome sequenced segments through larger-scale efficient oligonucleotide synthesis and PCR. paper_content: The recent ability to sequence whole genomes allows ready access to all genetic material. The approaches outlined here allow automated analysis of sequence for the synthesis of optimal primers in an automated multiplex oligonucleotide synthesizer (AMOS). The efficiency is such that all ORFs for an organism can be amplified by PCR. The resulting amplicons can be used directly in the construction of DNA arrays or can be cloned for a large variety of functional analyses. These tools allow a replacement of single-gene analysis with a highly efficient whole-genome analysis. --- paper_title: Applications of DNA microarrays in biology. paper_content: DNA microarrays have enabled biology researchers to conduct large-scale quantitative experiments. This capacity has produced qualitative changes in the breadth of hypotheses that can be explored. In what has become the dominant mode of use, changes in the transcription rate of nearly all the genes in a genome, taking place in a particular tissue or cell type, can be measured in disease states, during development, and in response to intentional experimental perturbations, such as gene disruptions and drug treatments. The response patterns have helped illuminate mechanisms of disease and identify disease subphenotypes, predict disease progression, assign function to previously unannotated genes, group genes into functional pathways, and predict activities of new compounds. Directed at the genome sequence itself, microarrays have been used to identify novel genes, binding sites of transcription factors, changes in DNA copy number, and variations from a baseline sequence, such as in emerging strains of pathogens or complex mutations in disease-causing human genes. They also serve as a general demultiplexing tool to sort spatially the sequence-tagged products of highly parallel reactions performed in solution. A brief review of microarray platform technology options, and of the process steps involved in complete experiment workflows, is included. --- paper_title: Single-chain polymorphism analysis in long QT syndrome using planar waveguide fluorescent biosensors. paper_content: Rapid detection of single nucleotide polymorphisms (SNPs) has potential applications in both genetic screening and pharmacogenomics. Planar waveguide fluorescent biosensor technology was employed to detect SNPs using a simple hybridization assay with the complementary strand ("capture oligo") immobilized on the waveguide. This technology allows real-time measurements of DNA hybridization kinetics. Under normal conditions, both the wild-type sequence and the SNP-containing sequence will hybridize with the capture oligo, but with different reaction kinetics and equilibrium duplex concentrations. A "design of experiments" approach was used to maximize the differences in the kinetics profiles of the two. Nearly perfect discrimination can be achieved at short times (2 min) with temperatures that destabilize or melt the heteroduplex while maintaining the stability of the homoduplex. The counter ion content of the solvent was shown to have significant effect not only on the melting point of the heteroduplex and the homoduplex but also on the hybridization rate. Changes in both the stability and the difference between the hybridization rates of the hetero- and homoduplex were observed with varying concentrations of three different cations (Na(+), K(+), Mg(2+)). With the difference in hybridization rates maximized, discrimination between the hetero- and the homoduplex can be obtained at lower, less rigorous temperatures at hybridization times of 7.5 min or longer. --- paper_title: Next-generation DNA sequencing paper_content: DNA sequence represents a single format onto which a broad range of biological phenomena can be projected for high-throughput data collection. Over the past three years, massively parallel DNA sequencing platforms have become widely available, reducing the cost of DNA sequencing by over two orders of magnitude, and democratizing the field by putting the sequencing capacity of a major genome center in the hands of individual investigators. These new technologies are rapidly evolving, and near-term challenges include the development of robust protocols for generating sequencing libraries, building effective new approaches to data-analysis, and often a rethinking of experimental design. Next-generation DNA sequencing has the potential to dramatically accelerate biological and biomedical research, by enabling the comprehensive analysis of genomes, transcriptomes and interactomes to become inexpensive, routine and widespread, rather than requiring significant production-scale efforts. --- paper_title: Nucleic acid biosensors for environmental pollution monitoring. paper_content: Nucleic acid-based biosensors are finding increasing use for the detection of environmental pollution and toxicity. A biosensor is defined as a compact analytical device incorporating a biological or biologically-derived sensing element either integrated within or intimately associated with a physicochemical transducer. A nucleic acid-based biosensor employs as the sensing element an oligonucleotide, with a known sequence of bases, or a complex structure of DNA or RNA. Nucleic acid biosensors can be used to detect DNA/RNA fragments or either biological or chemical species. In the first application, DNA/RNA is the analyte and it is detected through the hybridization reaction (this kind of biosensor is also called a genosensor). In the second application, DNA/RNA plays the role of the receptor of specific biological and/or chemical species, such as target proteins, pollutants or drugs. Recent advances in the development and applications of nucleic acid-based biosensors for environmental application are reviewed in this article with special emphasis on functional nucleic acid elements (aptamers, DNAzymes, aptazymes) and lab-on-a-chip technology. --- paper_title: Genomic Profiling and Identification of High-Risk Uveal Melanoma by Array CGH Analysis of Primary Tumors and Liver Metastases paper_content: PURPOSE. Incurable metastases develop in approximately 50% of patients with uveal melanoma (UM). The purpose of this study was to analyze genomic profiles in a large series of ocular tumors and liver metastases and design a genome-based classifier for metastatic risk assessment. METHODS. A series of 86 UM tumors and 66 liver metastases were analyzed by using a BAC CGH (comparative genomic hybridization) microarray. A clustering was performed, and correlation with the metastatic status was sought among a subset of 71 patients with a minimum follow-up of 24 months. The status of chromosome 3 was further examined in the tumors, and metastases with disomy 3 were checked with an SNP microarray. A prognostic classifier was constructed using a log-linear model on minimal regions and leave-one-out cross-validation. RESULTS. The clustering divides the groups of tumors with disomy 3 and monosomy 3 into two and three subgroups, respectively. Same subgroups are found in primary tumors and in metastases, but with different frequencies. Isolated monosomy 3 was present in 0% of metastatic ocular tumors and in 3% of metastases. The highest metastatic rate in ocular tumors was observed in a subgroup defined by the gain of 8q with a proximal breakpoint, and losses of 3, 8p, and 16q, also most represented in metastases. A prognostic classifier that included the status of these markers led to an 85.9% classification accuracy. CONCLUSIONS. The analysis of the status of these specific chromosome regions by genome profiling on SNP microarrays should be a reliable tool for identifying high-risk patients in future adjuvant therapy protocols. --- paper_title: Light-generated oligonucleotide arrays for rapid DNA sequence analysis. paper_content: In many areas of molecular biology there is a need to rapidly extract and analyze genetic information; however, current technologies for DNA sequence analysis are slow and labor intensive. We report here how modern photolithographic techniques can be used to facilitate sequence analysis by generating miniaturized arrays of densely packed oligonucleotide probes. These probe arrays, or DNA chips, can then be applied to parallel DNA hybridization analysis, directly yielding sequence information. In a preliminary experiment, a 1.28 x 1.28 cm array of 256 different octanucleotides was produced in 16 chemical reaction cycles, requiring 4 hr to complete. The hybridization pattern of fluorescently labeled oligonucleotide targets was then detected by epifluorescence microscopy. The fluorescence signals from complementary probes were 5-35 times stronger than those with single or double base-pair hybridization mismatches, demonstrating specificity in the identification of complementary sequences. This method should prove to be a powerful tool for rapid investigations in human genetics and diagnostics, pathogen detection, and DNA molecular recognition. --- paper_title: Biochemical applications of ultrathin films of enzymes, polyions and DNA. paper_content: This feature article summarizes recent applications of ultrathin films of enzymes and DNA assembled layer-by-layer (LbL). Using examples mainly from our own research, we focus on systems developed for biocatalysis and biosensors for toxicity screening. Enzyme-poly(L-lysine) (PLL) films, especially when stabilized by crosslinking, can be used for biocatalysis at unprecedented high temperatures or in acidic or basic solutions on electrodes or sub-micron sized beads. Such films have bright prospects for chiral synthesis and biofuel cells. Excellent bioactivity and retention of enzyme structure in these films facilitates their use in detailed kinetic studies. Biosensors and arrays employing DNA-enzyme films show great promise in predicting genotoxicity of new drug and chemical product candidates. These devices combine metabolic biocatalysis, reactive metabolite-DNA reactions, and DNA damage detection. Catalytic voltammetry or electrochemiluminescence (ECL) can be used for high throughput arrays utilizing multiple LbL "spots" of DNA, enzyme and metallopolymer. DNA-enzyme films can also be used to produce nucleobase adduct toxicity biomarkers for detection by LC-MS. These approaches provide valuable high throughput tools for drug and chemical product development and toxicity prediction. --- paper_title: Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray paper_content: A high-capacity system was developed to monitor the expression of many genes in parallel. Microarrays prepared by high-speed robotic printing of complementary DNAs on glass were used for quantitative expression measurements of the corresponding genes. Because of the small format and high density of the arrays, hybridization volumes of 2 microliters could be used that enabled detection of rare transcripts in probe mixtures derived from 2 micrograms of total cellular messenger RNA. Differential expression measurements of 45 Arabidopsis genes were made by means of simultaneous, two-color fluorescence hybridization. --- paper_title: Expression profiling using cDNA microarrays paper_content: cDNA microarrays are capable of profiling gene expression patterns of tens of thousands of genes in a single experiment. DNA targets, in the form of 3´ expressed sequence tags (ESTs), are arrayed onto glass slides (or membranes) and probed with fluorescent– or radioactively–labelled cDNAs. Here, we review technical aspects of cDNA microarrays, including the general principles, fabrication of the arrays, target labelling, image analysis and data extraction, management and mining. --- paper_title: Nucleic acid approaches for detection and identification of biological warfare and infectious disease agents. paper_content: Biological warfare agents are the most problematic of the weapons of mass destruction and terror. Both civilian and military sources predict that over the next decade the threat from proliferation of these agents will increase significantly. In this review we summarize the state of the art in detection and identification of biological threat agents based on PCR technology with emphasis on the new technology of microarrays. The advantages and limitations of real-time PCR technology and a review of the literature as it applies to pathogen and virus detection are presented. The paper covers a number of issues related to the challenges facing biological threat agent detection technologies and identifies critical components that must be overcome for the emergence of reliable PCR-based DNA technologies as bioterrorism countermeasures and for environmental applications. The review evaluates various system components developed for an integrated DNA microchip and the potential applications of the next generation of fully automated DNA analyzers with integrated sample preparation and biosensing elements. The article also reviews promising devices and technologies that are near to being, or have been, commercialized. --- paper_title: Part II: coordinated biosensors – development of enhanced nanobiosensors for biological and medical applications paper_content: In this review, we summarize recent developments in nanobiosensors and their applications in biology and potential in medical diagnostics. We first highlight the concept of coordinated nanobiosensors, which integrate desirable properties of the individual components: protein machinery for sensitivity and specificity of binding, peptide or nucleic acid chemistry for aligning the various electron-transducing units and the nanoelectrodes for enhancing sensitivity in electronic detection. The fundamental basis of coordinated nanobiosensing is in applying the precise 3D atomic resolution structural information to rationally design and fabricate biosensors with high specificity and sensitivity. Additionally, we describe several biosensors developed for detecting biologically relevant compounds, including those for hydrogen peroxide, dopamine, glucose, DNA and cytochrome C. Results from these systems highlight the potential advantages of using nanoscale biosensors and how further developments in this area will c... --- paper_title: Use of a cDNA microarray to analyse gene expression patterns in human cancer paper_content: The development and progression of cancer and the experimental reversal of tumorigenicity are accompanied by complex changes in patterns of gene expression. Microarrays of cDNA provide a powerful tool for studying these complex phenomena. The tumorigenic properties of a human melanoma cell line, UACC-903, can be suppressed by introduction of a normal human chromosome 6, resulting in a reduction of growth rate, restoration of contact inhibition, and suppression of both soft agar clonogenicity and tumorigenicity in nude mice. We used a high density microarray of 1,161 DNA elements to search for differences in gene expression associated with tumour suppression in this system. Fluorescent probes for hybridization were derived from two sources of cellular mRNA [UACC-903 and UACC-903(+6)] which were labelled with different fluors to provide a direct and internally controlled comparison of the mRNA levels corresponding to each arrayed gene. The fluorescence signals representing hybridization to each arrayed gene were analysed to determine the relative abundance in the two samples of mRNAs corresponding to each gene. Previously unrecognized alterations in the expression of specific genes provide leads for further investigation of the genetic basis of the tumorigenic phenotype of these cells. --- paper_title: Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale paper_content: DNA microarrays containing virtually every gene of Saccharomyces cerevisiae were used to carry out a comprehensive investigation of the temporal program of gene expression accompanying the metabolic shift from fermentation to respiration. The expression profiles observed for genes with known metabolic functions pointed to features of the metabolic reprogramming that occur during the diauxic shift, and the expression patterns of many previously uncharacterized genes provided clues to their possible functions. The same DNA microarrays were also used to identify genes whose expression was affected by deletion of the transcriptional co-repressor TUP1 or overexpression of the transcriptional activator YAP1. These results demonstrate the feasibility and utility of this approach to genomewide exploration of gene expression patterns. --- paper_title: Options available for profiling small samples: a review of sample amplification technology when combined with microarray profiling paper_content: The possibility of performing microarray analysis on limited material has been demonstrated in a number of publications. In this review we approach the technical aspects of mRNA amplification and several important implicit consequences, for both linear and exponential procedures. Amplification efficiencies clearly allow profiling of extremely small samples. The conservation of transcript abundance is the most important issue regarding the use of sample amplification in combination with microarray analysis, and this aspect has generally been found to be acceptable, although demonstrated to decrease in highly diluted samples. The fact that variability and discrepancies in microarray profiles increase with minute sample sizes has been clearly documented, but for many studies this does appear to have affected the biological conclusions. We suggest that this is due to the data analysis approach applied, and the consequence is the chance of presenting misleading results. We discuss the issue of amplification sensitivity limits in the light of reports on fidelity, published data from reviewed articles and data analysis approaches. These are important considerations to be reflected in the design of future studies and when evaluating biological conclusions from published microarray studies based on extremely low input RNA quantities. --- paper_title: Reconstruction and functional analysis of altered molecular pathways in human atherosclerotic arteries paper_content: BackgroundAtherosclerosis affects aorta, coronary, carotid, and iliac arteries most frequently than any other body vessel. There may be common molecular pathways sustaining this process. Plaque presence and diffusion is revealed by circulating factors that can mediate systemic reaction leading to plaque rupture and thrombosis.ResultsWe used DNA microarrays and meta-analysis to study how the presence of calcified plaque modifies human coronary and carotid gene expression. We identified a series of potential human atherogenic genes that are integrated in functional networks involved in atherosclerosis. Caveolae and JAK/STAT pathways, and S100A9/S100A8 interacting proteins are certainly involved in the development of vascular disease. We found that the system of caveolae is directly connected with genes that respond to hormone receptors, and indirectly with the apoptosis pathway.Cytokines, chemokines and growth factors released in the blood flux were investigated in parallel. High levels of RANTES, IL-1ra, MIP-1alpha, MIP-1beta, IL-2, IL-4, IL-5, IL-6, IL-7, IL-17, PDGF-BB, VEGF and IFN-gamma were found in plasma of atherosclerotic patients and might also be integrated in the molecular networks underlying atherosclerotic modifications of these vessels.ConclusionThe pattern of cytokine and S100A9/S100A8 up-regulation characterizes atherosclerosis as a proinflammatory disorder. Activation of the JAK/STAT pathway is confirmed by the up-regulation of IL-6, STAT1, ISGF3G and IL10RA genes in coronary and carotid plaques. The functional network constructed in our research is an evidence of the central role of STAT protein and the caveolae system to contribute to preserve the plaque. Moreover, Cav-1 is involved in SMC differentiation and dyslipidemia confirming the importance of lipid homeostasis in the atherosclerotic phenotype. --- paper_title: Terminal continuation (TC) RNA amplification without second strand synthesis paper_content: Terminal continuation (TC) RNA amplification was developed originally to reproducibly and inexpensively amplify RNA. The TC RNA amplification method has been improved further by obviating second strand DNA synthesis, a cost-effective protocol that takes less time to perform with fewer manipulations required for RNA amplification. Results demonstrate that TC RNA amplification without second strand synthesis does not differ from the original protocol using RNA harvested from mouse brain and from hippocampal neurons obtained via laser capture microdissection from postmortem human brains. The modified TC RNA amplification method can discriminate single cell gene expression profiles between normal control and Alzheimer's disease hippocampal neurons indistinguishable from the original protocol. Thus, TC RNA amplification without second strand synthesis is a reproducible, time- and cost-effective method for RNA amplification from minute amounts of input RNA, and is compatible with microaspiration strategies and subsequent microarray analysis as well as quantitative real-time PCR. --- paper_title: Electrochemical detection of a breast cancer susceptible gene using cDNA immobilized chitosan-co-polyaniline electrode paper_content: Abstract An electrochemical breast cancer biosensor based on a chitosan-co-polyaniline (CHIT-co-PANI) copolymer coated onto indium–tin-oxide (ITO) was fabricated by immobilizing the complementary DNA (cDNA) probe (42 bases long) associated with the breast cancer susceptible gene BRCA1. Both the CHIT-co-PANI/ITO and the cDNA/CHIT-co-PANI/ITO electrodes were characterized with Fourier transform infrared (FTIR) spectroscopy, atomic force microscopy (AFM), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). For the cDNA/CHIT-co-PANI/ITO electrode, the amperometric current decreased linearly with an increasing logarithm of molar concentration of the single-stranded target DNA (ssDNA) within the range of 0.05–25 fmol. The bioelectrode exhibited a sensitivity of 2.104 μA/fmol with a response time of 16 s. The cDNA/CHIT-co-PANI/ITO electrode had a shelf life of about six months, even when stored at room temperature. --- paper_title: Optimization of experimental design parameters for high-throughput chromatin immunoprecipitation studies paper_content: High-throughput, microarray-based chromatin immunoprecipitation (ChIP-chip) technology allows in vivo elucidation of transcriptional networks. However this complex is not yet readily accessible, in part because its many parameters have not been systematically evaluated and optimized. We address this gap by systematically assessing experimental-design parameters including antibody purity, dye-bias, array-batch, inter-day hybridization bias, amplification method and choice of hybridization control. The combined performance of these optimized parameters shows a 90% validation rate in ChIP-chip analysis of Myc genomic binding in HL60 cells using two different microarray platforms. Increased sensitivity and decreased noise in ChIP-chip assays will enable wider use of this methodology to accurately and affordably elucidate transcriptional networks. --- paper_title: Disposable nucleic acid biosensors based on gold nanoparticle probes and lateral flow strip. paper_content: In this article, we describe a disposable nucleic acid biosensor (DNAB) for low-cost and sensitive detection of nucleic acid samples in 15 min. Combining the unique optical properties of gold nanoparticles (Au-NP) and the high efficiency of chromatographic separation, sandwich-type DNA hybridization reactions were realized on the lateral flow strips, which avoid multiple incubation, separation, and washing steps in the conventional nucleic acid biosensors. The captured Au-NP probes on the test zone and control zone of the biosensor produced the characteristic red bands, enabling visual detection of nucleic acid samples without instrumentation. The quantitative detection was performed by reading the intensities of the produced red bands with a portable strip reader. The parameters (e.g., the concentration of reporter probe, the size of Au-NP, the amount of Au-NP-DNA probe, lateral flow membranes, and the concentration of running buffer) that govern the sensitivity and reproducibility of the sensor were optimized. The response of the optimized device is highly linear over the range of 1-100 nM target DNA, and the limit of detection is estimated to be 0.5 nM in association with a 15 min assay time. The sensitivity of the biosensor was further enhanced by using horseradish peroxidase (HRP)-Au-NP dual labels which ensure a quite low detection limit of 50 pM. The DNAB has been applied for the detection of human genomic DNA directly with a detection limit of 2.5 microg/mL (1.25 fM) by adopting well-designed DNA probes. The new nucleic acid biosensor thus provides a rapid, sensitive, low cost, and quantitative tool for the detection of nucleic acid samples. It shows great promise for in-field and point-of-care diagnosis of genetic diseases and detection of infectious agents or warning against biowarfare agents. --- paper_title: Genomewide Identification of Protein Binding Locations Using Chromatin Immunoprecipitation Coupled with Microarray paper_content: Interactions between cis-acting elements and proteins play a key role in transcriptional regulation of all known organisms. To better understand these interactions, researchers developed a method that couples chromatin immunoprecipitation with microarrays (also known as ChIP-chip), which is capable of providing a whole-genome map of protein-DNA interactions. This versatile and high-throughput strategy is initiated by formaldehyde-mediated cross-linking of DNA and proteins, followed by cell lysis, DNA fragmentation, and immunopurification. The immunoprecipitated DNA fragments are then purified from the proteins by reverse-cross-linking followed by amplification, labeling, and hybridization to a whole-genome tiling microarray against a reference sample. The enriched signals obtained from the microarray then are normalized by the reference sample and used to generate the whole-genome map of protein-DNA interactions. The protocol described here has been used for discovering the genomewide distribution of RNA polymerase and several transcription factors of Escherichia coli. --- paper_title: An optical DNA-based biosensor for the analysis of bioactive constituents with application in drug and herbal drug screening. paper_content: The efficient and rapid detection of bioactive compounds in complex matrices of different origins (natural or synthetic) is a key step in the discovery of molecules with potential application in therapy. Among them, molecules able to interact with nucleic acids can represent important targets. In this study, an optical DNA biosensor, based on surface plasmon resonance (SPR) transduction, has been studied in its potential application as new analytical device for drug screening. This device was applied to the analysis of pure synthetic or natural molecules and also to some fractions obtained by chromatographic separation of an extract of Chelidonium majus L. (great celandine), a plant containing benzo[c]phenanthridinium alkaloids having intercalating properties. The ability of these molecules to interact with the double stranded nucleic acid (dsDNA) immobilised on the sensor surface has been investigated. The optical sensing relies on the SPR-based bench instrument Biacore Xtrade mark and represents an example of multiuse sensor. The results obtained demonstrate the potential application of this device for the rapid screening of bioeffective compounds. The characteristics of the biosensor offer the possibility to be coupled to chemical analysis as in hyphenated technologies. --- paper_title: Differential gene expression of peripheral blood mononuclear cells from rheumatoid arthritis patients may discriminate immunogenetic, pathogenic and treatment features. paper_content: This study aimed to evaluate the association between the differential gene expression profiling of peripheral blood mononuclear cells of rheumatoid arthritis patients with their immunogenetic (human leucocyte antigen shared-epitope, HLA-SE), autoimmune response [anti-cyclic citrullinated peptide (CCP) antibodies], disease activity score (DAS-28) and treatment (disease-modifying antirheumatic drugs and tumour necrosis factor blocker) features. Total RNA samples were copied into Cy3-labelled complementary DNA probes, hybridized onto a glass slide microarray containing 4500 human IMAGE complementary DNA target sequences. The Cy3-monocolour microarray images from patients were quantified and normalized. Analysis of the data using the significance analysis of microarrays algorithm together with a Venn diagram allowed the identification of shared and of exclusively modulated genes, according to patient features. Thirteen genes were exclusively associated with the presence of HLA-SE alleles, whose major biological function was related to signal transduction, phosphorylation and apoptosis. Ninety-one genes were associated with disease activity, being involved in signal transduction, apoptosis, response to stress and DNA damage. One hundred and one genes were associated with the presence of anti-CCP antibodies, being involved in signal transduction, cell proliferation and apoptosis. Twenty-eight genes were associated with tumour necrosis factor blocker treatment, being involved in intracellular signalling cascade, phosphorylation and protein transport. Some of these genes had been previously associated with rheumatoid arthritis pathogenesis, whereas others were unveiled for future research. --- paper_title: Active microelectronic array system for DNA hybridization, genotyping and pharmacogenomic applications. paper_content: Microelectronic arrays have been developed for DNA hybridization analysis of point mutations, single nucleotide polymorphisms, short tandem repeats and gene expression. In addition to a variety of molecular biology and genomic research applications, such devices will also be used for infectious dise --- paper_title: MicroRNA detection by microarray paper_content: MicroRNAs (miRNAs) are a class of small noncoding RNAs ∼22 nt in length that regulate gene expression and play fundamental roles in multiple biological processes, including cell differentiation, proliferation and apoptosis as well as disease processes. The study of miRNA has thus become a rapidly emerging field in life science. The detection of miRNA expression is a very important first step in miRNA exploration. Several methodologies, including cloning, northern blotting, real-time RT-PCR, microRNA arrays and ISH (in situ hybridization), have been developed and applied successfully in miRNA profiling. This review discusses the main existing microRNA detection technologies, while emphasizing microRNA arrays. --- paper_title: Differential expression of microRNA species in human gastric cancer versus non-tumorous tissues paper_content: BACKGROUND AND AIM ::: MicroRNAs (miRNAs) play important roles in carcinogenesis. The global miRNA expression profile of gastric cancer has not been reported. The purpose of the present study was to determine the miRNA expression profile of gastric cancer. ::: ::: ::: METHODS ::: Total RNA were first extracted from primary gastric cancer tissues and adjacent non-tumorous tissues and then small isolated RNAs (< 300 nt) were 3'-extended with a poly(A) tail. Hybridization was carried out on a microParaflo microfluidic chip (LC Sciences, Houston, TX, USA). After hybridization detection by fluorescence labeling using tag-specific Cy3 and Cy5 dyes, hybridization images were collected using a laser scanner and digitized using Array-Pro image analysis software (Media Cybernetics, Silver Spring, MD, USA). To validate the results and investigate the biological meaning of differential expressed miRNAs, immunohistochemistry was used to detect the differential expression of target genes. ::: ::: ::: RESULTS ::: The most highly expressed miRNAs in non-tumorous tissues were miR-768-3p, miR-139-5p, miR-378, miR-31, miR-195, miR-497 and miR-133b. Three of them, miR-139-5p, miR-497 and miR-768-3p, were first found in non-tumorous tissues. The most highly expressed miRNAs in gastric cancer tissues were miR-20b, miR-20a, miR-17, miR-106a, miR-18a, miR-21, miR-106b, miR-18b, miR-421, miR-340, miR-19a and miR-658. Among them, miR-340, miR-421 and miR-658 were first found highly expressed in cancer cells. The expression of some target genes (such as Rb and PTEN) in cancer tissues was found to be decreased. ::: ::: ::: CONCLUSION ::: To our knowledge, this is the first report about these miRNAs associated with gastric cancer. This new information may suggest the potential roles of these miRNAs in the diagnosis of gastric cancer. --- paper_title: Electrochemical detection of harmful algae and other microbial contaminants in coastal waters using hand-held biosensors paper_content: Abstract Standard methods to identify microbial contaminants in the environment are slow, laborious, and can require specialized expertise. This study investigated electrochemical detection of microbial contaminants using commercially available, hand-held instruments. Electrochemical assays were developed for a red tide dinoflagellate ( Karenia brevis ), fecal-indicating bacteria ( Enterococcus spp.), markers indicative of human sources of fecal pollution (human cluster Bacteroides and the esp gene of Enterococcus faecium ), bacterial pathogens ( Escherichia coli 0157:H7, Salmonella spp., Campylobacter jejuni , Staphylococcus aureus ), and a viral pathogen (adenovirus). For K. brevis , two assay formats (Rapid PCR-Detect and Hybrid PCR-Detect) were tested and both provided detection limits of 10 genome equivalents for DNA isolated from K. brevis culture and amplified by PCR. Sensitivity with coastal water samples was sufficient to detect K. brevis that was “present” (⩽1000 cells/l) without yielding false positive results and the electrochemical signal was significantly different than for samples containing cells at “medium” concentrations (100,000 to 6 cells/l). Detection of K. brevis RNA was also shown. Multi-target capability was demonstrated with an 8-plex assay for bacterial and viral targets using isolated DNA, natural beach water spiked with human feces, and water and sediments collected from New Orleans, Louisiana following Hurricane Katrina. Furthermore, direct detection of dinoflagellate and bacterial DNA was achieved using lysed cells rather than extracted nucleic acids, allowing streamlining of the process. The methods presented can be used to rapidly (3–5 h) screen environmental water samples for the presence of microbial contaminants and have the potential to be integrated into semi-automated detection platforms. --- paper_title: Evaluation of a microarray-hybridization based method applicable for discovery of single nucleotide polymorphisms (SNPs) in the Pseudomonas aeruginosa genome paper_content: BackgroundWhole genome sequencing techniques have added a new dimension to studies on bacterial adaptation, evolution and diversity in chronic infections. By using this powerful approach it was demonstrated that Pseudomonas aeruginosa undergoes intense genetic adaptation processes, crucial in the development of persistent disease. The challenge ahead is to identify universal infection relevant adaptive bacterial traits as potential targets for the development of alternative treatment strategies.ResultsWe developed a microarray-based method applicable for discovery of single nucleotide polymorphisms (SNPs) in P. aeruginosa as an easy and economical alternative to whole genome sequencing. About 50% of all SNPs theoretically covered by the array could be detected in a comparative hybridization of PAO1 and PA14 genomes at high specificity (> 0.996). Variations larger than SNPs were detected at much higher sensitivities, reaching nearly 100% for genetic differences affecting multiple consecutive probe oligonucleotides. The detailed comparison of the in silico alignment with experimental hybridization data lead to the identification of various factors influencing sensitivity and specificity in SNP detection and to the identification of strain specific features such as a large deletion within the PA4684 and PA4685 genes in the Washington Genome Center PAO1.ConclusionThe application of the genome array as a tool to identify adaptive mutations, to depict genome organizations, and to identify global regulons by the "ChIP-on-chip" technique will expand our knowledge on P. aeruginosa adaptation, evolution and regulatory mechanisms of persistence on a global scale and thus advance the development of effective therapies to overcome persistent disease. --- paper_title: Differential microRNA expression between hepatitis B and hepatitis C leading disease progression to hepatocellular carcinoma paper_content: MicroRNA (miRNA) plays an important role in the pathology of various diseases, including infection and cancer. Using real-time polymerase chain reaction, we measured the expression of 188 miRNAs in liver tissues obtained from 12 patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) and 14 patients with hepatitis C virus (HCV)-related HCC, including background liver tissues and normal liver tissues obtained from nine patients. Global gene expression in the same tissues was analyzed via complementary DNA microarray to examine whether the differentially expressed miRNAs could regulate their target genes. Detailed analysis of the differentially expressed miRNA revealed two types of miRNA, one associated with HBV and HCV infections (n = 19), the other with the stage of liver disease (n = 31). Pathway analysis of targeted genes using infection-associated miRNAs revealed that the pathways related to cell death, DNA damage, recombination, and signal transduction were activated in HBV-infected liver, and those related to immune response, antigen presentation, cell cycle, proteasome, and lipid metabolism were activated in HCV-infected liver. The differences in the expression of infection-associated miRNAs in the liver correlated significantly with those observed in Huh7.5 cells in which infectious HBV or HCV clones replicated. Out of the 31 miRNAs associated with disease state, 17 were down-regulated in HCC, which up-regulated cancer-associated pathways such as cell cycle, adhesion, proteolysis, transcription, and translation; 6 miRNAs were up-regulated in HCC, which down-regulated anti-tumor immune response. Conclusion: miRNAs are important mediators of HBV and HCV infection as well as liver disease progression, and therefore could be potential therapeutic target molecules. (HEPATOLOGY 2009.) --- paper_title: DNA arrays, electronic noses and tongues, biosensors and receptors for rapid detection of toxigenic fungi and mycotoxins: A review paper_content: This paper presents an overview of how microsystem technology tools can be applied to the development of rapid, out–of–laboratory measurement capabilities for the determinations of toxigenic fungi and mycotoxins in foodstuffs. Most of the topics discussed are all under investigation within the European Commission–sponsored project Good–Food (FP6–IST). These are DNA arrays, electronic noses and electronic tongues for the detection of fungal contaminants in feed, and biosensors and chemical sensors based on microfabricated electrode systems, antibodies and novel synthetic receptors for the detection of specific mycotoxins. The approach to resolution of these difficult measurement problems in real matrices requires a multidisciplinary approach. The technology tools discussed can provide a route to the rapid, on–site generation of data that can aid the safe production of high–quality foodstuffs. --- paper_title: Polymorphisms in XRCC1 and XPG and response to platinum-based chemotherapy in advanced non-small cell lung cancer patients. paper_content: Platinum-based chemotherapeutics is the most common regimens for advanced NSCLC patients. However, it is difficult to identify platinum resistance in clinical treatment. Genetic factors are thought to represent important determinants of drug efficacy. In this study, we investigated whether single nucleotide polymorphisms (SNPs) in Xeroderma pigmentosum group G (XPG) and X-ray repair cross complementing group 1 (XRCC1) were associated with the tumor response in non-small cell lung cancer (NSCLC) patients treated with platinum-based chemotherapy in Chinese population. Totally 82 patients with advanced NSCLC were routinely treated with cisplatin or carboplatin-based chemotherapy, and clinical response was evaluated after 2-3 cycles. And 3D (three dimensions) polyacrylamide gel-based DNA microarray method was used to evaluate the genotypes of XRCC1 194 Arg/Trp, XRCC1 399Arg/Gln, XPG 46His/His and XPG 1104His/Asp in DNA from peripheral lymphocytes. We found that there was a significantly increased chance of treatment response to platinum-based chemotherapy with the XRCC1 194Arg/Trp genotype (odds ratio 0.429; 95% CI 0.137-1.671; P=0.035). The polymorphism of XPG 46His/His was found to be associated with clinical response in NSCLC patients P=0.047, not detected between chemotherapy response and SNPs of XRCC1 399Arg/Gln or XPG 1104His/Asp (P=0.997 0.561, respectively). Our study showed that the polymorphic status of XRCC1 194Arg/Trp might be a predictive marker of treatment response for advanced NSCLC patients and those of XPG His46His was associated with susceptibility of chemotherapy. The 3D polyacrylamide gel-based DNA microarray method was accurate, high-throughput and inexpensive, especially suitable for a large scale of SNP genotyping in population. --- paper_title: A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. paper_content: Detecting and determining the relative abundance of diverse individual sequences in complex DNA samples is a recurring experimental challenge in analyzing genomes. We describe a general experimental approach to this problem, using microscopic arrays of DNA fragments on glass substrates for differential hybridization analysis of fluorescently labeled DNA samples. To test the system, 864 physically mapped lambda clones of yeast genomic DNA, together representing >75% of the yeast genome, were arranged into 1.8-cm x 1.8-cm arrays, each containing a total of 1744 elements. The microarrays were characterized by simultaneous hybridization of two different sets of isolated yeast chromosomes labeled with two different fluorophores. A laser fluorescent scanner was used to detect the hybridization signals from the two fluorophores. The results demonstrate the utility of DNA microarrays in the analysis of complex DNA samples. This system should find numerous applications in genome-wide genetic mapping, physical mapping, and gene expression studies. --- paper_title: Surface Plasmon Resonance for Detection of Genetically Modified Organisms in the Food Supply paper_content: A review is presented demonstrating that biospecific interaction analysis, using surface plasmon resonance (SPR) and biosensor technologies is a simple, rapid, and automatable approach to detect genetically modified organisms (GMOs). Using SPR, we were able to monitor in real-time the hybridization between oligonucleotide or polymerase chain reaction (PCR)-generated probes and target single-stranded PCR products obtained by using as substrates DNA isolated from normal or transgenic soybean and maize. This procedure allows a one-step, nonradioactive detection of GMOs. PCR-generated probes are far more efficient in detecting GMOs than are oligodeoxyribonucleotide probes. This is expected to be a very important parameter, because information on low percentage of GMOs is of great value. Determination of the ability of SPR-based analysis to quantify GMOs should be considered a major research field for future studies, especially for the analyses of food supplies. --- paper_title: Biosensors: new approaches in drug discovery paper_content: The development of biosensors for analytical purposes has attracted a great deal of attention in recent years. A biosensor is defined as an analytical device consisting of a biological component (e.g., enzyme, antibody, entire cell, DNA) and a physical transducer (e.g., electrode, optical device). Biosensors are mostly designed for routine analysis, such as clinical diagnosis, quality control of food, in-process control of fermentations, and in environmental analysis. Many of these sensors are also suitable for screening purposes in order to find new drugs. Such systems should yield information either about compounds with known bioactivity or about the bioactivity of samples with known or unknown chemical composition. Biosensors intended for the latter purpose are essentially based on whole cells carrying receptors and ion channels at their surfaces. Miniaturization of structures, primarily based on silicon, allows integration of many sensors into arrays, which may be suitable for the screening of natural and chemical products as well as combinatorial libraries. Until now, no commercially available sensors of this kind exist but they are expected in the near future. Different biosensors, based on enzymes, antibodies, cells, artificial membranes and entire animal tissues, which can be used in drug discovery and may lead to efficient screening systems in the future, are described in this review. --- paper_title: New developments in the analysis of gene expression. paper_content: An understanding of the relationship between gene expression, protein expression and the influences of genetic responses upon gene function is vital before we can understand the complexity of genomes. Traditional methods for the study of gene expression are limited to studying small groups of genes at a time and a source of pure starting material has been difficult to obtain. Recent technological advances have enabled large numbers of genes, from specific cell populations, to be studied in a single experiment. Laser capture microdissection (LCM) and microarray technology are providing the next revolution in the study of gene expression. LCM-based molecular analysis of histopathological lesions can be applied to any disease process that is accessible through tissue sampling. Examples include: (i) mapping the field of genetic changes associated with oxidative stress; (ii) analysis of gene expression patterns in atherosclerotic tissues, sites of inflammation and Alzheimer's disease plaques; (iii) infectious micro-organism diagnosis; and (iv) typing of cells within disease foci. Microarray hybridisation glass chips spotted with sets of genes can then be used to obtain a molecular fingerprint of gene expression in the microdissected cells. The variation of expressed genes or alterations in the cellular DNA that correlate with a particular disease state can be compared within or between individual samples. The identification of gene expression patterns may provide vital information for the understanding of the disease process and may contribute to diagnostic decisions and therapies tailored to the individual patient. Molecules found to be associated with defined pathological lesions may provide clues about new therapeutic targets in the future. --- paper_title: Nucleic acid based biosensors: the desires of the user. paper_content: The need for nucleic acid based diagnostic tests has increased enormously in the last few years. On the one hand, this has been stimulated by the discovery of new hereditary genetic disease loci following the completion of the Human Genome Project, but also by the presence of new rapidly spreading viral threats, such as that of the SARS epidemic, or even micro-organisms released for the purpose of biological warfare. As in many instances rapid diagnoses of specific target genetic loci is required, new strategies have to be developed, which will allow this to be achieved directly at the point-of-care setting. One of these avenues being explored is that of biosensors. In this review, we provide an overview of the current state of the art concerning the high-throughput analysis of nucleic acids, and address future requirements, which will hopefully be met by new biosensor-based developments. --- paper_title: Detection of heterozygous mutations in BRCA1 using high density oligonucleotide arrays and two–colour fluorescence analysis paper_content: The ability to scan a large gene rapidly and accurately for all possible heterozygous mutations in large numbers of patient samples will be critical for the future of medicine. We have designed high-density arrays consisting of over 96,600 oligonucleotides 20-nucleotides (nt) in length to screen for a wide range of heterozygous mutations in the 3.45-kilobases (kb) exon 11 of the hereditary breast and ovarian cancer gene BRCA1. Reference and test samples were co-hybridized to these arrays and differences in hybridization patterns quantitated by two-colour analysis. Fourteen of fifteen patient samples with known mutations were accurately diagnosed, and no false positive mutations were identified in 20 control samples. Eight single nucleotide polymorphisms were also readily detected. DNA chip-based assays may provide a valuable new technology for high-throughput cost-efficient detection of genetic alterations. --- paper_title: The Cerebral Microvasculature in Schizophrenia: A Laser Capture Microdissection Study paper_content: BACKGROUND ::: Previous studies of brain and peripheral tissues in schizophrenia patients have indicated impaired energy supply to the brain. A number of studies have also demonstrated dysfunction of the microvasculature in schizophrenia patients. Together these findings are consistent with a hypothesis of blood-brain barrier dysfunction in schizophrenia. In this study, we have investigated the cerebral vascular endothelium of schizophrenia patients at the level of transcriptomics. ::: ::: ::: METHODOLOGY/PRINCIPAL FINDINGS ::: We used laser capture microdissection to isolate both microvascular endothelial cells and neurons from post mortem brain tissue from schizophrenia patients and healthy controls. RNA was isolated from these cell populations, amplified, and analysed using two independent microarray platforms, Affymetrix HG133plus2.0 GeneChips and CodeLink Whole Human Genome arrays. In the first instance, we used the dataset to compare the neuronal and endothelial data, in order to demonstrate that the predicted differences between cell types could be detected using this methodology. We then compared neuronal and endothelial data separately between schizophrenic subjects and controls. Analysis of the endothelial samples showed differences in gene expression between schizophrenics and controls which were reproducible in a second microarray platform. Functional profiling revealed that these changes were primarily found in genes relating to inflammatory processes. ::: ::: ::: CONCLUSIONS/SIGNIFICANCE ::: This study provides preliminary evidence of molecular alterations of the cerebral microvasculature in schizophrenia patients, suggestive of a hypo-inflammatory state in this tissue type. Further investigation of the blood-brain barrier in schizophrenia is warranted. --- paper_title: Genome-wide Analysis of BP1 Transcriptional Targets in Breast Cancer Cell Line Hs578T paper_content: Homeobox genes are known to be critically important in tumor development and progression. The BP1 (Beta Protein 1) gene, an isoform of DLX4, belongs to the Distal-less (DLX) subfamily of homeobox genes and encodes a homeodomain-containing transcription factor. Our studies have shown that the BP1 gene was overexpressed in 81% of primary breast cancer and its expression was closely correlated with the progression of breast cancer. However, the exact role of BP1 in breast has yet to be elucidated. Therefore, it is important to explore the potential transcriptional targets of BP1 via whole genome-scale screening. In this study, we used the chromatin immunoprecipitation on chip (ChIP-on-chip) and gene expression microarray assays to identify candidate target genes and gene networks, which are directly regulated by BP1 in ER negative (ER-) breast cancer cells. After rigorous bioinformatic and statistical analysis for both ChIP-on-chip and expression microarray gene lists, 18 overlapping genes were noted and verified. Those potential target genes are involved in a variety of tumorigenic pathways, which sheds light on the functional mechanisms of BP1 in breast cancer development and progression. --- paper_title: A genome-wide scalable SNP genotyping assay using microarray technology paper_content: Oligonucleotide probe arrays have enabled massively parallel analysis of gene expression levels from a single cDNA sample. Application of microarray technology to analyzing genomic DNA has been stymied by the sequence complexity of the entire human genome. A robust, single base-resolution direct genomic assay would extend the reach of microarray technology. We developed an array-based whole-genome genotyping assay that does not require PCR and enables effectively unlimited multiplexing. The assay achieves a high signal-to-noise ratio by combining specific hybridization of picomolar concentrations of whole genome-amplified DNA to arrayed probes with allele-specific primer extension and signal amplification. As proof of principle, we genotyped several hundred previously characterized SNPs. The conversion rate, call rate and accuracy were comparable to those of high-performance PCR-based genotyping assays. --- paper_title: A novel, sensitive detection system for high-density microarrays using dendrimer technology paper_content: To improve signal detection on cDNA microarrays, we adapted a fluorescent oligonucleotide dendrimeric signal amplification system to microarray technology. This signal detection method requires 16-fold less RNA for probe synthesis, does not depend on the incorporation of fluorescent dNTPs into a reverse transcription reaction, generates a high signal-to-background ratio, and can be used to allow for multichannel detection on a single chip. Furthermore, since the dendrimers can be detected individually, it may be possible, by employing dendrimer-binding standards, to calculate the numbers of bound cDNAs can be estimated. These features make the dendrimer signal detection reagent ideal for high-throughput functional genomics research. --- paper_title: Development of mussel mRNA profiling: Can gene expression trends reveal coastal water pollution? paper_content: Marine bivalves of the genus Mytilus are intertidal filter-feeders commonly used as biosensors of coastal pollution. Mussels adjust their functions to ordinary environmental changes, e.g. temperature fluctuations and emersion-related hypoxia, and react to various contaminants, accumulated from the surrounding water and defining a potential health risk for sea-food consumers. Despite the increasing use of mussels in environmental monitoring, their genome and gene functions are largely unexplored. Hence, we started the systematic identification of expressed sequence tags and prepared a cDNA microarray of Mytilus galloprovincialis including 1714 mussel probes (76% singletons, ∼50% putatively identified transcripts) plus unrelated controls. To assess the potential use of the gene set represented in MytArray 1.0, we tested different tissues and groups of mussels. The resulting data highlighted the transcriptional specificity of the mussel tissues. Further testing of the most responsive digestive gland allowed correct classification of mussels treated with mixtures of heavy metals or organic contaminants (expression changes of specific genes discriminated the two pollutant cocktails). Similar analyses made a distinction possible between mussels living in the Venice lagoon (Italy) at the petrochemical district and mussels close to the open sea. The suggestive presence of gene markers tracing organic contaminants more than heavy metals in mussels from the industrial district is consistent with reported trends of chemical contamination. Further study is necessary in order to understand how much gene expression profiles can disclose the signatures of pollutants in mussel cells and tissues. Nevertheless, the gene expression patterns described in this paper support a wider characterization of the mussel transcriptome and point to the development of novel environmental metrics. --- paper_title: Atypical 11q deletions identified by array CGH may be missed by FISH panels for prognostic markers in chronic lymphocytic leukemia paper_content: Atypical 11q deletions identified by array CGH may be missed by FISH panels for prognostic markers in chronic lymphocytic leukemia --- paper_title: Detection and haplotype differentiation of Southeast Asian alpha-thalassemia using polymerase chain reaction and a piezoelectric biosensor immobilized with a single oligonucleotide probe. paper_content: DNA-based diagnosis of alpha-thalassemias routinely relies on polymerase chain reaction (PCR) and gel electrophoresis. Here, we developed a new procedure for the detection and haplotype differentiation of Southeast Asian (SEA) alpha-thalassemia using a 3-primer system for PCR coupling with a DNA-based piezoelectric biosensor. PCR products amplified from genomic DNA were differentiated directly by using a quartz crystal microbalance immobilized with a single oligonucleotide probe. The frequency changes after hybridization of the PCR products amplified from a representative sample of normal alpha-globin, SEA alpha-thalassemia heterozygote, and homozygote were 206+/-11, 256+/-5, and 307+/-3 Hz, respectively. The fabricated biosensor was evaluated through an examination of 18 blind specimens. It could accurately discriminate between normal and SEA alpha-thalassemic samples, which suggests that this biosensor system is a promising alternative technique to detect SEA alpha-thalassemia because of its specificity and less hazardous exposure as compared with conventional methods. --- paper_title: Overview of mRNA expression profiling using DNA microarrays. paper_content: DNA microarray technology allows simultaneous measurement of the mRNA levels of thousands of genes. This powerful technology has applications in addressing many biological questions that were not approachable previously; however, the enormous size of microarray data sets leads to issues of experimental design and statistical analysis that are unfamiliar to many molecular biologists. The type of array used, the design of the biological experiment, the number of experimental replicates, and the statistical method for data analysis should all be chosen based on the scientific goals of the investigator. This overview presents a discussion of the relative merits and limitations of various methods with respect to some common applications of microarray experiments. --- paper_title: Gene transcription profiling in pollutant exposed mussels (Mytilus spp.) using a new low-density oligonucleotide microarray. paper_content: In this study we describe the design and implementation of a novel low-density oligonucleotide microarray (the "Mytox-chip"). It consists of 24 mussel genes involving both normalizing elements and stress response related genes, each represented on the array with one or two different 50 mer oligonucleotide-probe reporters spotted in replicated samples on glass-activated slides. Target genes were selected on the basis of their potential involvement in mechanisms of pollutant and xenobiotic response. They are implicated in both basic and stress related cellular processes such as shock response, biotransformation and excretion, cell-cycle regulation, immune defense, drug metabolism, etc. The microarray was tested on mussels exposed to sublethal concentrations of mercury or a crude North Sea oil mixture. RNA samples were extracted from digestive glands of control and treated mussels for the synthesis of fluorescence labeled cDNAs to be used in dual color hybridizations. Transcription rates of two metallothionein iso-genes (mt10 and mt20), a p53-like gene and actin were quantitatively estimated also by real-time PCR to confirm microarray data. Significant alterations in the gene transcription patterns were seen in response to both treatments. --- paper_title: Biosensors: Applications for Dairy Food Industry paper_content: Abstract Biosensors are defined as indicators of biological compounds that can be as simple as temperature-sensitive paint or as complex as DNA-RNA probes. Food microbiologists are constantly seeking rapid and reliable automated systems for the detection of biological activity. Biosensors provide sensitive, miniaturized systems that can be used to detect unwanted microbial activity or the presence of a biologically active compound, such as glucose or a pesticide. Immunodiagnostics and enzyme biosensors are two of the leading technologies that have had the greatest impact on the food industry. The use of these two systems has reduced the time for detection of pathogens such as Salmonella to 24 h and has provided detection of biological compounds such as cholesterol or chymotrypsin. The continued development of biosensor technology will soon make available "on-line quality control" of food production, which will not only reduce cost of food production but will also provide greater safety and increased food quality. --- paper_title: What would you do if you could sequence everything? paper_content: It could be argued that the greatest transformative aspect of the Human Genome Project has been not the sequencing of the genome itself, but the resultant development of new technologies. A host of new approaches has fundamentally changed the way we approach problems in basic and translational research. Now, a new generation of high-throughput sequencing technologies promises to again transform the scientific enterprise, potentially supplanting array-based technologies and opening up many new possibilities. By allowing DNA/RNA to be assayed more rapidly than previously possible, these next-generation platforms promise a deeper understanding of genome regulation and biology. Significantly enhancing sequencing throughput will allow us to follow the evolution of viral and bacterial resistance in real time, to uncover the huge diversity of novel genes that are currently inaccessible, to understand nucleic acid therapeutics, to better integrate biological information for a complete picture of health and disease at a personalized level and to move to advances that we cannot yet imagine. --- paper_title: Discovery and analysis of inflammatory disease-related genes using cDNA microarrays. paper_content: cDNA microarray technology is used to profile complex diseases and discover novel disease-related genes. In inflammatory disease such as rheumatoid arthritis, expression patterns of diverse cell types contribute to the pathology. We have monitored gene expression in this disease state with a microarray of selected human genes of probable significance in inflammation as well as with genes expressed in peripheral human blood cells. Messenger RNA from cultured macrophages, chondrocyte cell lines, primary chondrocytes, and synoviocytes provided expression profiles for the selected cytokines, chemokines, DNA binding proteins, and matrix-degrading metalloproteinases. Comparisons between tissue samples of rheumatoid arthritis and inflammatory bowel disease verified the involvement of many genes and revealed novel participation of the cytokine interleukin 3, chemokine Gro alpha and the metalloproteinase matrix metallo-elastase in both diseases. From the peripheral blood library, tissue inhibitor of metalloproteinase 1, ferritin light chain, and manganese superoxide dismutase genes were identified as expressed differentially in rheumatoid arthritis compared with inflammatory bowel disease. These results successfully demonstrate the use of the cDNA microarray system as a general approach for dissecting human diseases. --- paper_title: Optimized T7 amplification system for microarray analysis paper_content: Glass cDNA microarray technologies offer a highly parallel approach for profiling expressed gene sequences in disease-relevant tissues. However, standard hybridization and detection protocols are insufficient for milligram quantities of tissue, such as those derived from needle biopsies. Amplification systems utilizing T7 RNA polymerase can provide multiple cRNA copies from mRNA transcripts, permitting microarray studies with reduced sample inputs. Here, we describe an optimized T7-based amplification system for microarray analysis that yields between 200- and 700-fold amplification. This system was evaluated with both mRNA and total RNA samples and provided microarray sensitivity and precision that are comparable to our standard production process without amplification. The size distributions of amplified cRNA ranged from 200 bp to 4 kb and were similar to original mRNA profiles. These amplified cRNA samples were fluorescently labeled by reverse transcription and hybridized to microarrays comprising appr... --- paper_title: Yeast microarrays for genome wide parallel genetic and gene expression analysis paper_content: We have developed high-density DNA microarrays of yeast ORFs. These microarrays can monitor hybridization to ORFs for applications such as quantitative differential gene expression analysis and screening for sequence polymorphisms. Automated scripts retrieved sequence information from public databases to locate predicted ORFs and select appropriate primers for amplification. The primers were used to amplify yeast ORFs in 96-well plates, and the resulting products were arrayed using an automated micro arraying device. Arrays containing up to 2,479 yeast ORFs were printed on a single slide. The hybridization of fluorescently labeled samples to the array were detected and quantitated with a laser confocal scanning microscope. Applications of the microarrays are shown for genetic and gene expression analysis at the whole genome level. --- paper_title: Calibrating the Performance of SNP Arrays for Whole-Genome Association Studies paper_content: To facilitate whole-genome association studies (WGAS), several high-density SNP genotyping arrays have been developed. Genetic coverage and statistical power are the primary benchmark metrics in evaluating the performance of SNP arrays. Ideally, such evaluations would be done on a SNP set and a cohort of individuals that are both independently sampled from the original SNPs and individuals used in developing the arrays. Without utilization of an independent test set, previous estimates of genetic coverage and statistical power may be subject to an overfitting bias. Additionally, the SNP arrays' statistical power in WGAS has not been systematically assessed on real traits. One robust setting for doing so is to evaluate statistical power on thousands of traits measured from a single set of individuals. In this study, 359 newly sampled Americans of European descent were genotyped using both Affymetrix 500K (Affx500K) and Illumina 650Y (Ilmn650K) SNP arrays. From these data, we were able to obtain estimates of genetic coverage, which are robust to overfitting, by constructing an independent test set from among these genotypes and individuals. Furthermore, we collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Our genetic coverage estimates are lower than previous reports, providing evidence that previous estimates may be inflated due to overfitting. The Ilmn650K platform showed reasonable power (50% or greater) to detect SNPs associated with quantitative traits when the signal-to-noise ratio (SNR) is greater than or equal to 0.5 and the causal SNP's minor allele frequency (MAF) is greater than or equal to 20% (N = 359). In testing each of the more than 40,000 gene expression traits for association to each of the SNPs on the Ilmn650K and Affx500K arrays, we found that the Ilmn650K yielded 15% times more discoveries than the Affx500K at the same false discovery rate (FDR) level. --- paper_title: Exploring nature's plasticity with a flexible probing tool, and finding new ways for its electronic distribution. paper_content: Concepts and results are described for the use of a single, but extremely flexible, probing tool to address a wide variety of genomic questions. This is achieved by transforming genomic questions into a software file that is used as the design scheme for potentially any genomic assay in a microarray format. Microarray fabrication takes place in three-dimensional microchannel reaction carriers by in situ synthesis based on spatial light modulation. This set-up allows for maximum flexibility in design and realization of genomic assays. Flexibility is achieved at the molecular, genomic and assay levels. We have applied this technology to expression profiling and genotyping experiments. --- paper_title: Fabrication of high quality microarrays. paper_content: Fabrication of DNA microarray demands that between ten (diagnostic microarrays) and many hundred thousands of probes (research or screening microarrays) are efficiently immobilised to a glass or plastic surface using a suitable chemistry. DNA microarray performance is measured by parameters like array geometry, spot density, spot characteristics (morphology, probe density and hybridised density), background, specificity and sensitivity. At least 13 factors affect these parameters and factors affecting fabrication of microarrays are used in this review to compare different fabrication methods (spotted microarrays and in situ synthesis of microarrays) and immobilisation chemistries. --- paper_title: Applications of DNA microarrays in biology. paper_content: DNA microarrays have enabled biology researchers to conduct large-scale quantitative experiments. This capacity has produced qualitative changes in the breadth of hypotheses that can be explored. In what has become the dominant mode of use, changes in the transcription rate of nearly all the genes in a genome, taking place in a particular tissue or cell type, can be measured in disease states, during development, and in response to intentional experimental perturbations, such as gene disruptions and drug treatments. The response patterns have helped illuminate mechanisms of disease and identify disease subphenotypes, predict disease progression, assign function to previously unannotated genes, group genes into functional pathways, and predict activities of new compounds. Directed at the genome sequence itself, microarrays have been used to identify novel genes, binding sites of transcription factors, changes in DNA copy number, and variations from a baseline sequence, such as in emerging strains of pathogens or complex mutations in disease-causing human genes. They also serve as a general demultiplexing tool to sort spatially the sequence-tagged products of highly parallel reactions performed in solution. A brief review of microarray platform technology options, and of the process steps involved in complete experiment workflows, is included. --- paper_title: A novel, high-performance random array platform for quantitative gene expression profiling. paper_content: We have developed a new microarray technology for quantitative gene-expression profiling on the basis of randomly assembled arrays of beads. Each bead carries a gene-specific probe sequence. There are multiple copies of each sequence-specific bead in an array, which contributes to measurement precision and reliability. We optimized the system for specific and sensitive analysis of mammalian RNA, and using RNA controls of defined concentration, obtained the following estimates of system performance: specificity of 1:250,000 in mammalian poly(A(+)) mRNA; limit of detection 0.13 pM; dynamic range 3.2 logs; and sufficient precision to detect 1.3-fold differences with 95% confidence within the dynamic range. Measurements of expression differences between human brain and liver were validated by concordance with quantitative real-time PCR (R(2) = 0.98 for log-transformed ratios, and slope of the best-fit line = 1.04, for 20 genes). Quantitative performance was further verified using a mouse B- and T-cell model system. We found published reports of B- or T-cell-specific expression for 42 of 59 genes that showed the greatest differential expression between B- and T-cells in our system. All of the literature observations were concordant with our results. Our experiments were carried out on a 96-array matrix system that requires only 100 ng of input RNA and uses standard microtiter plates to process samples in parallel. Our technology has advantages for analyzing multiple samples, is scalable to all known genes in a genome, and is flexible, allowing the use of standard or custom probes in an array. --- paper_title: Next-generation DNA sequencing paper_content: DNA sequence represents a single format onto which a broad range of biological phenomena can be projected for high-throughput data collection. Over the past three years, massively parallel DNA sequencing platforms have become widely available, reducing the cost of DNA sequencing by over two orders of magnitude, and democratizing the field by putting the sequencing capacity of a major genome center in the hands of individual investigators. These new technologies are rapidly evolving, and near-term challenges include the development of robust protocols for generating sequencing libraries, building effective new approaches to data-analysis, and often a rethinking of experimental design. Next-generation DNA sequencing has the potential to dramatically accelerate biological and biomedical research, by enabling the comprehensive analysis of genomes, transcriptomes and interactomes to become inexpensive, routine and widespread, rather than requiring significant production-scale efforts. --- paper_title: Gene expression analysis using oligonucleotide arrays produced by maskless photolithography. paper_content: Microarrays containing 195,000 in situ synthesized oligonucleotide features have been created using a benchtop, maskless photolithographic instrument. This instrument, the Maskless Array Synthesizer (MAS), uses a digital light processor (DLP) developed by Texas Instruments. The DLP creates the patterns of UV light used in the light-directed synthesis of oligonucleotides. This digital mask eliminates the need for expensive and time-consuming chromium masks. In this report, we describe experiments in which we tested this maskless technology for DNA synthesis on glass surfaces. Parameters examined included deprotection rates, repetitive yields, and oligonucleotide length. Custom gene expression arrays were manufactured and hybridized to Drosophila melanogaster and mouse samples. Quantitative PCR was used to validate the gene expression data from the mouse arrays. --- paper_title: Light-generated oligonucleotide arrays for rapid DNA sequence analysis. paper_content: In many areas of molecular biology there is a need to rapidly extract and analyze genetic information; however, current technologies for DNA sequence analysis are slow and labor intensive. We report here how modern photolithographic techniques can be used to facilitate sequence analysis by generating miniaturized arrays of densely packed oligonucleotide probes. These probe arrays, or DNA chips, can then be applied to parallel DNA hybridization analysis, directly yielding sequence information. In a preliminary experiment, a 1.28 x 1.28 cm array of 256 different octanucleotides was produced in 16 chemical reaction cycles, requiring 4 hr to complete. The hybridization pattern of fluorescently labeled oligonucleotide targets was then detected by epifluorescence microscopy. The fluorescence signals from complementary probes were 5-35 times stronger than those with single or double base-pair hybridization mismatches, demonstrating specificity in the identification of complementary sequences. This method should prove to be a powerful tool for rapid investigations in human genetics and diagnostics, pathogen detection, and DNA molecular recognition. --- paper_title: Optical technologies for the read out and quality control of DNA and protein microarrays paper_content: Microarray formats have become an important tool for parallel (or multiplexed) monitoring of biomolecular interactions. Surface-immobilized probes like oligonucleotides, cDNA, proteins, or antibodies can be used for the screening of their complementary targets, covering different applications like gene or protein expression profiling, analysis of point mutations, or immunodiagnostics. Numerous reviews have appeared on this topic in recent years, documenting the intriguing progress of these miniaturized assay formats. Most of them highlight all aspects of microarray preparation, surface chemistry, and patterning, and try to give a systematic survey of the different kinds of applications of this new technique. This review places the emphasis on optical technologies for microarray analysis. As the fluorescent read out of microarrays is dominating the field, this topic will be the focus of the review. Basic principles of labeling and signal amplification techniques will be introduced. Recent developments in total internal reflection fluorescence, resonance energy transfer assays, and time-resolved imaging are addressed, as well as non-fluorescent imaging methods. Finally, some label-free detection modes are discussed, such as surface plasmon microscopy or ellipsometry, since these are particularly interesting for microarray development and quality control purposes. --- paper_title: Expression profiling using cDNA microarrays paper_content: cDNA microarrays are capable of profiling gene expression patterns of tens of thousands of genes in a single experiment. DNA targets, in the form of 3´ expressed sequence tags (ESTs), are arrayed onto glass slides (or membranes) and probed with fluorescent– or radioactively–labelled cDNAs. Here, we review technical aspects of cDNA microarrays, including the general principles, fabrication of the arrays, target labelling, image analysis and data extraction, management and mining. --- paper_title: Use of a cDNA microarray to analyse gene expression patterns in human cancer paper_content: The development and progression of cancer and the experimental reversal of tumorigenicity are accompanied by complex changes in patterns of gene expression. Microarrays of cDNA provide a powerful tool for studying these complex phenomena. The tumorigenic properties of a human melanoma cell line, UACC-903, can be suppressed by introduction of a normal human chromosome 6, resulting in a reduction of growth rate, restoration of contact inhibition, and suppression of both soft agar clonogenicity and tumorigenicity in nude mice. We used a high density microarray of 1,161 DNA elements to search for differences in gene expression associated with tumour suppression in this system. Fluorescent probes for hybridization were derived from two sources of cellular mRNA [UACC-903 and UACC-903(+6)] which were labelled with different fluors to provide a direct and internally controlled comparison of the mRNA levels corresponding to each arrayed gene. The fluorescence signals representing hybridization to each arrayed gene were analysed to determine the relative abundance in the two samples of mRNAs corresponding to each gene. Previously unrecognized alterations in the expression of specific genes provide leads for further investigation of the genetic basis of the tumorigenic phenotype of these cells. --- paper_title: DNA biochip arraying, detection and amplification strategies paper_content: Research on DNA biochips is advancing rapidly and has yielded several commercial platforms. This review presents, critically comments upon and compares DNA-biochip-arraying methods and techniques. It also discusses methods for detecting and amplifying hybridization events. The review focuses on miniaturization of these systems for diagnostic applications of biochip technology. --- paper_title: Reconstruction and functional analysis of altered molecular pathways in human atherosclerotic arteries paper_content: BackgroundAtherosclerosis affects aorta, coronary, carotid, and iliac arteries most frequently than any other body vessel. There may be common molecular pathways sustaining this process. Plaque presence and diffusion is revealed by circulating factors that can mediate systemic reaction leading to plaque rupture and thrombosis.ResultsWe used DNA microarrays and meta-analysis to study how the presence of calcified plaque modifies human coronary and carotid gene expression. We identified a series of potential human atherogenic genes that are integrated in functional networks involved in atherosclerosis. Caveolae and JAK/STAT pathways, and S100A9/S100A8 interacting proteins are certainly involved in the development of vascular disease. We found that the system of caveolae is directly connected with genes that respond to hormone receptors, and indirectly with the apoptosis pathway.Cytokines, chemokines and growth factors released in the blood flux were investigated in parallel. High levels of RANTES, IL-1ra, MIP-1alpha, MIP-1beta, IL-2, IL-4, IL-5, IL-6, IL-7, IL-17, PDGF-BB, VEGF and IFN-gamma were found in plasma of atherosclerotic patients and might also be integrated in the molecular networks underlying atherosclerotic modifications of these vessels.ConclusionThe pattern of cytokine and S100A9/S100A8 up-regulation characterizes atherosclerosis as a proinflammatory disorder. Activation of the JAK/STAT pathway is confirmed by the up-regulation of IL-6, STAT1, ISGF3G and IL10RA genes in coronary and carotid plaques. The functional network constructed in our research is an evidence of the central role of STAT protein and the caveolae system to contribute to preserve the plaque. Moreover, Cav-1 is involved in SMC differentiation and dyslipidemia confirming the importance of lipid homeostasis in the atherosclerotic phenotype. --- paper_title: Terminal continuation (TC) RNA amplification without second strand synthesis paper_content: Terminal continuation (TC) RNA amplification was developed originally to reproducibly and inexpensively amplify RNA. The TC RNA amplification method has been improved further by obviating second strand DNA synthesis, a cost-effective protocol that takes less time to perform with fewer manipulations required for RNA amplification. Results demonstrate that TC RNA amplification without second strand synthesis does not differ from the original protocol using RNA harvested from mouse brain and from hippocampal neurons obtained via laser capture microdissection from postmortem human brains. The modified TC RNA amplification method can discriminate single cell gene expression profiles between normal control and Alzheimer's disease hippocampal neurons indistinguishable from the original protocol. Thus, TC RNA amplification without second strand synthesis is a reproducible, time- and cost-effective method for RNA amplification from minute amounts of input RNA, and is compatible with microaspiration strategies and subsequent microarray analysis as well as quantitative real-time PCR. --- paper_title: The potential and challenges of nanopore sequencing paper_content: A nanopore-based device provides single-molecule detection and analytical capabilities that are achieved by electrophoretically driving molecules in solution through a nano-scale pore. The nanopore provides a highly confined space within which single nucleic acid polymers can be analyzed at high throughput by one of a variety of means, and the perfect processivity that can be enforced in a narrow pore ensures that the native order of the nucleobases in a polynucleotide is reflected in the sequence of signals that is detected. Kilobase length polymers (single-stranded genomic DNA or RNA) or small molecules (e.g., nucleosides) can be identified and characterized without amplification or labeling, a unique analytical capability that makes inexpensive, rapid DNA sequencing a possibility. Further research and development to overcome current challenges to nanopore identification of each successive nucleotide in a DNA strand offers the prospect of 'third generation' instruments that will sequence a diploid mammalian genome for ∼$1,000 in ∼24 h. --- paper_title: Fluorescent labelling of cRNA for microarray applications. paper_content: Microarrays of oligonucleotide expression libraries can be hybridised with either cDNA, generated from mRNA during reverse transcription, or cRNA, generated in an Eberwine mRNA amplification procedure. While methods for fluorescent labelling of cDNA have been thoroughly investigated, methods for cRNA labelling have not. To this purpose, we developed an aminoallyl-UTP (aa-UTP) driven cRNA labelling protocol and compared it in expression profiling studies using spotted 7.5 K 65mer murine oligonucleotide arrays with labelling via direct incorporation of Cy-UTPs. The presence of dimethylsulfoxide during coupling of aa-modified cRNA with N-hydroxysuccinimide-modified, fluorescent Cy dyes greatly enhanced the labelling efficiency, as analysed by spectrophotometry and fluorescent hybridisation signals. Indirect labelling using aa-UTP resulted in 2- to 3-fold higher degrees of labelling and fluorescent signals than labelling by direct incorporation of Cy-UTP. By variation of the aa-UTP:UTP ratio, a clear optimal degree of labelling was found (1 dye per 20-25 nt). Incorporation of more label increased Cy3 signal but lowered Cy5 fluorescence. This effect is probably due to quenching, which is more prominent for Cy5 than for Cy3. In conclusion, the currently developed method is an efficient, robust and inexpensive technique for fluorescent labelling of cRNA and allows sensitive detection of gene expression profiles on oligonucleotide microarrays. --- paper_title: Transcript copy number estimation using a mouse whole-genome oligonucleotide microarray paper_content: The ability to quantitatively measure the expression of all genes in a given tissue or cell with a single assay is an exciting promise of gene-expression profiling technology. An in situ-synthesized 60-mer oligonucleotide microarray designed to detect transcripts from all mouse genes was validated, as well as a set of exogenous RNA controls derived from the yeast genome (made freely available without restriction), which allow quantitative estimation of absolute endogenous transcript abundance. --- paper_title: Electrochemically Generated Acid and Its Containment to 100 Micron Reaction Areas for the Production of DNA Microarrays paper_content: An addressable electrode array was used for the production of acid at sufficient concentration to allow deprotection of the dimethoxytrityl (DMT) protecting group from an overlaying substrate bound to a porous reaction layer. Containment of the generated acid to an active electrode of 100 micron diameter was achieved by the presence of an organic base. This procedure was then used for the production of a DNA array, in which synthesis was directed by the electrochemical removal of the DMT group during synthesis. The product array was found to have a detection sensitivity to as low as 0.5 pM DNA in a complex background sample. --- paper_title: Electric field directed nucleic acid hybridization on microchips paper_content: Selection and adjustment of proper physical parameters enables rapid DNA transport, site selective concentration, and accelerated hybridization reactions to be carried out on active microelectronic arrays. These physical parameters include DC current, voltage, solution conductivity and buffer species. Generally, at any given current and voltage level, the transport or mobility of DNA is inversely proportional to electrolyte or buffer conductivity. However, only a subset of buffer species produce both rapid transport, site specific concentration and accelerated hybridization. These buffers include zwitterionic and low conductivity species such as: d- and l-histidine; 1- and 3-methylhistidines; carnosine; imidazole; pyridine; and collidine. In contrast, buffers such as glycine, beta-alanine and gamma-amino-butyric acid (GABA) produce rapid transport and site selective concentration but do not facilitate hybridization. Our results suggest that the ability of these buffers (histidine, etc.) to facilitate hybridization appears linked to their ability to provide electric field concentration of DNA; to buffer acidic conditions present at the anode; and in this process acquire a net positive charge which then shields or diminishes repulsion between the DNA strands, thus promoting hybridization. --- paper_title: Creation of the whole human genome microarray paper_content: Several companies have recently announced the availability of products that enable a scientist to probe gene expression from the entire human genome on a single DNA microarray. This review will focus on the underlying technological trends that have made this achievement possible, the particular methodologies which are employed to create such microarrays and the implications of the whole human genome microarray for future biological studies. The single genome array represents an important milestone on the path to unraveling the complexity of the cellular networks that control living processes. The microarrays being designed today may, however, become distant ancestors to the whole human genome arrays of the future as our understanding of the functioning of the human genome increases. --- paper_title: Annotating genomes with massive-scale RNA sequencing paper_content: Next generation technologies enable massive-scale cDNA sequencing (so-called RNA-Seq). Mainly because of the difficulty of aligning short reads on exon-exon junctions, no attempts have been made so far to use RNA-Seq for building gene models de novo, that is, in the absence of a set of known genes and/or splicing events. We present G-Mo.R-Se (Gene Modelling using RNA-Seq), an approach aimed at building gene models directly from RNA-Seq and demonstrate its utility on the grapevine genome. --- paper_title: Zero-Mode Waveguides for Single-Molecule Analysis at High Concentrations paper_content: Optical approaches for observing the dynamics of single molecules have required pico- to nanomolar concentrations of fluorophore in order to isolate individual molecules. However, many biologically relevant processes occur at micromolar ligand concentrations, necessitating a reduction in the conventional observation volume by three orders of magnitude. We show that arrays of zero-mode waveguides consisting of subwavelength holes in a metal film provide a simple and highly parallel means for studying single-molecule dynamics at micromolar concentrations with microsecond temporal resolution. We present observations of DNA polymerase activity as an example of the effectiveness of zero-mode waveguides for performing single-molecule experiments at high concentrations. --- paper_title: Overview of DNA Sequencing Strategies paper_content: Efficient and cost-effective DNA sequencing technologies have been, and may continue to be, critical to the progress of molecular biology. This overview of DNA sequencing strategies provides a high-level review of six distinct approaches to DNA sequencing: (a) dideoxy sequencing; (b) cyclic array sequencing; (c) sequencing-by-hybridization; (d) microelectrophoresis; (e) mass spectrometry; and (f) nanopore sequencing. The primary focus is on dideoxy sequencing, which has been the dominant technology since 1977, and on cyclic array strategies, for which several competitive implementations have been developed since 2005. Because the field of DNA sequencing is changing rapidly, this unit represents a snapshot of this particular moment. --- paper_title: Discovery and analysis of inflammatory disease-related genes using cDNA microarrays. paper_content: cDNA microarray technology is used to profile complex diseases and discover novel disease-related genes. In inflammatory disease such as rheumatoid arthritis, expression patterns of diverse cell types contribute to the pathology. We have monitored gene expression in this disease state with a microarray of selected human genes of probable significance in inflammation as well as with genes expressed in peripheral human blood cells. Messenger RNA from cultured macrophages, chondrocyte cell lines, primary chondrocytes, and synoviocytes provided expression profiles for the selected cytokines, chemokines, DNA binding proteins, and matrix-degrading metalloproteinases. Comparisons between tissue samples of rheumatoid arthritis and inflammatory bowel disease verified the involvement of many genes and revealed novel participation of the cytokine interleukin 3, chemokine Gro alpha and the metalloproteinase matrix metallo-elastase in both diseases. From the peripheral blood library, tissue inhibitor of metalloproteinase 1, ferritin light chain, and manganese superoxide dismutase genes were identified as expressed differentially in rheumatoid arthritis compared with inflammatory bowel disease. These results successfully demonstrate the use of the cDNA microarray system as a general approach for dissecting human diseases. --- paper_title: Optimized T7 amplification system for microarray analysis paper_content: Glass cDNA microarray technologies offer a highly parallel approach for profiling expressed gene sequences in disease-relevant tissues. However, standard hybridization and detection protocols are insufficient for milligram quantities of tissue, such as those derived from needle biopsies. Amplification systems utilizing T7 RNA polymerase can provide multiple cRNA copies from mRNA transcripts, permitting microarray studies with reduced sample inputs. Here, we describe an optimized T7-based amplification system for microarray analysis that yields between 200- and 700-fold amplification. This system was evaluated with both mRNA and total RNA samples and provided microarray sensitivity and precision that are comparable to our standard production process without amplification. The size distributions of amplified cRNA ranged from 200 bp to 4 kb and were similar to original mRNA profiles. These amplified cRNA samples were fluorescently labeled by reverse transcription and hybridized to microarrays comprising appr... --- paper_title: Amplified detection of single-base mismatches in DNA using microgravimetric quartz-crystal-microbalance transduction. paper_content: Three different methods for the amplified detection of a single-base mismatch in DNA are described using microgravimetric quartz-crystal-microbalance as transduction means. All methods involve the primary incorporation of a biotinylated base complementary to the mutation site in the analyzed double-stranded primer/DNA assembly. The double-stranded assembly is formed between 25 complementary bases of the probe DNA assembled on the Au-quartz crystal and the target DNA. One method of amplification includes the association of avidin- and biotin-labeled liposomes to the sensing interface. The second method of amplified detection of the base mismatch includes the association of an Au-nanoparticle-avidin conjugate to the sensing interface, and the secondary Au-nanoparticle-catalyzed deposition of gold on the particles. The third amplification route includes the binding of the avidin-alkaline phosphatase biocatalytic conjugate to the double-stranded surface followed by the oxidative hydrolysis of 5-bromo-4-chloro-3-indolyl phosphate to the insoluble product indigo derivative that precipitates on the transducer. Comparison of the three amplification routes reveals that the catalytic deposition of gold on the Au-nanoparticle/avidin conjugate is the most sensitive method, and the single-base mismatch in the analyzed DNA is detected with a sensitivity that corresponds to 3x10(-16) M. --- paper_title: Enzymatically Amplified Surface Plasmon Resonance Imaging Method Using RNase H and RNA Microarrays for the Ultrasensitive Detection of Nucleic Acids paper_content: A novel surface enzymatic amplification method that utilizes RNA microarrays in conjunction with the enzyme RNase H is developed for the ultrasensitve detection and analysis of target DNA molecules. The enzyme RNase H is shown to selectively and repeatedly destroy RNA from RNA−DNA heteroduplexes on gold surfaces; when used in conjunction with the label-free technique of surface plasmon resonance imaging, multiple DNA targets can be detected at a concentration of 10 fM on a single chip. In addition, this method is utilized for the sequence-specific detection of the TSPY gene in both purified and unpurified PCR products. Finally, in a series of kinetics measurements, the initial rate of hydrolysis is shown to depend directly on the surface concentration of DNA−RNA heteroduplexes. --- paper_title: A novel, high-performance random array platform for quantitative gene expression profiling. paper_content: We have developed a new microarray technology for quantitative gene-expression profiling on the basis of randomly assembled arrays of beads. Each bead carries a gene-specific probe sequence. There are multiple copies of each sequence-specific bead in an array, which contributes to measurement precision and reliability. We optimized the system for specific and sensitive analysis of mammalian RNA, and using RNA controls of defined concentration, obtained the following estimates of system performance: specificity of 1:250,000 in mammalian poly(A(+)) mRNA; limit of detection 0.13 pM; dynamic range 3.2 logs; and sufficient precision to detect 1.3-fold differences with 95% confidence within the dynamic range. Measurements of expression differences between human brain and liver were validated by concordance with quantitative real-time PCR (R(2) = 0.98 for log-transformed ratios, and slope of the best-fit line = 1.04, for 20 genes). Quantitative performance was further verified using a mouse B- and T-cell model system. We found published reports of B- or T-cell-specific expression for 42 of 59 genes that showed the greatest differential expression between B- and T-cells in our system. All of the literature observations were concordant with our results. Our experiments were carried out on a 96-array matrix system that requires only 100 ng of input RNA and uses standard microtiter plates to process samples in parallel. Our technology has advantages for analyzing multiple samples, is scalable to all known genes in a genome, and is flexible, allowing the use of standard or custom probes in an array. --- paper_title: Electrostatic readout of DNA microarrays with charged microspheres paper_content: Microarray platforms usually rely on fluorescence detection. Clack et al. present an equally sensitive, label-free technique that electrostatically detects DNA or RNA hybridization after randomly dispersing charged microspheres onto the microarray surface. --- paper_title: New trends in bioanalytical tools for the detection of genetically modified organisms: an update paper_content: Despite the controversies surrounding genetically modified organisms (GMOs), the production of GM crops is increasing, especially in developing countries. Thanks to new technologies involving genetic engineering and unprecedented access to genomic resources, the next decade will certainly see exponential growth in GMO production. Indeed, EU regulations based on the precautionary principle require any food containing more than 0.9% GM content to be labeled as such. The implementation of these regulations necessitates sampling protocols, the availability of certified reference materials and analytical methodologies that allow the accurate determination of the content of GMOs. In order to qualify for the validation process, a method should fulfil some criteria, defined as “acceptance criteria” by the European Network of GMO Laboratories (ENGL). Several methods have recently been developed for GMO detection and quantitation, mostly based on polymerase chain reaction (PCR) technology. PCR (including its different formats, e.g., double competitive PCR and real-time PCR) remains the technique of choice, thanks to its ability to detect even small amounts of transgenes in raw materials and processed foods. Other approaches relying on DNA detection are based on quartz crystal microbalance piezoelectric biosensors, dry reagent dipstick-type sensors and surface plasmon resonance sensors. The application of visible/near-infrared (vis/NIR) spectroscopy or mass spectrometry combined with chemometrics techniques has also been envisaged as a powerful GMO detection tool. Furthermore, in order to cope with the multiplicity of GMOs released onto the market, the new challenge is the development of routine detection systems for the simultaneous detection of numerous GMOs, including unknown GMOs. --- paper_title: Optical technologies for the read out and quality control of DNA and protein microarrays paper_content: Microarray formats have become an important tool for parallel (or multiplexed) monitoring of biomolecular interactions. Surface-immobilized probes like oligonucleotides, cDNA, proteins, or antibodies can be used for the screening of their complementary targets, covering different applications like gene or protein expression profiling, analysis of point mutations, or immunodiagnostics. Numerous reviews have appeared on this topic in recent years, documenting the intriguing progress of these miniaturized assay formats. Most of them highlight all aspects of microarray preparation, surface chemistry, and patterning, and try to give a systematic survey of the different kinds of applications of this new technique. This review places the emphasis on optical technologies for microarray analysis. As the fluorescent read out of microarrays is dominating the field, this topic will be the focus of the review. Basic principles of labeling and signal amplification techniques will be introduced. Recent developments in total internal reflection fluorescence, resonance energy transfer assays, and time-resolved imaging are addressed, as well as non-fluorescent imaging methods. Finally, some label-free detection modes are discussed, such as surface plasmon microscopy or ellipsometry, since these are particularly interesting for microarray development and quality control purposes. --- paper_title: Carbon and gold electrodes as electrochemical transducers for DNA hybridisation sensors paper_content: Genosensor technology relying on the use of carbon and gold electrodes is reviewed. The key steps of each analytical procedure, namely DNA-probe immobilisation, hybridisation, labelling and electrochemical investigation of the surface, are discussed in detail with separate sections devoted to label-free and newly emerging magnetic assays. Special emphasis has been given to protocols that have been used with real DNA samples. --- paper_title: Quartz crystal microbalance (QCM) affinity biosensor for genetically modified organisms (GMOs) detection. paper_content: Abstract A DNA piezoelectric sensor has been developed for the detection of genetically modified organisms (GMOs). Single stranded DNA (ssDNA) probes were immobilised on the sensor surface of a quartz crystal microbalance (QCM) device and the hybridisation between the immobilised probe and the target complementary sequence in solution was monitored. The probe sequences were internal to the sequence of the 35S promoter (P) and Nos terminator (T), which are inserted sequences in the genome of GMOs regulating the transgene expression. Two different probe immobilisation procedures were applied: (a) a thiol–dextran procedure and (b) a thiol-derivatised probe and blocking thiol procedure. The system has been optimised using synthetic oligonucleotides, which were then applied to samples of plasmidic and genomic DNA isolated from the pBI121 plasmid, certified reference materials (CRM), and real samples amplified by the polymerase chain reaction (PCR). The analytical parameters of the sensor have been investigated (sensitivity, reproducibility, lifetime etc.). The results obtained showed that both immobilisation procedures enabled sensitive and specific detection of GMOs, providing a useful tool for screening analysis in food samples. --- paper_title: Nanoparticles with Raman Spectroscopic Fingerprints for DNA and RNA Detection paper_content: Multiplexed detection of oligonucleotide targets has been performed with gold nanoparticle probes labeled with oligonucleotides and Raman-active dyes. The gold nanoparticles facilitate the formation of a silver coating that acts as a surface-enhanced Raman scattering promoter for the dye-labeled particles that have been captured by target molecules and an underlying chip in microarray format. The strategy provides the high-sensitivity and high-selectivity attributes of gray-scale scanometric detection but adds multiplexing and ratioing capabilities because a very large number of probes can be designed based on the concept of using a Raman tag as a narrow-band spectroscopic fingerprint. Six dissimilar DNA targets with six Raman-labeled nanoparticle probes were distinguished, as well as two RNA targets with single nucleotide polymorphisms. The current unoptimized detection limit of this method is 20 femtomolar. --- paper_title: BIACORE J: a new platform for routine biomolecular interaction analysis paper_content: SPR biosensor technology continues to evolve. The recently released platform from Biacore AB (Uppsala, Sweden), BIACORE J, is designed for the routine analysis of biomolecular interactions. Using an antibody-protein A and a ligand-receptor system, we demonstrate the utility of BIACORE J in determining active concentration and binding affinities. The results from these studies illustrate the high sensitivity of the instrument and its ability to generate reproducible binding responses. The BIACORE J is easy to operate and useful in diverse applications, making SPR technology widely accessible as a research tool. --- paper_title: Surface-Enhanced Raman Scattering Substrate Based on a Self-Assembled Monolayer for Use in Gene Diagnostics paper_content: The development of surface-enhanced Raman scattering (SERS)-active substrates for cancer gene detection is described. The detection method uses Raman active dye-labeled DNA gene probes, self-assembled monolayers, and nanostructured metallic substrates as SERS-active platforms. The mercaptohexane-labeled single-stranded DNA (SH−(CH2)6-ssDNA)/6-mercapto-1-hexanol system formed on a silver surface is characterized by atomic force microcopy. The surface-enhanced Raman gene (SERGen) probes developed in this study can be used to detect DNA targets via hybridization to complementary DNA probes. The probes do not require the use of radioactive labels and have a great potential to provide both sensitivity and selectivity. The effectiveness of this approach and its application in cancer gene diagnostics (BRCA1 breast cancer gene) are investigated. --- paper_title: Application of a miniature biochip using the molecular beacon probe in breast cancer gene BRCA1 detection. paper_content: We report for the first time the application of a biochip using the molecular beacon (MB) detection scheme. The usability of this biochip novel detection system for the analysis of the breast cancer gene BRCA1 is demonstrated using molecular beacon probes. The MB is designed for the BRCA1 gene and a miniature biochip system is used for detection. The performance of the biochip-MB detection system is evaluated. The optimum conditions for the MB system for highest fluorescence detection sensitivity are investigated for the detection system. The detection of BRCA1 gene is successfully demonstrated in solution and the limit of detection (LOD) is estimated as 70 nM. --- paper_title: Electrochemical DNA sensors paper_content: Electrochemistry-based sensors offer sensitivity, selectivity and low cost for the detection of selected DNA sequences or mutated genes associated with human disease. DNA-based electrochemical sensors exploit a range of different chemistries, but all take advantage of nanoscale interactions between the target in solution, the recognition layer and a solid electrode surface. Numerous approaches to electrochemical detection have been developed, including direct electrochemistry of DNA, electrochemistry at polymer-modified electrodes, electrochemistry of DNA-specific redox reporters, electrochemical amplifications with nanoparticles, and electrochemical devices based on DNA-mediated charge transport chemistry. --- paper_title: Piezoelectric Sensor for Determination of Genetically Modified Soybean Roundup Ready® in Samples not Amplified by PCR paper_content: The chemically modified piezoelectrodes were utilized to develop relatively cheap and easy to use biosensor for determination of genetically modified Roundup Ready soybean (RR soybean). The biosensor relies on the immobilization onto gold piezoelectrodes of the 21-mer single stranded oligonucleotide (probes) related to 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene, which is an active component of an insert integrated into RR soybean genome. The hybridization reaction between the probe and the target complementary sequence in solution was monitored. The system was optimized using synthetic oligonucleotides, which were applied for EPSPS gene detection in DNA samples extracted from animal feed containing 30% RR soybean amplified by the PCR and nonamplified by PCR. The detection limit for genomic DNA was in the range of 4.7·105 numbers of genom copies contained EPSPS gene in the QCM cell. The properties such as sensitivity and selectivity of piezoelectric senor presented here indicated that it could be applied for the direct determination of genetically modified RR soybean in the samples non-amplified by PCR. --- paper_title: Electrostatic surface plasmon resonance: direct electric field-induced hybridization and denaturation in monolayer nucleic acid films and label-free discrimination of base mismatches. paper_content: We demonstrate that in situ optical surface plasmon resonance spectroscopy can be used to monitor hybridization kinetics for unlabeled DNA in tethered monolayer nucleic acid films on gold in the presence of an applied electrostatic field. The dc field can enhance or retard hybridization and can also denature surface-immobilized DNA duplexes. Discrimination between matched and mismatched hybrids is achieved by simple adjustment of the electrode potential. Although the electric field at the interface is extremely large, the tethered single-stranded DNA thiol probes remain bound and can be reused for subsequent hybridization reactions without loss of efficiency. Only capacitive charging currents are drawn; redox reactions are avoided by maintaining the gold electrode potential within the ideally polarizable region. Because of potential-induced changes in the shape of the surface plasmon resonance curve, we account for the full curve rather than simply the shift in the resonance minimum. --- paper_title: DNA biochip arraying, detection and amplification strategies paper_content: Research on DNA biochips is advancing rapidly and has yielded several commercial platforms. This review presents, critically comments upon and compares DNA-biochip-arraying methods and techniques. It also discusses methods for detecting and amplifying hybridization events. The review focuses on miniaturization of these systems for diagnostic applications of biochip technology. --- paper_title: A fully electronic sensor for the measurement of cDNA hybridization kinetics paper_content: Abstract Ion sensitive field effect transistors (ISFET) are candidates for a new generation of fully electrical DNA sensors. To this purpose, we have modified ISFET sensors by adsorbing on their Si 3 N 4 surface poly- l -lysine and single (as well as double) stranded DNA. Once coupled to an accurate model of the oppositely charged layers adsorbed on the surface, the proposed sensor allows quantitatively evaluating the adsorbed molecules densities, as well as estimating DNA hybridization kinetics. --- paper_title: Piezoelectric crystal biosensors. paper_content: The recent development of piezoelectric devices as biosensors is reviewed. Biological materials, like enzymes, lipids, antibodies and antigens, have been used as specific coatings and were utilized for the determination of different substrates. Methods of protein coating and several applications are reported including microgravimetric immunoassays, microbial assays, DNA hybridization, enzyme detections and gas phase biosensors. Although the piezoelectric immunochemical sensor is convenient to use and very promising, a thorough understanding of the different phenomena associated with crystals frequency measurement in biological reactions is still lacking and deserves further investigation. --- paper_title: DNA based biosensors paper_content: There is described a method of detection of the protein-dependent coincidence of DNA in a sample which comprises detection using luminescence of one or more luminophores introduced into DNA with one, two or more DNA fragments which fragments are bound using one or more DNA-binding proteins. --- paper_title: Microgravimetric DNA sensor based on quartz crystal microbalance: comparison of oligonucleotide immobilization methods and the application in genetic diagnosis. paper_content: We report on the study of immobilization DNA probes onto quartz crystal oscillators by self-assembly technique to form variety types of mono- and multi-layered sensing films towards the realization of DNA diagnostic devices. A 18-mer DNA probe complementary to the site of genetic beta-thalassaemia mutations was immobilized on the electrodes of QCM by covalent bonding or electrostatic adsorption on polyelectrolyte films to form mono- or multi-layered sensing films by self-assembled process. Hybridization was induced by exposure of the QCMs immobilized with DNA probe to a test solution containing the target nucleic acid sequences. The kinetics of DNA probe immobilization and hybridization with the fabricated DNA sensors were studied via in-situ frequency changes. The characteristics of QCM sensors containing mono- or multi-layered DNA probe constructed by direct chemical bonding, avidin-biotin interaction or electrostatic adsorption on polyelectrolyte films were compared. Results indicated that the DNA sensing films fabricated by immobilization of biotinylated DNA probe to avidin provide fast sensor response and high hybridization efficiencies. The effects of ionic strength of the buffer solution and the concentration of target nucleic acid used in hybridization were also studied. The fabricated DNA biosensor was used to detect a set of real samples. We conclude that the microgravimetric DNA sensor with its direct detection of amplified products provide a rapid, low cost and convenient diagnostic method for genetic disease. --- paper_title: Surface Plasmon Resonance Spectroscopy as a Probe of In-Plane Polymerization in Monolayer Organic Conducting Films paper_content: Several groups have shown that alkanethiol-modified pyrroles can be tethered to a gold surface, but there is often little evidence that, once oxidized, the resulting monolayer film is an organic conducting polymer. Using surface plasma resonance (SPR) spectroscopy, we demonstrate for the first time that, upon electrochemical oxidation, self-assembled alkanethiol−pyrrole films on gold show behavior characteristic of organic conducting polymers: we observe reversible changes in the optical constants of the organic film upon doping/dedoping. Since the optical constants are related to film conductivity, we show that the effective isotropic dielectric constant of the film obtained in the standard SPR data analysis can be interpreted in terms of in-plane and out-of-plane contributions to film conductivity. We find that the in-plane conductivity of oxidized 3-(ω-mercaptoundecyl)pyrrole is smaller, but of the same order of magnitude, than that found for thick films of polypyrrole. Most importantly, we observe re... --- paper_title: Electrical biochip technology--a tool for microarrays and continuous monitoring. paper_content: Based on electrical biochips made in Si-technology cost effective portable devices have been constructed for field applications and point of care diagnosis. These miniaturized amperometric biosensor devices enable the evaluation of biomolecular interactions by measuring the redox recycling of ELISA products, as well as the electrical monitoring of metabolites. The highly sensitive redox recycling is facilitated by interdigitated ultramicroelectrodes of high spatial resolution. The application of these electrical biochips as DNA microarrays for the molecular diagnosis of viral infections demonstrates the measurement procedure. Self-assembling of capture oligonucleotides via thiol-gold coupling has been used to construct the DNA interface on-chip. Another application for this electrical detection principle is continuous measuring with bead-based biosensors. Here, paramagnetic nanoparticles are used as carriers of the bioanalytical interface in ELISA format. A Si-micromachined glucose sensor for continuous monitoring in interstitial fluid ex vivo shows the flexibility of the electrical platform. Here the novel approach is a pore membrane in micrometer-dimensions acting as a diffusion barrier. The electrochemical detection takes place in a cavity containing glucose oxidase and a Pt-electrode surface. The common hydrogen peroxide detection, together with Si technology, enable precise differential measurements using a second cavity. --- paper_title: DNA Biochip Using a Phototransistor Integrated Circuit paper_content: This work describes the development of an integrated biosensor based on phototransistor integrated circuits (IC) for use in medical detection, DNA diagnostics, and gene mapping. The evaluation of various system components developed for an integrated biosensor microchip is discussed. Methods to develop a microarray of DNA probes on nitrocellulose substrate are discussed. The biochip device has sensors, amplifiers, discriminators, and logic circuitry on board. Integration of light-emitting diodes into the device is also possible. To achieve improved sensitivity, we have designed an IC system having each phototransistor sensing element composed of 220 phototransistor cells connected in parallel. Measurements of fluorescent-labeled DNA probe microarrays and hybridization experiments with a sequence-specific DNA probe for the human immunodeficiency virus 1 system on nitrocellulose substrates illustrate the usefulness and potential of the DNA biochip. --- paper_title: Electronic detection of DNA by its intrinsic molecular charge. paper_content: We report the selective and real-time detection of label-free DNA using an electronic readout. Microfabricated silicon field-effect sensors were used to directly monitor the increase in surface charge when DNA hybridizes on the sensor surface. The electrostatic immobilization of probe DNA on a positively charged poly-l-lysine layer allows hybridization at low ionic strength where field-effect sensing is most sensitive. Nanomolar DNA concentrations can be detected within minutes, and a single base mismatch within 12-mer oligonucleotides can be distinguished by using a differential detection technique with two sensors in parallel. The sensors were fabricated by standard silicon microtechnology and show promise for future electronic DNA arrays and rapid characterization of nucleic acid samples. This approach demonstrates the most direct and simple translation of genetic information to microelectronics. --- paper_title: Plasmonics-Based Nanostructures for Surface-Enhanced Raman Scattering Bioanalysis paper_content: Surface-enhanced Raman scattering (SERS) spectroscopy is a plasmonics-based spectroscopic technique that combines modern laser spectroscopy with unique optical properties of metallic nanostructures, resulting in strongly increased Raman signals when molecules are adsorbed on or near nanometer-size structures of special metals such as gold, silver, and transition metals. This chapter provides a synopsis of the development and application of SERS-active metallic nanostructures, especially for the analysis of biologically relevant compounds. Some highlights of this chapter include reports of SERS as an immunoassay readout method, SERS gene nanoprobes, near-field scanning optical microscopy SERS probes, SERS as a tool for single-molecule detection, and SERS nanoprobes for cellular studies. --- paper_title: A novel microgravimetric DNA sensor with high sensitivity. paper_content: A novel method using an amplifier with a cantilever and gold nanoparticles successfully to extend the length of the target for the specific and high sensitive detection of DNA was reported. When the size of gold nanoparticle is 50 nm, a sensitivity of 10(-15)M for the single base mutation detection has been achieved. --- paper_title: Label-free and high-resolution protein/DNA nanoarray analysis using Kelvin probe force microscopy. paper_content: Using the scanning probe technique known as Kelvin probe force microscopy it is possible to successfully devise a sensor for charged biomolecules. The Kelvin probe force microscope is a tool for measuring local variations in surface potential across a substrate of interest. Because many biological molecules have a native state that includes the presence of charge centres (such as the negatively charged backbone of DNA), the formation of highly specific complexes between biomolecules will often be accompanied by local changes in charge density. By spatially resolving this variation in surface potential it is possible to measure the presence of a specific bound target biomolecule on a surface without the aid of special chemistries or any form of labelling. The Kelvin probe force microscope presented here is based on an atomic force microscopy nanoprobe offering high resolution (<10 nm), sensitivity (<50 nM) and speed (>1,100 microm s(-1)), and the ability to resolve as few as three nucleotide mismatches. --- paper_title: Biosensor technology and surface plasmon resonance for real-time detection of HIV-1 genomic sequences amplified by polymerase chain reaction. paper_content: BACKGROUND ::: The recent development of biosensor technologies for biospecific interaction analysis enables the monitoring of a variety of molecular reactions in real time by surface plasmon resonance (SPR). If the ligand is a biotinylated single stranded DNA, this technology could monitor DNA-DNA hybridization. This approach could be of great interest in virology, since the hybridization step is oftenly required to confirm specificity of molecular diagnosis. ::: ::: ::: OBJECTIVES ::: To determine whether real-time molecular diagnosis of human immunodeficiency virus type I (HIV-1) could be performed using biosensors and SPR technology. ::: ::: ::: STUDY DESIGN ::: Specific hybridization of a biotinylated HIV-1 oligonucleotide probe immobilized on a sensor chip to single stranded DNA obtained by asymmetric polymerase-chain reaction (PCR) was determined using the BIAcore biosensor. ::: ::: ::: RESULTS ::: Direct injection of asymmetric PCR to a sensor chip carrying an internal HIV-1 oligonucleotide probe allows detection of hybridization by SPR using biosensor technology. This enabled us to apply a real-time, one-step, non-radioactive protocol to demonstrate the specificity of amplification of HIV-1 genomic sequences by PCR. ::: ::: ::: CONCLUSION ::: The procedure described in this study for HIV-1 detection is simple, fast (PCR and SPR analyses take 30 min), reproducible and could be proposed as an integral part of automated diagnostic systems based on the use of laboratory workstations and biosensors for DNA isolation, preparation of PCR reactions and analysis of PCR products. --- paper_title: Rapid detection of single nucleotide polymorphisms associated with spinal muscular atrophy by use of a reusable fibre-optic biosensor. paper_content: Rapid (<2 min) and quantitative genotyping for single nucleotide polymorphisms (SNPs) associated with spinal muscular atrophy was done using a reusable (approximately 80 cycles of application) fibre-optic biosensor over a clinically relevant range (0-4 gene copies). Sensors were functionalized with oligonucleotide probes immobilized at high density (approximately 7 pmol/cm2) to impart enhanced selectivity for SNP discrimination and used in a total internal reflection fluorescence detection motif to detect 202 bp PCR amplicons from patient samples. Real-time detection may be done over a range of ionic strength conditions (0.1-1.0 M) without stringency rinsing to remove non-selectively bound materials and without loss of selectivity, permitting a means for facile sample preparation. By using the time-derivative of fluorescence intensity as the analytical parameter, linearity of response may be maintained while allowing for significant reductions in analysis time (10-100-fold), permitting for the completion of measurements in under 1 min. --- paper_title: Electrochemical Interrogation of DNA Monolayers on Gold Surfaces paper_content: In this report, we systematically investigated DNA immobilization at gold surfaces with electrochemical techniques. Comparative cyclic voltammetric and chronocoulometric studies suggested that DNA monolayers immobilized at gold surfaces were not homogeneous. Nonspecific Au-DNA interactions existed even with the treatment of mercaptohexanol, which was known to competitively remove loosely bound DNA at gold surfaces. While both thiolated and nonthiolated DNA formed monolayers on gold surfaces, their hybridization abilities were distinctly different. In contrast to thiolated DNA probes, nonthiolated DNA probes immobilized at gold surfaces were essentially nonhybridizable. The experimental results presented here might be useful for the design of high-performance electrochemical DNA sensors. --- paper_title: Carbon and gold electrodes as electrochemical transducers for DNA hybridisation sensors paper_content: Genosensor technology relying on the use of carbon and gold electrodes is reviewed. The key steps of each analytical procedure, namely DNA-probe immobilisation, hybridisation, labelling and electrochemical investigation of the surface, are discussed in detail with separate sections devoted to label-free and newly emerging magnetic assays. Special emphasis has been given to protocols that have been used with real DNA samples. --- paper_title: Immobilization of single-stranded deoxyribonucleic acid on gold electrode with self-assembled aminoethanethiol monolayer for DNA electrochemical sensor applications. paper_content: A synthesized 24-mer single-stranded deoxyribonucleic acid (ssDNA) was covalently immobilized onto a self-assembled aminoethanethiol monolayer modified gold electrode, using water-soluble 1-ethyl-3(3-dimethylaminopropyl)-carbodiimide (EDC). The covalently immobilized ssDNAs were hybridized with complementary ssDNA (cDNA) or yAL(3) gene in solution, forming double-stranded DNAs (dsDNA). Meanwhile, daunomycin as an electrochemical active intercalator in the hybridization buffer solution was intercalated into the dsDNA to form a dsDNA/daunomycin system on the gold electrode surface, which was used for DNA electrochemical sensor. The cathodic waves of daunomycin bound to the double-stranded DNA (dsDNA) by linear sweep voltammetry were utilized to detect the cDNA. The cathodic peak current (i(pc)) of duanomycin was linearly related to the concentrations of cDNA between 0.1 mug ml(-1) and 0.1 ng ml(-1). The detection limit was about 30 pg ml(-1). --- paper_title: Selective Release of DNA from the Surface of Indium−Tin Oxide Thin Electrode Films Using Thiol−Disulfide Exchange Chemistry paper_content: A new challenge in biointerfacial science is the development of dynamic surfaces with the ability to adjust and tune the chemical functionality at the interface between the biological and nonbiological entities. In this paper we describe fabrication of indium−tin oxide (ITO) electrodes and the design of a ligand that can be switched to enable selectively controlled interactions with DNA. Tailoring the surface composition of the ITO electrode to optimize its optical and electrical properties was also studied. The surface attachment chemistry investigated utilizes thiol−disulfide exchange chemistry. This chemistry involved the covalent attachment of a thiol-functionalized silane anchor to a hydroxyl-activated ITO electrode surface. Subsequent reaction with 2-(2-pyridinyldithio)ethanamine hydrochloride formed the disulfide bridge and provided the terminal amine group, which facilitates addition of a cross-linker. DNA was then covalently bound to the cross-linker, and hybridization with the complementary Cy3-... --- paper_title: A fully electronic sensor for the measurement of cDNA hybridization kinetics paper_content: Abstract Ion sensitive field effect transistors (ISFET) are candidates for a new generation of fully electrical DNA sensors. To this purpose, we have modified ISFET sensors by adsorbing on their Si 3 N 4 surface poly- l -lysine and single (as well as double) stranded DNA. Once coupled to an accurate model of the oppositely charged layers adsorbed on the surface, the proposed sensor allows quantitatively evaluating the adsorbed molecules densities, as well as estimating DNA hybridization kinetics. --- paper_title: Disposable DNA electrochemical sensor for hybridization detection. paper_content: A disposable electrochemical sensor for the detection of short DNA sequences is described. Synthetic single-stranded oligonucleotides have been immobilized onto graphite screen printed electrodes with two procedures, the first involving the binding of avidinbiotinylated oligonucleotide and the second adsorption at a controlled potential. The probes were hybridized with different concentrations of complementary sequences. The formed hybrids on the electrode surface were evaluated by differential pulse voltammetry and chronopotentiometric stripping analysis using daunomycin hydrochloride as indicator of hybridization reaction. The probe immobilization step, the hybridization event and the indicator detection, have been optimized. The DNA sensor obtained by adsorption at a controlled potential was able to detect 1 microgram/ml of target sequence in the buffer solution using chronopotentiometric stripping analysis. --- paper_title: Interface Layering Phenomena in Capacitance Detection of DNA with Biochips paper_content: Reliable DNA detection is of great importance for the development of the Lab-on-chip technology. The effort of the most recent projects on this field is to integrate all necessary operations, such as sample preparation (mixing, PCR amplification) together with the sensor user for DNA detection. Among the different ways to sense the DNA hybridization, fluorescence based detection has been favored by the market. However, fluorescence based approaches require that the DNA targets are labeled by means of chromophores. As an alternative label-free DNA detection method, capacitance detection was recently proposed by different authors. While this effect has been successfully demonstrated by several groups, the model used for data analysis is far too simple to describe the real behavior of a DNA sensor. The aim of the present paper is to propose a different electrochemical model to describe DNA capacitance detection. --- paper_title: Electrochemically Generated Acid and Its Containment to 100 Micron Reaction Areas for the Production of DNA Microarrays paper_content: An addressable electrode array was used for the production of acid at sufficient concentration to allow deprotection of the dimethoxytrityl (DMT) protecting group from an overlaying substrate bound to a porous reaction layer. Containment of the generated acid to an active electrode of 100 micron diameter was achieved by the presence of an organic base. This procedure was then used for the production of a DNA array, in which synthesis was directed by the electrochemical removal of the DMT group during synthesis. The product array was found to have a detection sensitivity to as low as 0.5 pM DNA in a complex background sample. --- paper_title: Electrochemical Biosensors for Sequence‐Specific DNA Detection paper_content: This review summarizes the most relevant work performed in the last years in the field of the DNA-based electrochemical biosensors for sequence-specific DNA detection. The approaches used for preparing the biosensing layer, as well as the schemes developed for the transduction of the hybridization event are also discussed. --- paper_title: Nanomaterial-based electrochemical biosensors. paper_content: The unique properties of nanoscale materials offer excellent prospects for interfacing biological recognition events with electronic signal transduction and for designing a new generation of bioelectronic devices exhibiting novel functions. In this Highlight I address recent research that has led to powerful nanomaterial-based electrical biosensing devices and examine future prospects and challenges. New nanoparticle-based signal amplification and coding strategies for bioaffinity assays are discussed, along with carbon-nanotube molecular wires for achieving efficient electrical communication with redox enzyme and nanowire-based label-free DNA sensors. --- paper_title: Array-Based Electrical Detection of DNA with Nanoparticle Probes paper_content: A DNA array detection method is reported in which the binding of oligonucleotides functionalized with gold nanoparticles leads to conductivity changes associated with target-probe binding events. The binding events localize gold nanoparticles in an electrode gap; silver deposition facilitated by these nanoparticles bridges the gap and leads to readily measurable conductivity changes. An unusual salt concentration–dependent hybridization behavior associated with these nanoparticle probes was exploited to achieve selectivity without a thermal-stringency wash. Using this method, we have detected target DNA at concentrations as low as 500 femtomolar with a point mutation selectivity factor of ∼ 100,000:1. --- paper_title: Electrochemical DNA biosensor based on silver nanoparticles/poly(3-(3-pyridyl) acrylic acid)/carbon nanotubes modified electrode. paper_content: Abstract In this work, we present an electrochemical DNA sensor based on silver nanoparticles/poly( trans -3-(3-pyridyl) acrylic acid) (PPAA)/multiwalled carbon nanotubes with carboxyl groups (MWCNTs–COOH) modified glassy carbon electrode (GCE). The polymer film was electropolymerized onto MWCNTs–COOH modified electrode by cyclic voltammetry (CV), and then silver nanoparticles were electrodeposited on the surface of PPAA/MWCNTs–COOH composite film. Thiol group end single-stranded DNA (HS–ssDNA) probe was easily covalently linked onto the surface of silver nanoparticles through a 5′ thiol linker. The DNA hybridization events were monitored based on the signal of the intercalated adriamycin by differential pulse voltammetry (DPV). Based on the response of adriamycin, only the complementary oligonucleotides gave an obvious current signal compared with the three-base mismatched and noncomplementary oligonucleotides. Under the optimal conditions, the increase of reduction peak current of adriamycin was linear with the logarithm of the concentration of the complementary oligonucleotides from 9.0 × 10 −12 to 9.0 × 10 −9 M with a detection limit of 3.2 × 10 −12 M. In addition, this DNA sensor exhibited an excellent reproducibility and stability during DNA hybridization assay. --- paper_title: Carbon Nanotube DNA Sensor and Sensing Mechanism paper_content: We report the fabrication of single-walled carbon nanotube (SWNT) DNA sensors and the sensing mechanism. The simple and generic protocol for label-free detection of DNA hybridization is demonstrated with random sequence 15mer and 30mer oligonucleotides. DNA hybridization on gold electrodes, instead of on SWNT sidewalls, is mainly responsible for the acute electrical conductance change due to the modulation of energy level alignment between SWNT and gold contact. This work provides concrete experimental evidence on the effect of SWNT−DNA binding on DNA functionality, which will help to pave the way for future designing of SWNT biocomplexes for applications in biotechnology in general and also DNA-assisted nanotube manipulation techniques. --- paper_title: Silicon nanowire arrays for label-free detection of DNA. paper_content: Arrays of highly ordered n-type silicon nanowires (SiNW) are fabricated using complementary metal-oxide semiconductor (CMOS) compatible technology, and their applications in biosensors are investigated. Peptide nucleic acid (PNA) capture probe-functionalized SiNW arrays show a concentration-dependent resistance change upon hybridization to complementary target DNA that is linear over a large dynamic range with a detection limit of 10 fM. As with other SiNW biosensing devices, the sensing mechanism can be understood in terms of the change in charge density at the SiNW surface after hybridization, the so-called "field effect". The SiNW array biosensor discriminates satisfactorily against mismatched target DNA. It is also able to monitor directly the DNA hybridization event in situ and in real time. The SiNW array biosensor described here is ultrasensitive, non-radioactive, and more importantly, label-free, and is of particular importance to the development of gene expression profiling tools and point-of-care applications. --- paper_title: Integrated nanoparticle-biomolecule systems for biosensing and bioelectronics. paper_content: Abstract The similar dimensions of biomolecules such as enzymes, antibodies or DNA, and metallic or semiconductor nanoparticles (NPs) enable the synthesis of biomolecule–NP hybrid systems where the unique electronic, photonic and catalytic properties of NPs are combined with the specific recognition and biocatalytic properties of biomolecules. The unique functions of biomolecule–NP hybrid systems are discussed with several examples: (i) the electrical contacting of redox enzymes with electrodes is the basis for the development of enzymatic electrodes for amperometric biosensors or biofuel cell elements. The reconstitution of the apo-glucose oxidase or apo-glucose dehydrogenase on flavin adenine dinucleotide (FAD)-functionalized Au NPs (1.4 nm) associated with electrodes, or on pyrroloquinoline quinone (PQQ)-functionalized Au NPs (1.4 nm) associated with electrodes, respectively, yields electrically contacted enzyme electrodes. The aligned, reconstituted enzymes on the electrode surfaces reveal effective electrical contacting, and the glucose oxidase and glucose dehydrogenase reveal turnover rates of 5000 and 11,800 s −1 , respectively. (ii) The photoexcitation of semiconductor nanoparticles yields fluorescence with a wavelength controlled by the size of the NPs. The fluorescence functions of semiconductor NPs are used to develop a fluorescence resonance energy transfer (FRET) assay for nucleic acids, and specifically, for analyzing telomerase activity in cancer cells. CdSe–ZnS NPs are functionalized by a primer recognized by telomerase, and this is elongated by telomerase extracted from HeLa cancer cells in the presence of dNTPs and Texas-red-functionalized dUTP. The dye integrated into the telomers allows the FRET process that is intensified as telomerization proceeds. Also, the photoexcited electron–hole pair generated in semiconductor NPs is used to generate photocurrents in a CdS–DNA hybrid system associated with an electrode. A redox-active intercalator, methylene blue, was incorporated into a CdS–duplex DNA monolayer associated with a Au electrode, and this facilitated the electron transfer between the electrode and the CdS NPs. The direction of the photocurrent was controlled by the oxidation state of the intercalator. (iii) Biocatalysts grow metallic NPs, and the absorbance of the NPs provides a means to assay the biocatalytic transformations. This is exemplified with the glucose oxidase-induced growth of Au NPs and with the tyrosinase-stimulated growth of Au NPs, in the presence of glucose or tyrosine, respectively. The biocatalytic growth of the metallic NPs is used to grow nanowires on surfaces. Glucose oxidase or alkaline phosphatase functionalized with Au NPs (1.4 nm) acted as ‘biocatalytic inks’ for the synthesis of metallic nanowires. The deposition of the Au NP-modified glucose oxidase, or the Au NP-modified alkaline phosphatase on Si surfaces by dip-pen nanolithography led to biocatalytic templates, that after interaction with glucose/AuCl 4 − or p -aminophenolphosphate/Ag + , allowed the synthesis of Au nanowires or Ag nanowires, respectively. --- paper_title: Carbon nanotubes for electrochemical biosensing. paper_content: The aim of this review is to summarize the most relevant contributions in the development of electrochemical (bio)sensors based on carbon nanotubes in the last years. Since the first application of carbon nanotubes in the preparation of an electrochemical sensor, an increasing number of publications involving carbon nanotubes-based sensors have been reported, demonstrating that the particular structure of carbon nanotubes and their unique properties make them a very attractive material for the design of electrochemical biosensors. The advantages of carbon nanotubes to promote different electron transfer reactions, in special those related to biomolecules; the different strategies for constructing carbon nanotubes-based electrochemical sensors, their analytical performance and future prospects are discussed in this article. --- paper_title: Allele-specific genotype detection of factor V Leiden mutation from polymerase chain reaction amplicons based on label-free electrochemical genosensor. paper_content: An electrochemical genosensor for the genotype detection of allele-specific factor V Leiden mutation from PCR amplicons using the intrinsic guanine signal is described. The biosensor relies on the immobilization of the 21-mer inosine-substituted oligonucleotide capture probes related to the wild-type or mutant-type amplicons, and these probes are hybridized with their complementary DNA sequences at a carbon paste electrode (CPE). The extent of hybridization between the probe and target sequences was determined by using the oxidation signal of guanine in connection with differential pulse voltammetry (DPV). The guanine signal was monitored as a result of the specific hybridization between the probe and amplicon at the CPE surface. No label-binding step was necessary, and the appearance of the guanine signal shortened the assay time and simplified the detection of the factor V Leiden mutation from polymerase chain reaction (PCR)-amplified amplicons. The discrimination between the homozygous and heterozygous mutations was also established by comparing the peak currents of the guanine signals. Numerous factors affecting the hybridization and nonspecific binding events were optimized to detect down to 51.14 fmol/mL target DNA. With the help of the appearance of the guanine signal, the yes/no system is established for the electrochemical detection of allele-specific mutation on factor V for the first time. Features of this protocol are discussed and optimized. --- paper_title: In situ DNA amplification with magnetic primers for the electrochemical detection of food pathogens. paper_content: A sensitive and selective genomagnetic assay for the electrochemical detection of food pathogens based on in situ DNA amplification with magnetic primers has been designed. The performance of the genomagnetic assay was firstly demonstrated for a DNA synthetic target by its double-hybridization with both a digoxigenin probe and a biotinylated capture probe, and further binding to streptavidin-modified magnetic beads. The DNA sandwiched target bound on the magnetic beads is then separated by using a magneto electrode based on graphite-epoxy composite. The electrochemical detection is finally achieved by an enzyme marker, anti-digoxigenin horseradish peroxidase (HRP). The novel strategy was used for the rapid and sensitive detection of polymerase chain reaction (PCR) amplified samples. Promising resultants were also achieved for the DNA amplification directly performed on magnetic beads by using a novel magnetic primer, i.e., the up PCR primer bound to magnetic beads. Moreover, the magneto DNA biosensing assay was able to detect changes at single nucleotide polymorphism (SNP) level, when stringent hybridization conditions were used. The reliability of the assay was tested for Salmonella spp., the most important pathogen affecting food safety. --- paper_title: Labelfree fully electronic nucleic acid detection system based on a field-effect transistor device. paper_content: The labelfree detection of nucleic acid sequences is one of the modern attempts to develop quick, cheap and miniaturised hand-held devices for the future genetic testing in biotechnology and medical diagnostics. We present an approach to detect the hybridisation of DNA sequences using electrolyte-oxide-semiconductor field-effect transistors (EOSFETs) with micrometer dimensions. These semiconductor devices are sensitive to electrical charge variations that occur at the surface/electrolyte interface, i.e. upon hybridisation of oligonucleotides with complementary single-stranded (ss) oligonucleotides, which are immobilised on the oxide surface of the transistor gate. This method allows direct, time-resolved and in situ detection of specific nucleic acid binding events without any labelling. We focus on the detection mechanism of our sensors by using oppositely charged polyelectrolytes (PAH and PSS) subsequently attached to the transistor structures. Our results indicate that the sensor output is charge sensitive and distance dependent from the gate surface, which pinpoints the need for very defined surface chemistry at the device surface. The hybridisation of natural 19 base-pair sequences has been successfully detected with the sensors. In combination with nano-transistors a PCR free detection system might be feasible in future. --- paper_title: Electrostatic surface plasmon resonance: direct electric field-induced hybridization and denaturation in monolayer nucleic acid films and label-free discrimination of base mismatches. paper_content: We demonstrate that in situ optical surface plasmon resonance spectroscopy can be used to monitor hybridization kinetics for unlabeled DNA in tethered monolayer nucleic acid films on gold in the presence of an applied electrostatic field. The dc field can enhance or retard hybridization and can also denature surface-immobilized DNA duplexes. Discrimination between matched and mismatched hybrids is achieved by simple adjustment of the electrode potential. Although the electric field at the interface is extremely large, the tethered single-stranded DNA thiol probes remain bound and can be reused for subsequent hybridization reactions without loss of efficiency. Only capacitive charging currents are drawn; redox reactions are avoided by maintaining the gold electrode potential within the ideally polarizable region. Because of potential-induced changes in the shape of the surface plasmon resonance curve, we account for the full curve rather than simply the shift in the resonance minimum. --- paper_title: Label-free detection of DNA hybridization using carbon nanotube network field-effect transistors paper_content: We report carbon nanotube network field-effect transistors (NTNFETs) that function as selective detectors of DNA immobilization and hybridization. NTNFETs with immobilized synthetic oligonucleotides have been shown to specifically recognize target DNA sequences, including H63D single-nucleotide polymorphism (SNP) discrimination in the HFE gene, responsible for hereditary hemochromatosis. The electronic responses of NTNFETs upon single-stranded DNA immobilization and subsequent DNA hybridization events were confirmed by using fluorescence-labeled oligonucleotides and then were further explored for label-free DNA detection at picomolar to micromolar concentrations. We have also observed a strong effect of DNA counterions on the electronic response, thus suggesting a charge-based mechanism of DNA detection using NTNFET devices. Implementation of label-free electronic detection assays using NTNFETs constitutes an important step toward low-cost, low-complexity, highly sensitive and accurate molecular diagnostics. --- paper_title: A fully electronic sensor for the measurement of cDNA hybridization kinetics paper_content: Abstract Ion sensitive field effect transistors (ISFET) are candidates for a new generation of fully electrical DNA sensors. To this purpose, we have modified ISFET sensors by adsorbing on their Si 3 N 4 surface poly- l -lysine and single (as well as double) stranded DNA. Once coupled to an accurate model of the oppositely charged layers adsorbed on the surface, the proposed sensor allows quantitatively evaluating the adsorbed molecules densities, as well as estimating DNA hybridization kinetics. --- paper_title: Microelectrodes on a Silicon Chip for Label-Free Capacitive DNA Sensing paper_content: This paper presents the experimental characterization of two-terminal microfabricated capacitors for microarrays with an electrical sensing of label-free deoxyribonucleic acid (DNA). So far, such a concept has been demonstrated only in experimental setups featuring dimensions much larger than those typical of microfabrication. Therefore, this paper investigates: 1) the compatibility of the silicon microelectronic processes with biological functionalization procedures; 2) the effects of parasitics when electrodes have realistic dimensions; 3) measurement stability and reproducibility; and 4) the possibility of a fully integrated stand-alone device. The obtained results clearly indicate that two-terminal capacitive sensing with fully integrated electronics represents a viable technology for a DNA label-free detection/recognition --- paper_title: An FET-type charge sensor for highly sensitive detection of DNA sequence. paper_content: We have fabricated an field effect transistor (FET)-type DNA charge sensor based on 0.5 microm standard complementary metal oxide semiconductor (CMOS) technology which can detect the deoxyribonucleic acid (DNA) probe's immobilization and information on hybridization by sensing the variation of drain current due to DNA charge and investigated its electrical characteristics. FET-type charge sensor for detecting DNA sequence is a semiconductor sensor measuring the change of electric charge caused by DNA probe's immobilization on the gate metal, based on the field effect mechanism of MOSFET. It was fabricated in p-channel (P) MOSFET-type because the phosphate groups present in DNA have a negative charge and this charge determines the effective gate potential of PMOSFET. Gold (Au) which has a chemical affinity with thiol was used as the gate metal in order to immobilize DNA. The gate potential is determined by the electric charge which DNA possesses. Variation of the drain current versus time was measured. The drain current increased when thiol DNA and target DNA were injected into the solution, because of the field effect due to the electrical charge of DNA molecules. The experimental validity was verified by the results of mass changes detected using quartz crystal microbalance (QCM) under the same measurement condition. Therefore it is confirmed that DNA sequence can be detected by measuring the variation of the drain current due to the variation of DNA charge and the proposed FET-type DNA charge sensor might be useful in the development for DNA chips. --- paper_title: Surface Plasmon Resonance Spectroscopy as a Probe of In-Plane Polymerization in Monolayer Organic Conducting Films paper_content: Several groups have shown that alkanethiol-modified pyrroles can be tethered to a gold surface, but there is often little evidence that, once oxidized, the resulting monolayer film is an organic conducting polymer. Using surface plasma resonance (SPR) spectroscopy, we demonstrate for the first time that, upon electrochemical oxidation, self-assembled alkanethiol−pyrrole films on gold show behavior characteristic of organic conducting polymers: we observe reversible changes in the optical constants of the organic film upon doping/dedoping. Since the optical constants are related to film conductivity, we show that the effective isotropic dielectric constant of the film obtained in the standard SPR data analysis can be interpreted in terms of in-plane and out-of-plane contributions to film conductivity. We find that the in-plane conductivity of oxidized 3-(ω-mercaptoundecyl)pyrrole is smaller, but of the same order of magnitude, than that found for thick films of polypyrrole. Most importantly, we observe re... --- paper_title: Electronic detection of DNA by its intrinsic molecular charge. paper_content: We report the selective and real-time detection of label-free DNA using an electronic readout. Microfabricated silicon field-effect sensors were used to directly monitor the increase in surface charge when DNA hybridizes on the sensor surface. The electrostatic immobilization of probe DNA on a positively charged poly-l-lysine layer allows hybridization at low ionic strength where field-effect sensing is most sensitive. Nanomolar DNA concentrations can be detected within minutes, and a single base mismatch within 12-mer oligonucleotides can be distinguished by using a differential detection technique with two sensors in parallel. The sensors were fabricated by standard silicon microtechnology and show promise for future electronic DNA arrays and rapid characterization of nucleic acid samples. This approach demonstrates the most direct and simple translation of genetic information to microelectronics. --- paper_title: A Fully Electronic Label-Free DNA Sensor Chip paper_content: This paper presents a microfabricated DNA chip for fully electronic, label-free DNA recognition based on capacitance measurements. The chip has been fabricated in 0.5-mum CMOS technology and it features an array of individually addressable sensing sites consisting of pairs of gold electrodes and addressing logic. Read-out circuitry is built externally using standard components to provide increased experimental flexibility. The chip has been electrically characterized and tested with various solutions containing DNA samples. Significant capacitance variations due to DNA hybridization have been measured, thus showing that the approach represents a viable solution for a single chip DNA sensor array --- paper_title: Label-free determination of picogram quantities of DNA by stripping voltammetry with solid copper amalgam or mercury electrodes in the presence of copper. paper_content: Highly sensitive label-free techniques of DNA determination are particularly interesting in relation to the present development of the DNA sensors. We show that subnanomolar concentrations (related to monomer content) of unlabeled DNA can be determined using copper solid amalgam electrodes or hanging mercury drop electrodes in the presence of copper. DNA is first treated with acid (e.g., 0.5 M perchloric acid), and the acid-released purine bases are directly determined by the cathodic stripping voltammetry. Volumes of 5−3 μL of acid-treated DNA can easily be analyzed, thus making possible the determination of picogram and subpicogram amounts of DNA corresponding to attomole and subattomole quantities of 1000-base pair DNA. Application of this determination in DNA hybridization detection is demonstrated using surface H for the hybridization (superparamagnetic beads with covalently attached DNA probe) and the mercury electrodes only for the determination of DNA selectively captured at surface H. --- paper_title: Room-temperature transistor based on a single carbon nanotube paper_content: The use of individual molecules as functional electronic devices was first proposed in the 1970s (ref. 1). Since then, molecular electronics2,3 has attracted much interest, particularly because it could lead to conceptually new miniaturization strategies in the electronics and computer industry. The realization of single-molecule devices has remained challenging, largely owing to difficulties in achieving electrical contact to individual molecules. Recent advances in nanotechnology, however, have resulted in electrical measurements on single molecules4,5,6,7. Here we report the fabrication of a field-effect transistor—a three-terminal switching device—that consists of one semiconducting8,9,10 single-wall carbon nanotube11,12 connected to two metal electrodes. By applying a voltage to a gate electrode, the nanotube can be switched from a conducting to an insulating state. We have previously reported5 similar behaviour for a metallic single-wall carbon nanotube operated at extremely low temperatures. The present device, in contrast, operates at room temperature, thereby meeting an important requirement for potential practical applications. Electrical measurements on the nanotube transistor indicate that its operation characteristics can be qualitatively described by the semiclassical band-bending models currently used for traditional semiconductor devices. The fabrication of the three-terminal switching device at the level of a single molecule represents an important step towards molecular electronics. --- paper_title: Modification of indium tin oxide electrodes with repeat polynucleotides: electrochemical detection of trinucleotide repeat expansion. paper_content: Genomic expansion of the triplet repeat sequences 5'-(CTG)n and 5'-(CGG)n leads to myotonic dystrophy and fragile X syndrome, respectively. Methods for determining the number of repeats in unprocessed nucleic acids would be useful in diagnosing diseases based on triplet repeat expansion. Electrochemical reactions based on the oxidation of guanine were expected to give larger signals per strand for expansion of repeats containing guanine. A novel PCR reaction was used to generate fragments containing 150, 230, 400, and 830 repeats of (CTG)n, which codes for myotonic dystrophy, and 130 and 600 repeats of (CGG)n, which codes for fragile X syndrome. These PCR fragments were immobilized to indium tin oxide electrodes, and oxidation of guanine in the fragments was realized using electrocatalysis by Ru(bpy)3(2+) (bpy = 2,2'-bipyridine). The catalytic currents due to oxidation of the immobilized guanines by Ru(bpy)3(3+) increased with the number of repeats and were a linear function of the repeat number when normalized to the number of strands immobilized. These results suggest a sensing strategy for repeat length based on the combination of the electrocatalytic strategy for determining the repeat length combined with existing methods for determining the number of strands. --- paper_title: Alkaline Phosphatase-Catalyzed Silver Deposition for Electrochemical Detection paper_content: Alkaline phosphatase (AP) is one of the most used enzymatic labels for the development of ELISAs, immunosensors, DNA hybridization assays, etc. This enzyme catalyzes the dephosphorylation of a substrate into a detectable product usually quantified by optical or electrochemical measurements. This work is based on a substrate (3-indoxyl phosphate) that produces a compound able to reduce silver ions in solution into a metallic deposit, which is localized where the enzymatic label AP is attached. The deposited silver is electrochemically stripped into solution and measured by anodic stripping voltammetry. Its application to an enzymatic genosensor on streptavidin-modified screen-printed carbon electrodes for the detection of virulence nucleic acid determinants of autolysin gene, exclusively present on the genome of the human pathogen Streptococcus pneumoniae, is described. Compared with the direct voltammetric detection of indigo carmine, the anodic stripping voltammetry of silver ions is 14-fold more sensitive. --- paper_title: Identification of Upper Respiratory Tract Pathogens Using Electrochemical Detection on an Oligonucleotide Microarray paper_content: Bacterial and viral upper respiratory infections (URI) produce highly variable clinical symptoms that cannot be used to identify the etiologic agent. Proper treatment, however, depends on correct identification of the pathogen involved as antibiotics provide little or no benefit with viral infections. Here we describe a rapid and sensitive genotyping assay and microarray for URI identification using standard amplification and hybridization techniques, with electrochemical detection (ECD) on a semiconductor-based oligonucleotide microarray. The assay was developed to detect four bacterial pathogens (Bordetella pertussis, Streptococcus pyogenes, Chlamydia pneumoniae and Mycoplasma pneumoniae) and 9 viral pathogens (adenovirus 4, coronavirus OC43, 229E and HK, influenza A and B, parainfluinza types 1, 2, and 3 and respiratory syncytial virus. This new platform forms the basis for a fully automated diagnostics system that is very flexible and can be customized to suit different or additional pathogens. Multiple probes on a flexible platform allow one to test probes empirically and then select highly reactive probes for further iterative evaluation. Because ECD uses an enzymatic reaction to create electrical signals that can be read directly from the array, there is no need for image analysis or for expensive and delicate optical scanning equipment. We show assay sensitivity and specificity that are excellent for a multiplexed format. --- paper_title: Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale paper_content: DNA microarrays containing virtually every gene of Saccharomyces cerevisiae were used to carry out a comprehensive investigation of the temporal program of gene expression accompanying the metabolic shift from fermentation to respiration. The expression profiles observed for genes with known metabolic functions pointed to features of the metabolic reprogramming that occur during the diauxic shift, and the expression patterns of many previously uncharacterized genes provided clues to their possible functions. The same DNA microarrays were also used to identify genes whose expression was affected by deletion of the transcriptional co-repressor TUP1 or overexpression of the transcriptional activator YAP1. These results demonstrate the feasibility and utility of this approach to genomewide exploration of gene expression patterns. --- paper_title: Enzyme-catalyzed signal amplification for electrochemical DNA detection with a PNA-modified electrode paper_content: The signal amplification technique of peptide nucleic acid (PNA)-based electrochemical DNA sensor was developed in a label-free and one-step method utilizing enzymatic catalysis. Electrochemical detection of DNAhybridization on a PNA-modified electrode is based on the change of surface charge caused by the hybridization of negatively charged DNA molecules. The negatively charged mediator, ferrocenedicarboxylic acid, cannot diffuse to the DNA hybridized electrode surface due to the charge repulsion with the hybridized DNA molecule while it can easily approach the neutral PNA-modified electrode surface without the hybridization. By employing glucose oxidase catalysis on this PNA-based electrochemical system, the oxidized mediator could be immediately reduced leading to greatly increased electrochemical signals. Using the enzymatic strategy, we successfully demonstrated its clinical utility by detecting one of the mutation sequences of the breast cancer susceptibility gene BRCA1 at a sample concentration lower than 10–9 M. Furthermore, a single base-mismatched sample could be also discriminated from a perfectly matched sample. --- paper_title: DNA single-base mismatch study with an electrochemical enzymatic genosensor paper_content: Abstract A thorough selectivity study of DNA hybridization employing an electrochemical enzymatic genosensor is discussed here. After immobilizing on a gold film a 30-mer 3′-thiolated DNA strand, hybridization with a biotinylated complementary one takes place. Then, alkaline phosphatase is incorporated to the duplex through the interaction streptavidin–biotin. Enzymatic generation of indigo blue from 3-indoxyl phosphate and subsequent electrochemical detection was made. The influence of hybridization conditions was studied in order to better discern between fully complementary and mismatched strands. Detection of 3, 2 and 1 mismatch was possible. The type and location of the single-base mismatch, as well as the influence of the length of the strands was studied too. Mutations that suppose displacement of the reading frame were also considered. The effect of the concentration on the selectivity was tested, resulting a highly selective genosensor with an adequate sensitivity and stability. --- paper_title: Allele-specific genotype detection of factor V Leiden mutation from polymerase chain reaction amplicons based on label-free electrochemical genosensor. paper_content: An electrochemical genosensor for the genotype detection of allele-specific factor V Leiden mutation from PCR amplicons using the intrinsic guanine signal is described. The biosensor relies on the immobilization of the 21-mer inosine-substituted oligonucleotide capture probes related to the wild-type or mutant-type amplicons, and these probes are hybridized with their complementary DNA sequences at a carbon paste electrode (CPE). The extent of hybridization between the probe and target sequences was determined by using the oxidation signal of guanine in connection with differential pulse voltammetry (DPV). The guanine signal was monitored as a result of the specific hybridization between the probe and amplicon at the CPE surface. No label-binding step was necessary, and the appearance of the guanine signal shortened the assay time and simplified the detection of the factor V Leiden mutation from polymerase chain reaction (PCR)-amplified amplicons. The discrimination between the homozygous and heterozygous mutations was also established by comparing the peak currents of the guanine signals. Numerous factors affecting the hybridization and nonspecific binding events were optimized to detect down to 51.14 fmol/mL target DNA. With the help of the appearance of the guanine signal, the yes/no system is established for the electrochemical detection of allele-specific mutation on factor V for the first time. Features of this protocol are discussed and optimized. --- paper_title: CombiMatrix oligonucleotide arrays: genotyping and gene expression assays employing electrochemical detection. paper_content: Electrochemical detection has been developed and assay performances studied for the CombiMatrix oligonucleotide microarray platform that contains 12,544 individually addressable microelectrodes (features) in a semiconductor matrix. The approach is based on the detection of redox active chemistries (such as horseradish peroxidase (HRP) and the associated substrate TMB) proximal to specific microarray electrodes. First, microarray probes are hybridized to biotin-labeled targets, second, the HRP-streptavidin conjugate binds to biotin, and enzymatic oxidation of the electron donor substrate then occurs. The detection current is generated due to electro-reduction of the HRP reaction product, and it is measured with the CombiMatrix ElectraSense Reader. Performance of the ElectraSense platform has been characterized using gene expression and genotyping assays to analyze: (i) signal to concentration dependence, (ii) assay resolution, (iii) coefficients of variation, (CV) and (iv) array-to-array reproducibility and data correlation. The ElectraSense platform was also compared to the standard fluorescent detection, and good consistency was observed between these two different detection techniques. A lower detection limit of 0.75 pM was obtained for ElectraSense as compared to the detection limit of 1.5 pM obtained for fluorescent detection. Thus, the ElectraSense platform has been used to develop nucleic acid assays for highly accurate genotyping of a variety of pathogens including bio-threat agents (such as Bacillus anthracis, Yersinia pestis, and other microorganisms including Escherichia coli, Bacillus subtilis, etc.) and common pathogens of the respiratory tract (e.g. influenza A virus). --- paper_title: Electrochemical detection of gene expression in tumor samples: overexpression of Rak nuclear tyrosine kinase. paper_content: Absolute quantification of Rak nuclear tyrosine kinase mRNA in breast tissue samples was determined by competitive RT-PCR. The total RNA from the same samples was also chemically amplified through conventional RT-PCR, and the relative amounts of these amplified RT-PCR products were determined by adsorption onto an indium tin oxide (ITO) electrode followed by electrochemical detection. The electrochemical detection was performed using the inorganic metal complex Ru(bpy)32+ (bpy = 2,2‘ bipyridine) to catalyze the oxidation of the guanine residues of the immobilized RT-PCR products. Using the competitive RT-PCR values as standards, it was found that an optimized conventional RT-PCR coupled with electrochemical detection provides a simple method for measuring relative gene expression among a series of mRNA samples from breast tumors. The use of electrochemical detection potentially eliminates the need for gel electrophoresis and fluorescent or radioactive labels in detecting the target genes. --- paper_title: DNA aptamers that recognize fluorophore using on-chip screening in combination with an in silico evolution paper_content: We successfully developed a novel screening method for the acquisition of DNA aptamers. The technique selectively recognizes resorufin using on-chip screening in combination with an in silico evolution method. This method proved efficient for screening for DNA aptamers of single-stranded oligo-DNAs. A genetic algorithm was applied to make oligonucleotide sequences for the combinatorial library. A fluorophore, resorufin was applied to the ligand screening as a target. The affinity of the library was analyzed by the DNA microarray. This method for screening DNA ligands includes on-chip selection and a computer-evolved sequence, where the highest affinity was chosen. The fluorescence intensity of the library on the DNA microarray increased after three repetitions of the selection round. --- paper_title: Array-based evolution of DNA aptamers allows modelling of an explicit sequence-fitness landscape paper_content: Mapping the landscape of possible macromolecular polymer sequences to their fitness in performing biological functions is a challenge across the biosciences. A paradigm is the case of aptamers, nucleic acids that can be selected to bind particular target molecules. We have characterized the sequence-fitness landscape for aptamers binding allophycocyanin (APC) protein via a novel Closed Loop Aptameric Directed Evolution (CLADE) approach. In contrast to the conventional SELEX methodology, selection and mutation of aptamer sequences was carried out in silico, with explicit fitness assays for 44 131 aptamers of known sequence using DNA microarrays in vitro. We capture the landscape using a predictive machine learning model linking sequence features and function and validate this model using 5500 entirely separate test sequences, which give a very high observed versus predicted correlation of 0.87. This approach reveals a complex sequence-fitness mapping, and hypotheses for the physical basis of aptameric binding; it also enables rapid design of novel aptamers with desired binding properties. We demonstrate an extension to the approach by incorporating prior knowledge into CLADE, resulting in some of the tightest binding sequences. --- paper_title: Electrochemically Generated Acid and Its Containment to 100 Micron Reaction Areas for the Production of DNA Microarrays paper_content: An addressable electrode array was used for the production of acid at sufficient concentration to allow deprotection of the dimethoxytrityl (DMT) protecting group from an overlaying substrate bound to a porous reaction layer. Containment of the generated acid to an active electrode of 100 micron diameter was achieved by the presence of an organic base. This procedure was then used for the production of a DNA array, in which synthesis was directed by the electrochemical removal of the DMT group during synthesis. The product array was found to have a detection sensitivity to as low as 0.5 pM DNA in a complex background sample. --- paper_title: CombiMatrix oligonucleotide arrays: genotyping and gene expression assays employing electrochemical detection. paper_content: Electrochemical detection has been developed and assay performances studied for the CombiMatrix oligonucleotide microarray platform that contains 12,544 individually addressable microelectrodes (features) in a semiconductor matrix. The approach is based on the detection of redox active chemistries (such as horseradish peroxidase (HRP) and the associated substrate TMB) proximal to specific microarray electrodes. First, microarray probes are hybridized to biotin-labeled targets, second, the HRP-streptavidin conjugate binds to biotin, and enzymatic oxidation of the electron donor substrate then occurs. The detection current is generated due to electro-reduction of the HRP reaction product, and it is measured with the CombiMatrix ElectraSense Reader. Performance of the ElectraSense platform has been characterized using gene expression and genotyping assays to analyze: (i) signal to concentration dependence, (ii) assay resolution, (iii) coefficients of variation, (CV) and (iv) array-to-array reproducibility and data correlation. The ElectraSense platform was also compared to the standard fluorescent detection, and good consistency was observed between these two different detection techniques. A lower detection limit of 0.75 pM was obtained for ElectraSense as compared to the detection limit of 1.5 pM obtained for fluorescent detection. Thus, the ElectraSense platform has been used to develop nucleic acid assays for highly accurate genotyping of a variety of pathogens including bio-threat agents (such as Bacillus anthracis, Yersinia pestis, and other microorganisms including Escherichia coli, Bacillus subtilis, etc.) and common pathogens of the respiratory tract (e.g. influenza A virus). --- paper_title: In Vitro Selection of DNA Aptamers on Chips Using a Method for Generating Point Mutations paper_content: Abstract We successfully developed a novel selection method for the acquisition of DNA aptamers that selectively recognize resorufin using on‐chip selection in combination with method for point mutations. This method proved efficient for selection for DNA aptamers of single‐stranded oligo‐DNAs. A genetic algorithm was applied to produce oligonucleotides for the combinatorial library. A fluorescent molecule, resorufin, was applied for the ligand selection as a target. The binding affinity of the library was analyzed by the DNA chip. This selection method of DNA ligands includes on‐chip selection and point‐mutated sequence, which the highest affinity was selected. The fluorescence intensity of the library on the DNA chip increased after three repetitions of the selection round. The average of response for the affinity test increased with each generation. --- paper_title: Mutation detection by electrocatalysis at DNA-modified electrodes paper_content: Detection of mutations and damaged DNA bases is important for the early diagnosis of genetic disease. Here we describe an electrocatalytic method for the detection of single-base mismatches as well as DNA base lesions in fully hybridized duplexes, based on charge transport through DNA films. Gold electrodes modified with preassembled DNA duplexes are used to monitor the electrocatalytic signal of methylene blue, a redox-active DNA intercalator, coupled to [Fe(CN)_6]^3-. The presence of mismatched or damaged DNA bases substantially diminishes the electrocatalytic signal. Because this assay is not a measure of differential hybridization, all single-base mismatches, including thermodynamically stable GT and GA mismatches, can be detected without stringent hybridization conditions. Furthermore, many common DNA lesions and "hot spot" mutations in the human p53 genome can be distinguished from perfect duplexes. Finally, we have demonstrated the application of this technology in a chip-based format. This system provides a sensitive method for probing the integrity of DNA sequences and a completely new approach to single-base mismatch detection. --- paper_title: Charge transport in DNA. paper_content: The base pair stack within double helical DNA provides an effective medium for charge transport. The DNA pi-stack mediates oxidative DNA damage over long molecular distances in a reaction that is exquisitely sensitive to the sequence-dependent conformation and dynamics of DNA. A mixture of tunneling and hopping mechanisms have been proposed to account for this long-range chemistry, which is gated by dynamical variations within the stack. Electrochemical sensors have also been developed, based upon the sensitivity of DNA charge transport to base pair stacking, and these sensors provide a completely new approach to diagnosing single base mismatches in DNA and monitoring protein-DNA interactions electrically. DNA charge transport, furthermore, may play a role within the cell and, indeed, oxidative damage to DNA from a distance has been demonstrated in the cell nucleus. As a result, the biological consequences of and opportunities for DNA-mediated charge transport now require consideration. --- paper_title: Sequence-selective biosensor for DNA based on electroactive hybridization indicators. paper_content: Deoxyribonucleic acid was covalently immobilized onto oxidized glassy carbon electrode surfaces that had been activated using 1-[3-(dimethylamino)-propyl]-3-ethylcarbodimide hydrochloride and N-hydroxysulfosuccinimide. This reaction is selective for immobilization through deoxyguanosine (dG) residues. Immobilized DNA was detected voltammetrically, using tris (2,2'-bipyridyl)cobalt(III) perchlorate and tris (1,10-phenanthroline)cobalt(III) perchlorate (Co(bpy)3(3+) and Co(phen)3(3+). These complexes are reversibly electroactive (1e-) and preconcentrate at the electrode surface through association with double-stranded DNA. Voltammetric peak currents obtained with a poly(dG)poly(dC)-modified electrode depend on [Co(bpy)3(3+)] and [Co(phen)3(3+)] in a nonlinear fashion and indicate saturation binding with immobilized DNA. Voltammetric peak currents for Co(phen)3(3+) reduction were used to estimate the (constant) local DNA concentration at the modified electrode surface; a binding site size of 5 base pairs and an association constant of 1.74 x 10(3) M(-1) yield 8.6 +/- 0.2 mM base pairs. Cyclic voltammetric peak separations indicate that heterogeneous electron transfer is slower at DNA-modified electrodes than at unmodified glassy carbon electrodes. A prototype sequence-selective DNA sensor was constructed by immobilizing a 20-mer oligo (deoxythymidylic acid) (oligo(dT)20), following its enzymatic elongation with dG residues, which yielded the species oligo(dT)20(dG)98. Cyclic voltammograms of 0.12 mM Co(bpy)3(3+) obtained before and after hybridization with poly-(dA) and oligo(dA)20 show increased cathodic peaks after hybridization. The single-stranded form is regenerated on the electrode surface by rinsing with hot deionized water. These results demonstrate the use of electroactive hybridization indicators in a reusable sequence-selective biosensor for DNA. --- paper_title: Electrochemical Quantitation of DNA Immobilized on Gold paper_content: We have developed an electrochemical method to quantify the surface density of DNA immobilized on gold. The surface density of DNA, more specifically the number of nucleotide phosphate residues, is calculated from the amount of cationic redox marker measured at the electrode surface. DNA was immobilized on gold by forming mixed monolayers of thiol-derivitized, single-stranded oligonucleotide and 6-mercapto-1-hexanol. The saturated amount of charge-compensating redox marker in the DNA monolayer, determined using chronocoulometry, is directly proportional to the number of phosphate residues and thereby the surface density of DNA. This method permits quantitative determination of both single- and double-stranded DNA at electrodes. Surface densities of single-stranded DNA were precisely varied in the range of (1−10) × 1012 molecules/cm2, as determined by the electrochemical method, using mixed monolayers. We measured the hybridization efficiency of immobilized single-stranded DNA to complementary strands as a... --- paper_title: Applications of DNA microarrays in biology. paper_content: DNA microarrays have enabled biology researchers to conduct large-scale quantitative experiments. This capacity has produced qualitative changes in the breadth of hypotheses that can be explored. In what has become the dominant mode of use, changes in the transcription rate of nearly all the genes in a genome, taking place in a particular tissue or cell type, can be measured in disease states, during development, and in response to intentional experimental perturbations, such as gene disruptions and drug treatments. The response patterns have helped illuminate mechanisms of disease and identify disease subphenotypes, predict disease progression, assign function to previously unannotated genes, group genes into functional pathways, and predict activities of new compounds. Directed at the genome sequence itself, microarrays have been used to identify novel genes, binding sites of transcription factors, changes in DNA copy number, and variations from a baseline sequence, such as in emerging strains of pathogens or complex mutations in disease-causing human genes. They also serve as a general demultiplexing tool to sort spatially the sequence-tagged products of highly parallel reactions performed in solution. A brief review of microarray platform technology options, and of the process steps involved in complete experiment workflows, is included. --- paper_title: Transcription profiling of rheumatic diseases paper_content: Rheumatic diseases are a diverse group of disorders. Most of these diseases are heterogeneous in nature and show varying responsiveness to treatment. Because our understanding of the molecular complexity of rheumatic diseases is incomplete and criteria for categorization are limited, we mainly refer to them in terms of group averages. The advent of DNA microarray technology has provided a powerful tool to gain insight into the molecular complexity of these diseases; this technology facilitates open-ended survey to identify comprehensively the genes and biological pathways that are associated with clinically defined conditions. During the past decade, encouraging results have been generated in the molecular description of complex rheumatic diseases, such as rheumatoid arthritis, systemic lupus erythematosus, Sjögren syndrome and systemic sclerosis. Here, we describe developments in genomics research during the past decade that have contributed to our knowledge of pathogenesis, and to the identification of biomarkers for diagnosis, patient stratification and prognostication. --- paper_title: Microarray-Based Genomic DNA Profiling Technologies in Clinical Molecular Diagnostics paper_content: BACKGROUND ::: Microarray-based genomic DNA profiling (MGDP) technologies are rapidly moving from translational research to clinical diagnostics and have revolutionized medical practices. Such technologies have shown great advantages in detecting genomic imbalances associated with genomic disorders and single-gene diseases. ::: ::: ::: CONTENT ::: We discuss the development and applications of the major array platforms that are being used in both academic and commercial laboratories. Although no standardized platform is expected to emerge soon, comprehensive oligonucleotide microarray platforms-both comparative genomic hybridization arrays and genotyping hybrid arrays-are rapidly becoming the methods of choice for their demonstrated analytical validity in detecting genomic imbalances, for their flexibility in incorporating customized designs and updates, and for the advantage of being easily manufactured. Copy number variants (CNVs), the form of genomic deletions/duplications detected through MGDP, are a common etiology for a variety of clinical phenotypes. The widespread distribution of CNVs poses great challenges in interpretation. A broad survey of CNVs in the healthy population, combined with the data accumulated from the patient population in clinical laboratories, will provide a better understanding of the nature of CNVs and enhance the power of identifying genetic risk factors for medical conditions. ::: ::: ::: SUMMARY ::: MGDP technologies for molecular diagnostics are still at an early stage but are rapidly evolving. We are in the process of extensive clinical validation and utility evaluation of different array designs and technical platforms. CNVs of currently unknown importance will be a rich source of novel discoveries. --- paper_title: A leukemia-enriched cDNA microarray platform identifies new transcripts with relevance to the biology of pediatric acute lymphoblastic leukemia. paper_content: BACKGROUND AND OBJECTIVES: Microarray gene expression profiling has been widely applied to characterize hematologic malignancies, has attributed a molecular signature to leukemia subclasses and has allowed new subclasses to be distinguished. We set out to use microarray technology to identify novel genes relevant for leukemogenesis. To this end we used a unique leukemia-enriched cDNA microarray platform. DESIGN AND METHODS: The systematic sequencing of cDNA libraries of normal and leukemic bone marrow allowed us to increase the number of genes to yield a new release of a previously generated cDNA microarray. Using this platform we analyzed the expression profiles of 4,670 genes in bone marrow samples from 18 pediatric patients with acute lymphoblastic leukemia (ALL). RESULTS: Expression profiling consistently distinguished the leukemia patients into three groups, those with T-ALL, B-ALL and B-ALL with MLL/AF4 rearrangement, in agreement with the clinical classification. Our platform identified 30 genes that best discriminate these three subtypes. Using mini-array technology these 30 genes were validated in another cohort of 17 patients. In particular we identified two novel genes not previously reported: endomucin (EMCN) and ubiquitin specific protease 33 (USP33) that appear to be over-expressed in B-ALL relative to their expression in T-ALL. INTERPRETATION AND CONCLUSIONS: Microarray technology not only allows the distinction between disease subclasses but also offers a chance to identify new genes involved in leukemogenesis. Our approach of using a unique platform has proven to be fruitful in identifying new genes and we suggest exploration of other malignancies using this approach. --- paper_title: Parallel protein and transcript profiles of FSHD patient muscles correlate to the D4Z4 arrangement and reveal a common impairment of slow to fast fibre differentiation and a general deregulation of MyoD‐dependent genes paper_content: Here, we present the first study of a human neuromuscular disorder at transcriptional and proteomic level. Autosomal dominant facio-scapulo-humeral muscular dystrophy (FSHD) is caused by a deletion of an integral number of 3.3-kb KpnI repeats inside the telomeric region D4Z4 at the 4q35 locus. We combined a muscle-specific cDNA microarray platform with a proteomic investigation to analyse muscle biopsies of patients carrying a variable number of KpnI repeats. Unsupervised cluster analysis divides patients into three classes, according to their KpnI repeat number. Expression data reveal a transition from fast-glycolytic to slow-oxidative phenotype in FSHD muscle, which is accompanied by a deficit of proteins involved in response to oxidative stress. Besides, FSHD individuals show a disruption in the MyoD-dependent gene network suggesting a coregulation at transcriptional level during myogenesis. We also discuss the hypothesis that D4Z4 contraction may affect in trans the expression of a set of genes involved in myogenesis, as well as in the regeneration pathway of satellite cells in adult tissue. Muscular wasting could result from the inability of satellite cells to successfully differentiate into mature fibres and from the accumulation of structural damages caused by a reactive oxygen species (ROS) imbalance induced by an increased oxidative metabolism in fibres. --- paper_title: Reconstruction and functional analysis of altered molecular pathways in human atherosclerotic arteries paper_content: BackgroundAtherosclerosis affects aorta, coronary, carotid, and iliac arteries most frequently than any other body vessel. There may be common molecular pathways sustaining this process. Plaque presence and diffusion is revealed by circulating factors that can mediate systemic reaction leading to plaque rupture and thrombosis.ResultsWe used DNA microarrays and meta-analysis to study how the presence of calcified plaque modifies human coronary and carotid gene expression. We identified a series of potential human atherogenic genes that are integrated in functional networks involved in atherosclerosis. Caveolae and JAK/STAT pathways, and S100A9/S100A8 interacting proteins are certainly involved in the development of vascular disease. We found that the system of caveolae is directly connected with genes that respond to hormone receptors, and indirectly with the apoptosis pathway.Cytokines, chemokines and growth factors released in the blood flux were investigated in parallel. High levels of RANTES, IL-1ra, MIP-1alpha, MIP-1beta, IL-2, IL-4, IL-5, IL-6, IL-7, IL-17, PDGF-BB, VEGF and IFN-gamma were found in plasma of atherosclerotic patients and might also be integrated in the molecular networks underlying atherosclerotic modifications of these vessels.ConclusionThe pattern of cytokine and S100A9/S100A8 up-regulation characterizes atherosclerosis as a proinflammatory disorder. Activation of the JAK/STAT pathway is confirmed by the up-regulation of IL-6, STAT1, ISGF3G and IL10RA genes in coronary and carotid plaques. The functional network constructed in our research is an evidence of the central role of STAT protein and the caveolae system to contribute to preserve the plaque. Moreover, Cav-1 is involved in SMC differentiation and dyslipidemia confirming the importance of lipid homeostasis in the atherosclerotic phenotype. --- paper_title: Expression profiling characterization of laminin alpha-2 positive MDC. paper_content: In the Caucasian population, patients affected by the most frequent forms of congenital muscular dystrophies (MDC) are commonly divided into two groups. The first is characterized by mutations of the gene for the laminin alpha-2 (LAMA2). The second is positive for this protein, highly heterogeneous, and has no specific genetic defect associated yet. We studied the skeletal muscle transcriptome of four LAMA2 deficient and six LAMA2 positive MDC patients by cDNA microarrays. The expression profiling defined two patients groups: one mild and one severe phenotype. This result was in agreement with histopathological features but only partially with the clinical classification. The mild phenotype is characterized by a delayed maturation from slow to fast muscle fibers. Other muscle transcripts, such as telethonin, myosin light-chains 3 and 1V, are underexpressed in this group. We suggest that expression profiling will provide important information to improve our understanding of the molecular basis of laminin alpha-2 positive MDC. --- paper_title: Differential gene expression profiling of laryngeal squamous cell carcinoma by laser capture microdissection and complementary DNA microarrays. paper_content: Background and Aims Genetic alteration associated with initiation and progression of laryngeal squamous cell carcinoma (LSCC) is largely unknown. The aim of this study was to identify genetic changes associated with the disease pathogenesis and pinpoint genes whose expression is impacted by these genetic alterations. Methods Tumor cells were collected from eight matched pairs of specimens of glottic carcinoma of the larynx and histologically normal epithelium tissues adjacent to the carcinoma by laser capture microdissection (LCM). RNAs prepared from these cells were used for genome-wide transcriptome analysis by probing 16 cDNA microarrays. Real-time quantitative RT-PCR and immunohistochemistry of tissue microarrays were used to validate a group of the differentially expressed genes identified by the cDNA microarrays. Results Hierarchical cluster analysis of the expressed genes showed that 2351 genes were differentially expressed and could distinguish cancerous and noncancerous samples. We also found 761 differentially expressed genes that were consistently different between early stage and later stage specimens. Furthermore, abnormal expression of some relevant genes such as MMP12 , HMGA2, and TIMP4 were validated by real-time quantitative RT-PCR and immunohistochemistry. Analysis of gene ontology and pathway distributions then highlighted genes that may be critically important to laryngeal carcinogenesis. Conclusions Our results suggest that using LCM plus DNA microarray analysis may facilitate the identification of clinical molecular markers for disease and novel potential therapeutic targets for LSCC. --- paper_title: Allele-specific genotype detection of factor V Leiden mutation from polymerase chain reaction amplicons based on label-free electrochemical genosensor. paper_content: An electrochemical genosensor for the genotype detection of allele-specific factor V Leiden mutation from PCR amplicons using the intrinsic guanine signal is described. The biosensor relies on the immobilization of the 21-mer inosine-substituted oligonucleotide capture probes related to the wild-type or mutant-type amplicons, and these probes are hybridized with their complementary DNA sequences at a carbon paste electrode (CPE). The extent of hybridization between the probe and target sequences was determined by using the oxidation signal of guanine in connection with differential pulse voltammetry (DPV). The guanine signal was monitored as a result of the specific hybridization between the probe and amplicon at the CPE surface. No label-binding step was necessary, and the appearance of the guanine signal shortened the assay time and simplified the detection of the factor V Leiden mutation from polymerase chain reaction (PCR)-amplified amplicons. The discrimination between the homozygous and heterozygous mutations was also established by comparing the peak currents of the guanine signals. Numerous factors affecting the hybridization and nonspecific binding events were optimized to detect down to 51.14 fmol/mL target DNA. With the help of the appearance of the guanine signal, the yes/no system is established for the electrochemical detection of allele-specific mutation on factor V for the first time. Features of this protocol are discussed and optimized. --- paper_title: Recovery of developmentally defined gene sets from high-density cDNA macroarrays. paper_content: New technologies for isolating differentially expressed genes from large arrayed cDNA libraries are reported. These methods can be used to identify genes that lie downstream of developmentally important transcription factors and genes that are expressed in specific tissues, processes, or stages of embryonic development. Though developed for the study of gene expression during the early embryogenesis of the sea urchin Strongylocentrotus purpuratus, these technologies can be applied generally. Hybridization parameters were determined for the reaction of complex cDNA probes to cDNA libraries carried on six nylon filters, each containing duplicate spots from 18,432 bacterial clones (macroarrays). These libraries are of sufficient size to include nearly all genes expressed in the embryo. The screening strategy we have devised is designed to overcome inherent sensitivity limitations of macroarray hybridization and thus to isolate differentially expressed genes that are represented only by low-prevalence mRNAs. To this end, we have developed improved methods for the amplification of cDNA from small amounts of tissue (as little as approximately 300 sea urchin embryos, or 2 x 10(5) cells, or about 10 ng of mRNA) and for the differential enhancement of probe sequence concentration by subtractive hybridization. Quantitative analysis of macroarray hybridization shows that these probes now suffice for detection of differentially expressed mRNAs down to a level below five molecules per average embryo cell. ---	Title: Overview of Electrochemical DNA Biosensors: New Approaches to Detect the Expression of Life Section 1: Introduction Description 1: Introduce the background and significance of DNA biosensors, comparing them with DNA microarrays, and specify the focus of the paper. Section 2: Conventional Microarrays Description 2: Describe the basics of conventional microarrays, including their use, process, and limitations, as a benchmark for DNA biosensors. Section 3: DNA Biosensors Description 3: Outline the components of biosensors and different methods used for signal transduction, specifically for DNA biosensors. Section 4: Electrochemical/Electrical DNA Biosensors Description 4: Explain the development and methods for electrical and electrochemical DNA biosensors, highlighting their advantages and methodologies. Section 5: Nano-Objects for the Electrochemical Biosensors Description 5: Discuss the impact of nanomaterials on electrochemical biosensors, including various nanomaterials used and their benefits. Section 6: Label-Free Electrochemical DNA Detection Description 6: Detail the methods and advantages of label-free detection in electrochemical DNA biosensors and compare these to other detection methods. Section 7: Indirect Electrochemical DNA Detection Description 7: Describe the indirect methods for electrochemical DNA detection, including the use of mediators and their applications. Section 8: CombiMatrix Chip: A High Throughput DNA Sensor Description 8: Introduce and explain the CombiMatrix 12K ElectraSense microarray, its functionality, and its applications in genomic testing. Section 9: Charge Transport by DNA Description 9: Provide an overview of DNA-mediated charge transport as an alternative approach for DNA detection in electrochemical sensors. Section 10: Conclusions Description 10: Summarize the potential and future directions of DNA biosensors, emphasizing the advancements in electrochemical detection and the role of nanotechnology.
A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments	8	--- paper_title: Early diagnosis of Parkinson's disease: recommendations from diagnostic clinical guidelines. paper_content: Therapeutic options for Parkinson's disease (PD) are currently limited to symptomatic agents. Levodopa is the most efficacious treatment; however, higher doses and long-term use are associated with adverse effects such as motor fluctuations and dyskinesia. Early treatment of PD with other agents such as dopamine agonists and monoamine oxidase type B inhibitors can provide symptomatic benefit and delay initiation of levodopa therapy. Early treatment of PD is contingent upon early and accurate diagnosis of the disease, which can be challenging because there are no biomarkers or neuroimaging or other clinical tests available to confirm the diagnosis. PD diagnosis is currently based on the presence or absence of various clinical features and the experience of the treating physician. A definitive diagnosis can be made only after autopsy. Moreover, the signs and symptoms present in early PD can resemble those of a number of other movement disorders, particularly other forms of parkinsonism, such as multiple system atrophy, drug-induced parkinsonism, and vascular parkinsonism, as well as diffuse Lewy body disease and essential tremor. Nevertheless, diagnosis of PD based on clinical features and response to antiparkinsonian medication can be achieved with a fairly high level of accuracy, particularly when made by a physician specializing in movement disorders. This article reviews and summarizes published recommendations for the clinical diagnosis of PD. --- paper_title: A Survey on Wireless Body Area Networks paper_content: The increasing use of wireless networks and the constant miniaturization of electrical devices has empowered the development of Wireless Body Area Networks (WBANs). In these networks various sensors are attached on clothing or on the body or even implanted under the skin. The wireless nature of the network and the wide variety of sensors offer numerous new, practical and innovative applications to improve health care and the Quality of Life. The sensors of a WBAN measure for example the heartbeat, the body temperature or record a prolonged electrocardiogram. Using a WBAN, the patient experiences a greater physical mobility and is no longer compelled to stay in the hospital. This paper offers a survey of the concept of Wireless Body Area Networks. First, we focus on some applications with special interest in patient monitoring. Then the communication in a WBAN and its positioning between the different technologies is discussed. An overview of the current research on the physical layer, existing MAC and network protocols is given. Further, cross layer and quality of service is discussed. As WBANs are placed on the human body and often transport private data, security is also considered. An overview of current and past projects is given. Finally, the open research issues and challenges are pointed out. --- paper_title: A Comprehensive Overview of Wireless Body Area Networks WBAN paper_content: In recent years, the wireless body area network WBAN has emerged as a new technology for e-healthcare applications. The WBANs promise to revolutionize health monitoring. However, this technology remains in the first stages and much research is underway. Designers of such systems face a number of challenging tasks, as they need to address conflicting requirements. This includes managing the network, the data, while maximizing the autonomy of each network node. Reducing the consumption of a node, the management of network resources and security insurance are therefore major challenges. This paper presents a survey of body area networks including the WBANs challenges and -architecture, the most important body sensor devices, as well as sensor board hardware and platforms. Further, various applications of WBANs in the medical field are discussed, as well as wireless communications standards and technologies. The newest researches related to WBANs at physical and MAC layers are presented. Finally the paper identifies data security and privacy in WBANs as well as open research issues. --- paper_title: Body Area Networks: A Survey paper_content: Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications. --- paper_title: A Review on Telemedicine-Based WBAN Framework for Patient Monitoring paper_content: Abstract Objective: In this article, we describe the important aspects like major characteristics, research issues, and challenges with body area sensor networks in telemedicine systems for patient monitoring in different scenarios. Present and emerging developments in communications integrated with the developments in microelectronics and embedded system technologies will have a dramatic impact on future patient monitoring and health information delivery systems. The important challenges are bandwidth limitations, power consumption, and skin or tissue protection. Materials and Methods: This article presents a detailed survey on wireless body area networks (WBANs). Results and Conclusions: We have designed the framework for integrating body area networks on telemedicine systems. Recent trends, overall WBAN-telemedicine framework, and future research scope have also been addressed in this article. --- paper_title: Review: Health Care Utilization and Costs of Elderly Persons With Multiple Chronic Conditions paper_content: This systematic literature review identified and summarized 35 studies that investigated the relationship between multiple chronic conditions (MCCs) and health care utilization outcomes (i.e. physician use, hospital use, medication use) and health care cost outcomes (medication costs, out-of-pocket costs, total health care costs) for elderly general populations. Although synthesis of studies was complicated because of ambiguous definitions and measurements of MCCs, and because of the multitude of outcomes investigated, almost all studies observed a positive association of MCCs and use/costs, many of which found that use/costs significantly increased with each additional condition. Several studies indicate a curvilinear, near exponential relationship between MCCs and costs. The rising prevalence, substantial costs, and the fear that current care arrangements may be inappropriate for many patients with MCCs, bring about a multitude of implications for research and policy, of which the most important are presented and discussed. --- paper_title: Early Diagnosis of Alzheimer's Disease: Clinical and Economic Benefits paper_content: An estimated four million individuals in the United States have Alzheimer's disease (AD). This number is expected to more than triple by mid-century. Primary care physicians have a key role in evaluating older patients for early signs of dementia and in initiating treatment that can significantly retard its progression over the maximum period of time. That role and its challenges will inevitably grow along with the expected increase in the population aged 65 and older. The tendency for physicians to dismiss memory complaints as normal aging must be replaced by awareness of the need to assess and possibly intervene. Early intervention is the optimal strategy, not only because the patient's level of function will be preserved for a longer period, but also because community-dwelling patients with AD incur less societal cost than those who require long-term institutional placement. Institutionalization contributes heavily to the annual cost of care for AD in the United States, which is estimated to be $100 billion annually. --- paper_title: An Ambient Assisted Living System for Telemedicine with Detection of Symptoms paper_content: Elderly people have a high risk of health problems. Hence, we propose an architecture for Ambient Assisted Living (AAL) that supports pre-hospital health emergencies, remote monitoring of patients with chronic conditions and medical collaboration through sharing of health-related information resources (using the European electronic health records CEN/ISO EN13606). Furthermore, it is going to use medical data from vital signs for, on the one hand, the detection of symptoms using a simple rule system (e.g. fever), and on the other hand, the prediction of illness using chronobiology algorithms (e.g. prediction of myocardial infarction eight days before). So this architecture provides a great variety of communication interfaces to get vital signs of patients from a heterogeneous set of sources, as well as it supports the more important technologies for Home Automation. Therefore, we can combine security, comfort and ambient intelligence with a telemedicine solution, thereby, improving the quality of life in elderly people. --- paper_title: The global burden of chronic diseases: overcoming impediments to prevention and control. paper_content: Chronic diseases are the largest cause of death in the world. In 2002, the leading chronic diseases--cardiovascular disease, cancer, chronic respiratory disease, and diabetes--caused 29 million deaths worldwide. Despite growing evidence of epidemiological and economic impact, the global response to the problem remains inadequate. Stakeholders include governments, the World Health Organization and other United Nations bodies, academic and research groups, nongovernmental organizations, and the private sector. Lack of financial support retards capacity development for prevention, treatment, and research in most developing countries. Reasons for this include that up-to-date evidence related to the nature of the burden of chronic diseases is not in the hands of decision makers and strong beliefs persist that chronic diseases afflict only the affluent and the elderly, that they arise solely from freely acquired risks, and that their control is ineffective and too expensive and should wait until infectious diseases are addressed. The influence of global economic factors on chronic disease risks impedes progress, as does the orientation of health systems toward acute care. We identify 3 policy levers to address these impediments elevating chronic diseases on the health agenda of key policymakers, providing them with better evidence about risk factor control, and persuading them of the need for health systems change. A more concerted, strategic, and multisectoral policy approach, underpinned by solid research, is essential to help reverse the negative trends in the global incidence of chronic disease. --- paper_title: Early detection of CKD: the benefits, limitations and effects on prognosis paper_content: The past decade has seen an increasing focus on chronic kidney disease (CKD) and its attendant complications, which has resulted in improved understanding of their impact on health-care resources. The early detection of CKD has been facilitated by the implementation of routine reporting of estimated glomerular filtration rates (eGFRs) and by education of primary care physicians on the implications of detecting a decreased eGFR with respect to patient safety as well as to cardiovascular and renal outcomes. The goals of early CKD detection are to prevent CKD progression and associated complications, thus improving patient outcomes and reducing the impact of CKD on health-care resources. This Review examines the benefits of the early detection of CKD, and describes the limitations of current knowledge with respect to screening, early detection and treatment, as well as the unintended consequences of detection. In addition, this article highlights what is currently known about cardiovascular and renal outcomes and the effects of intervention in patients with CKD. --- paper_title: Effects of exercise and diet on chronic disease paper_content: Currently, modern chronic diseases, including cardiovascular diseases, Type 2 diabetes, metabolic syndrome, and cancer, are the leading killers in Westernized society and are increasing rampantly in developing nations. In fact, obesity, diabetes, and hypertension are now even commonplace in children. Clearly, however, there is a solution to this epidemic of metabolic disease that is inundating today’s societies worldwide: exercise and diet. Overwhelming evidence from a variety of sources, including epidemiological, prospective cohort, and intervention studies, links most chronic diseases seen in the world today to physical inactivity and inappropriate diet consumption. The purpose of this review is to 1 ) discuss the effects of exercise and diet in the prevention of chronic disease, 2 ) highlight the effects of lifestyle modification for both mitigating disease progression and reversing existing disease, and 3 ) suggest potential mechanisms for beneficial effects. --- paper_title: A Comprehensive Survey of Wireless Body Area Networks paper_content: Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted. --- paper_title: The Growing Burden of Chronic Disease in America paper_content: a In 2000, approximately 125 million Americans (45% of the population) had chronic conditions and 61 million (21% of the population) had multiple chronic conditions. The number of people with chronic conditions is projected to increase steadily for the next 30 years. While current health care financing and delivery systems are designed primarily to treat acute conditions, 78% of health spending is devoted to people with chronic conditions. Quality medical care for people with chronic conditions requires a new orientation toward prevention of chronic disease and provision of ongoing care and care manage- ment to maintain their health status and functioning. Specific focus should be applied to people with multiple chronic conditions. --- paper_title: Improving transmission reliability of low-power medium access control protocols using average diversity combining paper_content: Embedded computer systems equipped with wireless communication transceivers are nowadays used in a vast number of application scenarios. Energy consumption is important in many of these scenarios, as systems are battery operated and long maintenance-free operation is required. To achieve this goal, embedded systems employ low-power communication transceivers and protocols. However, currently used protocols cannot operate efficiently when communication channels are highly erroneous. In this study, we show how average diversity combining (ADC) can be used in state-of-the-art low-power communication protocols. This novel approach improves transmission reliability and in consequence energy consumption and transmission latency in the presence of erroneous channels. Using a testbed, we show that highly erroneous channels are indeed a common occurrence in situations, where low-power systems are used and we demonstrate that ADC improves low-power communication dramatically. --- paper_title: Reliability comparison of transmit/receive diversity and error control coding in low-power medium access control protocols paper_content: Low-power medium access control (MAC) protocols used for communication of energy constraint wireless embedded devices do not cope well with situations where transmission channels are highly erroneous. Existing MAC protocols discard corrupted messages which lead to costly retransmissions. To improve transmission performance, it is possible to include an error correction scheme and transmit/receive diversity. It is possible to add redundant information to transmitted packets in order to recover data from corrupted packets. It is also possible to make use of transmit/receive diversity via multiple antennas to improve error resiliency of transmissions. Both schemes may be used in conjunction to further improve the performance. In this study, the authors show how an error correction scheme and transmit/receive diversity can be integrated in low-power MAC protocols. Furthermore, the authors investigate the achievable performance gains of both methods. This is important as both methods have associated costs (processing requirements; additional antennas and power) and for a given communication situation it must be decided which methods should be employed. The authors’ results show that, in many practical situations, error control coding outperforms transmission diversity; however, if very high reliability is required, it is useful to employ both schemes together. --- paper_title: Antennas and propagation for body centric communications paper_content: Body centric wireless communication is now accepted as an important part of 4th generation mobile communications systems. The design of antennas and the characterisation of radiowave propagation on the body are now being considered, by many groups around the world. The paper gives a brief overview of the current position and reports on some recent advances in the topic. --- paper_title: A Survey of Communications and Networking Technologies for Energy Management in Buildings and Home Automation paper_content: With the exploding power consumption in private households and increasing environmental and regulatory restraints, the need to improve the overall efficiency of electrical networks has never been greater. That being said, the most efficient way to minimize the power consumption is by voluntary mitigation of home electric energy consumption, based on energy-awareness and automatic or manual reduction of standby power of idling home appliances. Deploying bi-directional smart meters and home energy management (HEM) agents that provision real-time usage monitoring and remote control, will enable HEM in “smart households.” Furthermore, the traditionally inelastic demand curve has began to change, and these emerging HEM technologies enable consumers (industrial to residential) to respond to the energy market behavior to reduce their consumption at peak prices, to supply reserves on a as-needed basis, and to reduce demand on the electric grid. Because the development of smart grid-related activities has resulted in an increased interest in demand response (DR) and demand side management (DSM) programs, this paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand. The paper then focuses on reviewing and distinguishing the various state-of-the-art HEM control and networking technologies, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions. The paper also surveys the existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communications technologies that are at the core of future smart grids. It is envisioned that this paper will inspire future research and design efforts in developing standardized and user-friendly smart energy monitoring systems that are suitable for wide scale deployment in homes. --- paper_title: Antennas and propagation for on-body communication systems paper_content: Studies on the narrowband characterization of the on-body propagation channel show variations of up to 40dB in link loss for different antenna position and body posture. Channels on the body trunk show small variance of the path gain probability density function, whilst those involving the arms or legs a much greater variance. For most cases the monopole antenna located with the ground plane parallel to the body surface give least loss. For ultra wideband channels, path delay is highest for non line of sight links around the body and delay spreads are generally less than 10nsec. Link modeling using a free space assumption gives results within 10dB, but for more accurate modeling full body modeling is required and an example is given using locally distorted non-orthogonal FD-TD. --- paper_title: A Survey of Recent Developments in Home M2M Networks paper_content: Recent years have witnessed the emergence of machine-to-machine (M2M) networks as an efficient means for providing automated communications among distributed devices. Automated M2M communications can offset the overhead costs of conventional operations, thus promoting their wider adoption in fixed and mobile platforms equipped with embedded processors and sensors/actuators. In this paper, we survey M2M technologies for applications such as healthcare, energy management and entertainment. In particular, we examine the typical architectures of home M2M networks and discuss the performance tradeoffs in existing designs. Our investigation covers quality of service, energy efficiency and security issues. Moreover, we review existing home networking projects to better understand the real-world applicability of these systems. This survey contributes to better understanding of the challenges in existing M2M networks and further shed new light on future research directions. --- paper_title: A Power Line Communication Network Infrastructure for The Smart Home paper_content: Low voltage electrical wiring has largely been dismissed as too noisy and unpredictable to support high-speed communication signals. Advances in communication and modulation methodologies as well as in adaptive digital signal processing and error detection and correction have spawned novel protocols capable of supporting power line communication networks at speeds comparable to wired LANs. We motivate the use of power line LANs as a basic infrastructure for building integrated smart homes, wherein information appliances ranging from simple control or monitoring devices to multimedia entertainment systems are seamlessly interconnected by the very wires that provide them electricity. By simulation and actual measurements using "reference design" prototype commercial powerline products, we show that the HomePlug MAC and PHY layers can guarantee QoS for real-time communications, supporting delay-sensitive data streams for smart home applications. --- paper_title: VLSI Circuits for Biomedical Applications paper_content: VLSI (very large scale integration) is the process of creating integrated circuits by combining thousands of transistor based circuits into a single chip. Written by top-notch international experts in industry and academia, this groundbreaking resource presents a comprehensive, state-of-the-art overview of VLSI circuit design for a wide range of applications in biology and medicine.Supported with over 280 illustrations and over 160 equations, the book offers cutting-edge guidance on designing integrated circuits for wireless biosensing, body implants, biosensing interfaces, and molecular biology. Engineers discover innovative design techniques and novel materials to help them achieve higher levels circuit and system performance. This invaluable volume is essential reading for professionals and graduate students with a serious interest in circuit design and future biomedical technology. --- paper_title: Energy efficient communication in body area networks using collaborative communication in Rayleigh fading channel paper_content: Due to resource limited nature of nodes in body area networks (BAN), it is often very difficult to replace or recharge its power source. To prolong the network's life, only way out is energy efficient communication system. In this article an energy efficient communication system based on collaborative communication is proposed for BAN. Signals from the implanted nodes are received out-of-phase at the base station with no line-of-sight through an AWGN channel. Mathematical model derived here is based on three figures of merit i.e, received power, bit error rate and energy consumption. Analysis of the proposed model and Monte Carlo simulation show that the gain in received power increases as the number of collaborative nodes increase whereas BER is directly related to SNR $$(E_b/N_0)$$(Eb/N0). To evaluate energy consumption of the proposed system, it is compared with single-input-single-output (SISO) system. In this comparison it has been found that SISO performs well at short distances but collaborative communication outperforms SISO in case of long distances. It is also found that collaborative communication requires "N $$\times $$× Transmitted power", less transmission power in comparison to SISO systems. It is observed that collaborative communication achieve energy saving very close to 99 %. On the basis of these results it is safe to recommend collaborative communication for resource limited BAN. --- paper_title: Towards the fast and robust optimal design of Wireless Body Area Networks paper_content: Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search. Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with improved optimality gaps. --- paper_title: Energy efficient cooperative transmission in single-relay UWB based body area networks paper_content: Energy efficiency is one of the most critical parameters in ultra-wideband (UWB) based wireless body area networks (WBANs). In this paper, the energy efficiency optimization problem is investigated for cooperative transmission with a single relay in UWB based WBANs. Two practical onbody transmission scenarios are taken into account, namely, along-torso scenario and around-torso scenario. With a proposed single-relay WBAN model, a joint optimal scheme for the energy efficiency optimization is developed, which not only derives the optimal power allocation but also seeks the corresponding optimal relay location for each scenario. Simulation results show that the utilization of a relay node is necessary for the energy efficient transmission in particular for the around-torso scenario and the relay location is an important parameter. With the joint optimal relay location and power allocation, the proposed scheme is able to achieve up to 30 times improvement compared to direct transmission in terms of the energy efficiency when the battery of the sensor node is very limited, which indicates that it is an effective way to prolong the network lifetime in WBANs. --- paper_title: Energy-Efficient Resource Allocation with QoS Support in Wireless Body Area Networks paper_content: Wireless Body Area Network (WBAN) has become a promising type of networks to provide applications such as real-time health monitoring and ubiquitous e-Health services. One challenge in the design of WBAN is that energy efficiency needs to be ensured to increase the network lifetime in such a resourceconstrained network. Another critical challenge for WBAN is that quality of service (QoS) requirements, including packet loss rate (PLR), throughput and delay, should be guaranteed even under the highly dynamic environment due to changing of body postures. In this paper, we design a unified framework of energy efficient resource allocation scheme for WBAN, in which both constraints of QoS metrics and the characteristics of dynamic links are considered. A transmission rate allocation policy (TRAP) is proposed to carefully adjust the transmission rate at each sensor such that more strict PLR requirement could be achieved even when the link quality is very poor. A QoS optimization problem is then formulated to optimize the transmission power and allocated time slots for each sensor, which minimizes energy consumption subject to the QoS constraints. Numerical results demonstrate the effectiveness of the proposed transmission rate allocation policy and the resource allocation scheme. --- paper_title: An Energy-Efficient Hybrid System for Wireless Body Area Network Applications paper_content: Wireless Body Area Networks (WBANs) consist of a number of miniaturized wearable or implanted sensor nodes that are employed to monitor vital parameters of a patient over long duration of time. These sensors capture physiological data and wirelessly transfer the collected data to a local base station in order to be further processed. Almost all of these body sensors are expected to have low data-rate and to run on a battery. Since recharging or replacing the battery is not a simple task specifically in the case of implanted devices such as pacemakers, extending the lifetime of sensor nodes in WBANs is one of the greatest challenges. To achieve this goal, WBAN systems employ low-power communication transceivers and low duty cycle Medium Access Control (MAC) protocols. Although, currently used MAC protocols are able to reduce the energy consumption of devices for transmission and reception, yet they are still unable to offer an ultimate energy self-sustaining solution for low-power MAC protocols. This paper proposes to utilize energy harvesting technologies in low-power MAC protocols. This novel approach can further reduce energy consumption of devices in WBAN systems. --- paper_title: Power reduction by varying sampling rate paper_content: The rate at which a digital signal processing (DSP) system operates depends on the highest frequency component in the input signal. DSP applications must sample their inputs at a frequency at least twice the highest frequency in the input signal (i.e., the Nyquist rate) to accurately reproduce the signal. Typically a fixed sampling rate, guaranteed to always be high enough, is used. However, an input signal may have periods when the signal has little high frequency content as well as periods of silence. When the input signal has no perceptible high frequency components, the system can reduce its sampling rate, thereby reducing the number of samples processed per second, allowing the CPU speed to be scaled down without reducing output quality. This paper describes how to reduce power consumption in DSP applications by varying the amount of processing based on the input signal, and reports results of experiments with a prototype implementation. Experiments with a prototype show that when the system performs little processing, the added overhead of the variable sampling rate technique increased power consumption. When the system performs more processing, 18 FIR filters per frame, the power consumption was reduced to 40 % of the power required for a static sampling rate, while not reducing sound quality. --- paper_title: Co-LAEEBA: Cooperative link aware and energy efficient protocol for wireless body area networks paper_content: Proposed model gives energy efficient communications for human body in WBAN.Link aware communications.Cooperative routing decreases path loss.Cooperative routing allows more frequent data gathering. Performance evaluation of Wireless Body Area Networks (WBANs) is primarily conducted in terms of simulation based studies. From this perspective, recent research has focused on channel modeling, and energy conservation at Network/MAC layer. Most of these studies ignore collaborative learning and path loss. In this paper, we present Link-Aware and Energy Efficient protocol for wireless Body Area networks (LAEEBA) and Cooperative Link-Aware and Energy Efficient protocol for wireless Body Area networks (Co-LAEEBA) routing schemes. Unlike existing schemes, the proposed work factors in collaborative learning and path loss. Cost functions are introduced to learn and select the most feasible route from a given node to sink while sharing each others distance and residual energy information. Simulation results show improved performance of the proposed protocols in comparison to the selected existing ones in terms of the chosen performance metrics. --- paper_title: Trading off prediction accuracy and power consumption for context-aware wearable computing paper_content: Context-aware mobile computing requires wearable sensors to acquire information about the user. Continuous sensing rapidly depletes the -wearable system's energy, which is a critically constrained resource. In this paper, we analyze the trade-off between power consumption and prediction accuracy of context classifiers working on dual-axis accelerometer data collected from the eWaich sensing and notification platform. We improve power consumption techniques by providing competitive classification performance even in the low frequency region of 1-10 Hz and for the highly erratic wrist based sensing location. Furthermore, we propose and analyze a collection of selective sampling strategies in order to reduce the number of required sensor readings and the computation cycles even further. Our results indicate that optimized sampling schemes can increase the deployment lifetime of a wearable computing platform by a factor of four without a significant loss in prediction accuracy. --- paper_title: UWB on-body radio channel modeling using ray theory and subband FDTD method paper_content: This paper presents the ultra-wideband on-body radio channel modeling using a subband finite-difference time-domain (FDTD) method and a model combining the uniform geometrical theory of diffraction (UTD) and ray tracing (RT). In the subband FDTD model, the frequency band (3-9 GHz) is uniformly divided into 12 subbands in order to take into account the material frequency dispersion. Each subband is simulated separately and then a combination technique is used to recover all simulations at the receiver. In the UTD/RT model, the RT technique is used to find the surface diffracted ray path, while the UTD is applied for calculating the received signal. Respective modeling results from two- and three-dimensional subband FDTD and UTD/RT models indicate that antenna patterns have significant impacts on the on-body radio channel. The effect of different antenna types on on-body radio channels is also investigated through the UTD/RT approach. --- paper_title: Dynamic Channel Modeling for Multi-Sensor Body Area Networks paper_content: A channel model for time-variant multi-link wireless body area networks (WBANs) is proposed in this paper, based on an extensive measurement campaign using a multi-port channel sounder. A total of 12 nodes were placed on the body to measure the multi-link channel within the created WBAN. The resulting empirical model takes into account the received power, the link fading statistics, and the link auto- and cross-correlations. The distance dependence of the received power is investigated, and the link fading is modeled by a log-normal distribution. The link autocorrelation function is divided into a decaying component and a sinusoidal component to account for the periodical movement of the limbs caused by walking. The cross-correlation between different links is also shown to be high for a number of specific on-body links. Finally, the model is validated by considering several extraction-independent validation metrics: multi-hop link capacity, level crossing rate (LCR) and average fade duration (AFD). The capacity aims at validating the path-loss and fading model, while the LCR and AFD aim at validating the temporal behavior. For all validation metrics, the model is shown to satisfactorily reproduce the measurements, whereas its limits are pointed out. --- paper_title: Dynamic Channel Modeling at 2.4 GHz for On-Body Area Networks paper_content: In wireless body area networks, on-body radio propagation channels are typically time-varying, because of the frequent body movements. The dynamic local body scattering dominates the temporal and spatial properties of the on-body channels. The influence varies largely depending on the distribution of the channels and the modes of body movements. In this paper, we present some major achievements on the dynamic onbody channel modeling at 2.4 GHz under the framework of the COST 2100 action. Results of two complementary measurement campaigns are presented: a geometry-based one on a single subject, and a scenario-based one covering different subjects. Statistical models including the Doppler spectrum and the spatial correlation of on-body channels are presented. An analytical model is also introduced to offer a time-space description of the on-body channels, which is validated by the geometry-based measurement campaign. --- paper_title: A Power Line Communication Network Infrastructure for The Smart Home paper_content: Low voltage electrical wiring has largely been dismissed as too noisy and unpredictable to support high-speed communication signals. Advances in communication and modulation methodologies as well as in adaptive digital signal processing and error detection and correction have spawned novel protocols capable of supporting power line communication networks at speeds comparable to wired LANs. We motivate the use of power line LANs as a basic infrastructure for building integrated smart homes, wherein information appliances ranging from simple control or monitoring devices to multimedia entertainment systems are seamlessly interconnected by the very wires that provide them electricity. By simulation and actual measurements using "reference design" prototype commercial powerline products, we show that the HomePlug MAC and PHY layers can guarantee QoS for real-time communications, supporting delay-sensitive data streams for smart home applications. --- paper_title: A Review on Telemedicine-Based WBAN Framework for Patient Monitoring paper_content: Abstract Objective: In this article, we describe the important aspects like major characteristics, research issues, and challenges with body area sensor networks in telemedicine systems for patient monitoring in different scenarios. Present and emerging developments in communications integrated with the developments in microelectronics and embedded system technologies will have a dramatic impact on future patient monitoring and health information delivery systems. The important challenges are bandwidth limitations, power consumption, and skin or tissue protection. Materials and Methods: This article presents a detailed survey on wireless body area networks (WBANs). Results and Conclusions: We have designed the framework for integrating body area networks on telemedicine systems. Recent trends, overall WBAN-telemedicine framework, and future research scope have also been addressed in this article. --- paper_title: A Review on Telemedicine-Based WBAN Framework for Patient Monitoring paper_content: Abstract Objective: In this article, we describe the important aspects like major characteristics, research issues, and challenges with body area sensor networks in telemedicine systems for patient monitoring in different scenarios. Present and emerging developments in communications integrated with the developments in microelectronics and embedded system technologies will have a dramatic impact on future patient monitoring and health information delivery systems. The important challenges are bandwidth limitations, power consumption, and skin or tissue protection. Materials and Methods: This article presents a detailed survey on wireless body area networks (WBANs). Results and Conclusions: We have designed the framework for integrating body area networks on telemedicine systems. Recent trends, overall WBAN-telemedicine framework, and future research scope have also been addressed in this article. --- paper_title: An Overview of IEEE 802.15.6 Standard paper_content: Wireless Body Area Networks (WBAN) has emerged as a key technology to provide real-time health monitoring of a patient and to diagnose and treat many life threatening diseases. WBAN operates in close vicinity to, on, or inside a human body and supports a variety of medical and non-medical applications. IEEE 802 has established a Task Group called IEEE 802.15.6 for the standardization of WBAN. The purpose of the group is to establish a communication standard optimized for low-power in-body/on-body nodes to serve a variety of medical and non-medical applications. This paper explains the most important features of the new IEEE 802.15.6 standard. The standard defines a Medium Access Control (MAC) layer supporting several Physical (PHY) layers. We briefly overview the PHY and MAC layers specifications together with the bandwidth efficiency of IEEE 802.15.6 standard. We also discuss the security paradigm of the standard. --- paper_title: Design and Application of RuBee-Based Telemedicine Data Acquisition System paper_content: Telemedicine can be defined as the delivery of health care and sharing of medical knowledge over a distance using telecommunication. This paper introduced the new technology of RuBee, RuBee fills the drawback of RFID tags which have no network and cannot be programmable, which has made tremendous progress for the development of the telemedicine. A brief introduction of the RuBee protocol, and analyzed the design of RuBee Router and its application in the Telemedicine System. Provide a snapshot of the applications of electronic patient record, emergency telemedicine and home monitoring etc. in wireless telemedicine systems. --- paper_title: A comparative study of short range wireless sensor network on high density networks paper_content: ZigBee, Wibree, Z-Wave, and RuBee are four protocol standards for short range wireless communications with low power consumption. From an application point of view, ZigBee is designed for reliable wirelessly networked monitoring and control networks, RuBee is proposed for high security applications and use in harsh environment, Wibree considered for sports and healthcare while Z-Wave is planned for residential control systems. In this paper, after an overview of the mentioned four short-range wireless protocols, we attempt to make a preliminary comparison of them and then specifically study their radio frequency, data coding, security etc. At last we have compared different protocols capabilities in high density network. --- paper_title: Link Technologies and BlackBerry Mobile Health (mHealth) Solutions: A Review paper_content: The number of wearable wireless sensors is expected to grow to 400 million by the year 2014, while the number of operational mobile subscribers has already passed the 5.2 billion mark in 2011. This growth results in an increasing number of mobile applications including: Machine-to-Machine (M2M) communications, Electronic-Health (eHealth), and Mobile-Health (mHealth). A number of emerging mobile applications that require 3G and 4G mobile networks for data transport relate to telemedicine, including establishing, maintaining, and transmitting health-related information, research, education, and training. This review paper takes a closer look at these applications, specifically with regard to the healthcare industry and their underlying link technologies. The authors believe that the BlackBerry platform and the associated infrastructure (i.e., BlackBerry Enterprise Server) is a logical and practical solution for eHealth, mHealth, sensor and M2M deployments, which are considered in this paper. --- paper_title: Survey of the DASH7 Alliance Protocol for 433 MHz Wireless Sensor Communication paper_content: 433 MHz is getting more attention for Machine-to-Machine communication. This paper presents the DASH7 Alliance Protocol, an active RFID alliance standard for 433 MHz wireless sensor communication based on the ISO/IEC 18000-7. First, the major differences of 433 MHz communication compared to more frequently used frequencies, such as 2.4 GHz and 868/920 MHz are explained. Subsequently, the general concepts of DASH7 Alliance Protocol are described, such as the BLAST networking topology and the different OSI layer implementations, in a top-down method. Basic DASH7 features such as the advertising protocol, ad-hoc synchronization and query based addressing are used to explain the different layers. Finally, the paper introduces a software stack implementation named OSS-7, which is an open source implementation of the DASH7 alliance protocol used for testing, rapid prototyping, and demonstrations. --- paper_title: DASH7 alliance protocol 1.0: Low-power, mid-range sensor and actuator communication paper_content: This paper presents the DASH7 Alliance Protocol 1.0. It is an industry alliance standard for wireless sensor and actuator communication using the unlicensed sub-1 GHz bands. The paper explains its historic relation to active RFID standards ISO 18000-7 for 433 MHz communication, the basic concepts and communication paradigms of the protocol. Since the protocol is a full OSI stack specification, the paper discusses the implementation of every OSI layer. --- paper_title: Wireless Network Standards for Building Automation paper_content: This chapter gives an overview over several existing wireless network standards with their main usage, used frequency band, modulation, transfer speed as well as features and limitations, based on their data sheets. The main intention of this chapter is an assistance to choose the best fitting protocol for a given application. Therefore some typical scenarios for applications are described and after a preselection based on the main application the wireless network protocols are compared to find the best protocol for each scenario. --- paper_title: Link Technologies and BlackBerry Mobile Health (mHealth) Solutions: A Review paper_content: The number of wearable wireless sensors is expected to grow to 400 million by the year 2014, while the number of operational mobile subscribers has already passed the 5.2 billion mark in 2011. This growth results in an increasing number of mobile applications including: Machine-to-Machine (M2M) communications, Electronic-Health (eHealth), and Mobile-Health (mHealth). A number of emerging mobile applications that require 3G and 4G mobile networks for data transport relate to telemedicine, including establishing, maintaining, and transmitting health-related information, research, education, and training. This review paper takes a closer look at these applications, specifically with regard to the healthcare industry and their underlying link technologies. The authors believe that the BlackBerry platform and the associated infrastructure (i.e., BlackBerry Enterprise Server) is a logical and practical solution for eHealth, mHealth, sensor and M2M deployments, which are considered in this paper. --- paper_title: Mbps experimental acoustic through-tissue communications: MEAT-COMMS paper_content: Methods for digital, phase-coherent acoustic communication date to at least the work of Stojanjovic, et al [20], and the added robustness afforded by improved phase tracking and compensation of Johnson, et al [21]. This work explores the use of such methods for communications through tissue for potential biomedical applications, using the tremendous bandwidth available in commercial medical ultrasound transducers. While long-range ocean acoustic experiments have been at rates of under 100kbps, typically on the order of 1- 10kbps, data rates in excess of 120Mb/s have been achieved over cm-scale distances in ultrasonic testbeds [19]. This paper describes experimental transmission of digital communication signals through samples of real pork tissue and beef liver, achieving data rates of 20-30Mbps, demonstrating the possibility of real-time video-rate data transmission through tissue for inbody ultrasonic communications with implanted medical devices. --- paper_title: Signal Transmission by Galvanic Coupling Through the Human Body paper_content: Galvanic coupling is a promising approach for wireless intrabody data transmission between sensors. Using the human body as a transmission medium for electrical signals becomes a novel data communication technique in biomedical monitoring systems. In this paper, special attention is given to the coupling of the current into the human body. Safety requirements have to be fulfilled, and optimal signal coupling is of essence. Therefore, different electrodes are compared. A test system offers up to 1 mA contact current modulated in the frequency range of 10 kHz to 1 MHz. The injected current is up to 20 times below the maximum allowed contact current. Such a low-current approach enables data communication that is more energy saving than other wireless technologies. --- paper_title: Challenges and implications of using ultrasonic communications in intra-body area networks paper_content: Body area networks (BANs) promise to enable revolutionary biomedical applications by wirelessly interconnecting devices implanted or worn by humans. However, BAN wireless communications based on radio-frequency (RF) electromagnetic waves suffer from poor propagation of signals in body tissues, which leads to high levels of attenuation. In addition, in-body transmissions are constrained to be low-power to prevent overheating of tissues and consequent death of cells. To address the limitations of RF propagation in the human body, we propose a paradigm shift by exploring the use of ultrasonic waves as the physical medium to wirelessly interconnect in-body implanted devices. Acoustic waves are the transmission technology of choice for underwater communications, since they are known to propagate better than their RF counterpart in media composed mainly of water. Similarly, we envision that ultrasound (e.g., acoustic waves at non-audible frequencies) will provide support for communications in the human body, which is composed for 65% of water. In this paper, we first assess the feasibility of using ultrasonic communications in intra-body BANs, i.e., in-body networks where the devices are biomedical sensors that communicate with an actuator/gateway device located inside the body. We discuss the fundamentals of ultrasonic propagation in tissues, and explore important tradeoffs, including the choice of a transmission frequency, transmission power, bandwidth, and transducer size. Then, we discuss future research challenges for ultrasonic networking of intra-body devices at the physical, medium access and network layers of the protocol stack. --- paper_title: Comparison of low-power wireless communication technologies for wearable health-monitoring applications paper_content: Health monitoring technologies such as Body Area Network (BAN) systems has gathered a lot of attention during the past few years. Largely encouraged by the rapid increase in the cost of healthcare services and driven by the latest technological advances in Micro-Electro-Mechanical Systems (MEMS) and wireless communications. BAN technology comprises of a network of body worn or implanted sensors that continuously capture and measure the vital parameters such as heart rate, blood pressure, glucose levels and movement. The collected data must be transferred to a local base station in order to be further processed. Thus, wireless connectivity plays a vital role in such systems. However, wireless connectivity comes at a cost of increased power usage, mainly due to the high energy consumption during data transmission. Unfortunately, battery-operated devices are unable to operate for ultra-long duration of time and are expected to be recharged or replaced once they run out of energy. This is not a simple task especially in the case of implanted devices such as pacemakers. Therefore, prolonging the network lifetime in BAN systems is one of the greatest challenges. In order to achieve this goal, BAN systems take advantage of low-power in-body and on-body/off-body wireless communication technologies. This paper compares some of the existing and emerging low-power communication protocols that can potentially be employed to support the rapid development and deployment of BAN systems. --- paper_title: A Survey on Intrabody Communications for Body Area Network Applications paper_content: The rapid increase in healthcare demand has seen novel developments in health monitoring technologies, such as the body area networks (BAN) paradigm. BAN technology envisions a network of continuously operating sensors, which measure critical physical and physiological parameters e.g., mobility, heart rate, and glucose levels. Wireless connectivity in BAN technology is key to its success as it grants portability and flexibility to the user. While radio frequency (RF) wireless technology has been successfully deployed in most BAN implementations, they consume a lot of battery power, are susceptible to electromagnetic interference and have security issues. Intrabody communication (IBC) is an alternative wireless communication technology which uses the human body as the signal propagation medium. IBC has characteristics that could naturally address the issues with RF for BAN technology. This survey examines the on-going research in this area and highlights IBC core fundamentals, current mathematical models of the human body, IBC transceiver designs, and the remaining research challenges to be addressed. IBC has exciting prospects for making BAN technologies more practical in the future. --- paper_title: Wireless sensor devices for animal tracking and control paper_content: This paper describes some new wireless sensor hardware ::: developed for pastoral and environmental applications. ::: From our early experiments with Mote hardware we ::: were inspired to develop our devices with improved radio ::: range, solar power capability, mechanical and electrical robustness, ::: and with unique combinations of sensors. Here we ::: describe the design and evolution of a small family of devices: ::: radio/processor board, a soil moisture sensor interface, ::: and a single board multi-sensor unit for animal tracking ::: experiments. --- paper_title: Performance comparison of frequency flopping and direct sequence spread spectrum systems in the 2.4 GHz range paper_content: The FHSS and DSSS technologies are described as well as different types of spread coding. DSSS systems can provide higher access data rates (up to 11 Mbit/s) than FHSS systems (up to 3 Mbit/s). More FHSS systems can operate simultaneously in the same area than DSSS systems. DSSS systems have higher interference susceptibility tolerance levels. However, if there is a strong broadband interferer in the operational spectrum, FHSS systems will suffer less due to frequency hopping. Assuming equal transmit powers, DSSS systems have lower power spectral density due to wider operating spectrum. Monte Carlo analysis results show that DSSS systems are more likely to cause interference than FHSS systems. The worst case interference levels are higher when the interferer is an FHSS system. It was shown that this case has a very low probability of occurring. --- paper_title: Ubiquitous WSN for Healthcare: Recent Advances and Future Prospects paper_content: Wireless sensor networks (WSNs) have witnessed rapid advancement in medical applications from real-time telemonitoring and computer-assisted rehabilitation to emergency response systems. In this paper, we present the state-of-the-art research from the ubiquity perspective, and discuss the insights as well as vision of future directions in WSN-based healthcare systems. First, we propose a novel tiered architecture that can be generally applied to WSN-based healthcare systems. Then, we analyze the IEEE 802 series standards in the access layer on their capabilities in setting up WSNs for healthcare. We also explore some of the up-to-date work in the application layer, mostly on the smartphone platforms. Furthermore, in order to develop and integrate effective ubiquitous sensing for healthcare (USH), we highlight four important design goals (i.e., proactiveness, transparency, awareness, and trustworthiness) that should be taken into account in future systems. --- paper_title: Mobile Phone Sensing Systems: A Survey paper_content: Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction. --- paper_title: The MONARCA self-assessment system: Persuasive personal monitoring for bipolar patients paper_content: An increasing number of persuasive personal healthcare monitoring systems are being researched, designed and tested, many of them being based on Smartphone technology. These systems could help patients and clinicians monitor and manage mental illness. Mental illness is complex, difficult to treat, and carries social stigma. We describe our setup to support the treatment of bipolar patients using a persuasive mobile phone monitoring system and a web portal. --- paper_title: DistancePPG: Robust non-contact vital signs monitoring using a camera paper_content: Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios. --- paper_title: Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone paper_content: As a smartphone is becoming very popular and its performance is being improved fast, a smartphone shows its potential as a low-cost physiological measurement solution which is accurate and can be used beyond the clinical environment. Because cardiac pulse leads the subtle color change of a skin, a pulsatile signal which can be described as photoplethysmographic (PPG) signal can be measured through recording facial video using a digital camera. In this paper, we explore the potential that the reliable heart rate can be measured remotely by the facial video recorded using smartphone camera. First, using the front facing-camera of a smartphone, facial video was recorded. We detected facial region on the image of each frame using face detection, and yielded the raw trace signal from the green channel of the image. To extract more accurate cardiac pulse signal, we applied independent component analysis (ICA) to the raw trace signal. The heart rate was extracted using frequency analysis of the raw trace signal and the analyzed signal from ICA. The accuracy of the estimated heart rate was evaluated by comparing with the heart rate from reference electrocardiogram (ECG) signal. Finally, we developed FaceBEAT, an iPhone application for remote heart rate measurement, based on this study. --- paper_title: iSleep: unobtrusive sleep quality monitoring using smartphones paper_content: The quality of sleep is an important factor in maintaining a healthy life style. To date, technology has not enabled personalized, in-place sleep quality monitoring and analysis. Current sleep monitoring systems are often difficult to use and hence limited to sleep clinics, or invasive to users, e.g., requiring users to wear a device during sleep. This paper presents iSleep -- a practical system to monitor an individual's sleep quality using off-the-shelf smartphone. iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, including body movement, couch and snore, and infers quantitative measures of sleep quality. iSleep adopts a lightweight decision-tree-based algorithm to classify various events based on carefully selected acoustic features, and tracks the dynamic ambient noise characteristics to improve the robustness of classification. We have evaluated iSleep based on the experiment that involves 7 participants and total 51 nights of sleep, as well the data collected from real iSleep users. Our results show that iSleep achieves consistently above 90% accuracy for event classification in a variety of different settings. By providing a fine-grained sleep profile that depicts details of sleep-related events, iSleep allows the user to track the sleep efficiency over time and relate irregular sleep patterns to possible causes. --- paper_title: Adoption of telemedicine: from pilot stage to routine delivery paper_content: Today there is much debate about why telemedicine has stalled. Teleradiology is the only widespread telemedicine application. Other telemedicine applications appear to be promising candidates for widespread use, but they remain in the early adoption stage. The objective of this debate paper is to achieve a better understanding of the adoption of telemedicine, to assist those trying to move applications from pilot stage to routine delivery. We have investigated the reasons why telemedicine has stalled by focusing on two, high-level topics: 1) the process of adoption of telemedicine in comparison with other technologies; and 2) the factors involved in the widespread adoption of telemedicine. For each topic, we have formulated hypotheses. First, the advantages for users are the crucial determinant of the speed of adoption of technology in healthcare. Second, the adoption of telemedicine is similar to that of other health technologies and follows an S-shaped logistic growth curve. Third, evidence of cost-effectiveness is a necessary but not sufficient condition for the widespread adoption of telemedicine. Fourth, personal incentives for the health professionals involved in service provision are needed before the widespread adoption of telemedicine will occur. The widespread adoption of telemedicine is a major -- and still underdeveloped -- challenge that needs to be strengthened through new research directions. We have formulated four hypotheses, which are all susceptible to experimental verification. In particular, we believe that data about the adoption of telemedicine should be collected from applications implemented on a large-scale, to test the assumption that the adoption of telemedicine follows an S-shaped growth curve. This will lead to a better understanding of the process, which will in turn accelerate the adoption of new telemedicine applications in future. Research is also required to identify suitable financial and professional incentives for potential telemedicine users and understand their importance for widespread adoption. --- paper_title: Body and Visual Sensor Fusion for Motion Analysis in Ubiquitous Healthcare Systems paper_content: Human motion analysis provides a valuable solution for monitoring the wellbeing of the elderly, quantifying post-operative patient recovery and monitoring the progression of neurodegenerative diseases such as Parkinson’s. The development of accurate motion analysis models, however, requires the integration of multi-sensing modalities and the utilization of appropriate data analysis techniques. This paper describes a robust framework for improved patient motion analysis by integrating information captured by body and visual sensor networks. Real-time target extraction is applied and a skeletonization procedure is subsequently carried out to quantify the internal motion of moving target and compute two metrics, spatiotemporal cyclic motion between leg segments and head trajectory, for each vision node. Extracted motion metrics from multiple vision nodes and accelerometer information from a wearable body sensor are then fused at the feature level by using K-Nearest Neighbor algorithm and used to classify target’s walking gait into normal or abnormal. The potential value of the proposed framework for patient monitoring is demonstrated and the results obtained from practical experiments are described. --- paper_title: A Wearable Assistant for Gait Training for Parkinson’s Disease with Freezing of Gait in Out-of-the-Lab Environments paper_content: People with Parkinson’s disease (PD) suffer from declining mobility capabilities, which cause a prevalent risk of falling. Commonly, short periods of motor blocks occur during walking, known as freezing of gait (FoG). To slow the progressive decline of motor abilities, people with PD usually undertake stationary motor-training exercises in the clinics or supervised by physiotherapists. We present a wearable system for the support of people with PD and FoG. The system is designed for independent use. It enables motor training and gait assistance at home and other unsupervised environments. The system consists of three components. First, FoG episodes are detected in real time using wearable inertial sensors and a smartphone as the processing unit. Second, a feedback mechanism triggers a rhythmic auditory signal to the user to alleviate freeze episodes in an assistive mode. Third, the smartphone-based application features support for training exercises. Moreover, the system allows unobtrusive and long-term monitoring of the user’s clinical condition by transmitting sensing data and statistics to a telemedicine service. We investigate the at-home acceptance of the wearable system in a study with nine PD subjects. Participants deployed and used the system on their own, without any clinical support, at their homes during three protocol sessions in 1 week. Users’ feedback suggests an overall positive attitude toward adopting and using the system in their daily life, indicating that the system supports them in improving their gait. Further, in a data-driven analysis with sensing data from five participants, we study whether there is an observable effect on the gait during use of the system. In three out of five subjects, we observed a decrease in FoG duration distributions over the protocol days during gait-training exercises. Moreover, sensing data-driven analysis shows a decrease in FoG duration and FoG number in four out of five participants when they use the system as a gait-assistive tool during normal daily life activities at home. --- paper_title: A Personalized Exercise Trainer for Elderly paper_content: Physical activity provides many physiological benefits. On the one hand it reduces the risk of disease outcomes. On the other hand it is the basis for proper rehabilitation in case of or after a severe disease. Both aspects are especially important for the elderly population. Within this context, the present paper proposes a personalized, home-based exercise trainer for elderly people. The system is based on a wearable sensor network that enables capturing the user's motions. These are then evaluated by comparing them to a prescribed exercise, taking both exercise load and technique into account. Moreover, the results are translated into appropriate feedback to the user to assist the correct exercise execution. A novel part of the system is the generic personalization by means of a supervised teach-in phase. --- paper_title: Anxiety detection using wearable monitoring paper_content: Social Anxiety Disorder (SAD) might be confused with shyness. However, experiencing anxiety can have profound short and long-term implications. During an anxiety span, the subject suffers from blushing, sweating or trembling. Social activities are harder to accomplish and the subject might tend to avoid them. Although there are tested methods to treat SAD such as Exposure Therapy (ET) and Pharmacotherapy, patients do not treat themselves or suspend treatment due economic, time or space barriers. Wearable computing technologies can be used to constantyly monitor user context offering the possibility to detect anxiety spans. In this work we used Google Glass and the Zephyr HxM Bluetooth band to monitor Spontaneous Blink Rate (SBR) and Heart Rate (HR) respectively. We conducted an experiment that involved 8 subjects in two groups: Mild SAD and No SAD. The experiment consisted on an induced anxiety situation where each participant gave a 10 minutes speech in front of 2 professors. We found higher average heart rates after induced anxiety spans on the mild SAD group. However, we found no evidence of increased SBR as an anxiety indicator. These results indicate that wearable devices can be used to detect anxiety. --- paper_title: Managing Wearable Sensor Data through Cloud Computing paper_content: Mobile pervasive healthcare technologies can support a wide range of applications and services including patient monitoring and emergency response. At the same time they introduce several challenges, like data storage and management, interoperability and availability of heterogeneous resources, unified and ubiquitous access issues. One potential solution for addressing all aforementioned issues is the introduction of the Cloud Computing concept. Within this context, in this work we have developed and present a wearable -- textile platform based on open hardware and software that collects motion and heartbeat data and stores them wirelessly on an open Cloud infrastructure for monitoring and further processing. The proposed system may be used to promote the independent living of patient and elderly requiring constant surveillance. --- paper_title: Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor paper_content: Falling is one of the leading causes of serious health decline or injury-related deaths in the elderly. For survivors of a fall, the resulting health expenses can be a devastating burden, largely because of the long recovery time and potential comorbidities that ensue. The detection of a fall is, therefore, important in care of the elderly for decreasing the reaction time by the care-givers especially for those in care who are particularly frail or living alone. Recent advances in motion-sensor technology have enabled wearable sensors to be used efficiently for pervasive care of the elderly. In addition to fall detection, it is also important to determine the direction of a fall, which could help in the location of joint weakness or post-fall fracture. This work uses a waist-worn sensor, encompassing a 3D accelerometer and a barometric pressure sensor, for reliable fall detection and the determination of the direction of a fall. Also assessed is an efficient analysis framework suitable for on-node implementation using a low-power micro-controller that involves both feature extraction and fall detection. A detailed laboratory analysis is presented validating the practical application of the system. --- paper_title: Advancetid network based wireless, single PMS for multiple-patient monitoring paper_content: Real-time monitoring of the physical condition of the patients is one of the major challenges faced by hospital authorities nowadays. All the hospitals today have one Patient Monitoring System (PMS) per patient. Also human intervention is needed frequently for critical patients. In this paper we propose a network based Wireless Single PMS (NWSPMS), which can monitor multiple patients to measure various physical parameters. This must be precise, fast and effective for transmission of information about their health condition to the concerned. Also there lies a need to transmit more parameters and more data for the convenience and fast response by the staffs in the hospital. Since we need an immediate response, we make use of the voice-call facility apart from the SMS mechanism via an GSM modem. The system proposed here monitors blood pressure, heart beat rate, body temperature and ECG of the elderly patient thus giving an overall condition of the patient. --- paper_title: HOPE: An electronic gadget for home-bound patients and elders paper_content: Home-bound patients, mostly elders face many problems regarding their critical health parameter variations and timely assistance in case of emergencies. It is really a malady when they suffer from other severe diseases, heart problems etc. A constant and reliable assistive technology is essential while taking care of home-bound patients. The system, HOPE we have proposed has sensors to monitor the heart rate, body temperature, tilt and fall. The sensors are attached to the body of the elderly patient in a contented manner. The data can be sent to any Smartphone with Bluetooth support. In case of any emergency the caretaker will be given a notification about the critical situation. The provision to monitor the posture of the patient in the bed helps to reduce the cases of bedsore in bedridden elders. Day by day the menace of weakening health and chances of skin related problems, bed sores etc. are becoming critical in case of bed ridden patients. The experimental results presented in this paper give some insight into the behavior of the proposed system. --- paper_title: Data set for fall events and daily activities from inertial sensors paper_content: Wearable sensors are becoming popular for remote health monitoring as technology improves and cost reduces. One area in which wearable sensors are increasingly being used is falls monitoring. The elderly, in particular are vulnerable to falls and require continuous monitoring. Indeed, many attempts, with insufficient success have been made towards accurate, robust and generic falls and Activities of Daily Living (ADL) classification. A major challenge in developing solutions for fall detection is access to sufficiently large data sets. This paper presents a description of the data set and the experimental protocols designed by the authors for the simulation of falls, near-falls and ADL. Forty-two volunteers were recruited to participate in an experiment that involved a set of scripted protocols. Four types of falls (forward, backward, lateral left and right) and several ADL were simulated. This data set is intended for the evaluation of fall detection algorithms by combining daily activities and transitions from one posture to another with falls. In our prior work, machine learning based fall detection algorithms were developed and evaluated. Results showed that our algorithm was able to discriminate between falls and ADL with an F-measure of 94%. --- paper_title: Smart Multi-Level Tool for Remote Patient Monitoring Based on a Wireless Sensor Network and Mobile Augmented Reality paper_content: Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. One of the main advances is the development of real-time monitors that use intelligent and wireless communication technology. In this paper, a system is presented for the remote monitoring of the body temperature and heart rate of a patient by means of a wireless sensor network (WSN) and mobile augmented reality (MAR). The combination of a WSN and MAR provides a novel alternative to remotely measure body temperature and heart rate in real time during patient care. The system is composed of (1) hardware such as Arduino microcontrollers (in the patient nodes), personal computers (for the nurse server), smartphones (for the mobile nurse monitor and the virtual patient file) and sensors (to measure body temperature and heart rate), (2) a network layer using WiFly technology, and (3) software such as LabView, Android SDK, and DroidAR. The results obtained from tests show that the system can perform effectively within a range of 20 m and requires ten minutes to stabilize the temperature sensor to detect hyperthermia, hypothermia or normal body temperature conditions. Additionally, the heart rate sensor can detect conditions of tachycardia and bradycardia. --- paper_title: An integrated system for wireless monitoring of chronic patients and elderly people paper_content: In the last years the demographic changes and ageing of population increase health care demand. Increasing number of chronic patients and elders requires close attention to their health conditions. In this paper we present the realization of a wireless monitoring system capable to measure process and transmit patient's physiologic signals (electrocardiogram, respiratory rhythm, arterial saturation of oxygen, blood pressure and body temperature) to a central medical server. The use of our system is suitable for continuous long-time monitoring, as a part of a diagnostic procedure or can achieve medical assistance of a chronic condition. We use custom developed and commercially available devices, low power microcontrollers and RF transceivers that perform the measurements and transmit them to the Personal Server. The Personal Server, in form of a PDA, that running a monitor application, receives the physiological signals from monitoring devices, activates the alarms when the monitored parameters exceed the preset limits, and communicates periodically to the central server by using WiFi or GSM/GPRS connection. --- paper_title: CoolSpots: reducing the power consumption of wireless mobile devices with multiple radio interfaces paper_content: CoolSpots enable a wireless mobile device to automatically switch between multiple radio interfaces, such as WiFi and Bluetooth, in order to increase battery lifetime. The main contribution of this work is an exploration of the policies that enable a system to switch among these interfaces, each with diverse radio characteristics and different ranges, in order to save power - supported by detailed quantitative measurements. The system and policies do not require any changes to the mobile applications themselves, and changes required to existing infrastructure are minimal. Results are reported for a suite of commonly used applications, such as file transfer, web browsing, and streaming media, across a range of operating conditions. Experimental validation of the CoolSpot system on a mobile research platform shows substantial energy savings: more than a 50% reduction in energy consumption of the wireless subsystem is possible, with an associated increase in the effective battery lifetime. --- paper_title: Multi-parameters wireless shirt for physiological monitoring paper_content: The ability to monitor the health status of elderly patients or patients undergoing home therapy allows significant advantages in terms of cost and convenience of the subject. However, these non-clinical applications of biomedical signals acquisition require different monitoring devices having, between the other characteristics, reduced size, low power and environment compatibility. The research activity concerns the development of a new wearable device that can monitor the main physiological parameters of a person in a non-invasive manner. All sensors have contactless characteristics that permit to avoid the direct contact with the skin. This system is a useful solution for monitoring the health condition of patients at home. The wearable monitoring system consists of two subsystems: first, a wearable data acquisition hardware, in which the sensors are integrated for the acquisition of biomedical parameters, and secondly, a remote monitoring station located separately and connected to the Internet for telemedicine applications. The physiological parameters that are monitored are electrocardiogram (ECG), heart rate (HR), derived from ECG signals through the determination of RR intervals, respiratory rate, and three-axis motion (acceleration and position) of the subject measured using an accelerometer. All sensors are designed using contactless measurement techniques, thus avoiding the use of gel for the conduction of the signal and possible skin irritation due to contact. The electrodes for measuring ECG signal are capacitive, while the measure of respiration is obtained by plethysmography, which does not require direct contact with skin. In order to design and construct the signal acquisition circuits in an efficient and simple manner, modular design concept is adopted in this research. The flexible signal conditioning modules are designed and assembled together. The human parameters can be recorded and analyzed continuously during work activities at home. The correct evaluation of these parameters allows the medical staff to assess to the state of health, to know accidental injury or other danger occurred in patients at home. --- paper_title: An Integrated Architecture for Remote Healthcare Monitoring paper_content: Remote healthcare monitoring has attracted the interest of many research projects during last years. The need to address the issue of ageing population has lead to the exploitation of modern communication and software technologies in this domain too. This is instigated by related technological evolutions such as the evolutions in sensor networks which can now support self organization. This paper presents the INCASA architecture that provides advances towards mainly two directions. To start with, by using the appropriate middleware, it manages to transform the network of devices to a network of services following a Service Oriented Architecture. In this way, it turns the implementation of applications (e.g. clinical applications) on top of the middleware to be easy and efficient. Furthermore, INCASA architecture provides an integrated solution for profiling user habits. This is particularly important in case of elderly people who tend to follow daily activities in a repeating manner. In the proposed architecture, the procedure of modeling user habits is the necessary step in order to generate alert actions in case of divergence. --- paper_title: Noise Filtering, Channel Modeling and Energy Utilization in Wireless Body Area Networks paper_content: Constant monitoring of patients without disturbing their daily activities can be achieved through mobile networks. Sensor nodes distributed in a home environment to provide home assistance gives concept of Wireless Wearable Body Area Networks. Gathering useful information and its transmission to the required destination may face several problems. In this paper we figure out different issues and discuss their possible solutions in order to obtain an optimized infrastructure for the care of elderly people. Different channel models along with their characteristics, noise filtering in different equalization techniques, energy consumption and effect of different impairments have been discussed in our paper. The novelty of this work is that we highlighted multiple issues along with their possible solutions that a BAN infrastructure is still facing. --- paper_title: Wireless ECG monitoring and alarm system using ZigBee paper_content: This paper presents the development of a system for wireless ECG monitoring and alarm using ZigBee. The system is intended for home use by patients that are not in a critical condition but need to be constant or periodically monitored by clinicians or family. Patient monitoring is the cornerstone of proper medical care. It provides clinicians the much needed information about a person's current health status, so that they can act accordingly if anything goes wrong. Nowadays, complex patient monitoring systems offer the possibility of continuously monitoring a multitude of biological signals, analyze them, interpret them and take the appropriate action; or alert clinicians if necessary [1]. The usual shortcomings of most of these systems reside in affecting patient mobility and home comfort. A patient would need to be sitting on a bed wired to these devices in order for his vital signs to be monitored. This system measures, records and presents in real-time the electrical activity of the heart while preserving comfort of the patient. The device is built as a low-power, small-sized, low-cost solution suitable for monitoring elderly people at home or in a nursing facility without interfering with the daily activity of a patient. It should give sufficient information in real time, and make it available remotely. The intention is not to achieve perfect clinical accuracy but the device is able to detect anomalies in the measured data and it also has alerting features. Authorized observers (clinicians or family) can monitor at any moment the state of the patient through the internet. --- paper_title: A distributed wireless body area network for medical supervision paper_content: The emerging of wireless body area network has profound impacts on our daily life, such as pervasive medical supervision and outdoor exercises, and the large scale application of wireless body area network can effectively reduce higher cost burden owing to the aging society and long term healthcare for the chronic illness. It can also enhance the quality of life for elderly people and chronic patients, and decrease the harm of the sudden diseases. The paper presents a distributed wireless body area network for medical supervision. The system contains three layers: sensor network tier, mobile computing network tier, and remote monitoring network tier. It provides collection, demonstration, and storage of the vital information such as ECG, blood oxygen, body temperature, respiration rate. Furthermore, it also provides medical service management and disease warning. The system has many advantages such as comfort, low-cost, low-power, easy configuration, convenient carrying, easy transplantation, real-time reliable data, and friendly human-machine interaction. And then the design and implementation issues of the system composition are discussed in this paper. --- paper_title: Mobile Messaging Services-Based Personal Electrocardiogram Monitoring System paper_content: A mobile monitoring system utilizing Bluetooth and mobile messaging services (MMS/SMSs) with low-cost hardware equipment is proposed. A proof of concept prototype has been developed and implemented to enable transmission of an Electrocardiogram (ECG) signal and body temperature of a patient, which can be expanded to include other vital signs. Communication between a mobile smart-phone and the ECG and temperature acquisition apparatus is implemented using the popular personal area network standard specification Bluetooth. When utilizing MMS for transmission, the mobile phone plots the received ECG signal and displays the temperature using special application software running on the client mobile phone itself, where the plot can be captured and saved as an image before transmission. Alternatively, SMS can be selected as a transmission means, where in this scenario, dedicated application software is required at the receiving device. The experimental setup can be operated formonitoring from anywhere in the globe covered by a cellular network that offers data services. --- paper_title: Real life applicable fall detection system based on wireless body area network paper_content: Real-time health monitoring with wearable sensors is an active area of research. In this domain, observing the physical condition of elderly people or patients in personal environments such as home, office, and restroom has special significance because they might be unassisted in these locations. The elderly people have limited physical abilities and are more vulnerable to serious physical damages even with small accidents, e.g. fall. The falls are unpredictable and unavoidable. In case of a fall, early detection and prompt notification to emergency services is essential for quick recovery. However, the existing fall detection devices are bulky and uncomfortable to wear. Also, detection system using the devices requires the higher computation overhead to detect falls from activities of daily living (ADL). In this paper, we propose a new fall detection system using one sensor node which can be worn as a necklace to provide both the comfortable wearing and low computation overhead. The proposed necklace-shaped sensor node includes tri-axial accelerometer and gyroscope sensors to classify the behaviour and posture of the detection subject. The simulated experimental results performed 5 fall scenarios 50 times by 5 persons show that our proposed detection approach can successfully distinguish between ADL and fall, with sensitivities greater than 80% and specificities of 100%. --- paper_title: Feasibility Study of a Wearable System Based on a Wireless Body Area Network for Gait Assessment in Parkinson's Disease Patients paper_content: Parkinson's disease (PD) alters the motor performance of affected individuals. The dopaminergic denervation of the striatum, due to substantia nigra neuronal loss, compromises the speed, the automatism and smoothness of movements of PD patients. The development of a reliable tool for long-term monitoring of PD symptoms would allow the accurate assessment of the clinical status during the different PD stages and the evaluation of motor complications. Furthermore, it would be very useful both for routine clinical care as well as for testing novel therapies. Within this context we have validated the feasibility of using a Body Network Area (BAN) of wireless accelerometers to perform continuous at home gait monitoring of PD patients. The analysis addresses the assessment of the system performance working in real environments. --- paper_title: Leveraging Mobile Cloud for Telemedicine: A Performance Study in Medical Monitoring paper_content: Telemedicine has proven to be an effective and promising solution in promoting more affordable and higher quality healthcare. Wearable body sensors and mobile devices have been widely used to monitor health status of patients or elderly and generate alarms in case of imminent medical conditions. However, the limited computation power and energy supply of mobile devices result in either high false alarm rate or short battery life, prohibitive for medical monitoring. Cloud computing embraces new opportunities of transforming healthcare delivery into a more reliable and sustainable manner. In this paper, we propose a mobile cloud telemedicine framework and discuss its potential performance by taking advantage of the real-time, on-site monitoring capability of Android mobile device and the abundant computing power of the cloud. --- paper_title: An Open, Ubiquitous and Adaptive Chronic Disease Management Platform for Chronic Respiratory and Renal Diseases (CHRONIOUS) paper_content: CHRONIOUS is a highly innovative Information and Communication Technologies (ICT) research initiative that aspires to implement its vision for ubiquitous health and lifestyle monitoring of people with chronic diseases at a European level.CHRONIOUS is funded by the European Union under its 7th Framework Program. The project started on February 1st, 2008 and has a planned duration of three years and half. The CHRONIOUS team is a consortium of internationally renowned research labs in Europe, universities and private companies, who collaborate to define a European framework for a generic health status monitoring platform addressing people with chronic health conditions. CHRONIOUS addresses a smart wearable platform, based on multi-parametric sensors processing, for monitoring people suffering from chronic diseases. In particular CHRONIOUS will be tested with chronic obstructive pulmonary disease (COPD) and chronic kidney disease (CKD) and renal insufficiency inpatients at their home. --- paper_title: Using physiological signals for authentication in a group key agreement protocol paper_content: A Body Area Network (BAN) can be used to monitor the elderly people or patients with chronic diseases. Securing broadcasted data and commands within BANs is essential for preserving the privacy of health data and for ensuring the safety of the patient. We show how a group key can be securely established between the different sensors within a BAN. The proposed mechanism uses the inherent secure environmental values. An implementation of the protocols is carried out on mica2 motes and performance is examined in detail. The time elapsed, complexity of the code and memory requirements are analysed. The results confirm the potential benefits in real-world application. We show that a key establishment protocol based on RSA has advantages over a protocol based on ECC for this application. --- paper_title: Estimation of Basic Activities of Daily Living Using ZigBee 3D Accelerometer Sensor Network paper_content: Recently, how to support and care elderly people is serious problem. Modern approach is trying to support to keep health (body and mental) seniors living alone, but it is hard to keep a health by oneself. Our aim is monitoring a user's life for providing suitable services to keep his/her health. To providing suitable service, life log is very useful information. However, traditional monitoring system is only focusing elementary motion, walking, sleeping, and so on. We deal with recognizing advanced motions by using transportation data from an area to other areas in a house. These advanced motion logs support not only elderly but also care stuff or family who plans supports. In daily life, our motion is very variety. Therefore we focused on Basic Activities of Daily Living (ADL) which categories normally doing in daily life such as feeding, bathing, homemaking, and so on. In this research, we pick up 11 motion from ADL for recognition. --- paper_title: Automatic identification and placement verification of wearable wireless sensor nodes using atmospheric air pressure distribution paper_content: We present a new approach to identifying and verifying the location of wearable wireless sensor nodes (W2SNs) placed on a body by inferring differences in altitudes using atmospheric air pressure sensors. This technique is aimed at long-term, in-home monitoring applications of the elderly and patients with chronic conditions, where the user has the freedom to install and remove the W2SNs as required without caregiver assistance. Our prototype shows that each IEEE 802.15.4-capable W2SN, employing pressure sensors, is capable of detecting altitude changes sufficient to distinguish between the relative elevation of a patient's arm and leg, and recognize which W2SN is placed on which limb; preliminary results show detection in altitudes down to 39cm apart. --- paper_title: On Power and Throughput Tradeoffs of WiFi and Bluetooth in Smartphones paper_content: This paper describes a combined power and throughput performance study of WiFi and Bluetooth usage in smartphones. The work measures the obtained throughput in various settings while employing each of these technologies, and the power consumption level associated with them. In addition, the power requirements of Bluetooth and WiFi in their respective noncommunicating modes are also compared. The study reveals several interesting phenomena and tradeoffs. In particular, the paper identifies many situations in which WiFi is superior to Bluetooth, countering previous reports. The study also identifies a couple of scenarios that are better handled by Bluetooth. The conclusions from this study suggest preferred usage patterns, as well as operative suggestions for researchers and smartphone developers. This includes a cross-layer optimization for TCP/IP that could greatly improve the throughput to power ratio whenever the transmitter is more capable than the receiver. --- paper_title: Wireless sensor network based on multilevel femtocells for home monitoring paper_content: An intelligent femtocell-based sensor network is proposed for home monitoring elderly or people with chronic diseases. The femtocell is considered as a small sensor network which is placed into patient house and consists on both mobile and fixed sensors disposed on three layers. The first layer contains body sensors attached to the patient that monitor different health parameters, patient location, position and possible falls. The second layer is dedicated for ambient sensors and routing inside the cell. The third layer contains emergency ambient sensors that cover burglary events or toxic gas concentration, distributed by necessities. Cell implementation is based on IRIS family of motes. In order to reduce energy consumption and radiation level, adaptive rates of acquisition and communication are used. Experimental results on body sensors and ambient ones are presented in the last section. --- paper_title: Assistive Technology for Elders: Wireless Intelligent Healthcare Gadget paper_content: Improving the quality of life for the elderly persons and giving them the proper care at the right time is the responsibility of the younger generation. But due to lack of awareness on proper elder care, unavoidable busy schedule etc. the elderly population is seem to be quite ignored. In such a scenario we are trying to find an amicable solution to this problem using a compact and user friendly system design. The proposed system is a compact device which has various wearable sensors all attached inside a glove. It has a process flow controller module and a communication control module. These modules perform together to excite an alert mechanism to the relatives or to the doctors via an SMS or a voice call to their mobile phones, whenever some critical situations arise. The system measures the heart rate, oxygen content in blood, body temperature and the pressure of the elderly person, which are the important parameters for a proper diagnosis. --- paper_title: Synchronous Wearable Wireless Body Sensor Network Composed of Autonomous Textile Nodes paper_content: A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system. --- paper_title: Towards a low cost open architecture wearable sensor network for health care applications paper_content: Wireless sensor networks present a growing interest in health care applications since they can replace wired devices for detecting signals of physiological origin and continuously monitoring health parameters, offering a reliable and inexpensive solution. In this paper a low cost open architecture wearable sensor network for health care applications is presented. Through this study, an experimental wireless sensor network (WSN) architecture has been built from scratch in order to investigate and present the development procedure and the corresponding capabilities and limitations of such a system. Moreover, technological aspects regarding implementation are also presented. --- paper_title: Processing of wearable sensor data on the cloud - a step towards scaling of continuous monitoring of health and well-being paper_content: As part of a sleep monitoring project, we used actigraphy based on body-worn accelerometer sensors to remotely monitor and study the sleep-wake cycle of elderly staying at nursing homes. We have conducted a fifteen patient trial of a sleep activity pattern monitoring (SAPM) system at a local nursing home. The data was collected and stored in our server and the processing of the data was done offline after sleep diaries used for validation and ground truth were updated into the system. The processing algorithm matches and annotates the sensor data with manual sleep diary information and is processed asynchronously on the grid/cloud back end. In this paper we outline the mapping of the system for grid / cloud processing, and initial results that show expected near-linear performance for scaling the number of users. --- paper_title: Emergency Fall Incidents Detection in Assisted Living Environments Utilizing Motion, Sound, and Visual Perceptual Components paper_content: This paper presents the implementation details of a patient status awareness enabling human activity interpretation and emergency detection in cases, where the personal health is threatened like elder falls or patient collapses. The proposed system utilizes video, audio, and motion data captured from the patient's body using appropriate body sensors and the surrounding environment, using overhead cameras and microphone arrays. Appropriate tracking techniques are applied to the visual perceptual component enabling the trajectory tracking of persons, while proper audio data processing and sound directionality analysis in conjunction to motion information and subject's visual location can verify fall and indicate an emergency event. The postfall visual and motion behavior of the subject, which indicates the severity of the fall (e.g., if the person remains unconscious or patient recovers) is performed through a semantic representation of the patient's status, context and rules-based evaluation, and advanced classification. A number of advanced classification techniques have been examined in the framework of this study and their corresponding performance in terms of accuracy and efficiency in detecting an emergency situation has been thoroughly assessed. --- paper_title: Monitoring activities of daily living based on wearable wireless body sensor network paper_content: With recent advances in microprocessor chip technology, wireless communication, and biomedical engineering it is possible to develop miniaturized ubiquitous health monitoring devices that are capable of recording physiological and movement signals during daily life activities. The aim of the research is to implement and test the prototype of health monitoring system. The system consists of the body central unit with Bluetooth module and wearable sensors: the custom-designed ECG sensor, the temperature sensor, the skin humidity sensor and accelerometers placed on the human body or integrated with clothes and a network gateway to forward data to a remote medical server. The system includes custom-designed transmission protocol and remote web-based graphical user interface for remote real time data analysis. Experimental results for a group of humans who performed various activities (eg. working, running, etc.) showed maximum 5% absolute error compared to certified medical devices. The results are promising and indicate that developed wireless wearable monitoring system faces challenges of multi-sensor human health monitoring during performing daily activities and opens new opportunities in developing novel healthcare services. --- paper_title: Wireless sensor network based E-health system: Implementation and experimental results paper_content: With the increasing number senior citizens, E-health is targeted for home use with the special requirements of being usable in everyday life and low cost. A wireless sensor network application is proposed here for 24 hour constant monitoring without disturbing daily activities of elderly people and their caretakers. In the system proposed, both fixed and body (mobile) sensors are used. Since not every elder likes to have a sensor board attached to him/her, and in many cases, he/she may not carry the sensor; the home sensor network independently would have the ability to monitor the health status and living environment based on the multisensor data analysis and fusion. A mixed positioning algorithm is proposed to determine where the elderly person is. The purpose of the positioning is to help the system to determine the person's activities and further to make decisions about his/her health status. The system could take care of two types of the basic needs of an elderly person: everyday needs as abnormal events and emergency alarms to doctors and caretakers through telephone, SMS and e-mail, and day to day requirements such as taking of medicine, having lunch, turn off the microwave oven, and so on. At same time, the system is sensitive to security and privacy issues. --- paper_title: iCare: A Mobile Health Monitoring System for the Elderly paper_content: This paper describes a mobile health monitoring system called iCare for the elderly. We use wireless body sensors and smart phones to monitor the well being of the elderly. It can offer remote monitoring for the elderly anytime anywhere and provide tailored services for each person based on their personal health condition. When detecting an emergency, the smart phone will automatically alert pre-assigned people who could be the old people's family and friends, and call the ambulance of the emergency centre. It also acts as the personal health information system and the medical guidance which offers one communication platform and the medical knowledge database so that the family and friends of the served people can cooperate with doctors to take care of him/her. The system also features some unique functions that cater to the living demands of the elderly, including regular reminder, quick alarm, medical guidance, etc. iCare is not only a real-time health monitoring system for the elderly, but also a living assistant which can make their lives more convenient and comfortable. ---	Title: A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments Section 1: Introduction Description 1: Introduce the background and motivation for using Wireless Body Area Network (WBAN) systems in eHealthcare, especially in the context of an aging population and increasing chronic diseases. Section 2: Residential Environment eHealthcare System Architecture Description 2: Describe a typical architecture of a residential environment eHealthcare system, detailing its different layers and components. Section 3: Taxonomy and Requirements Description 3: Summarize the primary requirements and design considerations of wireless communication technologies applicable to WBAN systems, covering low power consumption, transmission reliability, latency, data rates, and security. Section 4: Candidate Wireless Technologies Description 4: Review the latest wireless communication technologies that support the development and deployment of WBAN systems, including BLE, ZigBee, RuBee, Sensium, Zarlink, Z-Wave, and others. Section 5: Discussion Description 5: Highlight important features of low-power communication technologies, discussing their protocol efficiency, user flexibility, communication range, and energy efficiency. Section 6: Future Prospects Description 6: Explore the potential future growth areas for WBAN systems in healthcare, focusing on smartphone-based healthcare applications, their advantages, challenges, and fastest-growing mHealth areas. Section 7: Summary of Recent Research Articles Description 7: Provide an overview of recent technological advances in eHealthcare systems, focusing on studies that use WBAN for remote patient monitoring and their findings. Section 8: Conclusions Description 8: Summarize the key points and findings from the survey, highlighting the challenges, opportunities, and potential solutions in the context of WBAN systems for residential eHealthcare.
A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems	21	--- paper_title: Fault Diagnosis based on Analytical Models for Linear and Nonlinear Systems - A Tutorial paper_content: Abstract The diagnosis systems considered in this paper rely on the inconsistency between the actual process behaviour and its expected behaviour as described by an analytical model. The inconsistency is exhibited in signals called residuals. Two methods for residual generation are presented in a tutorial way: the parity space and the observer based approaches. Linear and nonlinear models are successively considered as a basis for the design of the residual generators. --- paper_title: Engine data analysis using decision trees paper_content: Monitoring systems on aircraft engines typically record many parameters, sampled frequently, over the duration of a flight. On some newer engines such data have been recorded, and stored, over hundreds of flights. But data alone are not sufficient, and are too unwieldy, to help the analyst understand the state of the engine's health. Automated data mining, for knowledge discovery, is being successfully used in several fields. One well-known technique is a decision tree, which is induced from a subset of a known training set of engine data and outcome. Since engine parameters are continuous-valued a splitting criterion, known as the minimum description length principle, is used to define the branch points of the tree. The decision tree is induced from a subset of a known training set of engine data and outcome. Issues such as overfitting the training data, and the problem of large, or randomly-chosen, training sets are also discussed in this paper. Copyright ©2000 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. BACKGROUND Many airlines have invested in engine monitoring systems (EMS) on their newer aircraft. These systems record many parameters, such as temperatures, pressures, rotor speeds, fuel flow and others. The hope is that, by tracking the engines over hundreds of flights, one can discern component degradations which lead to diminished performance or safety. The large numbers of parameters, and the large volumes of time-series data, pose a challenge to the analyst sifting through the data. Simple checks such as minimum/maximum parameter bounds often lead to false alarms. Relaxing these bounds could lead to missed alerts. Several NASA Ames research efforts are directed at exploring ways of using computers to monitor the health of physical systems. In a previous paper [8] surveying aircraft engine health monitoring systems, various machine learning-based techniques, that are being researched for engine data analysis, were outlined. In this paper we focus on using the decision tree method for learning to classify engine's health based on its sensor data. (c)2000 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization. --- paper_title: A SURVEY OF AIRCRAFT ENGINE HEALTH MONITORING SYSTEMS paper_content: This paper presents a survey of engine health monitoring systems for commercial aircraft. The state of practice is explored first, with the purpose of identifying the shortcomings of current systems. The state of the research to address these shortcomings is them surveyed to explore the alternatives. Research and monitoring applications for various other types of engines provide a good basis for further exploring the topic. This survey is meant to serve as a precursor to engine health and monitoring research at the NASA Ames Research Center. --- paper_title: Fundamentals of model-based diagnosis paper_content: Abstract Over the last 25 years, the Computer Science community and particularly the Artificial Intelligence community, have developed a framework for system diagnosis, called Model-Based Diagnosis. This framework is extremely general and covers a broad range of capabilities including detecting malfunctions, isolating faulty components, handling multiple faults, identifying repair actions, and automatically generating embedded software. This field grew independently of the fault detection and isolation community (FDI) and has developed its own terminologies and conventions. This paper is an attempt to present the fundamental concepts of Model-Based Diagnosis (MBD) in onc place and in one consistent terminology, and thus make the field much more accessible to the FDI community. --- paper_title: Optimal discrete event supervisory control of aircraft gas turbine engines paper_content: This paper presents an application of the recently developed theory of optimal discrete event supervisory (DES) control that is based on a signed real measure of regular languages. The DES control techniques are validated on an aircraft gas turbine engine simulation test bed. The test bed is implemented on a networked computer system in which two computers operate in the client-server mode. Several DES controllers have been tested for engine performance and reliability. --- paper_title: Pilot's Associate: a cooperative, knowledge-based system application paper_content: The Pilot's Associate program, a joint effort of the Defense Advanced Research Projects Agency (DARPA) and the US Air Force to build a cooperative, knowledge-based system to help pilots make decisions is described, and the lessons learned are examined. The Pilot's Associate concept developed as a set of cooperating, knowledge-based subsystems: two assessor and two planning subsystems, and a pilot interface. The two assessors, situation assessment and system status, determine the state of the outside world and the aircraft systems, respectively. The two planners, tactics planner and mission planner, react to the dynamic environment by responding to immediate threats and their effects on the prebriefed mission plan. The pilot-vehicle interface subsystem provides the critical connection between the pilot and the rest of the system. The focus is on the air-to-air subsystems. > --- paper_title: Sensor Needs for Control and Health Management of Intelligent Aircraft Engines paper_content: ABSTRACT NASA and the U.S. Department of Defense are conducting programs which support the future vision of “intelligent” aircraft engines for enhancing the affordability, performance, operability, safety, and reliability of aircraft propulsion systems. Intelligent engines will have advanced control and health management capabilities enabling these engines to be self-diagnostic, self-prognostic, and adaptive to optimize performance based upon the current condition of the engine or the current mission of the vehicle. Sensors are a critical technology necessary to enable the intelligent engine vision as they are relied upon to accurately collect the data required for engine control and health management. This paper reviews the anticipated sensor requirements to support the future vision of intelligent engines from a control and health management perspective. Propulsion control and health management technologies are discussed in the broad areas of active component controls, propulsion health management and distributed controls. In each of these three areas individual technologies will be described, input parameters necessary for control feedback or health management will be discussed, and sensor performance specifications for measuring these parameters will be summarized. --- paper_title: A Study on the Requirements for Fast Active Turbine Tip Clearance Control Systems paper_content: This paper addresses the requirements of a control system for active turbine tip clearance control in a generic commercial turbofan engine through design and analysis. The control objective is to articulate the shroud in the high pressure turbine section in order to maintain a certain clearance set point given several possible engine transient events. The system must also exhibit reasonable robustness to modeling uncertainties and reasonable noise rejection properties. Two actuators were chosen to fulfill such a requirement, both of which possess different levels of technological readiness: electrohydraulic servovalves and piezoelectric stacks. Identification of design constraints, desired actuator parameters, and actuator limitations are addressed in depth; all of which are intimately tied with the hardware and controller design process. Analytical demonstrations of the performance and robustness characteristics of the two axisymmetric LQG clearance control systems are presented. Takeoff simulation results show that both actuators are capable of maintaining the clearance within acceptable bounds and demonstrate robustness to parameter uncertainty. The present model-based control strategy was employed to demonstrate the tradeoff between performance, control effort, and robustness and to implement optimal state estimation in a noisy engine environment with intent to eliminate ad hoc methods for designing reliable control systems. --- paper_title: An investigation of life extending control techniques for gas turbine engines paper_content: The consumption of engine life characterized by low EGT margin, expended life-limited parts, and slow engine accelerations is the principal cause of aircraft engine removal. Life extending control results from a conscious effort on the part of control system designers to extend the life of an engine by modifying the control logic or control hardware to influence one or more of these life-consuming factors. General Electric Aircraft Engines and NASA Glenn Research Center are currently engaged in a collaborative research programme to investigate control technologies applicable to extending on-wing life of aircraft engines. A trade study of potential schemes that may have a positive impact on engine life has been performed, and the results of this study are used to narrow the focus of further research under this programme. --- paper_title: Intelligent Life -Extending Controls for Aircraft Engines paper_content: Aircraft engine controllers are designed and operated to provide desired performance and stability margins. The purpose of life -extending -control (LEC) is to study the relationship between control action and engin e component life usage, and to design an intelligent control algorithm to provide proper trade -offs between performance and engine life usage. The benefit of this approach is that it is expected to maintain safety while minimizing the overall operating co sts. With the advances of computer technology, engine operation models, and damage physics, it is necessary to reevaluate the control strategy for overall operating cost consideration. This paper uses the thermo -mechanical fatigue (TMF) of a critical com ponent to demonstrate how an intelligent engine control algorithm can drastically reduce the engine life usage with minimum sacrifice in performance. A Monte Carlo simulation is also performed to evaluate the likely engine damage accumulation under variou s operating conditions. The simulation results show that an optimized acceleration schedule can provide a significant life saving in selected engine components. Nomenclature --- paper_title: Model-Based Life Extending Control for Aircraft Engines paper_content: ** * * The objective of this work is to extend the on-wing life of aircraft engines using advanced control techniques. Previous studies demonstrated that, out of various schemes that have a positive impact on engine life, improved active clearance control (ACC) is the one with the best potential. Modern commercial turbofan engines are already implementing various forms of ACC schemes to maintain turbine tip clearances within appropriate ranges for safety and efficiency reasons. However, one limitation of these schemes is that they are designed for a "nominal" engine, and as the engine deteriorates over time the ACC performance degrades. The present paper proposes to improve the high-pressure turbine ACC in a high bypass commercial engine to account for deterioration. A key component of this new ACC scheme is an estimator of the clearance variation due to deterioration. This estimator processes the difference between some of the actual engine outputs and outputs from a model of the nominal engine running in real-time. The new ACC scheme was implemented on a high-fidelity model of the commercial engine. Extensive simulations show that exhaust gas temperatures are clearly lowered by the new ACC, which translate into more cycles between engine overhauls. --- paper_title: Longitudinal emergency control system using thrust modulation demonstrated on an MD-11 airplane paper_content: This report describes how an MD-11 airplane landed using only thrust modulation, with the control surfaces locked. The propulsion-controlled aircraft system would be used if the aircraft suffered a major primary flight control system failure and lost most or all the hydraulics. The longitudinal and lateral–directional controllers were designed and flight tested, but only the longitudinal control of flightpath angle is addressed in this paper. A flight-test program was conducted to evaluate the aircraft’s high-altitude flying characteristics and to demonstrate its capacity to perform safe landings. In addition, over 50 low approaches and three landings without the movement of any aerodynamic control surfaces were performed. The longitudinal control modes include a wing engines only mode for flightpath control and a three-engine operation mode with speed control and dynamic control of the flightpath angle using the tail engine. These modes were flown in either a pilot-commanded mode or an instrument landing system coupled mode. Also included are the results of an analytical study of an autothrottle longitudinal controller designed to improve the phugoid damping. This mode requires the pilot to use differential throttles for lateral control. Nomenclature Alon longitudinal state derivative matrix Blon control input derivative matrix c.g. center of gravity Aerospace Engineer. †Chief, Propulsion Branch. Associate Fellow AIAA. ‡Flight Control Engineer. Copyright  1996 by the American Institute of Aeronautics and Astronautics, Inc. No copyright is asserted in the United States under Title 17, U.S. Code. The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental purposes. All other rights are reserved by the copyright owner. 1 American Institute of Aero Clon state output matrix Dlon control input observation matrix EPR engine pressure ratio (turbine and inlet total pressures) FADEC full-authority digital engine control computers FCC flight control computer FCP flight control panel sink rate, ft/sec ILS instrument landing system flightpath error feed-forward gain, deg pitch integrator error gain, 1/sec pitch rate feedback gain, deg/deg/sec velocity error feedback gain, deg/kn pitch angle feedback gain, deg/deg/sec center engine washout gain, lb MCDU multifunction control and display unit PCA propulsion-controlled aircraft PIO pilot induced oscillation q pitch rate, deg/sec t time, sec uu x axis velocity perturbation, ft/sec Vel velocity or airspeed, kn s Laplace transform ww z axis velocity perturbation, ft/sec xlon longitudinal state vector α angle of attack, deg γ flightpath angle, deg ḣ Kvc Kvi Kq K rs sec Kthad Kvm nautics and Astronautics flightpath angle command, deg velocity error θ pitch attitude, deg pitch attitude rate, deg/sec φ roll attitude, deg Introduction Aircraft flight control systems are designed with extensive redundancy to ensure a low probability of failure. During recent years, however, several aircraft have experienced major flight control system failures, leaving engine thrust as the only control effectors.1,2 In some of these emergency situations, the engines were used to maintain control of the airplane flightpath angle, γ. In the majority of the cases surveyed, crashes resulted, and over 1200 people have died.1 The challenge was to create a sufficient degree of control through thrust modulation to control and safely land an airplane with severely damaged or inoperative flight control surfaces. Meeting this challenge is the objective of the Propulsion-Controlled Aircraft (PCA) Emergency Backup System. The PCA emergency backup flight control system requires that the airplane have at least two engines, preferably two wing engines. In addition, the normal control surfaces can not be locked in a hardover position which could exceed the moments resulting from the thrust of the engines. The National Aeronautics and Space Administration, Dryden Flight Research Center, Edwards, California, has performed nonlinear and linear analytical studies and conducted several flight-test programs investigating the PCA concept. Results of these programs2–6 show that gross control can be obtained by manually moving the throttles. However, making a safe runway landing is exceedingly difficult because of low phugoid and dutch roll damping coupled with the high pilot work load near the ground. To improve the performance and reduce the pilot work load, the PCA program was developed. The goal was to make flying an airplane with the PCA system a viable task with minimal or no previous pilot training with this system. This report describes the longitudinal PCA control systems and flight test results of four modes: • Mode A—using the wing engines only for control of flightpath angle, γ. • Mode B—using the tail engine for speed control in conjunction with mode A. • Mode C—using all the wing and tail engines for dynamic control of γ and speed control. • Mode D—using an existing autothrottle system for γ control. The autothrottle system was developed to provide a simpler implementation that did not require changes to the engine controllers. This system was not flight tested, but simulation results are presented.§ Within control modes A, B, and C, the pilot has the option of selecting the instrument landing system (ILS)coupled with PCA for approach and landing. This option virtually eliminates the pilot work load. Two ILS landings using the wing engines (mode A) were performed, and one is presented in this report. The lateral–directional controller is described in reference 7. Test Vehicle Description The MD-11 airplane is a large, long-range, threeengine, wide-body transport. This airplane is 202 ft long, has a wing span of 170 ft, and a maximum takeoff gross weight of 618,000 lb (fig. 1). Flight Control Systems The MD-11 airplane has a mechanical flight control system with irreversible hydraulically powered actuators. The hydraulic power provided by three independent systems is intended for fail-safe capability. Essential control functions may be maintained by any one of these three systems. Pitch control is provided by dual elevators on each horizontal stabilizer, and pitch trim is provided by a moveable horizontal stabilizer. Inboard and outboard ailerons supplemented by wing spoilers provide roll control. A dual rudder mounted on a single vertical stabilizer provides yaw control. The lateral dynamics is controlled by the yaw damper. The longitudinal stability augmentation system controls the pitch dynamics. The aerodynamic surfaces are controlled by hydraulic actuators. The flight control computers (FCC) were built by Honeywell, Phoenix, Arizona, and operate at 20 samples/sec. The MD-11 airplane is equipped with a flight management system which integrates autopilot, navigation, and autoland functions. The automatic pilot control includes a thumbwheel for commanding flightpath angle, . §NASA has a patent pending for mode d. γ cmd γ err --- paper_title: Fuzzy Fuel Flow Selection Logic for a Real Time Embedded Full Authority Digital Engine Control paper_content: The control logic of the modern full authority digital engine control is comprised of many control loops, each of which has a specific purpose. Typical control loops include (but are not limited to) a high or low rotor speed governor, an acceleration and deceleration loop, and various limiting loops for temperature, speed, fuel flow, and rate of change of fuel flow. The logic that determines which of these loops is in control at any particular time has a history of being very simplistic. This selection logic is usually nothing more that a cascade of minimum and maximum selection logic gates. Since this logic is so simplistic, the control engineer often has to fine-tune the compensator design for each loop, in order to achieve proper performance. --- paper_title: Survivable Engine Control Algorithm Development (SECAD) paper_content: This presentation describes the work conducted as part of the Survivable Engine Control Algorithm Development (SECAD) project. The SECAD project was sponsored by the Joint Technical Coordinating Group on Aircraft Survivability (JTCG/AS) Vulnerability Reduction Subgroup and conducted by the Naval Air Warfare Center Weapons Division and General Electric Aircraft Engines, Lynn MA. The overall objective of the SECAD project is to develop turbine engine control algorithms that have the potential to reduce aircraft engine vulnerability to combat damage, including foreign object damage (FOD). The initial development effort was laid out in three-phases. Phase 1 conducted in early FY99, defined candidate engine damage modes to be addressed, and approaches to mitigate the damage effects. In phase 2 damage models, detection algorithms, and mitigation strategies were developed and evaluated using F414 computer engine models. Phase 3 integrated the damage detection algorithms with the F414 control system and an engine test was conducted to demonstrate the capabilities of the concepts developed. During these tests, SECAD successfully detected fan and compressor damage, combustor airflow loss and variable exhaust nozzle loss of control. This presentation documents the successful development and test of the damage detection and mitigation algorithms, and discusses the potential for merging these techniques with current prognostics and health monitoring technologies. --- paper_title: Adaptive Control paper_content: From the Publisher: ::: Written by two of the pioneers in the field, this book contains a wealth of practical information unavailable anywhere else. The authors give a comprehensive presentation of the field of adaptive control, carefully bending theory and implementation to provide the reader with insight and understanding. Benefiting from the feedback of users who are familiar with the first edition, the material has been reorganized and rewritten, giving a more balanced and teachable presentation of fundamentals and applications. --- paper_title: Multi-Variable Control of the GE T700 Engine using the LQG/LTR Design Methodology paper_content: In this paper we examine the design of scalar and multi-variable feedback control systems for the GE T700 turboshaft engine coupled to a helicopter rotor system. A series of linearized models are presented and analyzed. Robustness and performance specifications are posed in the frequency domain. The LQG/LTR methodology is used to obtain a sequence of three feedback designs. Even in the single-input single-output case, comparison of the current control system with that derived from the LQG/LTR approach shows significant performance improvement. The multi-variable designs, evaluated using linear and nonlinear simulations, show even more potential for performance improvement. --- paper_title: Performance benefits of adaptive in-flight propulsion system optimization paper_content: The communication throughput and data-processing capacities of integrated flight/propulsion control systems allow engine operating schedules to be adjusted in-flight, on the basis of adaptive optimization algorithms which identify engine component performance variations due to manufacturing, wear, and damage. A quantification is presently made of the performance benefits accruing to adaptive in-flight optimization, via comparisons of fuel consumption and turbine temperature data for variable geometry and component match optimized cases with conventional cases. A low-bypass mixed-flow turbofan and a high-bypass nonmixed turbofan are thus treated. 6 refs. --- paper_title: MODEL-BASED INTELLIGENT DIGITAL ENGINE CONTROL (MoBIDEC) paper_content: A model-based control system for an advanced gas turbine engine is described, and some preliminary performance benefits are derived based on simulation testing. The approach to plant model development and a method for adapting a nominal engine model to reflect the performance of a deteriorated engine is given. A general process for model-based control law design and validation, with emphasis on linear and nonlinear analysis, is then presented. Performance and Operability modes that implement closed loop control of model-derived parameters are demonstrated via simulation testing. Future plans include testing of the model-based control described in this paper on an IHPTET Phase II advanced technology engine. NOMENCLATURE --- paper_title: eSTORM: Enhanced self tuning on-board real-time engine model paper_content: Abstract : A key to producing reliable engine diagnostics and prognostics resides in the fusion of different processing techniques. Fusion of techniques has been shown to improve diagnostic performance while simultaneously reducing false alarms. Presented here is an approach that fuses a physical model called STORM (Self Tuning Onboard, Real-time engine Model) developed by Pratt & Whitney, with an empirical neural net model to provide a unique hybrid model called enhanced STORM (eSTORM) for engine diagnostics. STORM is a piecewise linear approximation of the engine cycle deck. Though STORM provides significant improvement over existing real-time engine model methods, there are several effects that impact engine performance that STORM does not capture. Integrating an empirical model with STORM accommodates the modeling errors. This paper describes the development of eSTORM for a Pratt & Whitney high bypass turbofan engine. Results of using STORM and eSTORM on simulated engine data are presented and compared. eSTORM is shown to work extremely well in reducing STORM modeling errors and biases for the conditions considered. --- paper_title: Performance-analysis-based gas turbine diagnostics: A review paper_content: Abstract Gas turbine diagnostics has a history almost as long as gas turbine development itself. Early engine fault diagnosis was carried out based on manufacturer information supplied in a technical manual combined with maintenance experience. In the late 1960s, when L. A. Urban introduced gas path analysis, gas turbine diagnostics made a big breakthrough. Since then different methods have been developed and used in both aerospace and industrial applications. To date, a substantial number of papers have been published in this area. This paper intends to give a comprehensive review of performance-analysis-based methods available thus far for gas turbine fault diagnosis in the open literature. --- paper_title: Sensor biases effect on the estimation algorithm for performance-seeking controllers paper_content: The performance-seeking-control algorithm (PSC) is designed to continuously optimize the performance of propulsion systems. The PSC uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters (EDPs) characterizing engine deviations with respect to nominal conditions. In practice, the measurement biases (or model uncertainties) may prevent the estimated EDPs from reflecting the engine's actual off-nominal condition. This factor has a direct impact on the PSC scheme exacerbated by the open-loop character of the algorithm. An observability analysis shows that the biases cannot be estimated together with the EDPs. Moreover, biases and EDPs turn out to have equivalent effects on the measurements, leaving it undecided whether the estimated EDPs represent the actual engine deviation or whether they simply reflect the measurement biases. In this article, the effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the PSC algorithm to an F100 engine. --- paper_title: Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics paper_content: ABSTRACT In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated. NOMENCLATURE A16 Variable bypass duct area A8 Nozzle area BST Booster CLM Component Level Model FAN Fan FDI Fault detection and isolation FOD Foreign object damage HPC High-pressure compressor HPT High-pressure turbine LPT Low-pressure turbine P27 HPC inlet pressure PS15 Bypass duct static pressure PS3 Combustor inlet static pressure PS56 LPT exit static pressure T27D Booster inlet temperature T56 LPT exit temperature TMPC Burner exit heat soak WF36 Fuel flow XN2 Low-pressure spool speed, measured XN25 High-pressure spool speed, measured XNH High-pressure spool speed, state variable XNL Low-pressure spool speed, state variable --- paper_title: Turbofan engine demonstration of sensor failure detection paper_content: In the paper, the results of a full-scale engine demonstration of a sensor failure detection algorithm are presented. The algorithm detects, isolates, and accommodates sensor failures using analytical redundancy. The experimental hardware, including the F100 engine, is described. Demonstration results were obtained over a large portion of a typical flight envelope for the F100 engine. They include both subsonic and supersonic conditions at hoth medium and full, nonafter burning, power. Estimated accuracy, minimum detectable levels of sensor failures, and failure accommodation performance for an F100 turbofan engine control system are discussed. HE objective of the advanced detection, isolation, and accommodation (ADIA) program was to improve demonstrated reliability of digital electronic control systems for turbine engines by detecting, isolating, and accommodat- ing sensor failures using analytical redundancy methods. This paper discusses the results of an engine demonstration of an analytical-redundancy-based algorithm developed as part of the ADIA program. Over the past 45 years, hydromechanical implementations of turbine engine control systems have ma- tured into highly reliable units. However, there is a trend toward increased engine complexity. Engine control has be- come increasingly complex and has evolved from a hydrome- chanical to a full authority digital electronic (FADEC) imple- mentation. These FADEC-type controls have to demonstrate the same or improved levels of reliability as their hydrome- chanical predecessors. Various redundancy management techniques have been ap- plied to both the total control system and to individual compo- nents. Studies1 have shown that the least reliable of the control system components are the engine sensors. Sensor redundancy will be required to achieve adequate control system reliability. One important type of sensor redundancy is analytical redun- dancy2 (AR), which uses a model to generate redundant infor- mation that can be compared to measured information to detect failures. AR-based systems can have cost and weight savings over hardware redundancy. Considerable progress has been made in the application of analytical redundancy to improve turbine engine control sys- tem reliability. However, little has been done to demonstrate AR-based techniques on real engine systems. One exception was a flightiest program3 where selected out-of-range (hard) sensor faults were induced and the resulting actions of the control evaluated. The test program included a ground-thrust stand evaluation and a flight test. The sensors that failed during the flight test included the compressor inlet variable geometry sensor, inlet static pressure, burner pressure, and fan turbine inlet temperature. Most failures were detected and accommodated. However, a recreation of a broken line (hard) burner pressure went undetected. Pilot response to aircraft performance after accommodation was favorable. --- paper_title: Using neural networks for sensor validation paper_content: This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a model-based approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component analysis on a set of redundant sensors to provide an estimate for a single failed sensor. The approaches are demonstrated using a nonlinear simulation of a turbofan engine. The fault detection and sensor estimation results are presented and the training of the auto-associative neural network to provide sensor estimates is discussed. --- paper_title: Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics paper_content: In this paper, an approach for in-flight fault detection and isolation (FDI) of aircraft engine sensors based on a bank of Kalman filters is developed. This approach utilizes multiple Kalman filters, each of which is designed based on a specific fault hypothesis. When the propulsion system experiences a fault, only one Kalman filter with the correct hypothesis is able to maintain the nominal estimation performance. Based on this knowledge, the isolation of faults is achieved. Since the propulsion system may experience component and actuator faults as well, a sensor FDI system must be robust in terms of avoiding misclassifications of any anomalies. The proposed approach utilizes a bank of (m+1) Kalman filters where m is the number of sensors being monitored. One Kalman filter is used for the detection of component and actuator faults while each of the other m filters detects a fault in a specific sensor. With this setup, the overall robustness of the sensor FDI system to anomalies is enhanced. Moreover, numerous component fault events can be accounted for by the FDI system. The sensor FDI system is applied to a commercial aircraft engine simulation, and its performance is evaluated at multiple power settings at a cruise operating point using various fault scenarios.Copyright © 2004 by ASME --- paper_title: Neural network-based sensor validation for turboshaft engines paper_content: Abstract : Sensor failure detection, isolation, and accommodation using a neural network approach is described. An autoassociative neural network is configured to perform dimensionality reduction on the sensor measurement vector and provide estimated sensor values. The sensor validation scheme is applied in a simulation of the T700 turboshaft engine in closed loop operation. Performance is evaluated based on the ability to detect faults correctly and maintain stable and responsive engine operation. The set of sensor outputs used for engine control forms the network input vector. Analytical redundancy is verified by training networks of successively smaller bottleneck layer sizes. Training data generation and strategy are discussed. The engine maintained stable behavior in the presence of sensor hard failures. With proper selection of fault determination thresholds, stability was maintained in the presence of sensor soft failures. --- paper_title: Sensor and Data Fusion Concepts and Applications paper_content: Multiple Sensor System Applications, Benefits, and Atmospheric Attenuation Data Fusion Algorithms and Architectures Bayesian Inference Dempster-Shafer Algorithm Artificial Neural Networks Voting Fusion Fuzzy Logic and Neural Networks Passive Data Association Techniques for Unambiguous Location of Targets. Appendices: Planck Radiation Law and Radiative Transfer Voting Fusion With Nested Confidence Levels. --- paper_title: Development of an information fusion system for engine diagnostics and health management paper_content: Aircraft gas-turbine engine data is available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C-17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P&W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM . Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples. --- paper_title: Fuzzy belief networks paper_content: The most common method for knowledge representation in an expert system is the production rule [Waterman 1986]. Unfortunately, the modularity inherent in a rule-based system is limiting, especially in an uncertain environment [Morawski 1989]. A fuzzy belief network (FBN) provides a more holistic, graphical approach and lends itself well to implementation in expert systems on personal and small computers. --- paper_title: Bayesian belief networks for fault identification in aircraft gas turbine engines paper_content: Describes the methodology for usage of Bayesian belief networks (BBNs) in fault detection for aircraft gas turbine engines. First, the basic theory of BBNs is discussed, followed by a discussion on the application of this theory to a specific engine. In particular, the selection of faults and the means by which operating regions for the BBN system are chosen are analyzed. This methodology is then illustrated using the GE CFM56-7 turbofan engine as an example. --- paper_title: PCA-based Fuzzy Classification of Turbine Blade Fatigue Modes paper_content: An efficient classification method is needed for th e health diagnosis of turbine blades. Traditional classification methods assign each dat a point or feature vector to one and only one of the categories, which poses significant prob lems in dealing with the data set containing overlapping structures. In this paper, a PCA-based fuzzy classification method is used to address this problem. After preprocessing o f the data, Principle Component Analysis (PCA) is first used to reduce the excessive dimensi onality in the feature space. Generalized multi-dimension Gaussian membership functions are then formulated for each category among the training patterns. Finally every new patt ern can be assigned appropriate membership value for each category. The proposed algorithm has been tested using a set of historical flight data from a group of engines. Exp erimental results have shown that this approach is effective in classifying turbine blade fatigue modes and agrees with the manual inspection findings. ---	Title: A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems Section 1: Introduction Description 1: Write an introductory section that outlines the paper’s intent, general background, and organization. Section 2: Engine Overview Description 2: Provide a detailed explanation of the turbofan engine components, their function, and their operational relevance. Section 3: Control Description 3: Discuss traditional and modern control techniques for aircraft engines, including specific applications and approaches beyond current practices. Section 4: Diagnostics Description 4: Explore various systems and approaches for on-wing health monitoring, including sensor validation and fault detection. Section 5: Intelligent Control Description 5: Explain modern intelligent control techniques and their potential to enhance aircraft propulsion system performance. Section 6: Life Extending Control Description 6: Describe life-extending control techniques that minimize component damage while maintaining acceptable engine response. Section 7: Adaptive Control Description 7: Discuss adaptive control techniques that adjust controller gains and schedules based on operating conditions. Section 8: Multivariable Control Description 8: Review multivariable control schemes emphasizing the optimization of system performance through state space representation. Section 9: Performance Seeking Control Description 9: Explain the concept and implementation of performance seeking control, aiming at optimizing engine operation based on current conditions. Section 10: Model Predictive Control Description 10: Describe model predictive control (MPC), an open loop control scheme that uses real-time models for optimization. Section 11: Gas Path Performance Diagnostics Description 11: Outline the techniques for on-line monitoring of gas path components and the challenges in estimating health parameters. Section 12: Vibration Diagnostics Description 12: Discuss the role of vibration diagnostics in assessing structural health and future advancements in high frequency measurements. Section 13: Lubrication System Diagnostics Description 13: Review the techniques used for monitoring lubrication systems and identifying part wear through contaminants. Section 14: Control System Integrity Assurance Description 14: Explore methods for ensuring the integrity of control systems through various diagnostic sensors and validation techniques. Section 15: Diagnostic Sensors Description 15: Discuss the types of sensors employed for propulsion diagnostics and the challenges associated with integrating new sensors. Section 16: Data/Information Fusion Description 16: Explain the application of data fusion techniques to integrate data from multiple sources for enhanced engine health management. Section 17: Prognostics Description 17: Describe prognostic methods for estimating the remaining useful life of engine components based on operating conditions. Section 18: Integration Description 18: Discuss the integration of various algorithms and systems into a cohesive engine health management system. Section 19: Integrated Fault Detection, Isolation, and Accommodation Logic Description 19: Elaborate on FDIA logic embedded within electronic engine controllers to detect and mitigate faults in real-time. Section 20: MPC for Integrated Control and Diagnostics Description 20: Explain how MPC can integrate control and diagnostics by accounting for faults within its optimization framework. Section 21: Engine Health Management, Support Engineering, Maintenance and Logistics Integration Description 21: Discuss the potential of automated engine health management to reduce maintenance effort and improve logistical processes. Section 22: Final Comments Description 22: Conclude the paper with final remarks on the potential contributions of intelligent control and health management technologies to future engines.
Transfer in Reinforcement Learning: a Framework and a Survey	12	--- paper_title: Using Options for Knowledge Transfer in Reinforcement Learning paper_content: One of the original motivations for the use of temporally extended actions, or options, in reinforcement learning was to enable t he transfer of learned value functions or policies to new problems. Many experimenters have used options to speed learning on single problems, but options have not been studied in depth as a tool for transfer. In this paper we introduce a formal model of a learning problem as a distribution of Markov Decision Problems (MDPs). Each MDP represents a task the agent will have to solve. Our model can also be viewed as a partially observable Markov decision problem (POMDP), with a special structure that we describe. We study two learning algorithms, one which keeps a single value function that generalizes across tasks, and an increm ental POMDPinspired method maintaining separate value functions for each task. We evaluate the learning algorithms on an extension of the Mountain Car domain, in terms of both learning speed and asymptotic performance. Empirically, we find that temporally extended options can fa cilitate transfer for both algorithms. In our domain, the single value func tion algorithm has much better learning speed because it generalizes its ex perience more broadly across tasks. We also observe that different sets of options can achieve tradeoffs of learning speed versus asymptotic perf ormance. --- paper_title: Schema induction and analogical transfer paper_content: An analysis of the process of analogical thinking predicts that analogies will be noticed on the basis of semantic retrieval cues and that the induction of a general schema from concrete analogs will facilitate analogical transfer. These predictions were tested in experiments in which subjects first read one or more stories illustrating problems and their solutions and then attempted to solve a disparate but analogous transfer problem. The studies in Part I attempted to foster the abstraction of a problem schema from a single story analog by means of summarization instructions, a verbal statement of the underlying principle, or a diagrammatic representation of it. None of these devices achieved a notable degree of sucess. In contrast, the experiments in Part II demonstrated that if two prior analogs were given, subjects often derived a problem schema as an incidental product of describing the similarities of the analogs. The quality of the induced schema was highly predictive of subsequent transfer performance. Furthermore, the verbal statements and diagrams that had failed to facilitate transfer from one analog proved highly beneficial when paired with two. The function of examples in learning was discussed in light of the present study. --- paper_title: Proto-transfer learning in markov decision processes using spectral methods paper_content: In this paper we introduce proto-transfer leaning, a new framework for transfer learning. We explore solutions to transfer learning within reinforcement learning through the use of spectral methods. Proto-value functions (PVFs) are basis functions computed from a spectral analysis of random walks on the state space graph. They naturally lead to the ability to transfer knowledge and representation between related tasks or domains. We investigate task transfer by using the same PVFs in Markov decision processes (MDPs) with different rewards functions. Additionally, our experiments in domain transfer explore applying the Nystrom method for interpolation of PVFs between MDPs of different sizes. 1. Problem Statement The aim of transfer learning is to reuse behavior by using the knowledge learned about one domain or task to accelerate learning in a related domain or task. In this paper we explore solutions to transfer learning within reinforcement learning (Sutton & Barto, 1998) through spectral methods. The new framework of proto-transfer learning transfers representations from one domain to another. This transfer entails the reuse of eigenvectors learned from one graph on another. We explore how to transfer knowledge learned on the source graph to a similar graph by modifying the eigenvectors of the Laplacian of the source domain to be reused for the target domain. Proto-value functions (PVFs) are a natural abstraction since they condense a domain by automatically learning an embedding of the Appearing in the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning, Pittsburgh, PA, 2006. Copyright 2006 by the author(s)/owner(s). state space based on its topology (Mahadevan, 2005). PVFs lead to the ability to transfer knowledge about domains and tasks, since they are constructed without taking reward into account. We define task transfer as the problem of transferring knowledge when the state space remains the same and only the reward differs. For task transfer, taskindependent basis functions, such as PVFs, can be reused from one task to the next without modification. Domain transfer refers to the more challenging problem of the state space changing. This change in state space can be a change in topology (i.e. obstacles moving to different locations) or a change in scale (i.e. a smaller or larger domain of the same shape). For domain transfer, the basis functions may need to be modified to reflect the changes in the state space. (Foster & Dayan, 2002) study the task transfer problem by applying unsupervised, mixture model, learning methods to a collection of optimal value functions of different tasks in order to decompose and extract the underlying structure. In this paper, we investigate task transfer in discrete domains by reusing PVFs in MDPs with different reward functions. For domain transfer, we apply the Nystrom extension for interpolation of PVFs between MDPs of different sizes (Mahadevan et al., 2006). Previous work has accelerated learning when transferring behaviors between tasks and domains (Taylor et al., 2005), but we transfer representation and reuse knowledge to learn comparably on a new task or domain. --- paper_title: Transfer Learning for Reinforcement Learning Domains: A Survey paper_content: The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work. --- paper_title: Learning and Transfer: A General Role for Analogical Encoding paper_content: Teaching by examples and cases is widely used to promote learning, but it varies widely in its effectiveness. The authors test an adaptation to case-based learning that facilitates abstracting problem-solving schemas from examples and using them to solve further problems: analogical encoding, or learning by drawing a comparison across examples. In 3 studies, the authors examined schema abstraction and transfer among novices learning negotiation strategies. Experiment 1 showed a benefit for analogical learning relative to no case study. Experiment 2 showed a marked advantage for comparing two cases over studying the 2 cases separately. Experiment 3 showed that increasing the degree of comparison support increased the rate of transfer in a face-to-face dynamic negotiation exercise. (PsycINFO Database Record (c) 2016 APA, all rights reserved) --- paper_title: Tree-based batch mode reinforcement learning paper_content: Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. In batch mode, it can be achieved by approximating the so-called Q-function based on a set of four-tuples (xt, ut , rt, xt+1) where xt denotes the system state at time t, ut the control action taken, rt the instantaneous reward obtained and xt+1 the successor state of the system, and by determining the control policy from this Q-function. The Q-function approximation may be obtained from the limit of a sequence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) and two newly proposed ensemble algorithms, namely extremely and totally randomized trees. We study their performances on several examples and find that the ensemble methods based on regression trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accuracy results are provided by the extremely randomized trees. --- paper_title: Reinforcement Learning: An Introduction paper_content: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning. --- paper_title: Selective transfer of neural network task knowledge paper_content: Within the context of artificial neural networks (ANN), we explore the question: How can a learning system retain and use previously learned knowledge to facilitate future learning? The research objectives are to develop a theoretical model and test a prototype system which sequentially retains ANN task knowledge and selectively uses that knowledge to bias the learning of a new task in an efficient and effective manner. ::: A theory of selective functional transfer is presented that requires a learning algorithm that employs a measure of task relatedness. ηMTL is introduced as a knowledge based inductive learning method that learns one or more secondary tasks within a back-propagation ANN as a source of inductive bias for a primary task. ηMTL employs a separate learning rate, ηk, for each secondary task output k. ηk varies as a function of a measure of relatedness, Rk, between the kth secondary task and the primary task of interest. Three categories of a priori measures of relatedness are developed for controlling inductive bias. ::: The task rehearsal method (TRM) is introduced to address the issue of sequential retention and generation of learned task knowledge. The representations of successfully learned tasks are stored within a domain knowledge repository. Virtual training examples generated from domain knowledge are rehearsed as secondary tasks in parallel with each new task using either standard multiple task learning (MTL) or ηMTL. ::: TRM using ηMTL is tested as a method of selective knowledge transfer and sequential learning on two synthetic domains and one medical diagnostic domain. Experiments show that the TRM provides an excellent method of retaining and generating accurate functional task knowledge. Hypotheses generated are compared statistically to single task learning and MTL hypotheses. We conclude that selective knowledge transfer with ηMTL develops more effective hypotheses but not necessarily with greater efficiency. The a priori measures of relatedness demonstrate significant value on certain domains of tasks but have difficulty scaling to large numbers of tasks. Several issues identified during the research indicate the importance of consolidating a representational form of domain knowledge. --- paper_title: A Survey on Transfer Learning paper_content: A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. --- paper_title: Transfer in variable-reward hierarchical reinforcement learning paper_content: Transfer learning seeks to leverage previously learned tasks to achieve faster learning in a new task. In this paper, we consider transfer learning in the context of related but distinct Reinforcement Learning (RL) problems. In particular, our RL problems are derived from Semi-Markov Decision Processes (SMDPs) that share the same transition dynamics but have different reward functions that are linear in a set of reward features. We formally define the transfer learning problem in the context of RL as learning an efficient algorithm to solve any SMDP drawn from a fixed distribution after experiencing a finite number of them. Furthermore, we introduce an online algorithm to solve this problem, Variable-Reward Reinforcement Learning (VRRL), that compactly stores the optimal value functions for several SMDPs, and uses them to optimally initialize the value function for a new SMDP. We generalize our method to a hierarchical RL setting where the different SMDPs share the same task hierarchy. Our experimental results in a simplified real-time strategy domain show that significant transfer learning occurs in both flat and hierarchical settings. Transfer is especially effective in the hierarchical setting where the overall value functions are decomposed into subtask value functions which are more widely amenable to transfer across different SMDPs. --- paper_title: Transfer learning via inter-task mappings for temporal difference learning paper_content: Temporal difference (TD) learning (Sutton and Barto, 1998) has become a popular reinforcement learning technique in recent years. TD methods, relying on function approximators to generalize learning to novel situations, have had some experimental successes and have been shown to exhibit some desirable properties in theory, but the most basic algorithms have often been found slow in practice. This empirical result has motivated the development of many methods that speed up reinforcement learning by modifying a task for the learner or helping the learner better generalize to novel situations. This article focuses on generalizing across tasks, thereby speeding up learning, via a novel form of transfer using handcoded task relationships. We compare learning on a complex task with three function approximators, a cerebellar model arithmetic computer (CMAC), an artificial neural network (ANN), and a radial basis function (RBF), and empirically demonstrate that directly transferring the action-value function can lead to a dramatic speedup in learning with all three. Using transfer via inter-task mapping (TVITM), agents are able to learn one task and then markedly reduce the time it takes to learn a more complex task. Our algorithms are fully implemented and tested in the RoboCup soccer Keepaway domain. ::: ::: This article contains and extends material published in two conference papers (Taylor and Stone, 2005; Taylor et al., 2005). --- paper_title: Autonomous transfer for reinforcement learning paper_content: Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typically must be provided either a full model of the tasks or an explicit relation mapping one task into the other. An autonomous agent may not have access to such high-level information, but would be able to analyze its experience to find similarities between tasks. In this paper we introduce Modeling Approximate State Transitions by Exploiting Regression (MASTER), a method for automatically learning a mapping from one task to another through an agent's experience. We empirically demonstrate that such learned relationships can significantly improve the speed of a reinforcement learning algorithm in a series of Mountain Car tasks. Additionally, we demonstrate that our method may also assist with the difficult problem of task selection for transfer. --- paper_title: A Survey on Transfer Learning paper_content: A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. --- paper_title: Intrinsically Motivated Reinforcement Learning paper_content: Psychologists call behavior intrinsically motivated when it is engaged in for its own sake rather than as a step toward solving a specific problem of clear practical value. But what we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated reinforcement learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy. --- paper_title: Transfer Learning for Reinforcement Learning Domains: A Survey paper_content: The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work. --- paper_title: Autonomous shaping: knowledge transfer in reinforcement learning paper_content: We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses prior experience on a sequence of tasks to learn a portable predictor that estimates intermediate rewards, resulting in accelerated learning in later tasks that are related but distinct. Such agents can be trained on a sequence of relatively easy tasks in order to develop a more informative measure of reward that can be transferred to improve performance on more difficult tasks without requiring a hand coded shaping function. We use a rod positioning task to show that this significantly improves performance even after a very brief training period. --- paper_title: Knowledge Transfer in Reinforcement Learning Agent paper_content: This manuscript is focused on transfer learning methods for reinforcement learning agents. An preview of contemporary papers in area of transfer Leaning and Knowledge transfer. We provided the background and overview of knowledge transfer methods with an emphasis on the topics of reinforcement learning. --- paper_title: Selective transfer of neural network task knowledge paper_content: Within the context of artificial neural networks (ANN), we explore the question: How can a learning system retain and use previously learned knowledge to facilitate future learning? The research objectives are to develop a theoretical model and test a prototype system which sequentially retains ANN task knowledge and selectively uses that knowledge to bias the learning of a new task in an efficient and effective manner. ::: A theory of selective functional transfer is presented that requires a learning algorithm that employs a measure of task relatedness. ηMTL is introduced as a knowledge based inductive learning method that learns one or more secondary tasks within a back-propagation ANN as a source of inductive bias for a primary task. ηMTL employs a separate learning rate, ηk, for each secondary task output k. ηk varies as a function of a measure of relatedness, Rk, between the kth secondary task and the primary task of interest. Three categories of a priori measures of relatedness are developed for controlling inductive bias. ::: The task rehearsal method (TRM) is introduced to address the issue of sequential retention and generation of learned task knowledge. The representations of successfully learned tasks are stored within a domain knowledge repository. Virtual training examples generated from domain knowledge are rehearsed as secondary tasks in parallel with each new task using either standard multiple task learning (MTL) or ηMTL. ::: TRM using ηMTL is tested as a method of selective knowledge transfer and sequential learning on two synthetic domains and one medical diagnostic domain. Experiments show that the TRM provides an excellent method of retaining and generating accurate functional task knowledge. Hypotheses generated are compared statistically to single task learning and MTL hypotheses. We conclude that selective knowledge transfer with ηMTL develops more effective hypotheses but not necessarily with greater efficiency. The a priori measures of relatedness demonstrate significant value on certain domains of tasks but have difficulty scaling to large numbers of tasks. Several issues identified during the research indicate the importance of consolidating a representational form of domain knowledge. --- paper_title: Proto-value functions: A laplacian framework for learning representation and control in markov decision processes paper_content: This paper introduces a novel spectral framework for solving Markov decision processes (MDPs) by jointly learning representations and optimal policies. The major components of the framework described in this paper include: (i) A general scheme for constructing representations or basis functions by diagonalizing symmetric diffusion operators (ii) A specific instantiation of this approach where global basis functions called proto-value functions (PVFs) are formed using the eigenvectors of the graph Laplacian on an undirected graph formed from state transitions induced by the MDP (iii) A three-phased procedure called representation policy iteration comprising of a sample collection phase, a representation learning phase that constructs basis functions from samples, and a final parameter estimation phase that determines an (approximately) optimal policy within the (linear) subspace spanned by the (current) basis functions. (iv) A specific instantiation of the RPI framework using least-squares policy iteration (LSPI) as the parameter estimation method (v) Several strategies for scaling the proposed approach to large discrete and continuous state spaces, including the Nystrom extension for out-of-sample interpolation of eigenfunctions, and the use of Kronecker sum factorization to construct compact eigenfunctions in product spaces such as factored MDPs (vi) Finally, a series of illustrative discrete and continuous control tasks, which both illustrate the concepts and provide a benchmark for evaluating the proposed approach. Many challenges remain to be addressed in scaling the proposed framework to large MDPs, and several elaboration of the proposed framework are briefly summarized at the end. --- paper_title: Regularized Policy Iteration paper_content: In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme we propose the use of non-parametric methods with regularization, providing a convenient way to control the complexity of the function approximator. We propose two novel regularized policy iteration algorithms by adding L2-regularization to two widely-used policy evaluation methods: Bellman residual minimization (BRM) and least-squares temporal difference learning (LSTD). We derive efficient implementation for our algorithms when the approximate value-functions belong to a reproducing kernel Hilbert space. We also provide finite-sample performance bounds for our algorithms and show that they are able to achieve optimal rates of convergence under the studied conditions. --- paper_title: Finite-Sample Analysis of LSTD paper_content: In this paper we consider the problem of policy evaluation in reinforcement learning, i.e., learning the value function of a fixed policy, using the least-squares temporal-difference (LSTD) learning algorithm. We report a finite-sample analysis of LSTD. We first derive a bound on the performance of the LSTD solution evaluated at the states generated by the Markov chain and used by the algorithm to learn an estimate of the value function. This result is general in the sense that no assumption is made on the existence of a stationary distribution for the Markov chain. We then derive generalization bounds in the case when the Markov chain possesses a stationary distribution and is β-mixing. --- paper_title: Transfer Learning for Reinforcement Learning Domains: A Survey paper_content: The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work. --- paper_title: Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path paper_content: In this paper we consider the problem of finding a near-optimal policy in a continuous space, discounted Markovian Decision Problem (MDP) by employing value-function-based methods when only a single trajectory of a fixed policy is available as the input. We study a policy-iteration algorithm where the iterates are obtained via empirical risk minimization with a risk function that penalizes high magnitudes of the Bellman-residual. Our main result is a finite-sample, high-probability bound on the performance of the computed policy that depends on the mixing rate of the trajectory, the capacity of the function set as measured by a novel capacity concept (the VC-crossing dimension), the approximation power of the function set and the controllability properties of the MDP. Moreover, we prove that when a linear parameterization is used the new algorithm is equivalent to Least-Squares Policy Iteration. To the best of our knowledge this is the first theoretical result for off-policy control learning over continuous state-spaces using a single trajectory. --- paper_title: Finite-Sample Analysis of Bellman Residual Minimization paper_content: We consider the Bellman residual minimization approach for solving discounted Markov decision problems, where we assume that a generative model of the dynamics and rewards is available. At each policy iteration step, an approximation of the value function for the current policy is obtained by minimizing an empirical Bellman residual defined on a set of n states drawn i.i.d. from a distribution , the immediate rewards, and the next states sampled from the model. Our main result is a generalization bound for the Bellman residual in linear approximation spaces. In particular, we prove that the empirical Bellman residual approaches the true (quadratic) Bellman residual in -norm with a rate of order O(1= √ n). This result implies that minimizing the empirical residual is indeed a sound approach for the minimization of the true Bellman residual which guarantees a good approximation of the value function for each policy. Finally, we derive performance bounds for the resulting approximate policy iteration algorithm in terms of the number of samples n and a measure of how well the function space is able to approximate the sequence of value functions. --- paper_title: Knowledge Transfer in Reinforcement Learning Agent paper_content: This manuscript is focused on transfer learning methods for reinforcement learning agents. An preview of contemporary papers in area of transfer Leaning and Knowledge transfer. We provided the background and overview of knowledge transfer methods with an emphasis on the topics of reinforcement learning. --- paper_title: Transferring instances for model-based reinforcement learning paper_content: Reinforcement learningagents typically require a significant amount of data before performing well on complex tasks. Transfer learningmethods have made progress reducing sample complexity, but they have primarily been applied to model-free learning methods, not more data-efficient model-based learning methods. This paper introduces timbrel , a novel method capable of transferring information effectively into a model-based reinforcement learning algorithm. We demonstrate that timbrel can significantly improve the sample efficiency and asymptotic performance of a model-based algorithm when learning in a continuous state space. Additionally, we conduct experiments to test the limits of timbrel 's effectiveness. --- paper_title: Transfer in variable-reward hierarchical reinforcement learning paper_content: Transfer learning seeks to leverage previously learned tasks to achieve faster learning in a new task. In this paper, we consider transfer learning in the context of related but distinct Reinforcement Learning (RL) problems. In particular, our RL problems are derived from Semi-Markov Decision Processes (SMDPs) that share the same transition dynamics but have different reward functions that are linear in a set of reward features. We formally define the transfer learning problem in the context of RL as learning an efficient algorithm to solve any SMDP drawn from a fixed distribution after experiencing a finite number of them. Furthermore, we introduce an online algorithm to solve this problem, Variable-Reward Reinforcement Learning (VRRL), that compactly stores the optimal value functions for several SMDPs, and uses them to optimally initialize the value function for a new SMDP. We generalize our method to a hierarchical RL setting where the different SMDPs share the same task hierarchy. Our experimental results in a simplified real-time strategy domain show that significant transfer learning occurs in both flat and hierarchical settings. Transfer is especially effective in the hierarchical setting where the overall value functions are decomposed into subtask value functions which are more widely amenable to transfer across different SMDPs. --- paper_title: Incremental Skill Acquisition for Self-Motivated Learning Animats paper_content: A central role in the development process of children is played by self-exploratory activities Through a playful interaction with the surrounding environment, they test their own capabilities, explore novel situations, and understand how their actions affect the world During this kind of exploration, interesting situations may be discovered By learning to reach these situations, a child incrementally develops more and more complex skills Inspired by studies from psychology, neuroscience, and machine learning, we designed SMILe (Self-Motivated Incremental Learning), a learning framework that allows artificial agents to autonomously identify and learn a set of abilities useful to face several different tasks, through an iterated three phase process: by means of a random exploration of the environment (babbling phase), the agent identifies interesting situations and generates an intrinsic motivation (motivating phase) aimed at learning how to get into these situations (skill acquisition phase) This process incrementally increases the skills of the agent, so that new interesting configurations can be experienced We present results on two gridworld environments to show how SMILe makes it possible to learn skills that enable the agent to perform well and robustly in many different tasks. --- paper_title: Near-optimal regret bounds for reinforcement learning paper_content: For undiscounted reinforcement learning in Markov decision processes (MDPs) we consider the total regret of a learning algorithm with respect to an optimal policy. In order to describe the transition structure of an MDP we propose a new parameter: An MDP has diameter D if for any pair of states s, s' there is a policy which moves from s to s' in at most D steps (on average). We present a reinforcement learning algorithm with total regret O(DS √AT) after T steps for any unknown MDP with S states, A actions per state, and diameter D. This bound holds with high probability. We also present a corresponding lower bound of Ω(√DSAT) on the total regret of any learning algorithm. --- paper_title: Using Homomorphisms to Transfer Options across Continuous Reinforcement Learning Domains paper_content: We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowledge acquired in one Markov Decision Process (MDP) to bootstrap learning in a more complex but related MDP. We build on work in model minimization in Reinforcement Learning to define relationships between state-action pairs of the two MDPs. Our main contribution in this work is to provide a way to compactly represent such mappings using relationships between state variables in the two domains. We use these functions to transfer a learned policy in the first domain into an option in the new domain, and apply intra-option learning methods to bootstrap learning in the new domain. We first evaluate our approach in the well known Blocksworld domain. We then demonstrate that our approach to transfer is viable in a complex domain with a continuous state space by evaluating it in the Robosoccer Keepaway domain. --- paper_title: Building Portable Options: Skill Transfer in Reinforcement Learning paper_content: The options framework provides methods for reinforcement learning agents to build new high-level skills. However, since options are usually learned in the same state space as the problem the agent is solving, they cannot be used in other tasks that are similar but have different state spaces. We introduce the notion of learning options in agentspace, the space generated by a feature set that is present and retains the same semantics across successive problem instances, rather than in problemspace. Agent-space options can be reused in later tasks that share the same agent-space but have different problem-spaces. We present experimental results demonstrating the use of agent-space options in building transferrable skills, and show that they perform best when used in conjunction with problem-space options. --- paper_title: Skill acquisition via transfer learning and advice taking paper_content: We describe a reinforcement learning system that transfers skills from a previously learned source task to a related target task. The system uses inductive logic programming to analyze experience in the source task, and transfers rules for when to take actions. The target task learner accepts these rules through an advice-taking algorithm, which allows learners to benefit from outside guidance that may be imperfect. Our system accepts a human-provided mapping, which specifies the similarities between the source and target tasks and may also include advice about the differences between them. Using three tasks in the RoboCup simulated soccer domain, we demonstrate that this system can speed up reinforcement learning substantially. --- paper_title: Identifying useful subgoals in reinforcement learning by local graph partitioning paper_content: We present a new subgoal-based method for automatically creating useful skills in reinforcement learning. Our method identifies subgoals by partitioning local state transition graphs---those that are constructed using only the most recent experiences of the agent. The local scope of our subgoal discovery method allows it to successfully identify the type of subgoals we seek---states that lie between two densely-connected regions of the state space while producing an algorithm with low computational cost. --- paper_title: Structure in the Space of Value Functions paper_content: Solving in an efficient manner many different optimal control tasks within the same underlying environment requires decomposing the environment into its computationally elemental fragments. We suggest how to find fragmentations using unsupervised, mixture model, learning methods on data derived from optimal value functions for multiple tasks, and show that these fragmentations are in accord with observable structure in the environments. Further, we present evidence that such fragments can be of use in a practical reinforcement learning context, by facilitating online, actor-critic learning of multiple goals MDPs. --- paper_title: Improving action selection in MDP’s via knowledge transfer paper_content: Temporal-difference reinforcement learning (RL) has been successfully applied in several domains with large state sets. Large action sets, however, have received considerably less attention. This paper demonstrates the use of knowledge transfer between related tasks to accelerate learning with large action sets. We introduce action transfer, a technique that extracts the actions from the (near-)optimal solution to the first task and uses them in place of the full action set when learning any subsequent tasks. When optimal actions make up a small fraction of the domain's action set, action transfer can substantially reduce the number of actions and thus the complexity of the problem. However, action transfer between dissimilar tasks can be detrimental. To address this difficulty, we contribute randomized task perturbation (RTP), an enhancement to action transfer that makes it robust to unrepresentative source tasks. We motivate RTP action transfer with a detailed theoretical analysis featuring a formalism of related tasks and a bound on the suboptimality of action transfer. The empirical results in this paper show the potential of RTP action transfer to substantially expand the applicability of RL to problems with large action sets. --- paper_title: Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning paper_content: Learning, planning, and representing knowledge at multiple levels of temporal ab- straction are key, longstanding challenges for AI. In this paper we consider how these challenges can be addressed within the mathematical framework of reinforce- ment learning and Markov decision processes (MDPs). We extend the usual notion of action in this framework to include options—closed-loop policies for taking ac- tion over a period of time. Examples of options include picking up an object, going to lunch, and traveling to a distant city, as well as primitive actions such as mus- cle twitches and joint torques. Overall, we show that options enable temporally abstract knowledge and action to be included in the reinforcement learning frame- work in a natural and general way. In particular, we show that options may be used interchangeably with primitive actions in planning methods such as dynamic pro- gramming and in learning methods such as Q-learning. Formally, a set of options defined over an MDP constitutes a semi-Markov decision process (SMDP), and the theory of SMDPs provides the foundation for the theory of options. However, the most interesting issues concern the interplay between the underlying MDP and the SMDP and are thus beyond SMDP theory. We present results for three such cases: 1) we show that the results of planning with options can be used during execution to interrupt options and thereby perform even better than planned, 2) we introduce new intra-option methods that are able to learn about an option from fragments of its execution, and 3) we propose a notion of subgoal that can be used to improve the options themselves. All of these results have precursors in the existing literature; the contribution of this paper is to establish them in a simpler and more general setting with fewer changes to the existing reinforcement learning framework. In particular, we show that these results can be obtained without committing to (or ruling out) any particular approach to state abstraction, hierarchy, function approximation, or the macro-utility problem. --- paper_title: Transfer of Experience Between Reinforcement Learning Environments with Progressive Difficulty paper_content: This paper describes an extension to reinforcement learning (RL), in which a standard RL algorithm is augmented with a mechanism for transferring experience gained in one problem to new but related problems. In this approach, named Progressive RL, an agent acquires experience of operating in a simple environment through experimentation, and then engages in a period of introspection, during which it rationalises the experience gained and formulates symbolic knowledge describing how to behave in that simple environment. When subsequently experimenting in a more complex but related environment, it is guided by this knowledge until it gains direct experience. A test domain with 15 maze environments, arranged in order of difficulty, is described. A range of experiments in this domain are presented, that demonstrate the benefit of Progressive RL relative to a basic RL approach in which each puzzle is solved from scratch. The experiments also analyse the knowledge formed during introspection, illustrate how domain knowledge may be incorporated, and show that Progressive Reinforcement Learning may be used to solve complex puzzles more quickly. --- paper_title: Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks paper_content: This paper discusses a system that accelerates reinforcement learning by using transfer from related tasks. Without such transfer, even if two tasks are very similar at some abstract level, an extensive re-learning effort is required. The system achieves much of its power by transferring parts of previously learned solutions rather than a single complete solution. The system exploits strong features in the multi-dimensional function produced by reinforcement learning in solving a particular task. These features are stable and easy to recognize early in the learning process. They generate a partitioning of the state space and thus the function. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. Experiments demonstrate that function composition often produces more than an order of magnitude increase in learning rate compared to a basic reinforcement learning algorithm. --- paper_title: Convex multi-task feature learning paper_content: We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the well-known single-task 1-norm regularization. It is based on a novel non-convex regularizer which controls the number of learned features common across the tasks. We prove that the method is equivalent to solving a convex optimization problem for which there is an iterative algorithm which converges to an optimal solution. The algorithm has a simple interpretation: it alternately performs a supervised and an unsupervised step, where in the former step it learns task-specific functions and in the latter step it learns common-across-tasks sparse representations for these functions. We also provide an extension of the algorithm which learns sparse nonlinear representations using kernels. We report experiments on simulated and real data sets which demonstrate that the proposed method can both improve the performance relative to learning each task independently and lead to a few learned features common across related tasks. Our algorithm can also be used, as a special case, to simply select--not learn--a few common variables across the tasks. --- paper_title: Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning paper_content: We present the Q-Cut algorithm, a graph theoretic approach for automatic detection of sub-goals in a dynamic environment, which is used for acceleration of the Q-Learning algorithm. The learning agent creates an on-line map of the process history, and uses an efficient Max-Flow/Min-Cut algorithm for identifying bottlenecks. The policies for reaching bottlenecks are separately learned and added to the model in a form of options (macro-actions). We then extend the basic Q-Cut algorithm to the Segmented Q-Cut algorithm, which uses previously identified bottlenecks for state space partitioning, necessary for finding additional bottlenecks in complex environments. Experiments show significant performance improvements, particulary in the initial learning phase. --- paper_title: Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density paper_content: This paper presents a method by which a reinforcement learning agent can automatically discover certain types of subgoals online. By creating useful new subgoals while learning, the agent is able to accelerate learning on the current task and to transfer its expertise to other, related tasks through the reuse of its ability to attain subgoals. The agent discovers subgoals based on commonalities across multiple paths to a solution. We cast the task of finding these commonalities as a multiple-instance learning problem and use the concept of diverse density to find solutions. We illustrate this approach using several gridworld tasks. --- paper_title: Effective Control Knowledge Transfer through Learning Skill and Representation Hierarchies paper_content: Learning capabilities of computer systems still lag far behind biological systems. One of the reasons can be seen in the inefficient re-use of control knowledge acquired over the lifetime of the artificial learning system. To address this deficiency, this paper presents a learning architecture which transfers control knowledge in the form of behavioral skills and corresponding representation concepts from one task to subsequent learning tasks. The presented system uses this knowledge to construct a more compact state space representation for learning while assuring bounded optimality of the learned task policy by utilizing a representation hierarchy. Experimental results show that the presented method can significantly outperform learning on a flat state space representation and the MAXQ method for hierarchical reinforcement learning. --- paper_title: Proto-value functions: A laplacian framework for learning representation and control in markov decision processes paper_content: This paper introduces a novel spectral framework for solving Markov decision processes (MDPs) by jointly learning representations and optimal policies. The major components of the framework described in this paper include: (i) A general scheme for constructing representations or basis functions by diagonalizing symmetric diffusion operators (ii) A specific instantiation of this approach where global basis functions called proto-value functions (PVFs) are formed using the eigenvectors of the graph Laplacian on an undirected graph formed from state transitions induced by the MDP (iii) A three-phased procedure called representation policy iteration comprising of a sample collection phase, a representation learning phase that constructs basis functions from samples, and a final parameter estimation phase that determines an (approximately) optimal policy within the (linear) subspace spanned by the (current) basis functions. (iv) A specific instantiation of the RPI framework using least-squares policy iteration (LSPI) as the parameter estimation method (v) Several strategies for scaling the proposed approach to large discrete and continuous state spaces, including the Nystrom extension for out-of-sample interpolation of eigenfunctions, and the use of Kronecker sum factorization to construct compact eigenfunctions in product spaces such as factored MDPs (vi) Finally, a series of illustrative discrete and continuous control tasks, which both illustrate the concepts and provide a benchmark for evaluating the proposed approach. Many challenges remain to be addressed in scaling the proposed framework to large MDPs, and several elaboration of the proposed framework are briefly summarized at the end. --- paper_title: Relativized Options: Choosing the Right Transformation paper_content: Relativized options combine model minimization methods and a hierarchical reinforcement learning framework to derive compact reduced representations of a related family of tasks. Relativized options are defined without an absolute frame of reference, and an option's policy is transformed suitably based on the circumstances under which the option is invoked. In earlier work we addressed the issue of learning the option policy online. In this article we develop an algorithm for choosing, from among a set of candidate transformations, the right transformation for each member of the family of tasks. --- paper_title: Transfer of samples in batch reinforcement learning paper_content: The main objective of transfer in reinforcement learning is to reduce the complexity of learning the solution of a target task by effectively reusing the knowledge retained from solving a set of source tasks. In this paper, we introduce a novel algorithm that transfers samples (i.e., tuples 〈s, a, s', r〉) from source to target tasks. Under the assumption that tasks have similar transition models and reward functions, we propose a method to select samples from the source tasks that are mostly similar to the target task, and, then, to use them as input for batch reinforcement-learning algorithms. As a result, the number of samples an agent needs to collect from the target task to learn its solution is reduced. We empirically show that, following the proposed approach, the transfer of samples is effective in reducing the learning complexity, even when some source tasks are significantly different from the target task. --- paper_title: Multitask reinforcement learning on the distribution of MDPs paper_content: In this paper we address a new problem in reinforcement learning. Here we consider an agent that faces multiple learning tasks within its lifetime. The agent's objective is to maximize its total reward in the lifetime as well as a conventional return in each task. To realize this, it has to be endowed an important ability to keep its past learning experiences and utilize them for improving future learning performance. This time we try to phrase this problem formally. The central idea is to introduce an environmental class, BV-MDPs that is defined with the distribution of MDPs. As an approach to exploiting past learning experiences, we focus on statistics (mean and deviation) about the agent's value tables. The mean can be used as initial values of the table when a new task is presented. The deviation can be viewed as measuring reliability of the mean, and we utilize it in calculating priority of simulated backups. We conduct experiments in computer simulation to evaluate the effectiveness. --- paper_title: Reinforcement learning with Gaussian processes paper_content: Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framework by addressing two pressing issues, which were not adequately treated in the original GPTD paper (Engel et al., 2003). The first is the issue of stochasticity in the state transitions, and the second is concerned with action selection and policy improvement. We present a new generative model for the value function, deduced from its relation with the discounted return. We derive a corresponding on-line algorithm for learning the posterior moments of the value Gaussian process. We also present a SARSA based extension of GPTD, termed GPSARSA, that allows the selection of actions and the gradual improvement of policies without requiring a world-model. --- paper_title: Using advice to transfer knowledge acquired in one reinforcement learning task to another paper_content: We present a method for transferring knowledge learned in one task to a related task. Our problem solvers employ reinforcement learning to acquire a model for one task. We then transform that learned model into advice for a new task. A human teacher provides a mapping from the old task to the new task to guide this knowledge transfer. Advice is incorporated into our problem solver using a knowledge-based support vector regression method that we previously developed. This advice-taking approach allows the problem solver to refine or even discard the transferred knowledge based on its subsequent experiences. We empirically demonstrate the effectiveness of our approach with two games from the RoboCup soccer simulator: KeepAway and BreakAway. Our results demonstrate that a problem solver learning to play BreakAway using advice extracted from KeepAway outperforms a problem solver learning without the benefit of such advice. --- paper_title: Transferring instances for model-based reinforcement learning paper_content: Reinforcement learningagents typically require a significant amount of data before performing well on complex tasks. Transfer learningmethods have made progress reducing sample complexity, but they have primarily been applied to model-free learning methods, not more data-efficient model-based learning methods. This paper introduces timbrel , a novel method capable of transferring information effectively into a model-based reinforcement learning algorithm. We demonstrate that timbrel can significantly improve the sample efficiency and asymptotic performance of a model-based algorithm when learning in a continuous state space. Additionally, we conduct experiments to test the limits of timbrel 's effectiveness. --- paper_title: Finite-Sample Analysis of LSTD paper_content: In this paper we consider the problem of policy evaluation in reinforcement learning, i.e., learning the value function of a fixed policy, using the least-squares temporal-difference (LSTD) learning algorithm. We report a finite-sample analysis of LSTD. We first derive a bound on the performance of the LSTD solution evaluated at the states generated by the Markov chain and used by the algorithm to learn an estimate of the value function. This result is general in the sense that no assumption is made on the existence of a stationary distribution for the Markov chain. We then derive generalization bounds in the case when the Markov chain possesses a stationary distribution and is β-mixing. --- paper_title: Transfer via inter-task mappings in policy search reinforcement learning paper_content: The ambitious goal of transfer learning is to accelerate learning on a target task after training on a different, but related, source task. While many past transfer methods have focused on transferring value-functions, this paper presents a method for transferring policies across tasks with different state and action spaces. In particular, this paper utilizes transfer via inter-task mappings for policy search methods (TVITM-PS) to construct a transfer functional that translates a population of neural network policies trained via policy search from a source task to a target task. Empirical results in robot soccer Keepaway and Server Job Scheduling show that TVITM-PS can markedly reduce learning time when full inter-task mappings are available. The results also demonstrate that TVITMPS still succeeds when given only incomplete inter-task mappings. Furthermore, we present a novel method for learning such mappings when they are not available, and give results showing they perform comparably to hand-coded mappings. --- paper_title: Multi-task reinforcement learning: a hierarchical Bayesian approach paper_content: We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknown distribution. We model the distribution over MDPs using a hierarchical Bayesian infinite mixture model. For each novel MDP, we use the previously learned distribution as an informed prior for modelbased Bayesian reinforcement learning. The hierarchical Bayesian framework provides a strong prior that allows us to rapidly infer the characteristics of new environments based on previous environments, while the use of a nonparametric model allows us to quickly adapt to environments we have not encountered before. In addition, the use of infinite mixtures allows for the model to automatically learn the number of underlying MDP components. We evaluate our approach and show that it leads to significant speedups in convergence to an optimal policy after observing only a small number of tasks. --- paper_title: Transfer in variable-reward hierarchical reinforcement learning paper_content: Transfer learning seeks to leverage previously learned tasks to achieve faster learning in a new task. In this paper, we consider transfer learning in the context of related but distinct Reinforcement Learning (RL) problems. In particular, our RL problems are derived from Semi-Markov Decision Processes (SMDPs) that share the same transition dynamics but have different reward functions that are linear in a set of reward features. We formally define the transfer learning problem in the context of RL as learning an efficient algorithm to solve any SMDP drawn from a fixed distribution after experiencing a finite number of them. Furthermore, we introduce an online algorithm to solve this problem, Variable-Reward Reinforcement Learning (VRRL), that compactly stores the optimal value functions for several SMDPs, and uses them to optimally initialize the value function for a new SMDP. We generalize our method to a hierarchical RL setting where the different SMDPs share the same task hierarchy. Our experimental results in a simplified real-time strategy domain show that significant transfer learning occurs in both flat and hierarchical settings. Transfer is especially effective in the hierarchical setting where the overall value functions are decomposed into subtask value functions which are more widely amenable to transfer across different SMDPs. --- paper_title: Transfer Learning for Reinforcement Learning Domains: A Survey paper_content: The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work. --- paper_title: An Experts Algorithm for Transfer Learning paper_content: A long-lived agent continually faces new tasks in its environment. Such an agent may be able to use knowledge learned in solving earlier tasks to produce candidate policies for its current task. There may, however, be multiple reasonable policies suggested by prior experience, and the agent must choose between them potentially without any a priori knowledge about their applicability to its current situation. We present an "experts" algorithm for efficiently choosing amongst candidate policies in solving an unknown Markov decision process task. We conclude with the results of experiments on two domains in which we generate candidate policies from solutions to related tasks and use our experts algorithm to choose amongst them. --- paper_title: Autonomous transfer for reinforcement learning paper_content: Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typically must be provided either a full model of the tasks or an explicit relation mapping one task into the other. An autonomous agent may not have access to such high-level information, but would be able to analyze its experience to find similarities between tasks. In this paper we introduce Modeling Approximate State Transitions by Exploiting Regression (MASTER), a method for automatically learning a mapping from one task to another through an agent's experience. We empirically demonstrate that such learned relationships can significantly improve the speed of a reinforcement learning algorithm in a series of Mountain Car tasks. Additionally, we demonstrate that our method may also assist with the difficult problem of task selection for transfer. --- paper_title: Transferring instances for model-based reinforcement learning paper_content: Reinforcement learningagents typically require a significant amount of data before performing well on complex tasks. Transfer learningmethods have made progress reducing sample complexity, but they have primarily been applied to model-free learning methods, not more data-efficient model-based learning methods. This paper introduces timbrel , a novel method capable of transferring information effectively into a model-based reinforcement learning algorithm. We demonstrate that timbrel can significantly improve the sample efficiency and asymptotic performance of a model-based algorithm when learning in a continuous state space. Additionally, we conduct experiments to test the limits of timbrel 's effectiveness. --- paper_title: Transfer of samples in batch reinforcement learning paper_content: The main objective of transfer in reinforcement learning is to reduce the complexity of learning the solution of a target task by effectively reusing the knowledge retained from solving a set of source tasks. In this paper, we introduce a novel algorithm that transfers samples (i.e., tuples 〈s, a, s', r〉) from source to target tasks. Under the assumption that tasks have similar transition models and reward functions, we propose a method to select samples from the source tasks that are mostly similar to the target task, and, then, to use them as input for batch reinforcement-learning algorithms. As a result, the number of samples an agent needs to collect from the target task to learn its solution is reduced. We empirically show that, following the proposed approach, the transfer of samples is effective in reducing the learning complexity, even when some source tasks are significantly different from the target task. --- paper_title: Transfer learning via inter-task mappings for temporal difference learning paper_content: Temporal difference (TD) learning (Sutton and Barto, 1998) has become a popular reinforcement learning technique in recent years. TD methods, relying on function approximators to generalize learning to novel situations, have had some experimental successes and have been shown to exhibit some desirable properties in theory, but the most basic algorithms have often been found slow in practice. This empirical result has motivated the development of many methods that speed up reinforcement learning by modifying a task for the learner or helping the learner better generalize to novel situations. This article focuses on generalizing across tasks, thereby speeding up learning, via a novel form of transfer using handcoded task relationships. We compare learning on a complex task with three function approximators, a cerebellar model arithmetic computer (CMAC), an artificial neural network (ANN), and a radial basis function (RBF), and empirically demonstrate that directly transferring the action-value function can lead to a dramatic speedup in learning with all three. Using transfer via inter-task mapping (TVITM), agents are able to learn one task and then markedly reduce the time it takes to learn a more complex task. Our algorithms are fully implemented and tested in the RoboCup soccer Keepaway domain. ::: ::: This article contains and extends material published in two conference papers (Taylor and Stone, 2005; Taylor et al., 2005). --- paper_title: Regularized Policy Iteration paper_content: In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme we propose the use of non-parametric methods with regularization, providing a convenient way to control the complexity of the function approximator. We propose two novel regularized policy iteration algorithms by adding L2-regularization to two widely-used policy evaluation methods: Bellman residual minimization (BRM) and least-squares temporal difference learning (LSTD). We derive efficient implementation for our algorithms when the approximate value-functions belong to a reproducing kernel Hilbert space. We also provide finite-sample performance bounds for our algorithms and show that they are able to achieve optimal rates of convergence under the studied conditions. --- paper_title: Finite-Sample Analysis of LSTD paper_content: In this paper we consider the problem of policy evaluation in reinforcement learning, i.e., learning the value function of a fixed policy, using the least-squares temporal-difference (LSTD) learning algorithm. We report a finite-sample analysis of LSTD. We first derive a bound on the performance of the LSTD solution evaluated at the states generated by the Markov chain and used by the algorithm to learn an estimate of the value function. This result is general in the sense that no assumption is made on the existence of a stationary distribution for the Markov chain. We then derive generalization bounds in the case when the Markov chain possesses a stationary distribution and is β-mixing. --- paper_title: Learning from multiple sources paper_content: We consider the problem of learning accurate models from multiple sources of "nearby" data. Given distinct samples from multiple data sources and estimates of the dissimilarities between these sources, we provide a general theory of which samples should be used to learn models for each source. This theory is applicable in a broad decision-theoretic learning framework, and yields general results for classification and regression. A key component of our approach is the development of approximate triangle inequalities for expected loss, which may be of independent interest. We discuss the related problem of learning parameters of a distribution from multiple data sources. Finally, we illustrate our theory through a series of synthetic simulations. --- paper_title: REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs paper_content: We provide an algorithm that achieves the optimal regret rate in an unknown weakly communicating Markov Decision Process (MDP). The algorithm proceeds in episodes where, in each episode, it picks a policy using regularization based on the span of the optimal bias vector. For an MDP with S states and A actions whose optimal bias vector has span bounded by H, we show a regret bound of O(HS√AT). We also relate the span to various diameter-like quantities associated with the MDP, demonstrating how our results improve on previous regret bounds. --- paper_title: Transfer Learning for Reinforcement Learning Domains: A Survey paper_content: The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work. --- paper_title: Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path paper_content: In this paper we consider the problem of finding a near-optimal policy in a continuous space, discounted Markovian Decision Problem (MDP) by employing value-function-based methods when only a single trajectory of a fixed policy is available as the input. We study a policy-iteration algorithm where the iterates are obtained via empirical risk minimization with a risk function that penalizes high magnitudes of the Bellman-residual. Our main result is a finite-sample, high-probability bound on the performance of the computed policy that depends on the mixing rate of the trajectory, the capacity of the function set as measured by a novel capacity concept (the VC-crossing dimension), the approximation power of the function set and the controllability properties of the MDP. Moreover, we prove that when a linear parameterization is used the new algorithm is equivalent to Least-Squares Policy Iteration. To the best of our knowledge this is the first theoretical result for off-policy control learning over continuous state-spaces using a single trajectory. --- paper_title: Finite-Sample Analysis of Bellman Residual Minimization paper_content: We consider the Bellman residual minimization approach for solving discounted Markov decision problems, where we assume that a generative model of the dynamics and rewards is available. At each policy iteration step, an approximation of the value function for the current policy is obtained by minimizing an empirical Bellman residual defined on a set of n states drawn i.i.d. from a distribution , the immediate rewards, and the next states sampled from the model. Our main result is a generalization bound for the Bellman residual in linear approximation spaces. In particular, we prove that the empirical Bellman residual approaches the true (quadratic) Bellman residual in -norm with a rate of order O(1= √ n). This result implies that minimizing the empirical residual is indeed a sound approach for the minimization of the true Bellman residual which guarantees a good approximation of the value function for each policy. Finally, we derive performance bounds for the resulting approximate policy iteration algorithm in terms of the number of samples n and a measure of how well the function space is able to approximate the sequence of value functions. ---	<format> Title: Transfer in Reinforcement Learning: a Framework and a Survey Section 1: Introduction Description 1: Summarize the main concepts of transfer learning in reinforcement learning, its origins, objectives, and significance in machine learning. Section 2: A Framework and a Taxonomy for Transfer in Reinforcement Learning Description 2: Introduce a formal framework and propose a taxonomy to classify transfer learning approaches in reinforcement learning. Section 3: Transfer Framework Description 3: Define the general transfer framework, including formal definitions and symbols, and illustrate how the framework applies to reinforcement learning. Section 4: Taxonomy Description 4: Propose a taxonomy categorizing transfer learning approaches based on the setting, transferred knowledge, and objectives. Section 5: The Settings Description 5: Discuss three different categories of transfer settings focusing on domain characteristics and transfer scenarios. Section 6: The Knowledge Description 6: Explain the different types of knowledge that can be transferred and how each approach uses this knowledge to improve learning performance. Section 7: The Objectives Description 7: Outline the performance evaluation metrics and objectives of transfer learning in reinforcement learning. Section 8: The Survey Description 8: Organize the surveyed transfer approaches based on the framework and taxonomy, and classify existing algorithms according to their settings, types of knowledge transfer, and performance objectives. Section 9: Methods for Transfer from Source to Target with a Fixed State-Action Space Description 9: Review approaches in the setting where transfer occurs from a single source task to a target task with a fixed state-action space. Section 10: Methods for Transfer across Tasks with a Fixed State-Action Space Description 10: Review transfer approaches that handle multiple source tasks sharing the same state-action space and discuss methods for merging and selectively transferring knowledge to avoid negative transfer. Section 11: Methods for Transfer from Source to Target Tasks with a Different State-Action Spaces Description 11: Review transfer approaches in the most general setting where source and target tasks differ in their state-action spaces, and discuss methods for mapping state-action variables. Section 12: Conclusions and Open Questions Description 12: Summarize the key findings, propose potential lines of research, and discuss open questions relevant to the advancement of transfer learning in reinforcement learning. </format>
A survey on reliability in distributed systems	12	--- paper_title: Reliability in grid computing systems paper_content: In recent years, grid technology has emerged as an important tool for solving compute-intensive problems within the scientific community and in industry. To further the development and adoption of this technology, researchers and practitioners from different disciplines have collaborated to produce standard specifications for implementing large-scale, interoperable grid systems. The focus of this activity has been the Open Grid Forum, but other standards development organizations have also produced specifications that are used in grid systems. To date, these specifications have provided the basis for a growing number of operational grid systems used in scientific and industrial applications. However, if the growth of grid technology is to continue, it will be important that grid systems also provide high reliability. In particular, it will be critical to ensure that grid systems are reliable as they continue to grow in scale, exhibit greater dynamism, and become more heterogeneous in composition. Ensuring grid system reliability in turn requires that the specifications used to build these systems fully support reliable grid services. This study surveys work on grid reliability that has been done in recent years and reviews progress made toward achieving these goals. The survey identifies important issues and problems that researchers are working to overcome in order to develop reliability methods for large-scale, heterogeneous, dynamic environments. The survey also illuminates reliability issues relating to standard specifications used in grid systems, identifying existing specifications that may need to be evolved and areas where new specifications are needed to better support the reliability. Published in 2009 by John Wiley & Sons, Ltd. ::: ::: This article is a U.S. Government work and is in the public domain in the U.S.A. --- paper_title: Survey of reliability and availability prediction methods from the viewpoint of software architecture paper_content: Many future software systems will be distributed across a network, extensively providing different kinds of services for their users. These systems must be highly reliable and provide services when required. Reliability and availability must be engineered into software from the onset of its development, and potential problems must be detected in the early stages, when it is easier and less expensive to implement modifications. The software architecture design phase is the first stage of software development in which it is possible to evaluate how well the quality requirements are being met. For this reason, a method is needed for analyzing software architecture with respect to reliability and availability. In this paper, we define a framework for comparing reliability and availability analysis methods from the viewpoint of software architecture. Our contribution is the comparison of the existing analysis methods and techniques that can be used for reliability and availability prediction at the architectural level. The objective is to discover which methods are suitable for the reliability and availability prediction of today’s complex systems, what are the shortcomings of the methods, and which research activities need to be conducted in order to overcome these identified shortcomings. The comparison reveals that none of the existing methods entirely fulfill the requirements that are defined in the framework. The comparison framework also defines the characteristics required of new reliability and availability analysis methods. Additionally, the framework is a valuable tool for selecting the best suitable method for architecture analysis. Furthermore, the framework can be extended and used for other evaluation methods as well. --- paper_title: Software Reliability Engineering: A Roadmap paper_content: Software reliability engineering is focused on engineering techniques for developing and maintaining software systems whose reliability can be quantitatively evaluated. In order to estimate as well as to predict the reliability of software systems, failure data need to be properly measured by various means during software development and operational phases. Moreover, credible software reliability models are required to track underlying software failure processes for accurate reliability analysis and forecasting. Although software reliability has remained an active research subject over the past 35 years, challenges and open questions still exist. In particular, vital future goals include the development of new software reliability engineering paradigms that take software architectures, testing techniques, and software failure manifestation mechanisms into consideration. In this paper, we review the history of software reliability engineering, the current trends and existing problems, and specific difficulties. Possible future directions and promising research subjects in software reliability engineering are also addressed. --- paper_title: Multimedia Object Placement for Transparent Data Replication paper_content: Transparent data replication is a promising technique for improving the system performance of a large distributed network. Transcoding is an important technology which adapts the same multimedia object to diverse mobile appliances; thus, users' requests for a specified version of a multimedia object could be served by a more detailed version cached according to transcoding. Therefore, it is particularly of theoretical and practical necessity to determine the proper version to be cached at each node such that the specified objective is achieved. In this paper, we address the problem of multimedia object placement for transparent data replication. The performance objective is to minimize the total access cost by considering both transmission cost and transcoding cost. We present optimal solutions for different cases for this problem. The performance of the proposed solutions is evaluated with a set of carefully designed simulation experiments for various performance metrics over a wide range of system parameters. The simulation results show that our solution consistently and significantly outperforms comparison solutions in terms of all the performance metrics considered --- paper_title: Collaborative reliability prediction of service-oriented systems paper_content: Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative reliability prediction approach obtains better reliability prediction accuracy than other approaches. --- paper_title: On distributed computing systems reliability analysis under program execution constraints paper_content: Presents an algorithm for computing the reliability of distributed computing systems (DCS). The algorithm, called the Fast Reliability Evaluation Algorithm, is based on the factoring theorem employing several reliability preserving reduction techniques. The effect of file distributions, program distributions, and various topologies on reliability of the DCS is studied in detail using the proposed algorithm. Compared with existing algorithms on various network topologies, file distributions, and program distributions, the proposed algorithm is much more economical in both time and space. To compute the distributed program reliability, the ARPA network is studied to illustrate the feasibility of the proposed algorithm. > --- paper_title: Quality Prediction of Service Compositions through Probabilistic Model Checking paper_content: The problem of composing services to deliver integrated business solutions has been widely studied in the last years. Besides addressing functional requirements, services compositions should also provide agreed service levels. Our goal is to support model-based analysis of service compositions, with a focus on the assessment of non-functional quality attributes, namely performance and reliability. We propose a model-driven approach, which automatically transforms a design model of service composition into an analysis model, which then feeds a probabilistic model checker for quality prediction. To bring this approach to fruition, we developed a prototype tool called ATOP , and we demonstrate its use on a simple case study. --- paper_title: Real-time distributed program reliability analysis paper_content: Distributed program reliability has been proposed as a reliability index for distributed computing systems to analyze the probability of the successful execution of a program, task, or mission in the system. However, current reliability models proposed for distributed program reliability evaluation do not capture the effects of real-time constraints. We propose an approach to the reliability analysis of distributed programs that addresses real-time constraints. Our approach is based on a model for evaluating transmission time, which allow us to find the time needed to complete execution of the program, task, or mission under evaluation. With information on time-constraints, the corresponding Markov state space can then be defined for reliability computation. To speed up the evaluation process and reduce the size of the Markov state space, several dynamic reliability-preserving reductions are developed. A simple distributed real-time system is used as an example to illustrate the feasibility and uniqueness of the proposed approach. > --- paper_title: Collaborative reliability prediction of service-oriented systems paper_content: Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative reliability prediction approach obtains better reliability prediction accuracy than other approaches. --- paper_title: Architecture-based reliability prediction for service-oriented computing paper_content: In service-oriented computing, services are dynamically built as an assembly of pre-existing, independently developed, network accessible services. Hence, predicting as much as possible automatically their dependability is important to appropriately drive the selection and assembly of services, in order to get some required dependability level. We present an approach to the reliability prediction of such services, based on the partial information published with each service, and that lends itself to automatization. The proposed methodology exploits ideas from the Software Architecture- and Component-based approaches to software design. --- paper_title: On distributed computing systems reliability analysis under program execution constraints paper_content: Presents an algorithm for computing the reliability of distributed computing systems (DCS). The algorithm, called the Fast Reliability Evaluation Algorithm, is based on the factoring theorem employing several reliability preserving reduction techniques. The effect of file distributions, program distributions, and various topologies on reliability of the DCS is studied in detail using the proposed algorithm. Compared with existing algorithms on various network topologies, file distributions, and program distributions, the proposed algorithm is much more economical in both time and space. To compute the distributed program reliability, the ARPA network is studied to illustrate the feasibility of the proposed algorithm. > --- paper_title: Real-time distributed program reliability analysis paper_content: Distributed program reliability has been proposed as a reliability index for distributed computing systems to analyze the probability of the successful execution of a program, task, or mission in the system. However, current reliability models proposed for distributed program reliability evaluation do not capture the effects of real-time constraints. We propose an approach to the reliability analysis of distributed programs that addresses real-time constraints. Our approach is based on a model for evaluating transmission time, which allow us to find the time needed to complete execution of the program, task, or mission under evaluation. With information on time-constraints, the corresponding Markov state space can then be defined for reliability computation. To speed up the evaluation process and reduce the size of the Markov state space, several dynamic reliability-preserving reductions are developed. A simple distributed real-time system is used as an example to illustrate the feasibility and uniqueness of the proposed approach. > --- paper_title: A quantitative and qualitative analysis of factors affecting software processes paper_content: Despite the growing body of research on software process improvement (SPI), there is still a great deal of variability in the success of SPI programmes. In this paper, we explore 26 factors that potentially affect SPI. We also consider the research strategies used to study these factors. We have used a multi-strategy approach for this study: first, by combining qualitative and quantitative analysis within case studies; second, by comparing our case study results with the results of a previously conducted survey study. Seven factors relevant to SPI (i.e. executive support, experienced staff, internal process ownership, metrics, procedures, reviews, and training) were identified by the case studies and the survey study. Two factors (reward schemes and estimating tools) were found, by both the case studies and the survey study, not to be relevant to SPI. Three additional factors (people, problems and change) were identified by the case studies. The frequency with which people, problems and change are discussed by practitioners suggests that these three factors may be pervasive in SPI, in a way that the other factors are not. These factors, however, require further investigation. --- paper_title: Reliability in grid computing systems paper_content: In recent years, grid technology has emerged as an important tool for solving compute-intensive problems within the scientific community and in industry. To further the development and adoption of this technology, researchers and practitioners from different disciplines have collaborated to produce standard specifications for implementing large-scale, interoperable grid systems. The focus of this activity has been the Open Grid Forum, but other standards development organizations have also produced specifications that are used in grid systems. To date, these specifications have provided the basis for a growing number of operational grid systems used in scientific and industrial applications. However, if the growth of grid technology is to continue, it will be important that grid systems also provide high reliability. In particular, it will be critical to ensure that grid systems are reliable as they continue to grow in scale, exhibit greater dynamism, and become more heterogeneous in composition. Ensuring grid system reliability in turn requires that the specifications used to build these systems fully support reliable grid services. This study surveys work on grid reliability that has been done in recent years and reviews progress made toward achieving these goals. The survey identifies important issues and problems that researchers are working to overcome in order to develop reliability methods for large-scale, heterogeneous, dynamic environments. The survey also illuminates reliability issues relating to standard specifications used in grid systems, identifying existing specifications that may need to be evolved and areas where new specifications are needed to better support the reliability. Published in 2009 by John Wiley & Sons, Ltd. ::: ::: This article is a U.S. Government work and is in the public domain in the U.S.A. --- paper_title: Evaluating the reliability of computational grids from the end user’s point of view paper_content: Reliability, in terms of Grid component fault tolerance and minimum quality of service, is an important aspect that has to be addressed to foster Grid technology adoption. Software reliability is critically important in today's integrated and distributed systems, as is often the weak link in system performance. In general, reliability is difficult to measure, and specially in Grid environments, where evaluation methodologies are novel and controversial matters. This paper describes a straightforward procedure to analyze the reliability of computational grids from the viewpoint of an end user. The procedure is illustrated in the evaluation of a research Grid infrastructure based on Globus basic services and the GridWay meta-scheduler. The GridWay support for fault tolerance is also demonstrated in a production-level environment. Results show that GridWay is a reliable workload management tool for dynamic and faulty Grid environments. Transparently to the end user, GridWay is able to detect and recover from any of the Grid element failure, outage and saturation conditions specified by the reliability analysis procedure. --- paper_title: Software Reliability Engineering: A Roadmap paper_content: Software reliability engineering is focused on engineering techniques for developing and maintaining software systems whose reliability can be quantitatively evaluated. In order to estimate as well as to predict the reliability of software systems, failure data need to be properly measured by various means during software development and operational phases. Moreover, credible software reliability models are required to track underlying software failure processes for accurate reliability analysis and forecasting. Although software reliability has remained an active research subject over the past 35 years, challenges and open questions still exist. In particular, vital future goals include the development of new software reliability engineering paradigms that take software architectures, testing techniques, and software failure manifestation mechanisms into consideration. In this paper, we review the history of software reliability engineering, the current trends and existing problems, and specific difficulties. Possible future directions and promising research subjects in software reliability engineering are also addressed. --- paper_title: Collaborative reliability prediction of service-oriented systems paper_content: Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative reliability prediction approach obtains better reliability prediction accuracy than other approaches. --- paper_title: Designing Fault Tolerant Web Services Using BPEL paper_content: The Web services technology provides an approach for developing distributed applications by using simple and well defined interfaces. Due to the flexibility of this architecture, it is possible to compose business processes integrating services from different domains. This paper presents an approach, which uses the specification of services orchestration, in order to create a fault tolerant model combining active and passive replication technique. This model support fault of crash. The characteristics and the results obtained by implementing this model are described along this paper. --- paper_title: Implementation and evaluation of transparent fault-tolerant Web service with kernel-level support paper_content: Most of the techniques used for increasing the availability of Web services do not provide fault tolerance for requests being processed at the time of server failure. Other schemes require deterministic servers or changes to the Web client. These limitations are unacceptable for many current and future applications of the Web. We have developed an efficient implementation of a client-transparent mechanism for providing fault-tolerant Web service that does not have the limitations mentioned above. The scheme is based on a hot standby backup server that maintains logs of requests and replies. The implementation includes modifications to the Linux kernel and to the Apache Web server, using their respective module mechanisms. We describe the implementation and present an evaluation of the impact of the backup scheme in terms of throughput, latency, and CPU processing cycles overhead. --- paper_title: Making services fault tolerant paper_content: With ever growing use of Internet, Web services become increasingly popular and their growth rate surpasses even the most optimistic predictions. Services are self-descriptive, self-contained, platform-independent and openly-available components that interact over the network. They are written strictly according to open specifications and/or standards and provide important and often critical functions for many business-to-business systems. Failures causing either service downtime or producing invalid results in such systems may range from a mere inconvenience to significant monetary penalties or even loss of human lives. In applications where sensing and control of machines and other devices take place via services, making the services highly dependable is one of main critical goals. Currently, there is no experimental investigation to evaluate the reliability and availability of Web services systems. In this paper, we identify parameters impacting the Web services dependability, describe the methods of dependability enhancement by redundancy in space and redundancy in time and perform a series of experiments to evaluate the availability of Web services. To increase the availability of the Web service, we use several replication schemes and compare them with a single service. The Web services are coordinated by a replication manager. The replication algorithm and the detailed system configuration are described in this paper. --- paper_title: Software Reliability Engineering: A Roadmap paper_content: Software reliability engineering is focused on engineering techniques for developing and maintaining software systems whose reliability can be quantitatively evaluated. In order to estimate as well as to predict the reliability of software systems, failure data need to be properly measured by various means during software development and operational phases. Moreover, credible software reliability models are required to track underlying software failure processes for accurate reliability analysis and forecasting. Although software reliability has remained an active research subject over the past 35 years, challenges and open questions still exist. In particular, vital future goals include the development of new software reliability engineering paradigms that take software architectures, testing techniques, and software failure manifestation mechanisms into consideration. In this paper, we review the history of software reliability engineering, the current trends and existing problems, and specific difficulties. Possible future directions and promising research subjects in software reliability engineering are also addressed. --- paper_title: Estimation of the Reliability of Distributed Applications paper_content: In this paper the reliability is presented as an important feature for use in mission-critical distributed applications. Certain aspects of distributed systems make the requested level of reliability more difficult. An obvious benefit of distributed systems is that they serve the global business and social environment in which we live and work. Another benefit is that they can improve the quality of services, in terms of reliability, availability and performance, for the complex systems. The paper presents results of a study conducted by the students of Economic Informatics at the University "Lucian Blaga" Sibiu, over four months. The studied population was represented by several distributed applications made under the object oriented programming techniques. This study aimed to estimate the reliability of these applications using object-oriented design metrics validation techniques. --- paper_title: Grid workflow: a flexible failure handling framework for the grid paper_content: The generic, heterogeneous, and dynamic nature of the grid requires a new from of failure recovery mechanism to address its unique requirements such as support for diverse failure handling strategies, separation of failure handling strategies from application codes, and user-defined exception handling. We here propose a grid workflow system (grid-WFS), a flexible failure handling framework for the grid, which addresses these grid-unique failure recovery requirements. Central to the framework is flexibility by the use of workflow structure as a high-level recovery policy specification. We show how this use of high-level workflow structure allows users to achieve failure recovery in a variety of ways depending on the requirements and constraints of their applications. We also demonstrate that this use of workflow structure enables users to not only rapidly prototype and investigate failure handling strategies, but also easily change them by simply modifying the encompassing workflow structure, while the application code remains intact. Finally, we present an experimental evaluation of our framework using a simulation, demonstrating the value of supporting multiple failure recovery techniques in grid systems to achieve high performance in the presence of failures. --- paper_title: FTWeb: a fault tolerant infrastructure for Web services paper_content: The Web services architecture came as answers to the search for interoperability among applications. There has been a growing interest in deploying on the Internet applications with high availability and reliability requirements. However, the technologies associated with this architecture still do not deliver adequate support to this requirement. The model proposed in this article is located in this context and provides a new layer of software that acts as a proxy between client requests and service delivery by providers. The main objective is to ensure client transparent fault tolerance by means of the active replication technique. This model supports the following faults: value, omission and stops. This paper describes the features and outcomes obtained through the implementation of this model. --- paper_title: Fault-tolerant grid services using primary-backup: feasibility and performance paper_content: The combination of grid technology and Web services has produced an attractive platform for deploying distributed applications: grid services, as represented by the Open Grid Services Infrastructure (OGSI) and its Globus toolkit implementation. As the use of grid services grows in popularity, tolerating failures becomes increasingly important. This work addresses the problem of building a reliable and highly-available grid service by replicating the service on two or more hosts using the primary-backup approach. The primary goal is to evaluate the ease and efficiency with which this can be done, by first designing a primary-backup protocol using OGSI, and then implementing it using Globus to evaluate performance implications and tradeoffs. We compared three implementations: one that makes heavy use of the notification interface defined in OGSI, one that uses standard grid service requests instead of notification, and one that uses low-level socket primitives. The overall conclusion is that, while the performance penalty of using Globus primitives - especially notification - for replica coordination can be significant, the OGSI model is suitable for building highly-available services and it makes the task of engineering such services easier. --- paper_title: Transparent Fault Tolerance for Web Services Based Architectures paper_content: Service-based architectures enable the development of new classes of Grid and distributed applications. One of the main capabilities provided by such systems is the dynamic and flexible integration of services, according to which services are allowed to be a part of more than one distributed system and simultaneously serve different applications. This increased flexibility in system composition makes it difficult to address classical distributed system issues such as fault-tolerance. While it is relatively easy to make an individual service fault-tolerant, improving fault-tolerance of services collaborating in multiple application scenarios is a challenging task. In this paper, we look at the issue of developing fault-tolerant service-based distributed systems, and propose an infrastructure to implement fault tolerance capabilities transparent to services. --- paper_title: Implementing e-Transactions with Asynchronous Replication paper_content: This paper describes a distributed algorithm that implements the abstraction of e-Transaction: a transaction that executes exactly-once despite failures. Our algorithm is based on an asynchronous replication scheme that generalizes well-known active-replication and primary-backup schemes. We devised the algorithm with a three-tier architecture in mind: the end-user interacts with front-end clients (e.g., browsers) that invoke middle-tier application servers (e.g., web servers) to access back-end databases. The algorithm preserves the three-tier nature of the architecture and introduces a very acceptable overhead with respect to unreliable solutions. --- paper_title: Client-Transparent Fault-Tolerant Web Service paper_content: Most of the existing fault tolerance schemes for Web servers detect server failure and route future client requests to backup servers. These techniques typically do not provide transparent handling of requests whose processing was in progress when the failure occurred. Thus, the system may fail to provide the user with confirmation for a requested transaction or clear indication that the transaction was not performed. We describe a client-transparent fault tolerance scheme for Web servers that ensures correct handling of requests in progress at the time of server failure. The scheme is based on a standby backup server and simple proxies. The error handling mechanisms of TCP are used to multicast requests to the primary and backup as well as to reliably deliver replies from a server that may fail while sending the reply. Our scheme does not involve OS kernel changes or use of user-level TCP implementations and requires minimal changes to the Web server software. --- paper_title: Software Reliability Engineering: A Roadmap paper_content: Software reliability engineering is focused on engineering techniques for developing and maintaining software systems whose reliability can be quantitatively evaluated. In order to estimate as well as to predict the reliability of software systems, failure data need to be properly measured by various means during software development and operational phases. Moreover, credible software reliability models are required to track underlying software failure processes for accurate reliability analysis and forecasting. Although software reliability has remained an active research subject over the past 35 years, challenges and open questions still exist. In particular, vital future goals include the development of new software reliability engineering paradigms that take software architectures, testing techniques, and software failure manifestation mechanisms into consideration. In this paper, we review the history of software reliability engineering, the current trends and existing problems, and specific difficulties. Possible future directions and promising research subjects in software reliability engineering are also addressed. --- paper_title: Collaborative reliability prediction of service-oriented systems paper_content: Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative reliability prediction approach obtains better reliability prediction accuracy than other approaches. --- paper_title: Tolerating hardware device failures in software paper_content: Hardware devices can fail, but many drivers assume they do not. When confronted with real devices that misbehave, these assumptions can lead to driver or system failures. While major operating system and device vendors recommend that drivers detect and recover from hardware failures, we find that there are many drivers that will crash or hang when a device fails. Such bugs cannot easily be detected by regular stress testing because the failures are induced by the device and not the software load. This paper describes Carburizer, a code-manipulation tool and associated runtime that improves system reliability in the presence of faulty devices. Carburizer analyzes driver source code to find locations where the driver incorrectly trusts the hardware to behave. Carburizer identified almost 1000 such bugs in Linux drivers with a false positive rate of less than 8 percent. With the aid of shadow drivers for recovery, Carburizer can automatically repair 840 of these bugs with no programmer involvement. To facilitate proactive management of device failures, Carburizer can also locate existing driver code that detects device failures and inserts missing failure-reporting code. Finally, the Carburizer runtime can detect and tolerate interrupt-related bugs, such as stuck or missing interrupts. --- paper_title: Software Reliability Engineering: A Roadmap paper_content: Software reliability engineering is focused on engineering techniques for developing and maintaining software systems whose reliability can be quantitatively evaluated. In order to estimate as well as to predict the reliability of software systems, failure data need to be properly measured by various means during software development and operational phases. Moreover, credible software reliability models are required to track underlying software failure processes for accurate reliability analysis and forecasting. Although software reliability has remained an active research subject over the past 35 years, challenges and open questions still exist. In particular, vital future goals include the development of new software reliability engineering paradigms that take software architectures, testing techniques, and software failure manifestation mechanisms into consideration. In this paper, we review the history of software reliability engineering, the current trends and existing problems, and specific difficulties. Possible future directions and promising research subjects in software reliability engineering are also addressed. --- paper_title: Implementation and evaluation of transparent fault-tolerant Web service with kernel-level support paper_content: Most of the techniques used for increasing the availability of Web services do not provide fault tolerance for requests being processed at the time of server failure. Other schemes require deterministic servers or changes to the Web client. These limitations are unacceptable for many current and future applications of the Web. We have developed an efficient implementation of a client-transparent mechanism for providing fault-tolerant Web service that does not have the limitations mentioned above. The scheme is based on a hot standby backup server that maintains logs of requests and replies. The implementation includes modifications to the Linux kernel and to the Apache Web server, using their respective module mechanisms. We describe the implementation and present an evaluation of the impact of the backup scheme in terms of throughput, latency, and CPU processing cycles overhead. --- paper_title: Making services fault tolerant paper_content: With ever growing use of Internet, Web services become increasingly popular and their growth rate surpasses even the most optimistic predictions. Services are self-descriptive, self-contained, platform-independent and openly-available components that interact over the network. They are written strictly according to open specifications and/or standards and provide important and often critical functions for many business-to-business systems. Failures causing either service downtime or producing invalid results in such systems may range from a mere inconvenience to significant monetary penalties or even loss of human lives. In applications where sensing and control of machines and other devices take place via services, making the services highly dependable is one of main critical goals. Currently, there is no experimental investigation to evaluate the reliability and availability of Web services systems. In this paper, we identify parameters impacting the Web services dependability, describe the methods of dependability enhancement by redundancy in space and redundancy in time and perform a series of experiments to evaluate the availability of Web services. To increase the availability of the Web service, we use several replication schemes and compare them with a single service. The Web services are coordinated by a replication manager. The replication algorithm and the detailed system configuration are described in this paper. --- paper_title: Software Reliability Engineering: A Roadmap paper_content: Software reliability engineering is focused on engineering techniques for developing and maintaining software systems whose reliability can be quantitatively evaluated. In order to estimate as well as to predict the reliability of software systems, failure data need to be properly measured by various means during software development and operational phases. Moreover, credible software reliability models are required to track underlying software failure processes for accurate reliability analysis and forecasting. Although software reliability has remained an active research subject over the past 35 years, challenges and open questions still exist. In particular, vital future goals include the development of new software reliability engineering paradigms that take software architectures, testing techniques, and software failure manifestation mechanisms into consideration. In this paper, we review the history of software reliability engineering, the current trends and existing problems, and specific difficulties. Possible future directions and promising research subjects in software reliability engineering are also addressed. --- paper_title: Multimedia Object Placement for Transparent Data Replication paper_content: Transparent data replication is a promising technique for improving the system performance of a large distributed network. Transcoding is an important technology which adapts the same multimedia object to diverse mobile appliances; thus, users' requests for a specified version of a multimedia object could be served by a more detailed version cached according to transcoding. Therefore, it is particularly of theoretical and practical necessity to determine the proper version to be cached at each node such that the specified objective is achieved. In this paper, we address the problem of multimedia object placement for transparent data replication. The performance objective is to minimize the total access cost by considering both transmission cost and transcoding cost. We present optimal solutions for different cases for this problem. The performance of the proposed solutions is evaluated with a set of carefully designed simulation experiments for various performance metrics over a wide range of system parameters. The simulation results show that our solution consistently and significantly outperforms comparison solutions in terms of all the performance metrics considered --- paper_title: An Effective Cache Replacement Algorithm in Transcoding-Enabled Proxies paper_content: In this paper, we address the problem of cache replacement for transcoding proxy caching. Transcoding proxy is a proxy that has the functionality of transcoding a multimedia object into an appropriate format or resolution for each client. We first propose an effective cache replacement algorithm for transcoding proxy. In general, when a new object is to be cached, cache replacement algorithms evict some of the cached objects with the least profit to accommodate the new object. Our algorithm takes into account of the inter-relationships among different versions of the same multimedia object, and selects the versions to replace according to their aggregate profit which usually differs from simple summation of their individual profits as assumed in the existing algorithms. It also considers cache consistency, which is not considered in the existing algorithms. We then present a complexity analysis to show the efficiency of our algorithm. Finally, we give extensive simulation results to compare the performance of our algorithm with some existing algorithms. The results show that our algorithm outperforms others in terms of various performance metrics. --- paper_title: Estimation of the Reliability of Distributed Applications paper_content: In this paper the reliability is presented as an important feature for use in mission-critical distributed applications. Certain aspects of distributed systems make the requested level of reliability more difficult. An obvious benefit of distributed systems is that they serve the global business and social environment in which we live and work. Another benefit is that they can improve the quality of services, in terms of reliability, availability and performance, for the complex systems. The paper presents results of a study conducted by the students of Economic Informatics at the University "Lucian Blaga" Sibiu, over four months. The studied population was represented by several distributed applications made under the object oriented programming techniques. This study aimed to estimate the reliability of these applications using object-oriented design metrics validation techniques. --- paper_title: Fault-tolerant and reliable computation in cloud computing paper_content: Cloud computing, with its great potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation clients. In this work, we investigate the security perspective of scientific computation in cloud computing. We investigate a cloud selection strategy to decompose the matrix multiplication problem into several tasks which will be submitted to different clouds. In particular, we propose techniques to improve the fault-tolerance and reliability of a rather general scientific computation: matrix multiplication. Through our techniques, we demonstrate that fault-tolerance and reliability against faulty and even malicious clouds in cloud computing can be achieved. --- paper_title: Evaluating the reliability of computational grids from the end user’s point of view paper_content: Reliability, in terms of Grid component fault tolerance and minimum quality of service, is an important aspect that has to be addressed to foster Grid technology adoption. Software reliability is critically important in today's integrated and distributed systems, as is often the weak link in system performance. In general, reliability is difficult to measure, and specially in Grid environments, where evaluation methodologies are novel and controversial matters. This paper describes a straightforward procedure to analyze the reliability of computational grids from the viewpoint of an end user. The procedure is illustrated in the evaluation of a research Grid infrastructure based on Globus basic services and the GridWay meta-scheduler. The GridWay support for fault tolerance is also demonstrated in a production-level environment. Results show that GridWay is a reliable workload management tool for dynamic and faulty Grid environments. Transparently to the end user, GridWay is able to detect and recover from any of the Grid element failure, outage and saturation conditions specified by the reliability analysis procedure. --- paper_title: Collaborative reliability prediction of service-oriented systems paper_content: Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative reliability prediction approach obtains better reliability prediction accuracy than other approaches. --- paper_title: WS-DREAM: A distributed reliability assessment Mechanism for Web Services paper_content: It is critical to guarantee the reliability of service-oriented applications. This is because they may employ remote Web services as components, which may easily become unavailable in the unpredictable Internet environment. This practical experience report presents a distribute reliability assessment mechanism for Web services (WS-DREAM), allowing users to carry out Web services reliability assessment in a collaborative manner. With WS-DREAM, users in different geography locations help each other to carry out testing, and share test cases under the coordination of a centralized server. Based on this collaborative mechanism, reliability assessment for Web services in real environment from different locations of the world becomes seamless. To illustrate the advantage of this mechanism, a prototype is implemented and a case study is carried out. Users from five locations all over the world perform reliability assessment to Web services distributed in six countries. Over 1,000,000 test cases are executed in a collaborative manner and detailed results are provided. --- paper_title: Real-time distributed program reliability analysis paper_content: Distributed program reliability has been proposed as a reliability index for distributed computing systems to analyze the probability of the successful execution of a program, task, or mission in the system. However, current reliability models proposed for distributed program reliability evaluation do not capture the effects of real-time constraints. We propose an approach to the reliability analysis of distributed programs that addresses real-time constraints. Our approach is based on a model for evaluating transmission time, which allow us to find the time needed to complete execution of the program, task, or mission under evaluation. With information on time-constraints, the corresponding Markov state space can then be defined for reliability computation. To speed up the evaluation process and reduce the size of the Markov state space, several dynamic reliability-preserving reductions are developed. A simple distributed real-time system is used as an example to illustrate the feasibility and uniqueness of the proposed approach. > --- paper_title: Collaborative reliability prediction of service-oriented systems paper_content: Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative reliability prediction approach obtains better reliability prediction accuracy than other approaches. --- paper_title: Architecture-based reliability prediction for service-oriented computing paper_content: In service-oriented computing, services are dynamically built as an assembly of pre-existing, independently developed, network accessible services. Hence, predicting as much as possible automatically their dependability is important to appropriately drive the selection and assembly of services, in order to get some required dependability level. We present an approach to the reliability prediction of such services, based on the partial information published with each service, and that lends itself to automatization. The proposed methodology exploits ideas from the Software Architecture- and Component-based approaches to software design. --- paper_title: A software reliability model for Web services paper_content: This paper proposes a service-oriented software reliability model that dynamically evaluates the reliability of Web services. There are two kinds of Web services: atomic services without the structural information and the composite services consisting of atomic services. The model first evaluates the reliability of atomic services based on group testing and majority voting. Group testing is the key technique proposed in this paper to support the service-oriented reliability model. Then, the reliability model evaluates the overall reliability of composite services using an architecture-based model and based on reliabilities of the atomic services, execution scenarios, and operational profiles. The reliability model is dynamic and the reliabilities of the services are evaluated in the actual operational environment. A case study is designed, implemented, and analyzed using the design of experiment technique. The results show the significances of the model and its components. --- paper_title: Quality Prediction of Service Compositions through Probabilistic Model Checking paper_content: The problem of composing services to deliver integrated business solutions has been widely studied in the last years. Besides addressing functional requirements, services compositions should also provide agreed service levels. Our goal is to support model-based analysis of service compositions, with a focus on the assessment of non-functional quality attributes, namely performance and reliability. We propose a model-driven approach, which automatically transforms a design model of service composition into an analysis model, which then feeds a probabilistic model checker for quality prediction. To bring this approach to fruition, we developed a prototype tool called ATOP , and we demonstrate its use on a simple case study. --- paper_title: Reliability in grid computing systems paper_content: In recent years, grid technology has emerged as an important tool for solving compute-intensive problems within the scientific community and in industry. To further the development and adoption of this technology, researchers and practitioners from different disciplines have collaborated to produce standard specifications for implementing large-scale, interoperable grid systems. The focus of this activity has been the Open Grid Forum, but other standards development organizations have also produced specifications that are used in grid systems. To date, these specifications have provided the basis for a growing number of operational grid systems used in scientific and industrial applications. However, if the growth of grid technology is to continue, it will be important that grid systems also provide high reliability. In particular, it will be critical to ensure that grid systems are reliable as they continue to grow in scale, exhibit greater dynamism, and become more heterogeneous in composition. Ensuring grid system reliability in turn requires that the specifications used to build these systems fully support reliable grid services. This study surveys work on grid reliability that has been done in recent years and reviews progress made toward achieving these goals. The survey identifies important issues and problems that researchers are working to overcome in order to develop reliability methods for large-scale, heterogeneous, dynamic environments. The survey also illuminates reliability issues relating to standard specifications used in grid systems, identifying existing specifications that may need to be evolved and areas where new specifications are needed to better support the reliability. Published in 2009 by John Wiley & Sons, Ltd. ::: ::: This article is a U.S. Government work and is in the public domain in the U.S.A. --- paper_title: Cloud Computing – Issues, Research and Implementations paper_content: "Cloud" computing – a relatively recent term, builds on decades of research in virtualization, distributed computing, utility computing, and more recently networking, web and software services. It implies a service oriented architecture, reduced information technology overhead for the end-user, great flexibility, reduced total cost of ownership, on-demand services and many other things. This paper discusses the concept of “cloud” computing, some of the issues it tries to address, related research topics, and a “cloud” implementation available today. --- paper_title: Elements of Applied Stochastic Processes paper_content: Fundamentals of Queueing Theory, 2nd Edition Donald Gross and Carl M. Harris A graduate text and reference treating queueing theory from the development of standard models to applications. The emphasis is on real analysis of queueing systems, applications, and problem solving. It has been brought up-to-date by modernizing older treatments. 1985 (0 471-89067-7) 475 pp. Multivariate Descriptive Analysis Correspondence Analysis and Related Techniques for Large Matrices Ludovic Lebart, Alain Morineau and Kenneth M. Warwick Presents a set of statistical methods for exploratory analysis of large date sets and categorical data. This unique approach uses graphical aspects of multidimensional scaling techniques within the context of exploratory data analysis. 1984 (0 471-86743-8) 231 pp. Introduction to Linear Regression Analysis Douglas C. Montgomery and Elizabeth A. Peck A definitive introduction to linear regression analysis covering basic topics as well as recent approaches in the field. It blends theory and application in a way that enables readers to apply regression methodology in a variety of practical settings. Many detailed examples drawn directly from various fields of engineering, physical science, and the management sciences provide clear guidance to the use of the techniques. The interface with widely available computer programs for regression analysis is illustrated throughout with numerous actual computer printouts. 1982 (0 471-05850-5) 504 pp. --- paper_title: Characterizing cloud computing hardware reliability paper_content: Modern day datacenters host hundreds of thousands of servers that coordinate tasks in order to deliver highly available cloud computing services. These servers consist of multiple hard disks, memory modules, network cards, processors etc., each of which while carefully engineered are capable of failing. While the probability of seeing any such failure in the lifetime (typically 3-5 years in industry) of a server can be somewhat small, these numbers get magnified across all devices hosted in a datacenter. At such a large scale, hardware component failure is the norm rather than an exception. Hardware failure can lead to a degradation in performance to end-users and can result in losses to the business. A sound understanding of the numbers as well as the causes behind these failures helps improve operational experience by not only allowing us to be better equipped to tolerate failures but also to bring down the hardware cost through engineering, directly leading to a saving for the company. To the best of our knowledge, this paper is the first attempt to study server failures and hardware repairs for large datacenters. We present a detailed analysis of failure characteristics as well as a preliminary analysis on failure predictors. We hope that the results presented in this paper will serve as motivation to foster further research in this area. --- paper_title: Reliability analysis of grid computing systems paper_content: Grid computing system is different from conventional distributed computing systems by its focus on large-scale resource sharing, where processors and communication have significant influence on grid computing reliability. Most previous research on conventional small-scale distributed systems ignored the communication time and processing time when studying the distributed program reliability, which is not practical in the analysis of grid computing systems. This paper describes the property of the grid computing systems and presents algorithms to analyze the grid program and system reliability. --- paper_title: Real-time distributed program reliability analysis paper_content: Distributed program reliability has been proposed as a reliability index for distributed computing systems to analyze the probability of the successful execution of a program, task, or mission in the system. However, current reliability models proposed for distributed program reliability evaluation do not capture the effects of real-time constraints. We propose an approach to the reliability analysis of distributed programs that addresses real-time constraints. Our approach is based on a model for evaluating transmission time, which allow us to find the time needed to complete execution of the program, task, or mission under evaluation. With information on time-constraints, the corresponding Markov state space can then be defined for reliability computation. To speed up the evaluation process and reduce the size of the Markov state space, several dynamic reliability-preserving reductions are developed. A simple distributed real-time system is used as an example to illustrate the feasibility and uniqueness of the proposed approach. > --- paper_title: A Hierarchical Modeling and Analysis for Grid Service Reliability paper_content: Grid computing is a recently developed technology. Although the developmental tools and techniques for the grid have been extensively studied, grid reliability analysis is not easy because of its complexity. This paper is the first one that presents a hierarchical model for the grid service reliability analysis and evaluation. The hierarchical modeling is mapped to the physical and logical architecture of the grid service system and makes the evaluation and calculation tractable by identifying the independence among layers. Various types of failures are interleaved in the grid computing environment, such as blocking failures, time-out failures, matchmaking failures, network failures, program failures, and resource failures. This paper investigates all of them to achieve a complete picture about grid service reliability. Markov models, queuing theory, and graph theory are mainly used to model, evaluate, and analyze the grid service reliability. Numerical examples are illustrated --- paper_title: Collaborative reliability prediction of service-oriented systems paper_content: Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative reliability prediction approach obtains better reliability prediction accuracy than other approaches. --- paper_title: A Taxonomy and Survey of Cloud Computing Systems paper_content: The computational world is becoming very large and complex. Cloud Computing has emerged as a popular computing model to support processing large volumetric data using clusters of commodity computers. According to J.Dean and S. Ghemawat [1], Google currently processes over 20 terabytes of raw web data. It's some fascinating, large-scale processing of data that makes your head spin and appreciate the years of distributed computing fine-tuning applied to today's large problems. The evolution of cloud computing can handle such massive data as per on demand service. Nowadays the computational world is opting for pay-for-use models and Hype and discussion aside, there remains no concrete definition of cloud computing. In this paper, we first develop a comprehensive taxonomy for describing cloud computing architecture. Then we use this taxonomy to survey several existing cloud computing services developed by various projects world-wide such as Google, force.com, Amazon. We use the taxonomy and survey results not only to identify similarities and differences of the architectural approaches of cloud computing, but also to identify areas requiring further research. --- paper_title: Collaborative reliability prediction of service-oriented systems paper_content: Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative reliability prediction approach obtains better reliability prediction accuracy than other approaches. ---	Title: A Survey on Reliability in Distributed Systems Section 1: Overview Description 1: Introduce the context of the survey and the importance of reliability in distributed systems. Section 2: Pragmatic Requirements Description 2: Explain the fundamental requirements for highly reliable distributed systems. Section 3: Conceptual Framework Description 3: Define basic concepts and highlight challenges and issues affecting reliability in various computing environments. Section 4: Reliability Description 4: Discuss the broader concept of reliability in software applications and its impact. Section 5: Various Challenges and Factors Affecting Reliability Description 5: Analyze different challenges and factors that impact the reliability of distributed systems. Section 6: Existing Works Description 6: Review existing models and approaches for fault tolerance and fault prediction in distributed systems. Section 7: Fault Tolerant Techniques Description 7: Describe the different fault tolerance mechanisms used in distributed systems. Section 8: Fault or Failure Forecasting Techniques Description 8: Analyze different techniques for forecasting faults or failures in distributed systems. Section 9: User Centric Approaches Description 9: Discuss approaches that focus on user behavior and usage to predict reliability. Section 10: Architecture Based Approaches Description 10: Explore approaches focusing on predicting reliability at the architectural design phase. Section 11: State Based Approaches Description 11: Describe the use of state-based models like the Markov chain for reliability prediction. Section 12: Discussion and Limitations of Existing Models Description 12: Critically analyze the limitations and open issues in existing reliability models for distributed systems. Section 13: Conclusion and Future Work Description 13: Summarize the findings of the survey, highlight the importance of comprehensive fault tolerance mechanisms, and propose areas for future research.
A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization	12	--- paper_title: Opportunistic sensing: Security challenges for the new paradigm paper_content: We study the security challenges that arise in opportunistic people-centric sensing, a new sensing paradigm leveraging humans as part of the sensing infrastructure. Most prior sensor-network research has focused on collecting and processing environmental data using a static topology and an application-aware infrastructure, whereas opportunistic sensing involves collecting, storing, processing and fusing large volumes of data related to everyday human activities. This highly dynamic and mobile setting, where humans are the central focus, presents new challenges for information security, because data originates from sensors carried by people— not tiny sensors thrown in the forest or attached to animals. In this paper we aim to instigate discussion of this critical issue, because opportunistic people-centric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that hold promise in this new sensing paradigm. --- paper_title: Challenge: ubiquitous location-aware computing and the "place lab" initiative paper_content: To be widely adopted, location-aware computing must be as effortless, familiar and rewarding as web search tools like Google. We envisage the global scale Place Lab, consisting of an open software base and a community building activity as a way to bootstrap the broad adoption of location-aware computing. The initiative is a laboratory because it will also be a vehicle for research and instruction, especially in the formative stages. The authors draw on their experiences with campus and building-scale location systems to identify the technological and social barriers to a truly ubiquitous deployment. With a grasp of these "barriers to adoption," we present a usage scenario, the problems in realizing this scenario, and how these problems will be addressed. We conclude with a sketch of the multi-organization cooperative being formed to move this effort forward. --- paper_title: Medusa: a programming framework for crowd-sensing applications paper_content: The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks. --- paper_title: The case for crowd computing paper_content: We introduce and motivate "crowd computing", which combines mobile devices and social interactions to achieve large-scale distributed computation. An opportunistic network of mobile devices offers substantial aggregate bandwidth and processing power. In this paper, we analyse encounter traces to place an upper bound on the amount of computation that is possible in such networks. We also investigate a practical task-farming algorithm that approaches this upper bound, and show that exploiting social structure can dramatically increase its performance. --- paper_title: reCAPTCHA: Human-Based Character Recognition via Web Security Measures paper_content: CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are widespread security measures on the World Wide Web that prevent automated programs from abusing online services. They do so by asking humans to perform a task that computers cannot yet perform, such as deciphering distorted characters. Our research explored whether such human effort can be channeled into a useful purpose: helping to digitize old printed material by asking users to decipher scanned words from books that computerized optical character recognition failed to recognize. We showed that this method can transcribe text with a word accuracy exceeding 99%, matching the guarantee of professional human transcribers. Our apparatus is deployed in more than 40,000 Web sites and has transcribed over 440 million words. --- paper_title: Social sensing for epidemiological behavior change paper_content: An important question in behavioral epidemiology and public health is to understand how individual behavior is affected by illness and stress. Although changes in individual behavior are intertwined with contagion, epidemiologists today do not have sensing or modeling tools to quantitatively measure its effects in real-world conditions. In this paper, we propose a novel application of ubiquitous computing. We use mobile phone based co-location and communication sensing to measure characteristic behavior changes in symptomatic individuals, reflected in their total communication, interactions with respect to time of day (e.g., late night, early morning), diversity and entropy of face-to-face interactions and movement. Using these extracted mobile features, it is possible to predict the health status of an individual, without having actual health measurements from the subject. Finally, we estimate the temporal information flux and implied causality between physical symptoms, behavior and mental health. --- paper_title: Motion Recognition Assisted Indoor Wireless Navigation on a Mobile Phone paper_content: The paper presents an indoor navigation solution combining physical motion recognition with WLAN positioning on a smart phone. Orientationindependent features are extracted from vertical and horizontal components of acceleration. The simple features such as the mean of horizontal acceleration, the variance of acceleration magnitude, and the variance of horizontal acceleration are selected as the nodes in a decision tree. Six common motion modes during indoor navigation, e.g., static, standing with hand swinging, normal walking with holding the phone in hand, normal walking with hand swinging, fast walking, and U-turning are detected. A fingerprinting based WLAN positioning approach offers the headings and initial positions for pedestrian dead reckoning with about 10 seconds interval. The velocities for six motion modes are trained by five testers and applied in the dead reckoning during the WLAN positioning gap. Test results indicate that the motion mode is recognized correctly in 95% of test cases. The field test shows 4.6m horizontal mean error for motion recognition assisted indoor wireless positioning. --- paper_title: Indoor Positioning and Navigation with Camera Phones paper_content: This low-cost indoor navigation system runs on off-the-shelf camera phones. More than 2,000 users at four different large-scale events have already used it. The system uses built-in cameras to determine user location in real time by detecting unobtrusive fiduciary markers. The required infrastructure is limited to paper markers and static digital maps, and common devices are used, facilitating quick deployment in new environments. The authors have studied the application quantitatively in a controlled environment and qualitatively during deployment at four large international events. According to test users, marker-based navigation is easier to use than conventional mobile digital maps. Moreover, the users' location awareness in navigation tasks improved. Experiences drawn from questionnaires, usage log data, and user interviews further highlight the benefits of this approach. --- paper_title: iParking: An Intelligent Indoor Location-Based Smartphone Parking Service paper_content: Indoor positioning technologies have been widely studied with a number of solutions being proposed, yet substantial applications and services are still fairly primitive. Taking advantage of the emerging concept of the connected car, the popularity of smartphones and mobile Internet, and precise indoor locations, this study presents the development of a novel intelligent parking service called iParking. With the iParking service, multiple parties such as users, parking facilities and service providers are connected through Internet in a distributed architecture. The client software is a light-weight application running on a smartphone, and it works essentially based on a precise indoor positioning solution, which fuses Wireless Local Area Network (WLAN) signals and the measurements of the built-in sensors of the smartphones. The positioning accuracy, availability and reliability of the proposed positioning solution are adequate for facilitating the novel parking service. An iParking prototype has been developed and demonstrated in a real parking environment at a shopping mall. The demonstration showed how the iParking service could improve the parking experience and increase the efficiency of parking facilities. The iParking is a novel service in terms of cost- and energy-efficient solution. --- paper_title: Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban Spaces paper_content: The recent wave of sensor-rich, Internet-enabled, smart mobile devices such as the Apple iPhone has opened the door for a novel paradigm for monitoring the urban landscape known as participatory sensing. Using this paradigm, ordinary citizens can collect multi-modal data streams from the surrounding environment using their mobile devices and share the same using existing communication infrastructure (e.g., 3G service or WiFi access points). The data contributed from multiple participants can be combined to build a spatiotemporal view of the phenomenon of interest and also to extract important community statistics. Given the ubiquity of mobile phones and the high density of people in metropolitan areas, participatory sensing can achieve an unprecedented level of coverage in both space and time for observing events of interest in urban spaces. Several exciting participatory sensing applications have emerged in recent years. For example, GPS traces uploaded by drivers and passengers can be used to generate real time traffic statistics. Similarly, street-level audio samples collected by pedestrians can be aggregated to create a citywide noise map. In this advanced seminar, we will provide a comprehensive overview of this new and exciting paradigm and outline the major research challenges. --- paper_title: Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning paper_content: The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in “Static Tests” and a 3.53 m in “Stop-Go Tests”. --- paper_title: Human computation: a survey and taxonomy of a growing field paper_content: The rapid growth of human computation within research and industry has produced many novel ideas aimed at organizing web users to do great things. However, the growth is not adequately supported by a framework with which to understand each new system in the context of the old. We classify human computation systems to help identify parallels between different systems and reveal "holes" in the existing work as opportunities for new research. Since human computation is often confused with "crowdsourcing" and other terms, we explore the position of human computation with respect to these related topics. --- paper_title: RADAR: an in-building RF-based user location and tracking system paper_content: The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy. --- paper_title: Visual-aided Two-dimensional Pedestrian Indoor Navigation with a Smartphone paper_content: Indoor pedestrian positioning sets severe challenges for a navigation system. To be applicable for pedestrian navigation the platform used has to be small in size and reasonably priced. Smartphones fulfill these requirements satisfyingly. GNSS signals are degraded indoors and in order to obtain accurate navigation aiding from other sensors is needed. Self-contained sensors provide valuable information about the motion of the pedestrian and when integrated with GNSS measurements a position solution is typically obtainable indoors. The accuracy is however decreased due to errors in the measurements of the self-contained sensors introduced by various environmental disturbances. When the effect of the disturbance is constrained using visualaiding the accuracy can be increased to an acceptable level. This paper introduces a visual-aided twodimensional indoor pedestrian navigation system integrating measurements from GNSS, Bluetooth, WLAN, self-contained sensors, and heading change information obtained from consecutive images. The integration is performed with an Extended Kalman filter. Reliability information of the heading change measurements calculated from images using vanishing points is provided to the filter and utilized in the integration. The visual-aiding algorithm is computationally lightweight taking into account the restricted resources of the smartphone. In the conducted experiment, the accuracy of the position solution is increased by 1.2 meters due to the visual-aiding. --- paper_title: Efficient WiFi fingerprint training using semi-supervised learning paper_content: Fingerfrinting based WiFi positioning approach needs an off-line training phase to build a radio map with received signal strength indication vector of each reference point. In existing systems, this training phase may cost a tremendous amount of workload to achieve satisfying location result. To cut down on the workload notably and guarantee the location result in the meantime, we will introduce an efficient WiFi fingerprint training method: Fa-Fi namely fast fingerprint generation, which uses semi-supervised learning in this article. This proposed method can reduce the training phase time cost to about 1/5, and guarantee the localization accuracy at the same time. --- paper_title: A Probabilistic Approach to WLAN User Location Estimation paper_content: We estimate the location of a WLAN user based on radio signal strength measurements performed by the user's mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths are distributed in different geographical areas based on a sample of measurements collected at several known locations. We present a probabilistic framework for solving the location estimation problem. In the empirical part of the paper we demonstrate the feasibility of this approach by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment. --- paper_title: A wireless LAN-based indoor positioning technology paper_content: Context-aware computing is an emerging computing paradigm that can provide new or improved services by exploiting user context information. In this paper, we present a wireless-local-area-network-based (WLAN-based) indoor positioning technology. The wireless device deploys a position-determination model to gather location information from collected WLAN signals. A model-based signal distribution training scheme is proposed to trade off the accuracy of signal distribution and training workload. A tracking-assistant positioning algorithm is presented to employ knowledge of the area topology to assist the procedure of position determination. We have set up a positioning system at the IBM China Research Laboratory. Our experimental results indicate an accuracy of 2 m with a 90% probability for static devices and, for moving (walking) devices, an accuracy of 5 m with a 90% probability. Moreover, the complexity of the training procedure is greatly reduced compared with other positioning algorithms. --- paper_title: WLAN location determination via clustering and probability distributions paper_content: We present a WLAN location determination technique, the Joint Clustering technique, that uses: (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy. --- paper_title: RADAR: an in-building RF-based user location and tracking system paper_content: The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy. --- paper_title: The uses of ambient light for ubiquitous positioning paper_content: This paper proposed ambient light (ambilight) as a new type of signal sources for positioning. The possibility and methods of ambilight positioning were presented in this paper. It has been shown that two kinds of observables of ambient light can be used for positioning through different principles. Ambilight intensity spectrum measurements have highly location dependency, and they can be used for positioning with the traditional fingerprinting approach. Total ambilight irradiance intensity is used to detect the proximity of a lighting source, and a location solution can be further resolved with the support of knowledge of lighting infrastructure. Ambilight positioning can work in areas where other traditional techniques are not able to function. An ambilight sensor is cost-efficient and miniature in size, and it can be easily integrated with other sensors to form a hybrid positioning system. This paper was concluded with discussions on the possibility, applicability, challenges and outlook of the new ambient light positioning techniques. --- paper_title: SurroundSense: mobile phone localization via ambience fingerprinting paper_content: A growing number of mobile computing applications are centered around the user's location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, there have been few attempts in recognizing logical locations. This paper argues that the increasing number of sensors on mobile phones presents new opportunities for logical localization. We postulate that ambient sound, light, and color in a place convey a photo-acoustic signature that can be sensed by the phone's camera and microphone. In-built accelerometers in some phones may also be useful in inferring broad classes of user-motion, often dictated by the nature of the place. By combining these optical, acoustic, and motion attributes, it may be feasible to construct an identifiable fingerprint for logical localization. Hence, users in adjacent stores can be separated logically, even when their physical positions are extremely close. We propose SurroundSense, a mobile phone based system that explores logical localization via ambience fingerprinting. Evaluation results from 51 different stores show that SurroundSense can achieve an average accuracy of 87% when all sensing modalities are employed. We believe this is an encouraging result, opening new possibilities in indoor localization. --- paper_title: WiFi-SLAM Using Gaussian Process Latent Variable Models paper_content: WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization. --- paper_title: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem paper_content: The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on an exact factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real-world data. --- paper_title: 1 A Tutorial on Graph-Based SLAM paper_content: Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular research topics in mobile robotics for the last two decades and efficient approaches for solving this task have been proposed. One intuitive way of formulating SLAM is to use a graph whose nodes correspond to the poses of the robot at different points in time and whose edges represent constraints between the poses. The latter are obtained from observations of the environment or from movement actions carried out by the robot. Once such a graph is constructed, the map can be computed by finding the spatial configuration of the nodes that is mostly consistent with the measurements modeled by the edges. In this paper, we provide an introductory description to the graph-based SLAM problem. Furthermore, we discuss a state-of-the-art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization. The goal of this tutorial is to enable the reader to implement the proposed methods from scratch. --- paper_title: Simultaneous Localization and Mapping for pedestrians using distortions of the local magnetic field intensity in large indoor environments paper_content: We present a Simultaneous Localization and Mapping (SLAM) algorithm based on measurements of the ambient magnetic field strength (MagSLAM) that allows quasi-real-time mapping and localization in buildings, where pedestrians with foot-mounted sensors are the subjects to be localized. We assume two components to be present: firstly a source of odometry (human step measurements), and secondly a sensor of the local magnetic field intensity. Our implementation follows the FastSLAM factorization using a particle filter. We augment the hexagonal transition map used in the pre-existing FootSLAM algorithm with local maps of the magnetic field strength, binned in a hierarchical hexagonal structure. We performed extensive experiments in a number of different buildings and present the results for five data sets for which we have ground truth location information. We consider the results obtained using MagSLAM to be strong evidence that scalable and accurate localization is possible without an a priori map. --- paper_title: Growing an organic indoor location system paper_content: Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments. --- paper_title: Crowdsourcing with Smartphones paper_content: Smartphones can reveal crowdsourcing's full potential and let users transparently contribute to complex and novel problem solving. This emerging area is illustrated through a taxonomy that classifies the mobile crowdsourcing field and through three new applications that optimize location-based search and similarity services based on crowd-generated data. Such applications can be deployed on SmartLab, a cloud of more than 40 Android devices deployed at the University of Cyprus that provides an open testbed to facilitate research and development of smartphone applications on a massive scale. --- paper_title: Growing an organic indoor location system paper_content: Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments. --- paper_title: Combining human and machine intelligence in large-scale crowdsourcing paper_content: We show how machine learning and inference can be harnessed to leverage the complementary strengths of humans and computational agents to solve crowdsourcing tasks. We construct a set of Bayesian predictive models from data and describe how the models operate within an overall crowd-sourcing architecture that combines the efforts of people and machine vision on the task of classifying celestial bodies defined within a citizens' science project named Galaxy Zoo. We show how learned probabilistic models can be used to fuse human and machine contributions and to predict the behaviors of workers. We employ multiple inferences in concert to guide decisions on hiring and routing workers to tasks so as to maximize the efficiency of large-scale crowdsourcing processes based on expected utility. --- paper_title: Quality Control in Crowdsourcing Systems: Issues and Directions paper_content: As a new distributed computing model, crowdsourcing lets people leverage the crowd's intelligence and wisdom toward solving problems. This article proposes a framework for characterizing various dimensions of quality control in crowdsourcing systems, a critical issue. The authors briefly review existing quality-control approaches, identify open issues, and look to future research directions. In the Web extra, the authors discuss both design-time and runtime approaches in more detail. --- paper_title: Covariance consistency methods for fault-tolerant distributed data fusion paper_content: Abstract This paper presents a general, rigorous, and fault-tolerant framework for maintaining consistent mean and covariance estimates in an arbitrary, dynamic, distributed network of information processing nodes. In particular, a solution is provided that addresses the information deconfliction problem that arises when estimates from two or more different nodes are determined to be inconsistent with each other, e.g., when two high precision (small covariance) estimates place the position of a particular object at very different locations. The challenge is to be able to resolve such inconsistencies without having to access and exploit global information to determine which of the estimates is spurious. The solution proposed in this paper is called Covariance Union. --- paper_title: Crowd IQ: aggregating opinions to boost performance paper_content: We show how the quality of decisions based on the aggregated opinions of the crowd can be conveniently studied using a sample of individual responses to a standard IQ questionnaire. We aggregated the responses to the IQ questionnaire using simple majority voting and a machine learning approach based on a probabilistic graphical model. The score for the aggregated questionnaire, Crowd IQ, serves as a quality measure of decisions based on aggregating opinions, which also allows quantifying individual and crowd performance on the same scale. ::: ::: We show that Crowd IQ grows quickly with the size of the crowd but saturates, and that for small homogeneous crowds the Crowd IQ significantly exceeds the IQ of even their most intelligent member. We investigate alternative ways of aggregating the responses and the impact of the aggregation method on the resulting Crowd IQ. We also discuss Contextual IQ, a method of quantifying the individual participant's contribution to the Crowd IQ based on the Shapley value from cooperative game theory. --- paper_title: Smartphone-Based Collaborative and Autonomous Radio Fingerprinting paper_content: Although active research has recently been conducted on received signal strength (RSS) fingerprint-based indoor localization, most of the current systems hardly overcome the costly and time-consuming offline training phase. In this paper, we propose an autonomous and collaborative RSS fingerprint collection and localization system. Mobile users track their position with inertial sensors and measure RSS from the surrounding access points. In this scenario, anonymous mobile users automatically collect data in daily life without purposefully surveying an entire building. The server progressively builds up a precise radio map as more users interact with their fingerprint data. The time drift error of inertial sensors is also compromised at run-time with the fingerprint-based localization, which runs with the collective fingerprints being currently built by the server. The proposed system has been implemented on a recent Android smartphone. The experiment results show that reasonable location accuracy is obtained with automatic fingerprinting in indoor environments. --- paper_title: LOF: identifying density-based local outliers paper_content: For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real-world datasets, we demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms we show that our approach of finding local outliers can be practical. --- paper_title: Learning From Crowds paper_content: For many supervised learning tasks it may be infeasible (or very expensive) to obtain objective and reliable labels. Instead, we can collect subjective (possibly noisy) labels from multiple experts or annotators. In practice, there is a substantial amount of disagreement among the annotators, and hence it is of great practical interest to address conventional supervised learning problems in this scenario. In this paper we describe a probabilistic approach for supervised learning when we have multiple annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the commonly used majority voting baseline. --- paper_title: Database updating through user feedback in fingerprint-based Wi-Fi location systems paper_content: Wi-Fi fingerprinting is a technique which can provide location in GPS-denied environments, relying exclusively on Wi-Fi signals. It first requires the construction of a database of “fingerprints”, i.e. signal strengths from different access points (APs) at different reference points in the desired coverage area. The location of the device is then obtained by measuring the signal strengths at its location, and comparing it with the different reference fingerprints in the database. The main disadvantage of this technique is the labour required to build and maintain the fingerprints database, which has to be rebuilt every time a significant change in the wireless environment occurs, such as installation or removal of new APs, changes in the layout of a building, etc. This paper investigates a new method to utilise user feedback as a way of monitoring changes in the wireless environment. It is based on a system of “points” given to each AP in the database. When an AP is switched off, the number of points associated with that AP will gradually reduce as the users give feedback, until it is eventually deleted from the database. If a new AP is installed, the system will detect it and update the database with new fingerprints. Our proposed system has two main advantages. First it can be used as a tool to monitor the wireless environment in a given place, detecting faulty APs or unauthorised installation of new ones. Second, it regulates the size of the database, unlike other systems where feedback is only used to insert new fingerprints in the database. --- paper_title: The multidimensional wisdom of crowds paper_content: Distributing labeling tasks among hundreds or thousands of annotators is an increasingly important method for annotating large datasets. We present a method for estimating the underlying value (e.g. the class) of each image from (noisy) annotations provided by multiple annotators. Our method is based on a model of the image formation and annotation process. Each image has different characteristics that are represented in an abstract Euclidean space. Each annotator is modeled as a multidimensional entity with variables representing competence, expertise and bias. This allows the model to discover and represent groups of annotators that have different sets of skills and knowledge, as well as groups of images that differ qualitatively. We find that our model predicts ground truth labels on both synthetic and real data more accurately than state of the art methods. Experiments also show that our model, starting from a set of binary labels, may discover rich information, such as different "schools of thought" amongst the annotators, and can group together images belonging to separate categories. --- paper_title: Trust-based fusion of untrustworthy information in crowdsourcing applications paper_content: In this paper, we address the problem of fusing untrustworthy reports provided from a crowd of observers, while simultaneously learning the trustworthiness of individuals. To achieve this, we construct a likelihood model of the users's trustworthiness by scaling the uncertainty of its multiple estimates with trustworthiness parameters. We incorporate our trust model into a fusion method that merges estimates based on the trust parameters and we provide an inference algorithm that jointly computes the fused output and the individual trustworthiness of the users based on the maximum likelihood framework. We apply our algorithm to cell tower local- isation using real-world data from the OpenSignal project and we show that it outperforms the state-of-the-art methods in both accuracy, by up to 21%, and consistency, by up to 50% of its predictions. --- paper_title: Spatial Weighted Outlier Detection paper_content: Spatial outliers are the spatial objects with distinct features from their surrounding neighbors. Detection of spatial outliers helps reveal valuable information from large spatial data sets. In many real applications, spatial objects can not be simply abstracted as isolated points. They have different boundary, size, volume, and location. These spatial properties affect the impact of a spatial object on its neighbors and should be taken into consideration. In this paper, we propose two spatial outlier detection methods which integrate the impact of spatial properties to the outlierness measurement. Experimental results on a real data set demonstrate the effectiveness of the proposed algorithms. --- paper_title: Whose vote should count more: Optimal integration of labels from labelers of unknown expertise paper_content: Modern machine learning-based approaches to computer vision require very large databases of hand labeled images. Some contemporary vision systems already require on the order of millions of images for training (e.g., Omron face detector [9]). New Internet-based services allow for a large number of labelers to collaborate around the world at very low cost. However, using these services brings interesting theoretical and practical challenges: (1) The labelers may have wide ranging levels of expertise which are unknown a priori, and in some cases may be adversarial; (2) images may vary in their level of difficulty; and (3) multiple labels for the same image must be combined to provide an estimate of the actual label of the image. Probabilistic approaches provide a principled way to approach these problems. In this paper we present a probabilistic model and use it to simultaneously infer the label of each image, the expertise of each labeler, and the difficulty of each image. On both simulated and real data, we demonstrate that the model outperforms the commonly used "Majority Vote" heuristic for inferring image labels, and is robust to both noisy and adversarial labelers. --- paper_title: WLAN location determination via clustering and probability distributions paper_content: We present a WLAN location determination technique, the Joint Clustering technique, that uses: (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy. --- paper_title: RADAR: an in-building RF-based user location and tracking system paper_content: The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy. --- paper_title: Prediction of Protein Structural Classes paper_content: A protein is usually classified into one of the following five struc- tural classes: a!, j3, a! +j3, a!/j3, and ( (irregular). The structural class of aprotein is correlated with its amino acid composition. However, given the amino acid composition of aprotein, how may one predict its structural class? Various efforts have been made in addressing this problem. This review addresses the progress in this field, with the focus on the state of the art, which is featured by a novel prediction algorithm and a recently developed database. The novel algorithm is characterized by a covariance matrix that takes into account the coupling effect among different amino acid components of a protein. The new database was established based on the requirement that the classes should have (1) as many nonhomologous structures as possible, (2) good quality structure, and (3) typical or distinguishable features for each of the structural classes concerned. The very high success rate for both the training-set proteins and the testing-set proteins, which has been further validated by a simulated analysis and a jackknife analysis, indicates that it is possible to predict the structural class of a protein according to its amino acid composition if an ideal and complete database can be established. It also suggests that the overall fold of a protein is basically determined by its amino acid composition. --- paper_title: Challenge: ubiquitous location-aware computing and the "place lab" initiative paper_content: To be widely adopted, location-aware computing must be as effortless, familiar and rewarding as web search tools like Google. We envisage the global scale Place Lab, consisting of an open software base and a community building activity as a way to bootstrap the broad adoption of location-aware computing. The initiative is a laboratory because it will also be a vehicle for research and instruction, especially in the formative stages. The authors draw on their experiences with campus and building-scale location systems to identify the technological and social barriers to a truly ubiquitous deployment. With a grasp of these "barriers to adoption," we present a usage scenario, the problems in realizing this scenario, and how these problems will be addressed. We conclude with a sketch of the multi-organization cooperative being formed to move this effort forward. --- paper_title: Reliability considerations of multi-sensor multi-network pedestrian navigation paper_content: This study discusses a simple multi-sensor multi-network positioning system that integrates global positioning satellite (GPS) measurements, accelerometers and a digital compass with wireless network localisation utilising a pedestrian motion model and dead reckoning. The feasibility of the multi-technology system for seamless outdoor to indoor pedestrian navigation is discussed with the emphasis on reliability issues and adaptability requirements. The multi-sensor multi-network positioning system is developed for challenging navigation environments such as indoors and deep urban canyons. This study considers how to estimate and improve such a multi-sensor multi-network system's reliability and estimate its accuracy. An outdoor to indoor pedestrian test is conducted. Adaptive filtering performance of the multi-technology solution as well as general measurement quality monitoring and error detection when an over-determined solution is at hand is shown. Reliability estimation utilising adaptation in the form of environment detection to estimate the final positioning accuracy is also presented. --- paper_title: A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS paper_content: Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user’s motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data. --- paper_title: Growing an organic indoor location system paper_content: Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments. --- paper_title: Redpin - adaptive, zero-configuration indoor localization through user collaboration paper_content: Redpin is a fingerprint-based indoor localization system designed and built to run on mobile phones. The basic principles of our system are based on known systems like Place Lab or Radar. However, with Redpin it is possible to consider the signal-strength of GSM, Bluetooth, and WiFi access points on a mobile phone. Moreover, we devised methods to omit the time-consuming training phase and instead incorporate a folksonomy-like approach where the users train the system while using it. Finally, this approach also enables the system to expeditiously adapt to changes in the environment, caused for example by replaced access points. --- paper_title: WiFi-SLAM Using Gaussian Process Latent Variable Models paper_content: WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization. --- paper_title: Crowdsourcing with Smartphones paper_content: Smartphones can reveal crowdsourcing's full potential and let users transparently contribute to complex and novel problem solving. This emerging area is illustrated through a taxonomy that classifies the mobile crowdsourcing field and through three new applications that optimize location-based search and similarity services based on crowd-generated data. Such applications can be deployed on SmartLab, a cloud of more than 40 Android devices deployed at the University of Cyprus that provides an open testbed to facilitate research and development of smartphone applications on a massive scale. --- paper_title: Zee: zero-effort crowdsourcing for indoor localization paper_content: Radio Frequency (RF) fingerprinting, based onWiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user's initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length,(c) back propagation to go back and improve the accuracy of ocalization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building. --- paper_title: Collaborative Pedestrian Mapping of Buildings Using Inertial Sensors and FootSLAM paper_content: The FeetSLAM technique builds on iterative processing of multiple sets of pedestrian odometry data, based on FootSLAM. The objective is to obtain maps of large areas based on many data sets. The central idea is that maps originating from other data sets are used as a so-called prior map for a given data set. We show that this follows from the optimal FeetSLAM derivation but is more suited to practical computation limitations such as limited memory. It also yields maps which are not overly dominated by one data set but rather balances the characteristics of each with the effect of averaging out errors. Over iterations, FootSLAM maps are gradually combined to yield a high-accuracy global map the iteration speed is controlled by employing concepts from simulated annealing. We validate our approach using two data sets from two locations, consisting of four and five walks respectively. --- paper_title: Simultaneous Localization and Mapping for pedestrians using distortions of the local magnetic field intensity in large indoor environments paper_content: We present a Simultaneous Localization and Mapping (SLAM) algorithm based on measurements of the ambient magnetic field strength (MagSLAM) that allows quasi-real-time mapping and localization in buildings, where pedestrians with foot-mounted sensors are the subjects to be localized. We assume two components to be present: firstly a source of odometry (human step measurements), and secondly a sensor of the local magnetic field intensity. Our implementation follows the FastSLAM factorization using a particle filter. We augment the hexagonal transition map used in the pre-existing FootSLAM algorithm with local maps of the magnetic field strength, binned in a hierarchical hexagonal structure. We performed extensive experiments in a number of different buildings and present the results for five data sets for which we have ground truth location information. We consider the results obtained using MagSLAM to be strong evidence that scalable and accurate localization is possible without an a priori map. --- paper_title: Locating in fingerprint space: wireless indoor localization with little human intervention paper_content: Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1600m2, and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey. --- paper_title: HiMLoc: Indoor smartphone localization via activity aware Pedestrian Dead Reckoning with selective crowdsourced WiFi fingerprinting paper_content: The large number of applications that rely on indoor positioning encourages more advancement in this field. Smartphones are becoming a common presence in our daily life, so taking advantage of their sensors can help to provide ubiquitous positioning solution. We propose HiMLoc, a novel solution that synergistically uses Pedestrian Dead Reckoning (PDR) and WiFi fingerprinting to exploit their positive aspects and limit the impact of their negative aspects. Specifically, HiMLoc combines location tracking and activity recognition using inertial sensors on mobile devices with location-specific weighted assistance from a crowd-sourced WiFi fingerprinting system via a particle filter. By using just the most common sensors available on the large majority of smartphones (accelerometer, compass, and WiFi card) and offering an easily deployable method (requiring just the locations of stairs, elevators, corners and entrances), HiMLoc is shown to achieve median accuracies lower than 3 meters in most cases. --- paper_title: No need to war-drive: unsupervised indoor localization paper_content: We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1:69m. War-driving is not necessary, neither are floorplans the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment. --- paper_title: Elekspot: A Platform for Urban Place Recognition via Crowdsourcing paper_content: The proliferation of Wi-Fi infrastructures has facilitated numerous indoor localization techniques using Wi-Fi location fingerprints. They make it possible to identify a room or a place in urban environment, which is especially important in enabling many interesting location-based services. As there are too many rooms and places such as cafes and restaurants to be recognized in urban environment, the crowdsourcing approach has been proposed to collect Wi-Fi location fingerprints based on user participation. However, its actual deployment in a large-scale urban environment presents numerous design and implementation challenges due to urban characteristics such as a large crowd, dense region, and device diversity. This paper presents Elekspot system, whose design goal is to support system scalability, device heterogeneity, robustness against lack of contributions, and localization accuracy. Through several experiments and implementation of actual applications targeting urban places we confirmed that the architecture and methods of Elekspot can effectively meet the design goals. --- paper_title: FreeLoc: Calibration-free crowdsourced indoor localization paper_content: Many indoor localization techniques that rely on RF signals from wireless Access Points have been proposed in the last decade. In recent years, research on crowdsourced (also known as “Organic”) Wi-Fi fingerprint positioning systems has been attracting much attention. This participatory approach introduces new challenges that no previously proposed techniques have taken into account. This paper proposes “FreeLoc”, an efficient localization method addressing three major technical issues posed in crowdsourcing based systems. Our novel solution facilitates 1) extracting accurate fingerprint values from short RSS measurement times 2) calibration-free positioning across different devices and 3) maintaining a single fingerprint for each location in a radio map, irrespective of any number of uploaded data sets for a given location. Through experiments using four different smartphones, we evaluate our new indoor positioning method. The experimental results confirm that the proposed scheme provides consistent localization accuracy in an environment where the device heterogeneity and the multiple surveyor problems exist. --- paper_title: WicLoc: An indoor localization system based on WiFi fingerprints and crowdsourcing paper_content: WiFi fingerprint-based indoor localization techniques have been proposed and widely used in recent years. Most solutions need a site survey to collect fingerprints from interested locations to construct the fingerprint database. However, the site survey is labor-intensive and time-consuming. To overcome this shortcoming, we record user motions as well as WiFi signals without the active participation of the users to construct the fingerprint database, in place of the previous site survey. In this paper, we develop an indoor localization system called WicLoc, which is based on WiFi fingerprinting and crowdsourcing. We design a fingerprint model to form fingerprints of each location of interest after fingerprint collection. We propose a weighted KNN (K-Nearest Neighbor) algorithm to assign different weights to APs and achieve room-level localization. To obtain the absolute coordinate of users, we design a novel MDS (Multi-Dimensional Scaling) algorithm called MDS-C (Multi-Dimensional Scaling with Calibrations) to calculate coordinates of interested locations in the corridor and rooms, where anchor points are used to calibrate absolute coordinates of users. Experimental results show that our system can achieve a competitive localization accuracy compared with state-of-the-art WiFi fingerprint-based methods while avoiding the labor-intensive site survey. --- paper_title: Rank based fingerprinting algorithm for indoor positioning paper_content: A novel Received Signal Strength (RSS) rank based fingerprinting algorithm for indoor positioning is presented. Because RSS rank is invariant to bias and scaling, the algorithm provides the same accuracy for any receiver device, without the need for RSS calibration. Similarity measures to compare ranked vectors are introduced and their impact on positioning accuracy is investigated in experiments. Experimental results shown that proposed algorithm can achieve better accuracy than commonly used NN and WKNN fingerprinting algorithms. ---	Title: A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization Section 1: Introduction Description 1: This section provides background information on mobile indoor localization and crowd sensing, setting the context for the survey. Section 2: Foundation of Crowd Sensing for Indoor Localization Description 2: This section describes the basic principles of crowd sensing and its application in fingerprinting-based indoor localization, including the learning and positioning phases. Section 3: Scheme of a Crowd Sensing Based Mobile Indoor Localization Approach Description 3: This section outlines the structure of a crowd sensing-based localization system, focusing on the roles of the frontend and backend components. Section 4: Crowd Sensing versus Expert Survey Description 4: This section compares crowd sensing with traditional expert surveys, evaluating factors like time consumption, labor cost, data quality, and more. Section 5: Opportunistic Signals Description 5: This section examines various types of opportunistic signals used for fingerprinting-based localization, including Wi-Fi, Bluetooth, magnetic fields, image features, and others. Section 6: Walking Trajectory Description 6: This section discusses methods for obtaining the walking trajectory of participants, focusing on Pedestrian Dead Reckoning (PDR) and Simultaneous Localization and Mapping (SLAM). Section 7: Indoor Maps Description 7: This section explores the use of indoor maps, such as raster and vector maps, in indoor localization. Section 8: Organic Fingerprint Description 8: This section defines the concept of organic fingerprints and discusses the challenges and solutions related to data fusion in crowd sensing. Section 9: Fingerprinting-Based Positioning Algorithms Description 9: This section reviews various positioning algorithms, including deterministic, probabilistic, and hybrid solutions for indoor localization. Section 10: The State-of-the-Art Solutions Description 10: This section presents a comparison of recent state-of-the-art solutions in crowd sensing-based indoor localization. Section 11: Challenges Description 11: This section outlines the challenges facing crowd sensing-based indoor localization, such as device diversity, quality control, power consumption, and privacy protection. Section 12: Conclusion and Future Trends Description 12: This section concludes the survey by summarizing the findings and discussing future research directions in crowd sensing-based indoor localization.
Variability in quality attributes of service-based software systems: A systematic literature review	37	--- paper_title: Variability management in software product lines: an investigation of contemporary industrial challenges paper_content: Variability management is critical for achieving the large scale reuse promised by the software product line paradigm. It has been studied for almost 20 years. We assert that it is important to explore how well the body of knowledge of variability management solves the challenges faced by industrial practitioners, and what are the remaining and (or) emerging challenges. To gain such understanding of the challenges of variability management faced by practitioners, we have conducted an empirical study using focus group as data collection method. The results of the study highlight several technical challenges that are often faced by practitioners in their daily practices. Different from previous studies, the results also reveal and shed light on several non-technical challenges that were almost neglected by existing research. --- paper_title: Systematic literature reviews in software engineering - A tertiary study paper_content: Context: In a previous study, we reported on a systematic literature review (SLR), based on a manual search of 13 journals and conferences undertaken in the period 1st January 2004 to 30th June 2007. Objective: The aim of this on-going research is to provide an annotated catalogue of SLRs available to software engineering researchers and practitioners. This study updates our previous study using a broad automated search. Method: We performed a broad automated search to find SLRs published in the time period 1st January 2004 to 30th June 2008. We contrast the number, quality and source of these SLRs with SLRs found in the original study. Results: Our broad search found an additional 35 SLRs corresponding to 33 unique studies. Of these papers, 17 appeared relevant to the undergraduate educational curriculum and 12 appeared of possible interest to practitioners. The number of SLRs being published is increasing. The quality of papers in conferences and workshops has improved as more researchers use SLR guidelines. Conclusion: SLRs appear to have gone past the stage of being used solely by innovators but cannot yet be considered a main stream software engineering research methodology. They are addressing a wide range of topics but still have limitations, such as often failing to assess primary study quality. --- paper_title: Systematic Review in Software Engineering paper_content: A kit of assemblable components for implantation into the bone of a mammal for use in distraction osteogenesis. The kit comprises a fixture, a footing and a distracter, the fixture including a longitudinally extending body portion with a proximal end and a distal end, the body portion having an exterior surface adapted for contact and integration with bone tissue, the body portion having a generally longitudinally extending bore extending from a proximal opening adjacent the proximal end to a distal opening adjacent the distal end. The footing includes a proximal surface and a distal surface. The distracter comprises a generally rod-shaped body including a distal end and a proximal end, and the proximal end of the distracter is adapted to bear against the footing. There are first and second engaging means on the fixture and the distracter respectively for adjustably locating the fixture relative to the distracter. --- paper_title: An Overview of Software Engineering Approaches to Service Oriented Architectures in Various Fields paper_content: For the last few years, a rise has been observed in research activity in Service Oriented Architectures, with applications in different sectors. Several new technologies have been introduced and even more are being currently researched and aimed to the future. In this paper we present and analyze some of the most influential approaches from a software engineer’s point of view that belong either to the academic or to the industrial field. Despite their differences though, all of these approaches share a service oriented mentality, with the purpose of lessening the issues of clients and companies, students and teachers, citizens and government employees alike. Lastly, we discuss our findings from the comparison and present possible new research opportunities for the immediate future. --- paper_title: Exploring service-oriented system engineering challenges: a systematic literature review paper_content: Service-oriented system engineering (SOSE) has drawn increasing attention since service-oriented computing was introduced in the beginning of this decade. A large number of SOSE challenges that call for special software engineering efforts have been proposed in the research community. Our goal is to gain insight into the current status of SOSE research issues as published to date. To this end, we conducted a systematic literature review exploring SOSE challenges that have been claimed between January 2000 and July 2008. This paper presents the results of the systematic review as well as the empirical research method we followed. In this review, of the 729 publications that have been examined, 51 were selected as primary studies, from which more than 400 SOSE challenges were elicited. By applying qualitative data analysis methods to the extracted data from the review, we proved our hypotheses about the classification scheme. We are able to conclude that the SOSE challenges can be classified along two dimensions: (a) based on themes (or topics) that they cover and (b) based on characteristics (or types) that they reveal. By analyzing the distribution of the SOSE challenges on the topics and types in the years 2000–2008, we are able to point out the trend in SOSE research activities. The findings of this review further provide empirical evidence for establishing future SOSE research agendas. --- paper_title: Systematic literature reviews in software engineering – A systematic literature review paper_content: Background: In 2004 the concept of evidence-based software engineering (EBSE) was introduced at the ICSE04 conference. Aims: This study assesses the impact of systematic literature reviews (SLRs) which are the recommended EBSE method for aggregating evidence. Method: We used the standard systematic literature review method employing a manual search of 10 journals and 4 conference proceedings. Results: Of 20 relevant studies, eight addressed research trends rather than technique evaluation. Seven SLRs addressed cost estimation. The quality of SLRs was fair with only three scoring less than 2 out of 4. Conclusions: Currently, the topic areas covered by SLRs are limited. European researchers, particularly those at the Simula Laboratory appear to be the leading exponents of systematic literature reviews. The series of cost estimation SLRs demonstrate the potential value of EBSE for synthesising evidence and making it available to practitioners. --- paper_title: Experiences conducting systematic reviews from novices' perspective paper_content: A systematic review (SR) is a sound methodology for collecting evidence on a research topic of interest and establishing the context of future research. Unlike ordinary or even expert literature reviews, SRs are systematic thus increasing the confidence in the findings from the previous published literature. SRs can be carried out by both experienced and novice researchers; however, while expert researchers? experiences with conducting SRs are important for improving the SR body of knowledge, we believe that novice researchers? experiences are equally important to establish what distinct problems they face while carrying out SRs. With a prior knowledge of these issues, novice researchers can better plan their SRs and seek guidance from expert researchers. --- paper_title: Variability management in software product lines: a systematic review paper_content: Variability Management (VM) in Software Product Line (SPL) is a key activity that usually affects the degree to which a SPL is successful. SPL community has spent huge amount of resources on developing various approaches to dealing with variability related challenges over the last decade. To provide an overview of different aspects of the proposed VM approaches, we carried out a systematic literature review of the papers reporting VM in SPL. This paper presents and discusses the findings from this systematic literature review. The results reveal the chronological backgrounds of various approaches over the history of VM research, and summarize the key issues that drove the evolution of different approaches. This study has also identified several gaps that need to be filled by future efforts in this line of research. --- paper_title: Variability management in software product lines: an investigation of contemporary industrial challenges paper_content: Variability management is critical for achieving the large scale reuse promised by the software product line paradigm. It has been studied for almost 20 years. We assert that it is important to explore how well the body of knowledge of variability management solves the challenges faced by industrial practitioners, and what are the remaining and (or) emerging challenges. To gain such understanding of the challenges of variability management faced by practitioners, we have conducted an empirical study using focus group as data collection method. The results of the study highlight several technical challenges that are often faced by practitioners in their daily practices. Different from previous studies, the results also reveal and shed light on several non-technical challenges that were almost neglected by existing research. --- paper_title: An Overview of Software Engineering Approaches to Service Oriented Architectures in Various Fields paper_content: For the last few years, a rise has been observed in research activity in Service Oriented Architectures, with applications in different sectors. Several new technologies have been introduced and even more are being currently researched and aimed to the future. In this paper we present and analyze some of the most influential approaches from a software engineer’s point of view that belong either to the academic or to the industrial field. Despite their differences though, all of these approaches share a service oriented mentality, with the purpose of lessening the issues of clients and companies, students and teachers, citizens and government employees alike. Lastly, we discuss our findings from the comparison and present possible new research opportunities for the immediate future. --- paper_title: Exploring service-oriented system engineering challenges: a systematic literature review paper_content: Service-oriented system engineering (SOSE) has drawn increasing attention since service-oriented computing was introduced in the beginning of this decade. A large number of SOSE challenges that call for special software engineering efforts have been proposed in the research community. Our goal is to gain insight into the current status of SOSE research issues as published to date. To this end, we conducted a systematic literature review exploring SOSE challenges that have been claimed between January 2000 and July 2008. This paper presents the results of the systematic review as well as the empirical research method we followed. In this review, of the 729 publications that have been examined, 51 were selected as primary studies, from which more than 400 SOSE challenges were elicited. By applying qualitative data analysis methods to the extracted data from the review, we proved our hypotheses about the classification scheme. We are able to conclude that the SOSE challenges can be classified along two dimensions: (a) based on themes (or topics) that they cover and (b) based on characteristics (or types) that they reveal. By analyzing the distribution of the SOSE challenges on the topics and types in the years 2000–2008, we are able to point out the trend in SOSE research activities. The findings of this review further provide empirical evidence for establishing future SOSE research agendas. --- paper_title: Software Architecture in Practice paper_content: The award-winning and highly influential Software Architecture in Practice, Third Edition, has been substantially revised to reflect the latest developments in the field. In a real-world setting, the book once again introduces the concepts and best practices of software architecturehow a software system is structured and how that systems elements are meant to interact. Distinct from the details of implementation, algorithm, and data representation, an architecture holds the key to achieving system quality, is a reusable asset that can be applied to subsequent systems, and is crucial to a software organizations business strategy. The authors have structured this edition around the concept of architecture influence cycles. Each cycle shows how architecture influences, and is influenced by, a particular context in which architecture plays a critical role. Contexts include technical environment, the life cycle of a project, an organizations business profile, and the architects professional practices. The authors also have greatly expanded their treatment of quality attributes, which remain central to their architecture philosophywith an entire chapter devoted to each attributeand broadened their treatment of architectural patterns. If you design, develop, or manage large software systems (or plan to do so), you will find this book to be a valuable resource for getting up to speed on the state of the art. Totally new material covers Contexts of software architecture: technical, project, business, and professional Architecture competence: what this means both for individuals and organizations The origins of business goals and how this affects architecture Architecturally significant requirements, and how to determine them Architecture in the life cycle, including generate-and-test as a design philosophy; architecture conformance during implementation; architecture and testing; and architecture and agile development Architecture and current technologies, such as the cloud, social networks, and end-user devices --- paper_title: Quality Attributes for Service-Oriented Architectures paper_content: The SOA approach is a very popular choice today for the implementation of distributed systems. The use of SOA or more specifically the Web services technology is an important architecture decision. An architect should understand how different quality attributes for a system are impacted by that decision. While there are significant benefits with respect to interoperability and modifiability, other qualities such as performance, security and testability are concerns. This paper discusses how the different quality attributes of a system can be positively or negatively affected by the use of such technology. It describes the factors related to each attribute, as well as possible tradeoffs and existing efforts to achieve that quality. The paper also discusses open issues in service level agreements that are used to contract the level of service quality between service providers and users. --- paper_title: Managing Variation in Services in a Software Product Line Context paper_content: Abstract : Software product line (SPL) and service-oriented architecture (SOA) approaches both enable an organization to reuse existing assets and capabilities rather than repeatedly redeveloping them for new systems. Organizations can capitalize on such reuse in software-reliant systems to achieve business goals such as productivity gains, decreased development costs, improved time to market, increased reliability, increased agility, and competitive advantage. Both approaches accommodate variation in the software that is being reused or the way in which it is employed. Meeting business goals through a product line or a set of service-oriented systems requires managing the variation of assets, including services. This report examines combining existing SOA and software product line approaches for variation management. This examination has two objectives: (1) for service-oriented systems development, to present an approach for managing variation by identifying and designing services explicitly targeted to multiple service-oriented systems, and (2) for SPL systems, to present an approach for managing variation where services are a mechanism for variation within a product line or for expanding the product line scope. --- paper_title: Quality Attributes and Service-Oriented Architectures paper_content: Abstract : This report examines the relationship between service-oriented architectures (SOAs) and quality attributes. Because software architecture is the bridge between mission/business goals and a software-intensive system, and quality attribute requirements drive software architecture design, it is important to understand how SOAs support these requirements. This report gives a short introduction to SOAs and outlines some of the main business goals that may lead an organization to choose an SOA to design and implement a system. The report outlines a set of quality attributes that may be derived from an organization's business goals and examines how those attributes relate to an SOA. In addition, the report describes how the SOA impacts those attributes and how choosing an SOA can help an organization achieve its business goals. --- paper_title: Requirements and Tools for Variability Management paper_content: Explicit and software-supported Business Process Management has become the core infrastructure of any medium and large organization that has a need to be efficient and effective. The number of processes of a single organization can be very high, furthermore, they might be very similar, be in need of momentary change, or evolve frequently. If the adhoc adaptation and customization of processes is currently the dominant way, it clearly is not the best. In fact, providing tools for supporting the explicit management of variation in processes (due to customization or evolution needs) has a profound impact on the overall life-cycle of processes in organizations. Additionally, with the increasing adoption of Service-Oriented Architectures, the infrastructure to support automatic reconfiguration and adaptation of business process is solid. In this paper, after defining variability in business process management, we consider the requirements for explicit variation handling for (service based) business process systems. eGovernment serves as an illustrative example of reuse. In this case study, all local municipalities need to implement the same general legal process while adapting it to the local business practices and IT infrastructure needs. Finally, an evaluation of existing tools for explicit variability management is provided with respect to the requirements identified. --- paper_title: The Landscape of Service-Oriented Systems: A Research Perspective paper_content: Service orientation has been touted as one of the most important technologies for designing, implementing and deploying large scale service provision software systems. In this position paper we attempt to investigate an initial classification of challenge areas related to service orientation and service-oriented systems. We start by organizing the research issues related to service orientation in three general categories- business, engineering and operations, plus a set of cross-cutting concerns across domain. We further propose the notion of Service Strategy as a binding model for these three categories. Finally, concluding this position paper, we outline a set of emerging opportunities to be used for further discussion. --- paper_title: A systematic review of quality attributes and measures for software product lines paper_content: It is widely accepted that software measures provide an appropriate mechanism for understanding, monitoring, controlling, and predicting the quality of software development projects. In software product lines (SPL), quality is even more important than in a single software product since, owing to systematic reuse, a fault or an inadequate design decision could be propagated to several products in the family. Over the last few years, a great number of quality attributes and measures for assessing the quality of SPL have been reported in literature. However, no studies summarizing the current knowledge about them exist. This paper presents a systematic literature review with the objective of identifying and interpreting all the available studies from 1996 to 2010 that present quality attributes and/or measures for SPL. These attributes and measures have been classified using a set of criteria that includes the life cycle phase in which the measures are applied; the corresponding quality characteristics; their support for specific SPL characteristics (e.g., variability, compositionality); the procedure used to validate the measures, etc. We found 165 measures related to 97 different quality attributes. The results of the review indicated that 92% of the measures evaluate attributes that are related to maintainability. In addition, 67% of the measures are used during the design phase of Domain Engineering, and 56% are applied to evaluate the product line architecture. However, only 25% of them have been empirically validated. In conclusion, the results provide a global vision of the state of the research within this area in order to help researchers in detecting weaknesses, directing research efforts, and identifying new research lines. In particular, there is a need for new measures with which to evaluate both the quality of the artifacts produced during the entire SPL life cycle and other quality characteristics. There is also a need for more validation (both theoretical and empirical) of existing measures. In addition, our results may be useful as a reference guide for practitioners to assist them in the selection or the adaptation of existing measures for evaluating their software product lines. --- paper_title: Issues in the Design of Flexible and Dynamic Service-Oriented Systems paper_content: Due to the use of the concepts embedded in Service- Oriented Architecture (SOA), software design, now more than ever, involves the use of incomplete information. Applications that utilizeWeb Services are also highly impacted by the problem of deployment and subsequent undeployment of services. Specifically, there is a level of uncertainty caused by the potential for services to become unavailable (either temporarily or permanently). In a scenario where an application must switch from one service to another due to the undeployment problem, the client application may require that new or different handlers be used to cope with the properties of the alternative service. In this current development climate, the design issues become clear: there is a need to reason about how a design is impacted by discovered services, design analysis must consider the transaction and event properties of discovered services, and design of systems must incorporate fault tolerance and high-integrity issues to cope with the dynamic landscape caused by the uncertainty associated with using services. --- paper_title: Testing in Service Oriented Architectures with Dynamic Binding: A Mapping Study paper_content: Context: Service Oriented Architectures (SOA) have emerged as a new paradigm to develop interoperable and highly dynamic applications. Objective: This paper aims to identify the state of the art in the research on testing in Service Oriented Architectures with dynamic binding. Method: A mapping study has been performed employing both manual and automatic search in journals, conference/workshop proceedings and electronic databases. Results: A total of 33 studies have been reviewed in order to extract relevant information regarding a previously defined set of research questions. The detection of faults and the decision making based on the information gathered from the tests have been identified as the main objectives of these studies. To achieve these goals, monitoring and test case generation are the most proposed techniques testing both functional and non-functional properties. Furthermore, different stakeholders have been identified as participants in the tests, which are performed in specific points in time during the life cycle of the services. Finally, it has been observed that a relevant group of studies have not validated their approach yet. Conclusions: Although we have only found 33 studies that address the testing of SOA where the discovery and binding of the services are performed at runtime, this number can be considered significant due to the specific nature of the reviewed topic. The results of this study have contributed to provide a body of knowledge that allows identifying current gaps in improving the quality of the dynamic binding in SOA using testing approaches. --- paper_title: Adaptation of service-based systems paper_content: The advances in modern technology development and future technology changes dictate new challenges and requirements to the engineering and provision of services and service-based systems (SBS). These services and systems should become drastically more flexible; they should be able to operate and evolve in highly dynamic environments and to adequately react to various changes in these environments. In these settings, adaptability becomes a key feature of services as it provides a way for an application to continuously change itself in order to satisfy new contextual requirements. --- paper_title: Exploring service-oriented system engineering challenges: a systematic literature review paper_content: Service-oriented system engineering (SOSE) has drawn increasing attention since service-oriented computing was introduced in the beginning of this decade. A large number of SOSE challenges that call for special software engineering efforts have been proposed in the research community. Our goal is to gain insight into the current status of SOSE research issues as published to date. To this end, we conducted a systematic literature review exploring SOSE challenges that have been claimed between January 2000 and July 2008. This paper presents the results of the systematic review as well as the empirical research method we followed. In this review, of the 729 publications that have been examined, 51 were selected as primary studies, from which more than 400 SOSE challenges were elicited. By applying qualitative data analysis methods to the extracted data from the review, we proved our hypotheses about the classification scheme. We are able to conclude that the SOSE challenges can be classified along two dimensions: (a) based on themes (or topics) that they cover and (b) based on characteristics (or types) that they reveal. By analyzing the distribution of the SOSE challenges on the topics and types in the years 2000–2008, we are able to point out the trend in SOSE research activities. The findings of this review further provide empirical evidence for establishing future SOSE research agendas. --- paper_title: Systematic literature reviews in software engineering – A systematic literature review paper_content: Background: In 2004 the concept of evidence-based software engineering (EBSE) was introduced at the ICSE04 conference. Aims: This study assesses the impact of systematic literature reviews (SLRs) which are the recommended EBSE method for aggregating evidence. Method: We used the standard systematic literature review method employing a manual search of 10 journals and 4 conference proceedings. Results: Of 20 relevant studies, eight addressed research trends rather than technique evaluation. Seven SLRs addressed cost estimation. The quality of SLRs was fair with only three scoring less than 2 out of 4. Conclusions: Currently, the topic areas covered by SLRs are limited. European researchers, particularly those at the Simula Laboratory appear to be the leading exponents of systematic literature reviews. The series of cost estimation SLRs demonstrate the potential value of EBSE for synthesising evidence and making it available to practitioners. --- paper_title: The Landscape of Service-Oriented Systems: A Research Perspective paper_content: Service orientation has been touted as one of the most important technologies for designing, implementing and deploying large scale service provision software systems. In this position paper we attempt to investigate an initial classification of challenge areas related to service orientation and service-oriented systems. We start by organizing the research issues related to service orientation in three general categories- business, engineering and operations, plus a set of cross-cutting concerns across domain. We further propose the notion of Service Strategy as a binding model for these three categories. Finally, concluding this position paper, we outline a set of emerging opportunities to be used for further discussion. --- paper_title: Variability management in software product lines: a systematic review paper_content: Variability Management (VM) in Software Product Line (SPL) is a key activity that usually affects the degree to which a SPL is successful. SPL community has spent huge amount of resources on developing various approaches to dealing with variability related challenges over the last decade. To provide an overview of different aspects of the proposed VM approaches, we carried out a systematic literature review of the papers reporting VM in SPL. This paper presents and discusses the findings from this systematic literature review. The results reveal the chronological backgrounds of various approaches over the history of VM research, and summarize the key issues that drove the evolution of different approaches. This study has also identified several gaps that need to be filled by future efforts in this line of research. --- paper_title: The Goal Question Metric Approach paper_content: As with any engineering discipline, software development requires a measurement mechanism for feedback and evaluation. Measurement is a mechanism for creating a corporate memory and an aid in answering a variety of questions associated with the enactment of any software process. It helps support project planning (e.g., How much will a new project cost?); it allows us to determine the strengths and weaknesses of the current processes and products (e.g., What is the frequency of certain types of errors?); it provides a rationale for adopting/refining techniques (e.g., What is the impact of the technique XX on the productivity of the projects?); it allows us to evaluate the quality of specific processes and products (e.g., What is the defect density in a specific system after deployment?). Measurement also helps, during the course of a project, to assess its progress, to take corrective action based on this assessment, and to evaluate the impact of such action. --- paper_title: On searching relevant studies in software engineering paper_content: BACKGROUND: Systematic Literature Review (SLR) has become an important research methodology in software engineering since 2004. One critical step in applying this methodology is to design and execute appropriate and effective search strategy. This is quite time consuming and error-prone step, which needs to be carefully planned and implemented. There is an apparent need of a systematic approach to designing, executing, and evaluating a suitable search strategy for optimally retrieving the target literature from digital libraries. ::: ::: OBJECTIVE: The main objective of the research reported in this paper is to improve the search step of doing SLRs in SE by devising and evaluating systematic and practical approaches to identifying relevant studies in SE. ::: ::: OUTCOMES: We have systematically selected and analytically studied a large number of papers to understand the state-of-the-practice of search strategies in EBSE. Having identified the limitations of the current ad-hoc nature of search strategies used by SE researchers for SLR, we have devised a systematic approach to developing and executing optimal search strategies in SLRs. The proposed approach incorporates the concept of 'quasi-gold standard', which consists of collection of known studies and corresponding 'quasi-sensitivity' into the search process for evaluating search performance. We report the case study and its finding to demonstrate that the approach is able to improve the rigor of search process in an SLR, and can serves as the supplements to the guidelines for SLRs in EBSE. We plan to further evaluate the proposed approach using several case studies with varying topics in software engineering. --- paper_title: Exploring service-oriented system engineering challenges: a systematic literature review paper_content: Service-oriented system engineering (SOSE) has drawn increasing attention since service-oriented computing was introduced in the beginning of this decade. A large number of SOSE challenges that call for special software engineering efforts have been proposed in the research community. Our goal is to gain insight into the current status of SOSE research issues as published to date. To this end, we conducted a systematic literature review exploring SOSE challenges that have been claimed between January 2000 and July 2008. This paper presents the results of the systematic review as well as the empirical research method we followed. In this review, of the 729 publications that have been examined, 51 were selected as primary studies, from which more than 400 SOSE challenges were elicited. By applying qualitative data analysis methods to the extracted data from the review, we proved our hypotheses about the classification scheme. We are able to conclude that the SOSE challenges can be classified along two dimensions: (a) based on themes (or topics) that they cover and (b) based on characteristics (or types) that they reveal. By analyzing the distribution of the SOSE challenges on the topics and types in the years 2000–2008, we are able to point out the trend in SOSE research activities. The findings of this review further provide empirical evidence for establishing future SOSE research agendas. --- paper_title: Variability management in software product lines: a systematic review paper_content: Variability Management (VM) in Software Product Line (SPL) is a key activity that usually affects the degree to which a SPL is successful. SPL community has spent huge amount of resources on developing various approaches to dealing with variability related challenges over the last decade. To provide an overview of different aspects of the proposed VM approaches, we carried out a systematic literature review of the papers reporting VM in SPL. This paper presents and discusses the findings from this systematic literature review. The results reveal the chronological backgrounds of various approaches over the history of VM research, and summarize the key issues that drove the evolution of different approaches. This study has also identified several gaps that need to be filled by future efforts in this line of research. --- paper_title: A systematic review of comparative evidence of aspect-oriented programming paper_content: Context: Aspect-oriented programming (AOP) promises to improve many facets of software quality by providing better modularization and separation of concerns, which may have system wide affect. There have been numerous claims in favor and against AOP compared with traditional programming languages such as Objective Oriented and Structured Programming Languages. However, there has been no attempt to systematically review and report the available evidence in the literature to support the claims made in favor or against AOP compared with non-AOP approaches. Objective: This research aimed to systematically identify, analyze, and report the evidence published in the literature to support the claims made in favor or against AOP compared with non-AOP approaches. Method: We performed a systematic literature review of empirical studies of AOP based development, published in major software engineering journals and conference proceedings. Results: Our search strategy identified 3307 papers, of which 22 were identified as reporting empirical studies comparing AOP with non-AOP approaches. Based on the analysis of the data extracted from those 22 papers, our findings show that for performance, code size, modularity, and evolution related characteristics, a majority of the studies reported positive effects, a few studies reported insignificant effects, and no study reported negative effects; however, for cognition and language mechanism, negative effects were reported. Conclusion: AOP is likely to have positive effect on performance, code size, modularity, and evolution. However its effect on cognition and language mechanism is less likely to be positive. Care should be taken using AOP outside the context in which it has been validated. --- paper_title: On searching relevant studies in software engineering paper_content: BACKGROUND: Systematic Literature Review (SLR) has become an important research methodology in software engineering since 2004. One critical step in applying this methodology is to design and execute appropriate and effective search strategy. This is quite time consuming and error-prone step, which needs to be carefully planned and implemented. There is an apparent need of a systematic approach to designing, executing, and evaluating a suitable search strategy for optimally retrieving the target literature from digital libraries. ::: ::: OBJECTIVE: The main objective of the research reported in this paper is to improve the search step of doing SLRs in SE by devising and evaluating systematic and practical approaches to identifying relevant studies in SE. ::: ::: OUTCOMES: We have systematically selected and analytically studied a large number of papers to understand the state-of-the-practice of search strategies in EBSE. Having identified the limitations of the current ad-hoc nature of search strategies used by SE researchers for SLR, we have devised a systematic approach to developing and executing optimal search strategies in SLRs. The proposed approach incorporates the concept of 'quasi-gold standard', which consists of collection of known studies and corresponding 'quasi-sensitivity' into the search process for evaluating search performance. We report the case study and its finding to demonstrate that the approach is able to improve the rigor of search process in an SLR, and can serves as the supplements to the guidelines for SLRs in EBSE. We plan to further evaluate the proposed approach using several case studies with varying topics in software engineering. --- paper_title: Exploring service-oriented system engineering challenges: a systematic literature review paper_content: Service-oriented system engineering (SOSE) has drawn increasing attention since service-oriented computing was introduced in the beginning of this decade. A large number of SOSE challenges that call for special software engineering efforts have been proposed in the research community. Our goal is to gain insight into the current status of SOSE research issues as published to date. To this end, we conducted a systematic literature review exploring SOSE challenges that have been claimed between January 2000 and July 2008. This paper presents the results of the systematic review as well as the empirical research method we followed. In this review, of the 729 publications that have been examined, 51 were selected as primary studies, from which more than 400 SOSE challenges were elicited. By applying qualitative data analysis methods to the extracted data from the review, we proved our hypotheses about the classification scheme. We are able to conclude that the SOSE challenges can be classified along two dimensions: (a) based on themes (or topics) that they cover and (b) based on characteristics (or types) that they reveal. By analyzing the distribution of the SOSE challenges on the topics and types in the years 2000–2008, we are able to point out the trend in SOSE research activities. The findings of this review further provide empirical evidence for establishing future SOSE research agendas. --- paper_title: Refining the systematic literature review process—two participant-observer case studies paper_content: Systematic literature reviews (SLRs) are a major tool for supporting evidence-based software engineering. Adapting the procedures involved in such a review to meet the needs of software engineering and its literature remains an ongoing process. As part of this process of refinement, we undertook two case studies which aimed 1) to compare the use of targeted manual searches with broad automated searches and 2) to compare different methods of reaching a consensus on quality. For Case 1, we compared a tertiary study of systematic literature reviews published between January 1, 2004 and June 30, 2007 which used a manual search of selected journals and conferences and a replication of that study based on a broad automated search. We found that broad automated searches find more studies than manual restricted searches, but they may be of poor quality. Researchers undertaking SLRs may be justified in using targeted manual searches if they intend to omit low quality papers, or they are assessing research trends in research methodologies. For Case 2, we analyzed the process used to evaluate the quality of SLRs. We conclude that if quality evaluation of primary studies is a critical component of a specific SLR, assessments should be based on three independent evaluators incorporating at least two rounds of discussion. --- paper_title: A systematic review of comparative evidence of aspect-oriented programming paper_content: Context: Aspect-oriented programming (AOP) promises to improve many facets of software quality by providing better modularization and separation of concerns, which may have system wide affect. There have been numerous claims in favor and against AOP compared with traditional programming languages such as Objective Oriented and Structured Programming Languages. However, there has been no attempt to systematically review and report the available evidence in the literature to support the claims made in favor or against AOP compared with non-AOP approaches. Objective: This research aimed to systematically identify, analyze, and report the evidence published in the literature to support the claims made in favor or against AOP compared with non-AOP approaches. Method: We performed a systematic literature review of empirical studies of AOP based development, published in major software engineering journals and conference proceedings. Results: Our search strategy identified 3307 papers, of which 22 were identified as reporting empirical studies comparing AOP with non-AOP approaches. Based on the analysis of the data extracted from those 22 papers, our findings show that for performance, code size, modularity, and evolution related characteristics, a majority of the studies reported positive effects, a few studies reported insignificant effects, and no study reported negative effects; however, for cognition and language mechanism, negative effects were reported. Conclusion: AOP is likely to have positive effect on performance, code size, modularity, and evolution. However its effect on cognition and language mechanism is less likely to be positive. Care should be taken using AOP outside the context in which it has been validated. --- paper_title: Generalizing a Model of Software Architecture Design from Five Industrial Approaches paper_content: We compare five industrial software architecture design methods and we extract from their commonalities a general software architecture design approach. Using this general approach, we compare across the five methods the artifacts and activities they use or recommend, and we pinpoint similarities and differences. Once we get beyond the great variance in terminology and description, we find that the 5 approaches have a lot in common and match more or less the "ideal" pattern we introduced. --- paper_title: Requirements Engineering for Software Product Lines: A Systematic Literature Review paper_content: Context: Software product line engineering (SPLE) is a growing area showing promising results in research and practice. In order to foster its further development and acceptance in industry, it is necessary to assess the quality of the research so that proper evidence for adoption and validity are ensured. This holds in particular for requirements engineering (RE) within SPLE, where a growing number of approaches have been proposed. Objective: This paper focuses on RE within SPLE and has the following goals: assess research quality, synthesize evidence to suggest important implications for practice, and identify research trends, open problems, and areas for improvement. Method: A systematic literature review was conducted with three research questions and assessed 49 studies, dated from 1990 to 2009. Results: The evidence for adoption of the methods is not mature, given the primary focus on toy examples. The proposed approaches still have serious limitations in terms of rigor, credibility, and validity of their findings. Additionally, most approaches still lack tool support addressing the heterogeneity and mostly textual nature of requirements formats as well as address only the proactive SPLE adoption strategy. Conclusions: Further empirical studies should be performed with sufficient rigor to enhance the body of evidence in RE within SPLE. In this context, there is a clear need for conducting studies comparing alternative methods. In order to address scalability and popularization of the approaches, future research should be invested in tool support and in addressing combined SPLE adoption strategies. --- paper_title: Applying Systematic Reviews to Diverse Study Types: An Experience Report paper_content: Systematic reviews are one of the key building blocks of evidence-based software engineering. Current guidelines for such reviews are, for a large part, based on standard meta-analytic techniques. However, such quantitative techniques have only limited applicability to software engineering research. In this paper, therefore, we describe our experience with an approach to combine diverse study types in a systematic review of empirical research of agile software development. --- paper_title: A systematic review of evaluation of variability management approaches in software product lines paper_content: ContextVariability management (VM) is one of the most important activities of software product-line engineering (SPLE), which intends to develop software-intensive systems using platforms and mass customization. VM encompasses the activities of eliciting and representing variability in software artefacts, establishing and managing dependencies among different variabilities, and supporting the exploitation of the variabilities for building and evolving a family of software systems. Software product line (SPL) community has allocated huge amount of effort to develop various approaches to dealing with variability related challenges during the last two decade. Several dozens of VM approaches have been reported. However, there has been no systematic effort to study how the reported VM approaches have been evaluated. ObjectiveThe objectives of this research are to review the status of evaluation of reported VM approaches and to synthesize the available evidence about the effects of the reported approaches. MethodWe carried out a systematic literature review of the VM approaches in SPLE reported from 1990s until December 2007. ResultsWe selected 97 papers according to our inclusion and exclusion criteria. The selected papers appeared in 56 publication venues. We found that only a small number of the reviewed approaches had been evaluated using rigorous scientific methods. A detailed investigation of the reviewed studies employing empirical research methods revealed significant quality deficiencies in various aspects of the used quality assessment criteria. The synthesis of the available evidence showed that all studies, except one, reported only positive effects. ConclusionThe findings from this systematic review show that a large majority of the reported VM approaches have not been sufficiently evaluated using scientifically rigorous methods. The available evidence is sparse and the quality of the presented evidence is quite low. The findings highlight the areas in need of improvement, i.e., rigorous evaluation of VM approaches. However, the reported evidence is quite consistent across different studies. That means the proposed approaches may be very beneficial when they are applied properly in appropriate situations. Hence, it can be concluded that further investigations need to pay more attention to the contexts under which different approaches can be more beneficial. --- paper_title: Increasing business flexibility and SOA adoption through effective SOA governance paper_content: Most organizations understand the need to address service-oriented architecture (SOA) governance during SOA adoption. An abundance of information is available defining SOA governance: what it is and what it is not, why it is important, and why organizational change must be addressed. Increasingly business and information technology (IT) stakeholders, executive and technical, acknowledge that SOA governance is essential for realizing the benefits of SOA adoption: building more-flexible IT architectures, improving the fusion between business and IT models, and making business processes more flexible and reusable. However, what is not clear is how an organization gets started. What works and what does not work? More importantly, what is required in SOA governance for organizations to see sustained and realized benefits? This paper describes a framework, the SOA governance model, that can be used to scope and identify what is required for effective SOA governance. Based on client experiences, we describe four approaches to getting started with SOA governance, and we describe how to use these four approaches to make shared services (services used by two or more consumers), reuse, and flexibility a reality. We also discuss lessons learned in using these four approaches. --- paper_title: Variability management in software product lines: a systematic review paper_content: Variability Management (VM) in Software Product Line (SPL) is a key activity that usually affects the degree to which a SPL is successful. SPL community has spent huge amount of resources on developing various approaches to dealing with variability related challenges over the last decade. To provide an overview of different aspects of the proposed VM approaches, we carried out a systematic literature review of the papers reporting VM in SPL. This paper presents and discusses the findings from this systematic literature review. The results reveal the chronological backgrounds of various approaches over the history of VM research, and summarize the key issues that drove the evolution of different approaches. This study has also identified several gaps that need to be filled by future efforts in this line of research. --- paper_title: A method for evaluating rigor and industrial relevance of technology evaluations paper_content: One of the main goals of an applied research field such as software engineering is the transfer and widespread use of research results in industry. To impact industry, researchers developing technologies in academia need to provide tangible evidence of the advantages of using them. This can be done trough step-wise validation, enabling researchers to gradually test and evaluate technologies to finally try them in real settings with real users and applications. The evidence obtained, together with detailed information on how the validation was conducted, offers rich decision support material for industry practitioners seeking to adopt new technologies and researchers looking for an empirical basis on which to build new or refined technologies. This paper presents model for evaluating the rigor and industrial relevance of technology evaluations in software engineering. The model is applied and validated in a comprehensive systematic literature review of evaluations of requirements engineering technologies published in software engineering journals. The aim is to show the applicability of the model and to characterize how evaluations are carried out and reported to evaluate the state-of-research. The review shows that the model can be applied to characterize evaluations in requirements engineering. The findings from applying the model also show that the majority of technology evaluations in requirements engineering lack both industrial relevance and rigor. In addition, the research field does not show any improvements in terms of industrial relevance over time. --- paper_title: Variability management in software product lines: a systematic review paper_content: Variability Management (VM) in Software Product Line (SPL) is a key activity that usually affects the degree to which a SPL is successful. SPL community has spent huge amount of resources on developing various approaches to dealing with variability related challenges over the last decade. To provide an overview of different aspects of the proposed VM approaches, we carried out a systematic literature review of the papers reporting VM in SPL. This paper presents and discusses the findings from this systematic literature review. The results reveal the chronological backgrounds of various approaches over the history of VM research, and summarize the key issues that drove the evolution of different approaches. This study has also identified several gaps that need to be filled by future efforts in this line of research. --- paper_title: A systematic review of evaluation of variability management approaches in software product lines paper_content: ContextVariability management (VM) is one of the most important activities of software product-line engineering (SPLE), which intends to develop software-intensive systems using platforms and mass customization. VM encompasses the activities of eliciting and representing variability in software artefacts, establishing and managing dependencies among different variabilities, and supporting the exploitation of the variabilities for building and evolving a family of software systems. Software product line (SPL) community has allocated huge amount of effort to develop various approaches to dealing with variability related challenges during the last two decade. Several dozens of VM approaches have been reported. However, there has been no systematic effort to study how the reported VM approaches have been evaluated. ObjectiveThe objectives of this research are to review the status of evaluation of reported VM approaches and to synthesize the available evidence about the effects of the reported approaches. MethodWe carried out a systematic literature review of the VM approaches in SPLE reported from 1990s until December 2007. ResultsWe selected 97 papers according to our inclusion and exclusion criteria. The selected papers appeared in 56 publication venues. We found that only a small number of the reviewed approaches had been evaluated using rigorous scientific methods. A detailed investigation of the reviewed studies employing empirical research methods revealed significant quality deficiencies in various aspects of the used quality assessment criteria. The synthesis of the available evidence showed that all studies, except one, reported only positive effects. ConclusionThe findings from this systematic review show that a large majority of the reported VM approaches have not been sufficiently evaluated using scientifically rigorous methods. The available evidence is sparse and the quality of the presented evidence is quite low. The findings highlight the areas in need of improvement, i.e., rigorous evaluation of VM approaches. However, the reported evidence is quite consistent across different studies. That means the proposed approaches may be very beneficial when they are applied properly in appropriate situations. Hence, it can be concluded that further investigations need to pay more attention to the contexts under which different approaches can be more beneficial. --- paper_title: A method for evaluating rigor and industrial relevance of technology evaluations paper_content: One of the main goals of an applied research field such as software engineering is the transfer and widespread use of research results in industry. To impact industry, researchers developing technologies in academia need to provide tangible evidence of the advantages of using them. This can be done trough step-wise validation, enabling researchers to gradually test and evaluate technologies to finally try them in real settings with real users and applications. The evidence obtained, together with detailed information on how the validation was conducted, offers rich decision support material for industry practitioners seeking to adopt new technologies and researchers looking for an empirical basis on which to build new or refined technologies. This paper presents model for evaluating the rigor and industrial relevance of technology evaluations in software engineering. The model is applied and validated in a comprehensive systematic literature review of evaluations of requirements engineering technologies published in software engineering journals. The aim is to show the applicability of the model and to characterize how evaluations are carried out and reported to evaluate the state-of-research. The review shows that the model can be applied to characterize evaluations in requirements engineering. The findings from applying the model also show that the majority of technology evaluations in requirements engineering lack both industrial relevance and rigor. In addition, the research field does not show any improvements in terms of industrial relevance over time. --- paper_title: Mathematical notation in formal specification: too difficult for the masses? paper_content: The phrase "not much mathematics required" can imply a variety of skill levels. When this phrase is applied to computer scientists, software engineers, and clients in the area of formal specification, the word "much" can be widely misinterpreted with disastrous consequences. A small experiment in reading specifications revealed that students already trained in discrete mathematics and the specification notation performed very poorly; much worse than could reasonably be expected if formal methods proponents are to be believed. --- paper_title: A method for evaluating rigor and industrial relevance of technology evaluations paper_content: One of the main goals of an applied research field such as software engineering is the transfer and widespread use of research results in industry. To impact industry, researchers developing technologies in academia need to provide tangible evidence of the advantages of using them. This can be done trough step-wise validation, enabling researchers to gradually test and evaluate technologies to finally try them in real settings with real users and applications. The evidence obtained, together with detailed information on how the validation was conducted, offers rich decision support material for industry practitioners seeking to adopt new technologies and researchers looking for an empirical basis on which to build new or refined technologies. This paper presents model for evaluating the rigor and industrial relevance of technology evaluations in software engineering. The model is applied and validated in a comprehensive systematic literature review of evaluations of requirements engineering technologies published in software engineering journals. The aim is to show the applicability of the model and to characterize how evaluations are carried out and reported to evaluate the state-of-research. The review shows that the model can be applied to characterize evaluations in requirements engineering. The findings from applying the model also show that the majority of technology evaluations in requirements engineering lack both industrial relevance and rigor. In addition, the research field does not show any improvements in terms of industrial relevance over time. --- paper_title: A method for evaluating rigor and industrial relevance of technology evaluations paper_content: One of the main goals of an applied research field such as software engineering is the transfer and widespread use of research results in industry. To impact industry, researchers developing technologies in academia need to provide tangible evidence of the advantages of using them. This can be done trough step-wise validation, enabling researchers to gradually test and evaluate technologies to finally try them in real settings with real users and applications. The evidence obtained, together with detailed information on how the validation was conducted, offers rich decision support material for industry practitioners seeking to adopt new technologies and researchers looking for an empirical basis on which to build new or refined technologies. This paper presents model for evaluating the rigor and industrial relevance of technology evaluations in software engineering. The model is applied and validated in a comprehensive systematic literature review of evaluations of requirements engineering technologies published in software engineering journals. The aim is to show the applicability of the model and to characterize how evaluations are carried out and reported to evaluate the state-of-research. The review shows that the model can be applied to characterize evaluations in requirements engineering. The findings from applying the model also show that the majority of technology evaluations in requirements engineering lack both industrial relevance and rigor. In addition, the research field does not show any improvements in terms of industrial relevance over time. --- paper_title: On searching relevant studies in software engineering paper_content: BACKGROUND: Systematic Literature Review (SLR) has become an important research methodology in software engineering since 2004. One critical step in applying this methodology is to design and execute appropriate and effective search strategy. This is quite time consuming and error-prone step, which needs to be carefully planned and implemented. There is an apparent need of a systematic approach to designing, executing, and evaluating a suitable search strategy for optimally retrieving the target literature from digital libraries. ::: ::: OBJECTIVE: The main objective of the research reported in this paper is to improve the search step of doing SLRs in SE by devising and evaluating systematic and practical approaches to identifying relevant studies in SE. ::: ::: OUTCOMES: We have systematically selected and analytically studied a large number of papers to understand the state-of-the-practice of search strategies in EBSE. Having identified the limitations of the current ad-hoc nature of search strategies used by SE researchers for SLR, we have devised a systematic approach to developing and executing optimal search strategies in SLRs. The proposed approach incorporates the concept of 'quasi-gold standard', which consists of collection of known studies and corresponding 'quasi-sensitivity' into the search process for evaluating search performance. We report the case study and its finding to demonstrate that the approach is able to improve the rigor of search process in an SLR, and can serves as the supplements to the guidelines for SLRs in EBSE. We plan to further evaluate the proposed approach using several case studies with varying topics in software engineering. --- paper_title: Systematic literature reviews in software engineering - A tertiary study paper_content: Context: In a previous study, we reported on a systematic literature review (SLR), based on a manual search of 13 journals and conferences undertaken in the period 1st January 2004 to 30th June 2007. Objective: The aim of this on-going research is to provide an annotated catalogue of SLRs available to software engineering researchers and practitioners. This study updates our previous study using a broad automated search. Method: We performed a broad automated search to find SLRs published in the time period 1st January 2004 to 30th June 2008. We contrast the number, quality and source of these SLRs with SLRs found in the original study. Results: Our broad search found an additional 35 SLRs corresponding to 33 unique studies. Of these papers, 17 appeared relevant to the undergraduate educational curriculum and 12 appeared of possible interest to practitioners. The number of SLRs being published is increasing. The quality of papers in conferences and workshops has improved as more researchers use SLR guidelines. Conclusion: SLRs appear to have gone past the stage of being used solely by innovators but cannot yet be considered a main stream software engineering research methodology. They are addressing a wide range of topics but still have limitations, such as often failing to assess primary study quality. --- paper_title: A systematic review of evaluation of variability management approaches in software product lines paper_content: ContextVariability management (VM) is one of the most important activities of software product-line engineering (SPLE), which intends to develop software-intensive systems using platforms and mass customization. VM encompasses the activities of eliciting and representing variability in software artefacts, establishing and managing dependencies among different variabilities, and supporting the exploitation of the variabilities for building and evolving a family of software systems. Software product line (SPL) community has allocated huge amount of effort to develop various approaches to dealing with variability related challenges during the last two decade. Several dozens of VM approaches have been reported. However, there has been no systematic effort to study how the reported VM approaches have been evaluated. ObjectiveThe objectives of this research are to review the status of evaluation of reported VM approaches and to synthesize the available evidence about the effects of the reported approaches. MethodWe carried out a systematic literature review of the VM approaches in SPLE reported from 1990s until December 2007. ResultsWe selected 97 papers according to our inclusion and exclusion criteria. The selected papers appeared in 56 publication venues. We found that only a small number of the reviewed approaches had been evaluated using rigorous scientific methods. A detailed investigation of the reviewed studies employing empirical research methods revealed significant quality deficiencies in various aspects of the used quality assessment criteria. The synthesis of the available evidence showed that all studies, except one, reported only positive effects. ConclusionThe findings from this systematic review show that a large majority of the reported VM approaches have not been sufficiently evaluated using scientifically rigorous methods. The available evidence is sparse and the quality of the presented evidence is quite low. The findings highlight the areas in need of improvement, i.e., rigorous evaluation of VM approaches. However, the reported evidence is quite consistent across different studies. That means the proposed approaches may be very beneficial when they are applied properly in appropriate situations. Hence, it can be concluded that further investigations need to pay more attention to the contexts under which different approaches can be more beneficial. --- paper_title: Requirements Engineering for Software Product Lines: A Systematic Literature Review paper_content: Context: Software product line engineering (SPLE) is a growing area showing promising results in research and practice. In order to foster its further development and acceptance in industry, it is necessary to assess the quality of the research so that proper evidence for adoption and validity are ensured. This holds in particular for requirements engineering (RE) within SPLE, where a growing number of approaches have been proposed. Objective: This paper focuses on RE within SPLE and has the following goals: assess research quality, synthesize evidence to suggest important implications for practice, and identify research trends, open problems, and areas for improvement. Method: A systematic literature review was conducted with three research questions and assessed 49 studies, dated from 1990 to 2009. Results: The evidence for adoption of the methods is not mature, given the primary focus on toy examples. The proposed approaches still have serious limitations in terms of rigor, credibility, and validity of their findings. Additionally, most approaches still lack tool support addressing the heterogeneity and mostly textual nature of requirements formats as well as address only the proactive SPLE adoption strategy. Conclusions: Further empirical studies should be performed with sufficient rigor to enhance the body of evidence in RE within SPLE. In this context, there is a clear need for conducting studies comparing alternative methods. In order to address scalability and popularization of the approaches, future research should be invested in tool support and in addressing combined SPLE adoption strategies. --- paper_title: Systematic literature reviews in software engineering – A systematic literature review paper_content: Background: In 2004 the concept of evidence-based software engineering (EBSE) was introduced at the ICSE04 conference. Aims: This study assesses the impact of systematic literature reviews (SLRs) which are the recommended EBSE method for aggregating evidence. Method: We used the standard systematic literature review method employing a manual search of 10 journals and 4 conference proceedings. Results: Of 20 relevant studies, eight addressed research trends rather than technique evaluation. Seven SLRs addressed cost estimation. The quality of SLRs was fair with only three scoring less than 2 out of 4. Conclusions: Currently, the topic areas covered by SLRs are limited. European researchers, particularly those at the Simula Laboratory appear to be the leading exponents of systematic literature reviews. The series of cost estimation SLRs demonstrate the potential value of EBSE for synthesising evidence and making it available to practitioners. ---	Title: Variability in Quality Attributes of Service-Based Software Systems: A Systematic Literature Review Section 1: Introduction Description 1: Introduce the concept of variability and its significance, especially in the context of service-based software systems. State the objective of the paper. Section 2: Background Description 2: Provide definitions and descriptions of service-based systems, quality attributes, and variability as used in the paper. Section 2.1: Service-based systems and quality attributes Description 2.1: Describe the principles of service-orientation and the adopted definitions of quality attributes. Section 2.2: Variability Description 2.2: Explain the concept of variability, its introduction through variation points, and differentiate between design time and runtime variability. Section 2.3: Lack of existing reviews Description 2.3: Highlight the existing gap in systematic reviews specifically focusing on variability in quality attributes of service-based systems. Section 3: Paper goal and contributions Description 3: Articulate the specific goals of the paper following the Goal-Question-Metric (GQM) perspectives and identify the target audience. Section 4: Paper structure Description 4: Provide an overview of the paper's organization and structure. Section 5: Research method Description 5: Describe the systematic literature review method used, its significance, and the protocol followed. Section 5.1: Research questions Description 5.1: List and explain the research questions that the review seeks to answer. Section 5.2: Search strategy Description 5.2: Detail the search strategy used to identify relevant studies, including preliminary searches, reviews, trial searches, and expert consultation. Section 5.3: Search method Description 5.3: Explain the method of conducting an automatic search using search strings on electronic data sources and manual searches to form a "quasi-gold" standard. Section 5.4: Search terms for automatic search Description 5.4: Outline the steps and terms used to generate search strings for automatic searches. Section 5.5: Search scope and sources to be searched Description 5.5: Define the scope of the search in terms of publication period and source. Section 5.6: Search process Description 5.6: Describe the stages of the study selection process, from searching databases to final inclusion/exclusion decisions. Section 5.7: Inclusion and exclusion criteria Description 5.7: List the inclusion and exclusion criteria used to filter relevant studies. Section 5.8: Quality criteria Description 5.8: Explain the quality criteria used to evaluate the papers, relating to rationale, context, research design, data analysis, findings, bias, and credibility. Section 5.9: Data collection Description 5.9: Describe the data extraction process and the use of data extraction forms for analysis. Section 5.10: Data analysis Description 5.10: Explain how data were summarized, synthetized, and analyzed using descriptive statistics and frequency analysis. Section 6: Results and analysis Description 6: Present the findings of the systematic review in response to the research questions. Section 6.1: Results overview and demographics Description 6.1: Provide an overview of the identified studies and highlight trends in published papers. Section 6.2: RQ1: What quality attributes do existing methods for variability in quality attributes of service-based systems handle? Description 6.2: Analyze the quality attributes addressed by the identified studies, identifying common sets and specific domains. Section 6.3: RQ2: What software development activities are addressed by existing methods for handling variability in quality attributes of service-based systems? Description 6.3: Evaluate and categorize the software development activities considered by the studies. Section 6.4: RQ3: What solution types are used by methods to handle variability in quality attributes of service-based systems? Description 6.4: Identify and classify the types of solutions used in the proposed methods. Section 6.5: RQ4: What evidence is available to adopt proposed methods for handling variability in quality attributes of service-based systems? Description 6.5: Assess the credibility of the studies based on citation counts, quality scores, evidence levels, and evaluation approaches. Section 6.6: RQ5: Are methods only applicable to variability of design-time or run-time quality attributes? Description 6.6: Determine the focus of the methods on design-time or runtime quality attributes. Section 6.7: RQ6: Is there support for practitioners concerning how to use current methods? Description 6.7: Evaluate the support available for practitioners in terms of practical implementations and tool support. Section 7: Discussion of results Description 7: Summarize the main findings, discuss limitations, and address threats to validity. Section 7.1: Focus on certain quality attributes Description 7.1: Discuss the emphasis on specific quality attributes in current research. Section 7.2: Impact of product line engineering Description 7.2: Reflect on the influence of product line engineering on current variability methods. Section 7.3: Poor evidence of proposed methods Description 7.3: Evaluate the evidence supporting proposed methods, highlighting gaps in industrial evidence. Section 7.4: Implications for practitioners and researchers Description 7.4: Discuss the relevance of the findings for both academic and industrial audiences. Section 7.5: Research direction for future work Description 7.5: Suggest future research directions based on the identified gaps and limitations. Section 7.6: Inaccuracy and bias in selected papers for review Description 7.6: Address potential inaccuracies and biases during the paper selection process. Section 7.7: Inaccuracy and bias in data extraction Description 7.7: Discuss measures taken to mitigate bias and inaccuracy in data extraction. Section 7.8: Deviations from the procedures for systematic reviews Description 7.8: Note deviations from standard systematic review procedures and their implications. Section 7.9: Evaluation of review Description 7.9: Evaluate the systematic review based on predefined quality questions. Section 8: Conclusions Description 8: Summarize the conclusions of the systematic review, highlighting key findings and recommendations for future research.
A survey on heterogeneous transfer learning	18	--- paper_title: A Survey on Transfer Learning paper_content: A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. --- paper_title: Cross-Domain Learning Based Traditional Chinese Medicine Medical Record Classification paper_content: In Traditional Chinese Medicine(TCM) area, medical records are the objective record of a doctor's diagnosis and treatment and they are the basis of the TCM development. However, existing medical records of TCM are derived from books, medical cases, Web and most of them lack the categories information. In this paper, we propose a text classification method for the TCM medical record based on cross-domain topic model. First, we transform the physical books into the digital documents, then tokenize and filter the documents with domain lexicons to achieve the significative sequences of words which largely maintain the topics of original documents. Second, we use the cross domain topic model named Topic Relevance Weighting Model(TRWM) to generate the features. Finally, the generated features are leveraged for the medical records classification and compared with the baselines. The experimental results validate the effectiveness of our method. --- paper_title: A Survey on Transfer Learning paper_content: A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. --- paper_title: A survey of transfer learning paper_content: Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. --- paper_title: A survey of transfer learning paper_content: Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. --- paper_title: A survey of transfer learning paper_content: Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. --- paper_title: Deep learning applications and challenges in big data analytics paper_content: Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning. --- paper_title: A survey of open source tools for machine learning with big data in the Hadoop ecosystem paper_content: With an ever-increasing amount of options, the task of selecting machine learning tools for big data can be difficult. The available tools have advantages and drawbacks, and many have overlapping uses. The world’s data is growing rapidly, and traditional tools for machine learning are becoming insufficient as we move towards distributed and real-time processing. This paper is intended to aid the researcher or professional who understands machine learning but is inexperienced with big data. In order to evaluate tools, one should have a thorough understanding of what to look for. To that end, this paper provides a list of criteria for making selections along with an analysis of the advantages and drawbacks of each. We do this by starting from the beginning, and looking at what exactly the term “big data” means. From there, we go on to the Hadoop ecosystem for a look at many of the projects that are part of a typical machine learning architecture and an understanding of how everything might fit together. We discuss the advantages and disadvantages of three different processing paradigms along with a comparison of engines that implement them, including MapReduce, Spark, Flink, Storm, and H2O. We then look at machine learning libraries and frameworks including Mahout, MLlib, SAMOA, and evaluate them based on criteria such as scalability, ease of use, and extensibility. There is no single toolkit that truly embodies a one-size-fits-all solution, so this paper aims to help make decisions smoother by providing as much information as possible and quantifying what the tradeoffs will be. Additionally, throughout this paper, we review recent research in the field using these tools and talk about possible future directions for toolkit-based learning. --- paper_title: A survey of transfer learning paper_content: Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. --- paper_title: Heterogeneous Unsupervised Cross-domain Transfer Learning. paper_content: Transfer learning addresses the problem of how to leverage previously acquired knowledge (a source domain) to improve the efficiency of learning in a new domain (the target domain). Although transfer learning has been widely researched in the last decade, existing research still has two restrictions: 1) the feature spaces of the domains must be homogeneous; and 2) the target domain must have at least a few labeled instances. These restrictions significantly limit transfer learning models when transferring knowledge across domains, especially in the big data era. To completely break through both of these bottlenecks, a theorem for reliable unsupervised knowledge transfer is proposed to avoid negative transfers, and a Grassmann manifold is applied to measure the distance between heterogeneous feature spaces. Based on this theorem and the Grassmann manifold, this study proposes two heterogeneous unsupervised knowledge transfer (HeUKT) models - known as RLG and GLG. The RLG uses a linear monotonic map (LMM) to reliably project two heterogeneous feature spaces onto a latent feature space and applies geodesic flow kernel (GFK) model to transfers knowledge between two the projected domains. The GLG optimizes the LMM to achieve the highest possible accuracy and guarantees that the geometric properties of the domains remain unchanged during the transfer process. To test the overall effectiveness of two models, this paper reorganizes five public datasets into ten heterogeneous cross-domain tasks across three application fields: credit assessment, text classification, and cancer detection. Extensive experiments demonstrate that the proposed models deliver superior performance over current benchmarks, and that these HeUKT models are a promising way to give computers the associative ability to judge unknown things using related known knowledge. --- paper_title: A Survey on Transfer Learning paper_content: A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. --- paper_title: Transfer Learning across Feature-Rich Heterogeneous Feature Spaces via Feature-Space Remapping (FSR) paper_content: Transfer learning aims to improve performance on a target task by utilizing previous knowledge learned from source tasks. In this paper we introduce a novel heterogeneous transfer learning technique, Feature-Space Remapping (FSR), which transfers knowledge between domains with different feature spaces. This is accomplished without requiring typical feature-feature, feature instance, or instance-instance co-occurrence data. Instead we relate features in different feature-spaces through the construction of metafeatures. We show how these techniques can utilize multiple source datasets to construct an ensemble learner which further improves performance. We apply FSR to an activity recognition problem and a document classification problem. The ensemble technique is able to outperform all other baselines and even performs better than a classifier trained using a large amount of labeled data in the target domain. These problems are especially difficult because, in addition to having different feature-spaces, the marginal probability distributions and the class labels are also different. This work extends the state of the art in transfer learning by considering large transfer across dramatically different spaces. --- paper_title: A survey of transfer learning paper_content: Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. --- paper_title: Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation paper_content: In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured. We then propose a new feature mapping function for each domain, which augments the transformed samples with their original features and zeros. Existing supervised learning methods ( e.g., SVM and SVR) can be readily employed by incorporating our newly proposed augmented feature representations for supervised HDA. As a showcase, we propose a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM. We show that the proposed formulation can be equivalently derived as a standard Multiple Kernel Learning (MKL) problem, which is convex and thus the global solution can be guaranteed. To additionally utilize the unlabeled data in the target domain, we further propose the semi-supervised HFA (SHFA) which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples. Comprehensive experiments on three different applications clearly demonstrate that our SHFA and HFA outperform the existing HDA methods. --- paper_title: Semi-supervised Subspace Co-Projection for Multi-class Heterogeneous Domain Adaptation paper_content: Heterogeneous domain adaptation aims to exploit labeled training data from a source domain for learning prediction models in a target domain under the condition that the two domains have different input feature representation spaces. In this paper, we propose a novel semi-supervised subspace co-projection method to address multi-class heterogeneous domain adaptation. The proposed method projects the instances of the two domains into a co-located latent subspace to bridge the feature divergence gap across domains, while simultaneously training prediction models in the co-projected representation space with labeled training instances from both domains. It also exploits the unlabeled data to promote the consistency of co-projected subspaces from the two domains based on a maximum mean discrepancy criterion. Moreover, to increase the stability and discriminative informativeness of the subspace co-projection, we further exploit the error-correcting output code schemes to incorporate more binary prediction tasks shared across domains into the learning process. We formulate this semi-supervised learning process as a non-convex joint minimization problem and develop an alternating optimization algorithm to solve it. To investigate the empirical performance of the proposed approach, we conduct experiments on cross-lingual text classification and cross-domain digit image classification tasks with heterogeneous feature spaces. The experimental results demonstrate the efficacy of the proposed method on these heterogeneous domain adaptation problems. --- paper_title: Heterogeneous domain adaptation network based on autoencoder paper_content: Abstract Heterogeneous domain adaptation is a more challenging problem than homogeneous domain adaptation. The transfer effect is not ideally caused by shallow structure which cannot adequately describe the probability distribution and obtain more effective features. In this paper, we propose a heterogeneous domain adaptation network based on autoencoder, in which two sets of autoencoder networks are used to project the source-domain and target-domain data to a shared feature space to obtain more abstractive feature representations. In the last feature and classification layer, the marginal and conditional distributions can be matched by empirical maximum mean discrepancy metric to reduce distribution difference. To preserve the consistency of geometric structure and label information, a manifold alignment term based on labels is introduced. The classification performance can be improved further by making full use of label information of both domains. The experimental results of 16 cross-domain transfer tasks verify that HDANA outperforms several state-of-the-art methods. --- paper_title: Efficient Learning of Domain-invariant Image Representations paper_content: Abstract: We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches. --- paper_title: Transfer Neural Trees for Heterogeneous Domain Adaptation paper_content: Heterogeneous domain adaptation (HDA) addresses the task of associating data not only across dissimilar domains but also described by different types of features. Inspired by the recent advances of neural networks and deep learning, we propose Transfer Neural Trees (TNT) which jointly solves cross-domain feature mapping, adaptation, and classification in a NN-based architecture. As the prediction layer in TNT, we further propose Transfer Neural Decision Forest (Transfer-NDF), which effectively adapts the neurons in TNT for adaptation by stochastic pruning. Moreover, to address semi-supervised HDA, a unique embedding loss term for preserving prediction and structural consistency between target-domain data is introduced into TNT. Experiments on classification tasks across features, datasets, and modalities successfully verify the effectiveness of our TNT. --- paper_title: Feature Space Independent Semi-Supervised Domain Adaptation via Kernel Matching paper_content: Domain adaptation methods aim to learn a good prediction model in a label-scarce target domain by leveraging labeled patterns from a related source domain where there is a large amount of labeled data. However, in many practical domain adaptation learning scenarios, the feature distribution in the source domain is different from that in the target domain. In the extreme, the two distributions could differ completely when the feature representation of the source domain is totally different from that of the target domain. To address the problems of substantial feature distribution divergence across domains and heterogeneous feature representations of different domains, we propose a novel feature space independent semi-supervised kernel matching method for domain adaptation in this work. Our approach learns a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. We evaluate the proposed kernel matching method using both cross domain sentiment classification tasks of Amazon product reviews and cross language text classification tasks of Reuters multilingual newswire stories. Our empirical results demonstrate that the proposed kernel matching method consistently and significantly outperforms comparison methods on both cross domain classification problems with homogeneous feature spaces and cross domain classification problems with heterogeneous feature spaces. --- paper_title: Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification paper_content: In this paper, we present the supervised multi-view canonical correlation analysis ensemble (SMVCCAE) and its semi-supervised version (SSMVCCAE), which are novel techniques designed to address heterogeneous domain adaptation problems, i.e., situations in which the data to be processed and recognized are collected from different heterogeneous domains. Specifically, the multi-view canonical correlation analysis scheme is utilized to extract multiple correlation subspaces that are useful for joint representations for data association across domains. This scheme makes homogeneous domain adaption algorithms suitable for heterogeneous domain adaptation problems. Additionally, inspired by fusion methods such as Ensemble Learning (EL), this work proposes a weighted voting scheme based on canonical correlation coefficients to combine classification results in multiple correlation subspaces. Finally, the semi-supervised MVCCAE extends the original procedure by incorporating multiple speed-up spectral regression kernel discriminant analysis (SRKDA). To validate the performances of the proposed supervised procedure, a single-view canonical analysis (SVCCA) with the same base classifier (Random Forests) is used. Similarly, to evaluate the performance of the semi-supervised approach, a comparison is made with other techniques such as Logistic label propagation (LLP) and the Laplacian support vector machine (LapSVM). All of the approaches are tested on two real hyperspectral images, which are considered the target domain, with a classifier trained from synthetic low-dimensional multispectral images, which are considered the original source domain. The experimental results confirm that multi-view canonical correlation can overcome the limitations of SVCCA. Both of the proposed procedures outperform the ones used in the comparison with respect to not only the classification accuracy but also the computational efficiency. Moreover, this research shows that canonical correlation weighted voting (CCWV) is a valid option with respect to other ensemble schemes and that because of their ability to balance diversity and accuracy, canonical views extracted using partially joint random view generation are more effective than those obtained by exploiting disjoint random view generation. --- paper_title: Co-transfer learning via joint transition probability graph based method paper_content: This paper studies a new machine learning strategy called co-transfer learning. Unlike many previous learning problems, we focus on how to use labeled data of different feature spaces to enhance the classification of different learning spaces simultaneously. For instance, we make use of both labeled images and labeled text data to help learn models for classifying image data and text data together. An important component of co-transfer learning is to build different relations to link different feature spaces, thus knowledge can be co-transferred across different spaces. Our idea is to model the problem as a joint transition probability graph. The transition probabilities can be constructed by using the intra-relationships based on affinity metric among instances and the inter-relationships based on co-occurrence information among instances from different spaces. The proposed algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the random walk with restart. The main contribution of this paper is to (i) propose a co-transfer learning (CT-Learn) framework that can perform learning simultaneously by co-transferring knowledge across different spaces; (ii) show the theoretical properties of the random walk for such joint transition probability graph so that the proposed learning model can be used effectively; (iii) develop an efficient algorithm to compute ranking scores and generate the possible labels for a given instance. Experimental results on benchmark data (image-text and English-Chinese-French classification data sets) have shown that the proposed algorithm is computationally efficient, and effective in learning across different spaces. In the comparison, we find that the classification performance of the CT-Learn algorithm is better than those of the other tested transfer learning algorithms. --- paper_title: Heterogeneous Domain Adaptation and Classification by Exploiting the Correlation Subspace paper_content: We present a novel domain adaptation approach for solving cross-domain pattern recognition problems, i.e., the data or features to be processed and recognized are collected from different domains of interest. Inspired by canonical correlation analysis (CCA), we utilize the derived correlation subspace as a joint representation for associating data across different domains, and we advance reduced kernel techniques for kernel CCA (KCCA) if nonlinear correlation subspace are desirable. Such techniques not only makes KCCA computationally more efficient, potential over-fitting problems can be alleviated as well. Instead of directly performing recognition in the derived CCA subspace (as prior CCA-based domain adaptation methods did), we advocate the exploitation of domain transfer ability in this subspace, in which each dimension has a unique capability in associating cross-domain data. In particular, we propose a novel support vector machine (SVM) with a correlation regularizer, named correlation-transfer SVM, which incorporates the domain adaptation ability into classifier design for cross-domain recognition. We show that our proposed domain adaptation and classification approach can be successfully applied to a variety of cross-domain recognition tasks such as cross-view action recognition, handwritten digit recognition with different features, and image-to-text or text-to-image classification. From our empirical results, we verify that our proposed method outperforms state-of-the-art domain adaptation approaches in terms of recognition performance. --- paper_title: Cross-Language Text Classification Using Structural Correspondence Learning paper_content: We present a new approach to cross-language text classification that builds on structural correspondence learning, a recently proposed theory for domain adaptation. The approach uses unlabeled documents, along with a simple word translation oracle, in order to induce task-specific, cross-lingual word correspondences. We report on analyses that reveal quantitative insights about the use of unlabeled data and the complexity of inter-language correspondence modeling. ::: ::: We conduct experiments in the field of cross-language sentiment classification, employing English as source language, and German, French, and Japanese as target languages. The results are convincing; they demonstrate both the robustness and the competitiveness of the presented ideas. --- paper_title: Heterogeneous defect prediction paper_content: Software defect prediction is one of the most active research areas in software engineering. We can build a prediction model with defect data collected from a software project and predict defects in the same project, i.e. within-project defect prediction (WPDP). Researchers also proposed cross-project defect prediction (CPDP) to predict defects for new projects lacking in defect data by using prediction models built by other projects. In recent studies, CPDP is proved to be feasible. However, CPDP requires projects that have the same metric set, meaning the metric sets should be identical between projects. As a result, current techniques for CPDP are difficult to apply across projects with heterogeneous metric sets. To address the limitation, we propose heterogeneous defect prediction (HDP) to predict defects across projects with heterogeneous metric sets. Our HDP approach conducts metric selection and metric matching to build a prediction model between projects with heterogeneous metric sets. Our empirical study on 28 subjects shows that about 68% of predictions using our approach outperform or are comparable to WPDP with statistical significance. --- paper_title: Heterogeneous domain adaptation method for video annotation paper_content: In this study, the authors study the video annotation problem over heterogeneous domains, in which data from the image source domain and the video target domain is represented by heterogeneous features with different dimensions and physical meanings. A novel feature learning method, called heterogeneous discriminative analysis of canonical correlation (HDCC), is proposed to discover a common feature subspace in which heterogeneous features can be compared. The HDCC utilises discriminative information from the source domain as well as topology information from the target domain to learn two different projection matrices. By using these two matrices, heterogeneous data can be projected onto a common subspace and different features can be compared. They additionally design a group weighting learning framework for multi-domain adaptation to effectively leverage knowledge learned from the source domain. Under this framework, source domain images are organised in groups according to their semantic meanings, and different weights are assigned to these groups according to their relevancies to the target domain videos. Extensive experiments on the Columbia Consumer Video and Kodak datasets demonstrate the effectiveness of their HDCC and group weighting methods. --- paper_title: Hybrid heterogeneous transfer learning through deep learning paper_content: Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between heterogeneous feature spaces based on a few cross-domain instance-correspondences, and these corresponding instances are assumed to be representative in the source and target domains respectively. However, in many real-world scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be precise due to the bias issue of the correspondences in the target or (and) source domain(s). In this case, a classifier trained on the labeled transformed-source-domain data may not be useful for the target domain. In this paper, we present a new transfer learning framework called Hybrid Heterogeneous Transfer Learning (HHTL), which allows the corresponding instances across domains to be biased in either the source or target domain. Specifically, we propose a deep learning approach to learn a feature mapping between cross-domain heterogeneous features as well as a better feature representation for mapped data to reduce the bias issue caused by the cross-domain correspondences. Extensive experiments on several multilingual sentiment classification tasks verify the effectiveness of our proposed approach compared with some baseline methods. --- paper_title: Heterogeneous transfer learning for image classification paper_content: Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset. --- paper_title: Large Margin Classification Using the Perceptron Algorithm paper_content: We introduce and analyze a new algorithm for linear classification which combines Rosenblatt's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large margins. Compared to Vapnik's algorithm, however, ours is much simpler to implement, and much more efficient in terms of computation time. We also show that our algorithm can be efficiently used in very high dimensional spaces using kernel functions. We performed some experiments using our algorithm, and some variants of it, for classifying images of handwritten digits. The performance of our algorithm is close to, but not as good as, the performance of maximal-margin classifiers on the same problem, while saving significantly on computation time and programming effort. --- paper_title: OTL: A Framework of Online Transfer Learning paper_content: In this paper, we investigate a new machine learning framework called Online Transfer Learning (OTL) that aims to transfer knowledge from some source domain to an online learning task on a target domain. We do not assume the target data follows the same class or generative distribution as the source data, and our key motivation is to improve a supervised online learning task in a target domain by exploiting the knowledge that had been learned from large amount of training data in source domains. OTL is in general challenging since data in both domains not only can be different in their class distributions but can be also different in their feature representations. As a first attempt to this problem, we propose techniques to address two kinds of OTL tasks: one is to perform OTL in a homogeneous domain, and the other is to perform OTL across heterogeneous domains. We show the mistake bounds of the proposed OTL algorithms, and empirically examine their performance on several challenging OTL tasks. Encouraging results validate the efficacy of our techniques. --- paper_title: Online Passive-Aggressive Algorithms paper_content: We present a unified view for online classification, regression, and uni-class problems. This view leads to a single algorithmic framework for the three problems. We prove worst case loss bounds for various algorithms for both the realizable case and the non-realizable case. A conversion of our main online algorithm to the setting of batch learning is also discussed. The end result is new algorithms and accompanying loss bounds for the hinge-loss. --- paper_title: Online Heterogeneous Transfer Learning by Weighted Offline and Online Classifiers paper_content: In this paper, we study online heterogeneous transfer learning (HTL) problems where offline labeled data from a source domain is transferred to enhance the online classification performance in a target domain. The main idea of our proposed algorithm is to build an offline classifier based on heterogeneous similarity constructed by using labeled data from a source domain and unlabeled co-occurrence data which can be easily collected from web pages and social networks. We also construct an online classifier based on data from a target domain, and combine the offline and online classifiers by using the Hedge weighting strategy to update their weights for ensemble prediction. The theoretical analysis of error bound of the proposed algorithm is provided. Experiments on a real-world data set demonstrate the effectiveness of the proposed algorithm. --- paper_title: Online learning: Theory, algorithms, and applications paper_content: Online learning is the process of answering a sequence of questions given knowledge of the correct answers to previous questions and possibly additional available information. Answering questions in an intelligent fashion and being able to make rational decisions as a result is a basic feature of everyday life. Will it rain today (so should I take an umbrella)? Should I fight the wild animal that is after me, or should I run away? Should I open an attachment in an email message or is it a virus? The study of online learning algorithms is thus an important domain in machine learning, and one that has interesting theoretical properties and practical applications. This dissertation describes a novel framework for the design and analysis of online learning algorithms. We show that various online learning algorithms can all be derived as special cases of our algorithmic framework. This unified view explains the properties of existing algorithms and also enables us to derive several new interesting algorithms. Online learning is performed in a sequence of consecutive rounds, where at each round the learner is given a question and is required to provide an answer to this question. After predicting an answer, the correct answer is revealed and the learner suffers a loss if there is a discrepancy between his answer and the correct one. The algorithmic framework for online learning we propose in this dissertation stems from a connection that we make between the notions of regret in online learning and weak duality in convex optimization. Regret bounds are the common thread in the analysis of online learning algorithms. A regret bound measures the performance of an online algorithm relative to the performance of a competing prediction mechanism, called a competing hypothesis. The competing hypothesis can be chosen in hindsight from a class of hypotheses, after observing the entire sequence of question- answer pairs. Over the years, competitive analysis techniques have been refined and extended to numerous prediction problems by employing complex and varied notions of progress toward a good competing hypothesis. We propose a new perspective on regret bounds which is based on the notion of duality in convex optimization. Regret bounds are universal in the sense that they hold for any possible fixed hypothesis in a given hypothesis class. We therefore cast the universal bound as a lower bound --- paper_title: Heterogeneous Domain Adaptation Using Manifold Alignment paper_content: We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. --- paper_title: Online Heterogeneous Transfer Learning by Weighted Offline and Online Classifiers paper_content: In this paper, we study online heterogeneous transfer learning (HTL) problems where offline labeled data from a source domain is transferred to enhance the online classification performance in a target domain. The main idea of our proposed algorithm is to build an offline classifier based on heterogeneous similarity constructed by using labeled data from a source domain and unlabeled co-occurrence data which can be easily collected from web pages and social networks. We also construct an online classifier based on data from a target domain, and combine the offline and online classifiers by using the Hedge weighting strategy to update their weights for ensemble prediction. The theoretical analysis of error bound of the proposed algorithm is provided. Experiments on a real-world data set demonstrate the effectiveness of the proposed algorithm. --- paper_title: Frustratingly Easy Domain Adaptation paper_content: We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough ``target'' data to do slightly better than just using only ``source'' data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outperforms state-of-the-art approaches on a range of datasets. Moreover, it is trivially extended to a multi-domain adaptation problem, where one has data from a variety of different domains. --- paper_title: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms paper_content: In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks. --- paper_title: Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation paper_content: Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task. --- paper_title: Learning with Augmented Features for Heterogeneous Domain Adaptation paper_content: We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods. --- paper_title: Kernel Methods for Pattern Analysis paper_content: Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so. --- paper_title: Heterogeneous domain adaptation using previously learned classifier for object detection problem paper_content: When a trained classifier on specific domain (source domain) is applied in a different domain (target domain) the accuracy is degraded significantly. The main reason for this degradation is the distribution difference between the source and target domains. Domain adaptation aims to lessen this accuracy degradation. In this paper, we focus on adaptation for heterogeneous domains (where the source and target domain may have different feature spaces) and propose a novel algorithm which uses the pre-learned source classifier to adapt a trained target classifier. In this method, a max-margin classifier is trained on the target data and is adapted using the offset of the source classifier. The main strength of this adaptation is its low complexity and high speed which makes it a proper adaptation choice for problems with large-size datasets such as object detection. We test our method on human detection datasets and the experimental results show the significant improvement in accuracy, in comparison to several baselines. --- paper_title: A SVM-based model-transferring method for heterogeneous domain adaptation paper_content: In many real classification scenarios the distribution of test (target) domain is different from the training (source) domain. The distribution shift between the source and target domains may cause the source classifier not to gain the expected accuracy on the target data. Domain adaptation has been introduced to solve the accuracy-dropping problem caused by distribution shift phenomenon between domains. In this paper, we study model-transferring methods as a practical branch of adaptation methods, which adapt the source classifier to new domains without using the source samples. We introduce a new SVM-based model-transferring method, in which a max-margin classifier is trained on labeled target samples and is adapted using the offset of the source classifier. We call it Heterogeneous Max-Margin Classifier Adaptation Method, abbreviated as HMCA. The main strength of HMCA is its applicability for heterogeneous domains where the source and target domains may have different feature types. This property is important because the previously proposed model-transferring methods do not provide any solution for heterogeneous problems. We also introduce a new similarity metric that reliably measures adaptability between two domains according to HMCA structure. In the situation that we have access to several source classifiers, the metric can be used to select the most appropriate one for adaptation. We test HMCA on two different computer vision problems (pedestrian detection and image classification). The experimental results show the advantage in accuracy rate for our approach in comparison to several baselines. We propose a new SVM-based model-transferring method for adaptation.Our method applies adaptation in the one-dimensional discrimination space.The proposed method can handle heterogeneous domains.Based on proposed model-transferring method, we design a new metric for measuring the adaptability between two domains. --- paper_title: Heterogeneous Domain Adaptation for Multiple Classes paper_content: In this paper, we present an efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions. Specifically, we propose to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain. We cast this learning task as a compressed sensing problem, where each binary classifier induced from multiple classes can be deemed as a measurement sensor. Based on the compressive sensing theory, the estimation error of the transformation matrix decreases with the increasing number of classifiers. Therefore, to guarantee reconstruction performance, we construct sufficiently many binary classifiers based on the error correcting output coding. Extensive experiments are conducted on both a toy dataset and three real-world datasets to verify the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy. --- paper_title: Compressed sensing paper_content: Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible by transform coding with a known transform, and we reconstruct via the nonlinear procedure defined here, the number of measurements n can be dramatically smaller than the size m. Thus, certain natural classes of images with m pixels need only n=O(m1/4log5/2(m)) nonadaptive nonpixel samples for faithful recovery, as opposed to the usual m pixel samples. More specifically, suppose x has a sparse representation in some orthonormal basis (e.g., wavelet, Fourier) or tight frame (e.g., curvelet, Gabor)-so the coefficients belong to an lscrp ball for 0<ples1. The N most important coefficients in that expansion allow reconstruction with lscr2 error O(N1/2-1p/). It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients. Moreover, a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing. The nonadaptive measurements have the character of "random" linear combinations of basis/frame elements. Our results use the notions of optimal recovery, of n-widths, and information-based complexity. We estimate the Gel'fand n-widths of lscrp balls in high-dimensional Euclidean space in the case 0<ples1, and give a criterion identifying near- optimal subspaces for Gel'fand n-widths. We show that "most" subspaces are near-optimal, and show that convex optimization (Basis Pursuit) is a near-optimal way to extract information derived from these near-optimal subspaces --- paper_title: Heterogeneous Domain Adaptation Using Manifold Alignment paper_content: We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. --- paper_title: Principal Component Analysis paper_content: When large multivariate datasets are analyzed, it is often desirable to reduce their dimensionality. Principal component analysis is one technique for doing this. It replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables. Often, it is possible to retain most of the variability in the original variables with q very much smaller than p. Despite its apparent simplicity, principal component analysis has a number of subtleties, and it has many uses and extensions. A number of choices associated with the technique are briefly discussed, namely, covariance or correlation, how many components, and different normalization constraints, as well as confusion with factor analysis. Various uses and extensions are outlined. ::: ::: ::: Keywords: ::: ::: dimension reduction; ::: factor analysis; ::: multivariate analysis; ::: variance maximization --- paper_title: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms paper_content: In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks. --- paper_title: Solving Multiclass Learning Problems via Error-Correcting Output Codes paper_content: Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k "classes"). The definition is acquired by studying collections of training examples of the form (xi, f(xi)). Existing approaches to multiclass learning problems include direct application of multiclass algorithms such as the decision-tree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and application of binary concept learning algorithms with distributed output representations. This paper compares these three approaches to a new technique in which error-correcting codes are employed as a distributed output representation. We show that these output representations improve the generalization performance of both C4.5 and backpropagation on a wide range of multiclass learning tasks. We also demonstrate that this approach is robust with respect to changes in the size of the training sample, the assignment of distributed representations to particular classes, and the application of overfitting avoidance techniques such as decision-tree pruning. Finally, we show that--like the other methods--the error-correcting code technique can provide reliable class probability estimates. Taken together, these results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems. --- paper_title: A framework for learning predictive structures from multiple tasks and unlabeled data paper_content: One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods are proposed, at the current stage, we still don't have a complete understanding of their effectiveness. This paper investigates a closely related problem, which leads to a novel approach to semi-supervised learning. Specifically we consider learning predictive structures on hypothesis spaces (that is, what kind of classifiers have good predictive power) from multiple learning tasks. We present a general framework in which the structural learning problem can be formulated and analyzed theoretically, and relate it to learning with unlabeled data. Under this framework, algorithms for structural learning will be proposed, and computational issues will be investigated. Experiments will be given to demonstrate the effectiveness of the proposed algorithms in the semi-supervised learning setting. --- paper_title: Learning with Augmented Features for Heterogeneous Domain Adaptation paper_content: We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods. --- paper_title: Heterogeneous transfer learning for image classification paper_content: Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset. --- paper_title: Towards Semantic Knowledge Propagation from Text Corpus to Web Images paper_content: In this paper, we study the problem of transfer learning from text to images in the context of network data in which link based bridges are available to transfer the knowledge between the different domains. The problem of classification of image data is often much more challenging than text data because of the following two reasons: (a) Labeled text data is very widely available for classification purposes. On the other hand, this is often not the case for image data, in which a lot of images are available from many sources, but many of them are often not labeled. (b) The image features are not directly related to semantic concepts inherent in class labels. On the other hand, since text data tends to have natural semantic interpretability (because of their human origins), they are often more directly related to class labels. Therefore, the relationships between the images and text features also provide additional hints for the classification process in terms of the image feature transformations which provide the most effective results. The semantic challenges of image features are glaringly evident, when we attempt to recognize complex abstract concepts, and the visual features often fail to discriminate such concepts. However, the copious availability of bridging relationships between text and images in the context of web and social network data can be used in order to design for effective classifiers for image data. One of our goals in this paper is to develop a mathematical model for the functional relationships between text and image features, so as indirectly transfer semantic knowledge through feature transformations. This feature transformation is accomplished by mapping instances from different domains into a common space of unspecific topics. This is used as a bridge to semantically connect the two heterogeneous spaces. This is also helpful for the cases where little image data is available for the classification process. We evaluate our knowledge transfer techniques on an image classification task with labeled text corpora and show the effectiveness with respect to competing algorithms. --- paper_title: Translated Learning : Transfer Learning across Different Feature Spaces † paper_content: This paper investigates a new machine learning strategy called translated learning. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled text data to help learn a model for classifying image data, when the labeled images are difficult to obtain. An important aspect of translated learning is to build a "bridge" to link one feature space (known as the "source space") to another space (known as the "target space") through a translator in order to migrate the knowledge from source to target. The translated learning solution uses a language model to link the class labels to the features in the source spaces, which in turn is translated to the features in the target spaces. Finally, this chain of linkages is completed by tracing back to the instances in the target spaces. We show that this path of linkage can be modeled using a Markov chain and risk minimization. Through experiments on the text-aided image classification and cross-language classification tasks, we demonstrate that our translated learning framework can greatly outperform many state-of-the-art baseline methods. --- paper_title: Document Language Models, Query Models, and Risk Minimization for Information Retrieval paper_content: We present a framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory. The framework suggests an operational retrieval model that extends recent developments in the language modeling approach to information retrieval. A language model for each document is estimated, as well as a language model for each query, and the retrieval problem is cast in terms of risk minimization. The query language model can be exploited to model user preferences, the context of a query, synonomy and word senses. While recent work has incorporated word translation models for this purpose, we introduce a new method using Markov chains defined on a set of documents to estimate the query models. The Markov chain method has connections to algorithms from link analysis and social networks. The new approach is evaluated on TREC collections and compared to the basic language modeling approach and vector space models together with query expansion using Rocchio. Significant improvements are obtained over standard query expansion methods for strong baseline TF-IDF systems, with the greatest improvements attained for short queries on Web data. --- paper_title: Towards Semantic Knowledge Propagation from Text Corpus to Web Images paper_content: In this paper, we study the problem of transfer learning from text to images in the context of network data in which link based bridges are available to transfer the knowledge between the different domains. The problem of classification of image data is often much more challenging than text data because of the following two reasons: (a) Labeled text data is very widely available for classification purposes. On the other hand, this is often not the case for image data, in which a lot of images are available from many sources, but many of them are often not labeled. (b) The image features are not directly related to semantic concepts inherent in class labels. On the other hand, since text data tends to have natural semantic interpretability (because of their human origins), they are often more directly related to class labels. Therefore, the relationships between the images and text features also provide additional hints for the classification process in terms of the image feature transformations which provide the most effective results. The semantic challenges of image features are glaringly evident, when we attempt to recognize complex abstract concepts, and the visual features often fail to discriminate such concepts. However, the copious availability of bridging relationships between text and images in the context of web and social network data can be used in order to design for effective classifiers for image data. One of our goals in this paper is to develop a mathematical model for the functional relationships between text and image features, so as indirectly transfer semantic knowledge through feature transformations. This feature transformation is accomplished by mapping instances from different domains into a common space of unspecific topics. This is used as a bridge to semantically connect the two heterogeneous spaces. This is also helpful for the cases where little image data is available for the classification process. We evaluate our knowledge transfer techniques on an image classification task with labeled text corpora and show the effectiveness with respect to competing algorithms. --- paper_title: Translated Learning : Transfer Learning across Different Feature Spaces † paper_content: This paper investigates a new machine learning strategy called translated learning. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled text data to help learn a model for classifying image data, when the labeled images are difficult to obtain. An important aspect of translated learning is to build a "bridge" to link one feature space (known as the "source space") to another space (known as the "target space") through a translator in order to migrate the knowledge from source to target. The translated learning solution uses a language model to link the class labels to the features in the source spaces, which in turn is translated to the features in the target spaces. Finally, this chain of linkages is completed by tracing back to the instances in the target spaces. We show that this path of linkage can be modeled using a Markov chain and risk minimization. Through experiments on the text-aided image classification and cross-language classification tasks, we demonstrate that our translated learning framework can greatly outperform many state-of-the-art baseline methods. --- paper_title: Information-theoretic metric learning paper_content: In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the distance function. We express this problem as a particular Bregman optimization problem---that of minimizing the LogDet divergence subject to linear constraints. Our resulting algorithm has several advantages over existing methods. First, our method can handle a wide variety of constraints and can optionally incorporate a prior on the distance function. Second, it is fast and scalable. Unlike most existing methods, no eigenvalue computations or semi-definite programming are required. We also present an online version and derive regret bounds for the resulting algorithm. Finally, we evaluate our method on a recent error reporting system for software called Clarify, in the context of metric learning for nearest neighbor classification, as well as on standard data sets. --- paper_title: Heterogeneous Domain Adaptation Using Manifold Alignment paper_content: We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. --- paper_title: Frustratingly Easy Domain Adaptation paper_content: We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough ``target'' data to do slightly better than just using only ``source'' data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outperforms state-of-the-art approaches on a range of datasets. Moreover, it is trivially extended to a multi-domain adaptation problem, where one has data from a variety of different domains. --- paper_title: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms paper_content: In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks. --- paper_title: Random Forests paper_content: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression. --- paper_title: Supervised heterogeneous domain adaptation via random forests paper_content: Heterogeneity of features and lack of correspondence between data points of different domains are the two primary challenges while performing feature transfer. In this paper, we present a novel supervised domain adaptation algorithm (SHDA-RF) that learns the mapping between heterogeneous features of different dimensions. Our algorithm uses the shared label distributions present across the domains as pivots for learning a sparse feature transformation. The shared label distributions and the relationship between the feature spaces and the label distributions are estimated in a supervised manner using random forests. We conduct extensive experiments on three diverse datasets of varying dimensions and sparsity to verify the superiority of the proposed approach over other baseline and state of the art transfer approaches. --- paper_title: Relations Between Two Sets of Variates paper_content: Concepts of correlation and regression may be applied not only to ordinary one-dimensional variates but also to variates of two or more dimensions. Marksmen side by side firing simultaneous shots at targets, so that the deviations are in part due to independent individual errors and in part to common causes such as wind, provide a familiar introduction to the theory of correlation; but only the correlation of the horizontal components is ordinarily discussed, whereas the complex consisting of horizontal and vertical deviations may be even more interesting. The wind at two places may be compared, using both components of the velocity in each place. A fluctuating vector is thus matched at each moment with another fluctuating vector. The study of individual differences in mental and physical traits calls for a detailed study of the relations between sets of correlated variates. For example the scores on a number of mental tests may be compared with physical measurements on the same persons. The questions then arise of determining the number and nature of the independent relations of mind and body shown by these data to exist, and of extracting from the multiplicity of correlations in the system suitable characterizations of these independent relations. As another example, the inheritance of intelligence in rats might be studied by applying not one but s different mental tests to N mothers and to a daughter of each --- paper_title: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples paper_content: We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning algorithms and standard methods including support vector machines and regularized least squares can be obtained as special cases. We use properties of reproducing kernel Hilbert spaces to prove new Representer theorems that provide theoretical basis for the algorithms. As a result (in contrast to purely graph-based approaches) we obtain a natural out-of-sample extension to novel examples and so are able to handle both transductive and truly semi-supervised settings. We present experimental evidence suggesting that our semi-supervised algorithms are able to use unlabeled data effectively. Finally we have a brief discussion of unsupervised and fully supervised learning within our general framework. --- paper_title: Heterogeneous Domain Adaptation Using Manifold Alignment paper_content: We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. --- paper_title: A General Framework for Manifold Alignment paper_content: Manifold alignment has been found to be useful in many fields of machine learning and data mining. In this paper we summarize our work in this area and introduce a general framework for manifold alignment. This framework generates a family of approaches to align manifolds by simultaneously matching the corresponding instances and preserving the local geometry of each given manifold. Some approaches like semi-supervised alignment and manifold projections can be obtained as special cases. Our framework can also solve multiple manifold alignment problems and be adapted to handle the situation when no correspondence information is available. The approaches are described and evaluated both theoretically and experimentally, providing results showing useful knowledge transfer from one domain to another. Novel applications of our methods including identification of topics shared by multiple document collections, and biological structure alignment are discussed in the paper. --- paper_title: Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation paper_content: In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured. We then propose a new feature mapping function for each domain, which augments the transformed samples with their original features and zeros. Existing supervised learning methods ( e.g., SVM and SVR) can be readily employed by incorporating our newly proposed augmented feature representations for supervised HDA. As a showcase, we propose a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM. We show that the proposed formulation can be equivalently derived as a standard Multiple Kernel Learning (MKL) problem, which is convex and thus the global solution can be guaranteed. To additionally utilize the unlabeled data in the target domain, we further propose the semi-supervised HFA (SHFA) which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples. Comprehensive experiments on three different applications clearly demonstrate that our SHFA and HFA outperform the existing HDA methods. --- paper_title: Efficient Learning of Domain-invariant Image Representations paper_content: Abstract: We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches. --- paper_title: Heterogeneous Domain Adaptation Using Manifold Alignment paper_content: We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. --- paper_title: Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation paper_content: While domain adaptation (DA) aims to associate the learning tasks across data domains, heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain data which are of different types of features. In other words, for HDA, data from source and target domains are observed in separate feature spaces and thus exhibit distinct distributions. In this paper, we propose a novel learning algorithm of Cross-Domain Landmark Selection (CDLS) for solving the above task. With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation. In addition, the adaptation capabilities of such cross-domain landmarks can be determined accordingly. This is the reason why our CDLS is able to achieve promising HDA performance when comparing to state-of-the-art HDA methods. We conduct classification experiments using data across different features, domains, and modalities. The effectiveness of our proposed method can be successfully verified. --- paper_title: Heterogeneous Domain Adaptation Using Manifold Alignment paper_content: We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. --- paper_title: Incremental Discriminant Learning for Heterogeneous Domain Adaptation paper_content: This paper proposes a new incremental learning method for heterogeneous domain adaptation, in which the training data from both source domain and target domains are acquired sequentially, represented by heterogeneous features. Two different projection matrices are learned to map the data from two domains into a discriminative common subspace, where the intra-class samples are closely-related to each other, the inter-class samples are well-separated from each other, and the data distribution mismatch between the source and target domains is reduced. Different from previous work, our method is capable of incrementally optimizing the projection matrices when the training data becomes available as a data stream instead of being given completely in advance. With the gradually coming training data, the new projection matrices are computed by updating the existing ones using an eigenspace merging algorithm, rather than repeating the learning from the begin by keeping the whole training data set. Therefore, our incremental learning solution for the projection matrices can significantly reduce the computational complexity and memory space, which makes it applicable to a wider set of heterogeneous domain adaptation scenarios with a large training dataset. Furthermore, our method is neither restricted to the corresponding training instances in the source and target domains nor restricted to the same type of feature, which meaningfully relaxes the requirement of training data. Comprehensive experiments on three benchmark datasets clearly demonstrate the effectiveness and efficiency of our method. --- paper_title: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms paper_content: In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks. --- paper_title: Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation paper_content: Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task. --- paper_title: Learning with Augmented Features for Heterogeneous Domain Adaptation paper_content: We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods. --- paper_title: Kernel Methods for Pattern Analysis paper_content: Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so. --- paper_title: Learning from Multiple Outlooks paper_content: We propose a novel problem formulation of learning a single task when the data are provided in different feature spaces. Each such space is called an outlook, and is assumed to contain both labeled and unlabeled data. The objective is to take advantage of the data from all the outlooks to better classify each of the outlooks. We devise an algorithm that computes optimal affine mappings from different outlooks to a target outlook by matching moments of the empirical distributions. We further derive a probabilistic interpretation of the resulting algorithm and a sample complexity bound indicating how many samples are needed to adequately find the mapping. We report the results of extensive experiments on activity recognition tasks that show the value of the proposed approach in boosting performance. --- paper_title: Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation paper_content: Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task. --- paper_title: Predictive Modeling with Heterogeneous Sources paper_content: Lack of labeled training examples is a common problem for many applications. In the same time, there is usually an abundance of labeled data from related tasks. But they have different distributions and outputs (e.g., different class labels, and different scales of regression values). Conjecture that there is only a limited number of vaccine efficacy examples against the new epidemic swine flu H1N1, whereas there exists a large amount of labeled vaccine data against previous years’ flu. However, it is difficult to directly apply the older flu vaccine data as training examples because of the difference in data distribution and efficacy output criteria between different viruses. To increase the sources of labeled data, we propose a method to utilize these examples whose marginal distribution and output criteria can be different. The idea is to first select a subset of source examples similar in distribution to the target data; all the selected instances are then “re-scaled” and assigned new output values from the labeled space of the target task. A new predictive model is built on the enlarged training set. We derive a generalization bound that specifically considers distribution difference and further evaluate the model on a number of applications. For an siRNA efficacy prediction problem, we extract examples from 4 heterogeneous regression tasks and 2 classification tasks to learn the target model, and achieve an average improvement of 30% in accuracy. --- paper_title: Efficient Estimation of Word Representations in Vector Space paper_content: We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities. --- paper_title: Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation paper_content: In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured. We then propose a new feature mapping function for each domain, which augments the transformed samples with their original features and zeros. Existing supervised learning methods ( e.g., SVM and SVR) can be readily employed by incorporating our newly proposed augmented feature representations for supervised HDA. As a showcase, we propose a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM. We show that the proposed formulation can be equivalently derived as a standard Multiple Kernel Learning (MKL) problem, which is convex and thus the global solution can be guaranteed. To additionally utilize the unlabeled data in the target domain, we further propose the semi-supervised HFA (SHFA) which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples. Comprehensive experiments on three different applications clearly demonstrate that our SHFA and HFA outperform the existing HDA methods. --- paper_title: Heterogeneous Domain Adaptation Using Manifold Alignment paper_content: We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. --- paper_title: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms paper_content: In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks. --- paper_title: Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation paper_content: Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task. --- paper_title: Learning with Augmented Features for Heterogeneous Domain Adaptation paper_content: We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods. --- paper_title: Heterogeneous transfer learning for image classification paper_content: Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset. --- paper_title: Cotransfer Learning Using Coupled Markov Chains with Restart paper_content: This article studies cotransfer learning, a machine learning strategy that uses labeled data to enhance the classification of different learning spaces simultaneously. The authors model the problem as a coupled Markov chain with restart. The transition probabilities in the coupled Markov chain can be constructed using the intrarelationships based on the affinity metric among instances in the same space, and the interrelationships based on co-occurrence information among instances from different spaces. The learning algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the coupled Markov chain with restart. Experimental results on benchmark data (multiclass image-text and English-Spanish-French classification datasets) have shown that the learning algorithm is computationally efficient, and effective in learning across different spaces. --- paper_title: Towards Semantic Knowledge Propagation from Text Corpus to Web Images paper_content: In this paper, we study the problem of transfer learning from text to images in the context of network data in which link based bridges are available to transfer the knowledge between the different domains. The problem of classification of image data is often much more challenging than text data because of the following two reasons: (a) Labeled text data is very widely available for classification purposes. On the other hand, this is often not the case for image data, in which a lot of images are available from many sources, but many of them are often not labeled. (b) The image features are not directly related to semantic concepts inherent in class labels. On the other hand, since text data tends to have natural semantic interpretability (because of their human origins), they are often more directly related to class labels. Therefore, the relationships between the images and text features also provide additional hints for the classification process in terms of the image feature transformations which provide the most effective results. The semantic challenges of image features are glaringly evident, when we attempt to recognize complex abstract concepts, and the visual features often fail to discriminate such concepts. However, the copious availability of bridging relationships between text and images in the context of web and social network data can be used in order to design for effective classifiers for image data. One of our goals in this paper is to develop a mathematical model for the functional relationships between text and image features, so as indirectly transfer semantic knowledge through feature transformations. This feature transformation is accomplished by mapping instances from different domains into a common space of unspecific topics. This is used as a bridge to semantically connect the two heterogeneous spaces. This is also helpful for the cases where little image data is available for the classification process. We evaluate our knowledge transfer techniques on an image classification task with labeled text corpora and show the effectiveness with respect to competing algorithms. --- paper_title: Co-transfer learning via joint transition probability graph based method paper_content: This paper studies a new machine learning strategy called co-transfer learning. Unlike many previous learning problems, we focus on how to use labeled data of different feature spaces to enhance the classification of different learning spaces simultaneously. For instance, we make use of both labeled images and labeled text data to help learn models for classifying image data and text data together. An important component of co-transfer learning is to build different relations to link different feature spaces, thus knowledge can be co-transferred across different spaces. Our idea is to model the problem as a joint transition probability graph. The transition probabilities can be constructed by using the intra-relationships based on affinity metric among instances and the inter-relationships based on co-occurrence information among instances from different spaces. The proposed algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the random walk with restart. The main contribution of this paper is to (i) propose a co-transfer learning (CT-Learn) framework that can perform learning simultaneously by co-transferring knowledge across different spaces; (ii) show the theoretical properties of the random walk for such joint transition probability graph so that the proposed learning model can be used effectively; (iii) develop an efficient algorithm to compute ranking scores and generate the possible labels for a given instance. Experimental results on benchmark data (image-text and English-Chinese-French classification data sets) have shown that the proposed algorithm is computationally efficient, and effective in learning across different spaces. In the comparison, we find that the classification performance of the CT-Learn algorithm is better than those of the other tested transfer learning algorithms. --- paper_title: Translated Learning : Transfer Learning across Different Feature Spaces † paper_content: This paper investigates a new machine learning strategy called translated learning. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled text data to help learn a model for classifying image data, when the labeled images are difficult to obtain. An important aspect of translated learning is to build a "bridge" to link one feature space (known as the "source space") to another space (known as the "target space") through a translator in order to migrate the knowledge from source to target. The translated learning solution uses a language model to link the class labels to the features in the source spaces, which in turn is translated to the features in the target spaces. Finally, this chain of linkages is completed by tracing back to the instances in the target spaces. We show that this path of linkage can be modeled using a Markov chain and risk minimization. Through experiments on the text-aided image classification and cross-language classification tasks, we demonstrate that our translated learning framework can greatly outperform many state-of-the-art baseline methods. --- paper_title: Random walk with restart: fast solutions and applications paper_content: How closely related are two nodes in a graph? How to compute this score quickly, on huge, disk-resident, real graphs? Random walk with restart (RWR) provides a good relevance score between two nodes in a weighted graph, and it has been successfully used in numerous settings, like automatic captioning of images, generalizations to the “connection subgraphs”, personalized PageRank, and many more. However, the straightforward implementations of RWR do not scale for large graphs, requiring either quadratic space and cubic pre-computation time, or slow response time on queries. We propose fast solutions to this problem. The heart of our approach is to exploit two important properties shared by many real graphs: (a) linear correlations and (b) block-wise, community-like structure. We exploit the linearity by using low-rank matrix approximation, and the community structure by graph partitioning, followed by the Sherman–Morrison lemma for matrix inversion. Experimental results on the Corel image and the DBLP dabasets demonstrate that our proposed methods achieve significant savings over the straightforward implementations: they can save several orders of magnitude in pre-computation and storage cost, and they achieve up to 150 × speed up with 90%+ quality preservation. --- paper_title: Measuring statistical dependence with Hilbert-Schmidt norms paper_content: We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods. --- paper_title: Heterogeneous Domain Adaptation Using Manifold Alignment paper_content: We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. --- paper_title: Feature Space Independent Semi-Supervised Domain Adaptation via Kernel Matching paper_content: Domain adaptation methods aim to learn a good prediction model in a label-scarce target domain by leveraging labeled patterns from a related source domain where there is a large amount of labeled data. However, in many practical domain adaptation learning scenarios, the feature distribution in the source domain is different from that in the target domain. In the extreme, the two distributions could differ completely when the feature representation of the source domain is totally different from that of the target domain. To address the problems of substantial feature distribution divergence across domains and heterogeneous feature representations of different domains, we propose a novel feature space independent semi-supervised kernel matching method for domain adaptation in this work. Our approach learns a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. We evaluate the proposed kernel matching method using both cross domain sentiment classification tasks of Amazon product reviews and cross language text classification tasks of Reuters multilingual newswire stories. Our empirical results demonstrate that the proposed kernel matching method consistently and significantly outperforms comparison methods on both cross domain classification problems with homogeneous feature spaces and cross domain classification problems with heterogeneous feature spaces. --- paper_title: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms paper_content: In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks. --- paper_title: Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation paper_content: Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task. --- paper_title: Learning with Augmented Features for Heterogeneous Domain Adaptation paper_content: We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods. --- paper_title: Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation paper_content: In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured. We then propose a new feature mapping function for each domain, which augments the transformed samples with their original features and zeros. Existing supervised learning methods ( e.g., SVM and SVR) can be readily employed by incorporating our newly proposed augmented feature representations for supervised HDA. As a showcase, we propose a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM. We show that the proposed formulation can be equivalently derived as a standard Multiple Kernel Learning (MKL) problem, which is convex and thus the global solution can be guaranteed. To additionally utilize the unlabeled data in the target domain, we further propose the semi-supervised HFA (SHFA) which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples. Comprehensive experiments on three different applications clearly demonstrate that our SHFA and HFA outperform the existing HDA methods. --- paper_title: Integrating structured biological data by kernel maximum mean discrepancy paper_content: MOTIVATION ::: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic. The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology. ::: ::: ::: RESULTS ::: We study the practical feasibility of an MMD-based test on three central data integration tasks: Testing cross-platform comparability of microarray data, cancer diagnosis, and data-content based schema matching for two different protein function classification schemas. In all of these experiments, including high-dimensional ones, MMD is very accurate in finding samples that were generated from the same distribution, and outperforms its best competitors. ::: ::: ::: CONCLUSIONS ::: We have defined a novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by our experiments. ::: ::: ::: AVAILABILITY ::: http://www.dbs.ifi.lmu.de/~borgward/MMD. --- paper_title: Semi-supervised Subspace Co-Projection for Multi-class Heterogeneous Domain Adaptation paper_content: Heterogeneous domain adaptation aims to exploit labeled training data from a source domain for learning prediction models in a target domain under the condition that the two domains have different input feature representation spaces. In this paper, we propose a novel semi-supervised subspace co-projection method to address multi-class heterogeneous domain adaptation. The proposed method projects the instances of the two domains into a co-located latent subspace to bridge the feature divergence gap across domains, while simultaneously training prediction models in the co-projected representation space with labeled training instances from both domains. It also exploits the unlabeled data to promote the consistency of co-projected subspaces from the two domains based on a maximum mean discrepancy criterion. Moreover, to increase the stability and discriminative informativeness of the subspace co-projection, we further exploit the error-correcting output code schemes to incorporate more binary prediction tasks shared across domains into the learning process. We formulate this semi-supervised learning process as a non-convex joint minimization problem and develop an alternating optimization algorithm to solve it. To investigate the empirical performance of the proposed approach, we conduct experiments on cross-lingual text classification and cross-domain digit image classification tasks with heterogeneous feature spaces. The experimental results demonstrate the efficacy of the proposed method on these heterogeneous domain adaptation problems. --- paper_title: Efficient Learning of Domain-invariant Image Representations paper_content: Abstract: We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches. --- paper_title: Heterogeneous Domain Adaptation Using Manifold Alignment paper_content: We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. --- paper_title: Solving Multiclass Learning Problems via Error-Correcting Output Codes paper_content: Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k "classes"). The definition is acquired by studying collections of training examples of the form (xi, f(xi)). Existing approaches to multiclass learning problems include direct application of multiclass algorithms such as the decision-tree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and application of binary concept learning algorithms with distributed output representations. This paper compares these three approaches to a new technique in which error-correcting codes are employed as a distributed output representation. We show that these output representations improve the generalization performance of both C4.5 and backpropagation on a wide range of multiclass learning tasks. We also demonstrate that this approach is robust with respect to changes in the size of the training sample, the assignment of distributed representations to particular classes, and the application of overfitting avoidance techniques such as decision-tree pruning. Finally, we show that--like the other methods--the error-correcting code technique can provide reliable class probability estimates. Taken together, these results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems. --- paper_title: Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation paper_content: Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task. --- paper_title: Efficient Learning of Domain-invariant Image Representations paper_content: Abstract: We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches. --- paper_title: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms paper_content: In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks. --- paper_title: Learning with Augmented Features for Heterogeneous Domain Adaptation paper_content: We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods. --- paper_title: Random Forests paper_content: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression. --- paper_title: Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification paper_content: In this paper, we present the supervised multi-view canonical correlation analysis ensemble (SMVCCAE) and its semi-supervised version (SSMVCCAE), which are novel techniques designed to address heterogeneous domain adaptation problems, i.e., situations in which the data to be processed and recognized are collected from different heterogeneous domains. Specifically, the multi-view canonical correlation analysis scheme is utilized to extract multiple correlation subspaces that are useful for joint representations for data association across domains. This scheme makes homogeneous domain adaption algorithms suitable for heterogeneous domain adaptation problems. Additionally, inspired by fusion methods such as Ensemble Learning (EL), this work proposes a weighted voting scheme based on canonical correlation coefficients to combine classification results in multiple correlation subspaces. Finally, the semi-supervised MVCCAE extends the original procedure by incorporating multiple speed-up spectral regression kernel discriminant analysis (SRKDA). To validate the performances of the proposed supervised procedure, a single-view canonical analysis (SVCCA) with the same base classifier (Random Forests) is used. Similarly, to evaluate the performance of the semi-supervised approach, a comparison is made with other techniques such as Logistic label propagation (LLP) and the Laplacian support vector machine (LapSVM). All of the approaches are tested on two real hyperspectral images, which are considered the target domain, with a classifier trained from synthetic low-dimensional multispectral images, which are considered the original source domain. The experimental results confirm that multi-view canonical correlation can overcome the limitations of SVCCA. Both of the proposed procedures outperform the ones used in the comparison with respect to not only the classification accuracy but also the computational efficiency. Moreover, this research shows that canonical correlation weighted voting (CCWV) is a valid option with respect to other ensemble schemes and that because of their ability to balance diversity and accuracy, canonical views extracted using partially joint random view generation are more effective than those obtained by exploiting disjoint random view generation. --- paper_title: Semi-supervised Subspace Co-Projection for Multi-class Heterogeneous Domain Adaptation paper_content: Heterogeneous domain adaptation aims to exploit labeled training data from a source domain for learning prediction models in a target domain under the condition that the two domains have different input feature representation spaces. In this paper, we propose a novel semi-supervised subspace co-projection method to address multi-class heterogeneous domain adaptation. The proposed method projects the instances of the two domains into a co-located latent subspace to bridge the feature divergence gap across domains, while simultaneously training prediction models in the co-projected representation space with labeled training instances from both domains. It also exploits the unlabeled data to promote the consistency of co-projected subspaces from the two domains based on a maximum mean discrepancy criterion. Moreover, to increase the stability and discriminative informativeness of the subspace co-projection, we further exploit the error-correcting output code schemes to incorporate more binary prediction tasks shared across domains into the learning process. We formulate this semi-supervised learning process as a non-convex joint minimization problem and develop an alternating optimization algorithm to solve it. To investigate the empirical performance of the proposed approach, we conduct experiments on cross-lingual text classification and cross-domain digit image classification tasks with heterogeneous feature spaces. The experimental results demonstrate the efficacy of the proposed method on these heterogeneous domain adaptation problems. --- paper_title: Heterogeneous Domain Adaptation for Multiple Classes paper_content: In this paper, we present an efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions. Specifically, we propose to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain. We cast this learning task as a compressed sensing problem, where each binary classifier induced from multiple classes can be deemed as a measurement sensor. Based on the compressive sensing theory, the estimation error of the transformation matrix decreases with the increasing number of classifiers. Therefore, to guarantee reconstruction performance, we construct sufficiently many binary classifiers based on the error correcting output coding. Extensive experiments are conducted on both a toy dataset and three real-world datasets to verify the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy. --- paper_title: Efficient Learning of Domain-invariant Image Representations paper_content: Abstract: We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches. --- paper_title: Transfer Neural Trees for Heterogeneous Domain Adaptation paper_content: Heterogeneous domain adaptation (HDA) addresses the task of associating data not only across dissimilar domains but also described by different types of features. Inspired by the recent advances of neural networks and deep learning, we propose Transfer Neural Trees (TNT) which jointly solves cross-domain feature mapping, adaptation, and classification in a NN-based architecture. As the prediction layer in TNT, we further propose Transfer Neural Decision Forest (Transfer-NDF), which effectively adapts the neurons in TNT for adaptation by stochastic pruning. Moreover, to address semi-supervised HDA, a unique embedding loss term for preserving prediction and structural consistency between target-domain data is introduced into TNT. Experiments on classification tasks across features, datasets, and modalities successfully verify the effectiveness of our TNT. --- paper_title: Deep Neural Decision Forests paper_content: We present a novel approach to enrich classification trees with the representation learning ability of deep (neural) networks within an end-to-end trainable architecture. We combine these two worlds via a stochastic and differentiable decision tree model, which steers the formation of latent representations within the hidden layers of a deep network. The proposed model differs from conventional deep networks in that a decision forest provides the final predictions and it differs from conventional decision forests by introducing a principled, joint and global optimization of split and leaf node parameters. Our approach compares favourably to other state-of-the-art deep models on a large-scale image classification task like ImageNet. --- paper_title: Random Forests paper_content: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression. --- paper_title: Learning with Augmented Features for Heterogeneous Domain Adaptation paper_content: We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods. --- paper_title: Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation paper_content: In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured. We then propose a new feature mapping function for each domain, which augments the transformed samples with their original features and zeros. Existing supervised learning methods ( e.g., SVM and SVR) can be readily employed by incorporating our newly proposed augmented feature representations for supervised HDA. As a showcase, we propose a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM. We show that the proposed formulation can be equivalently derived as a standard Multiple Kernel Learning (MKL) problem, which is convex and thus the global solution can be guaranteed. To additionally utilize the unlabeled data in the target domain, we further propose the semi-supervised HFA (SHFA) which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples. Comprehensive experiments on three different applications clearly demonstrate that our SHFA and HFA outperform the existing HDA methods. --- paper_title: Heterogeneous domain adaptation network based on autoencoder paper_content: Abstract Heterogeneous domain adaptation is a more challenging problem than homogeneous domain adaptation. The transfer effect is not ideally caused by shallow structure which cannot adequately describe the probability distribution and obtain more effective features. In this paper, we propose a heterogeneous domain adaptation network based on autoencoder, in which two sets of autoencoder networks are used to project the source-domain and target-domain data to a shared feature space to obtain more abstractive feature representations. In the last feature and classification layer, the marginal and conditional distributions can be matched by empirical maximum mean discrepancy metric to reduce distribution difference. To preserve the consistency of geometric structure and label information, a manifold alignment term based on labels is introduced. The classification performance can be improved further by making full use of label information of both domains. The experimental results of 16 cross-domain transfer tasks verify that HDANA outperforms several state-of-the-art methods. --- paper_title: Recognizing heterogeneous cross-domain data via generalized joint distribution adaptation paper_content: In this paper, we propose a novel algorithm of Generalized Joint Distribution Adaptation (G-JDA) for heterogeneous domain adaptation (HDA), which associates and recognizes cross-domain data observed in different feature spaces (and thus with different dimensionality). With the objective to derive a domain-invariant feature subspace for relating source and target-domain data, our G-JDA learns a pair of feature projection matrices (one for each domain), which allows us to eliminate the difference between projected cross-domain heterogeneous data by matching their marginal and class-conditional distributions. We conduct experiments on cross-domain classification tasks using data across different features, datasets, and modalities. We confirm that our G-JDA would perform favorably against state-of-the-art HDA approaches. --- paper_title: Efficient Learning of Domain-invariant Image Representations paper_content: Abstract: We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches. --- paper_title: Transfer Neural Trees for Heterogeneous Domain Adaptation paper_content: Heterogeneous domain adaptation (HDA) addresses the task of associating data not only across dissimilar domains but also described by different types of features. Inspired by the recent advances of neural networks and deep learning, we propose Transfer Neural Trees (TNT) which jointly solves cross-domain feature mapping, adaptation, and classification in a NN-based architecture. As the prediction layer in TNT, we further propose Transfer Neural Decision Forest (Transfer-NDF), which effectively adapts the neurons in TNT for adaptation by stochastic pruning. Moreover, to address semi-supervised HDA, a unique embedding loss term for preserving prediction and structural consistency between target-domain data is introduced into TNT. Experiments on classification tasks across features, datasets, and modalities successfully verify the effectiveness of our TNT. --- paper_title: Heterogeneous Domain Adaptation Using Manifold Alignment paper_content: We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. --- paper_title: Deep learning paper_content: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. --- paper_title: Heterogeneous Domain Adaptation and Classification by Exploiting the Correlation Subspace paper_content: We present a novel domain adaptation approach for solving cross-domain pattern recognition problems, i.e., the data or features to be processed and recognized are collected from different domains of interest. Inspired by canonical correlation analysis (CCA), we utilize the derived correlation subspace as a joint representation for associating data across different domains, and we advance reduced kernel techniques for kernel CCA (KCCA) if nonlinear correlation subspace are desirable. Such techniques not only makes KCCA computationally more efficient, potential over-fitting problems can be alleviated as well. Instead of directly performing recognition in the derived CCA subspace (as prior CCA-based domain adaptation methods did), we advocate the exploitation of domain transfer ability in this subspace, in which each dimension has a unique capability in associating cross-domain data. In particular, we propose a novel support vector machine (SVM) with a correlation regularizer, named correlation-transfer SVM, which incorporates the domain adaptation ability into classifier design for cross-domain recognition. We show that our proposed domain adaptation and classification approach can be successfully applied to a variety of cross-domain recognition tasks such as cross-view action recognition, handwritten digit recognition with different features, and image-to-text or text-to-image classification. From our empirical results, we verify that our proposed method outperforms state-of-the-art domain adaptation approaches in terms of recognition performance. --- paper_title: Relations Between Two Sets of Variates paper_content: Concepts of correlation and regression may be applied not only to ordinary one-dimensional variates but also to variates of two or more dimensions. Marksmen side by side firing simultaneous shots at targets, so that the deviations are in part due to independent individual errors and in part to common causes such as wind, provide a familiar introduction to the theory of correlation; but only the correlation of the horizontal components is ordinarily discussed, whereas the complex consisting of horizontal and vertical deviations may be even more interesting. The wind at two places may be compared, using both components of the velocity in each place. A fluctuating vector is thus matched at each moment with another fluctuating vector. The study of individual differences in mental and physical traits calls for a detailed study of the relations between sets of correlated variates. For example the scores on a number of mental tests may be compared with physical measurements on the same persons. The questions then arise of determining the number and nature of the independent relations of mind and body shown by these data to exist, and of extracting from the multiplicity of correlations in the system suitable characterizations of these independent relations. As another example, the inheritance of intelligence in rats might be studied by applying not one but s different mental tests to N mothers and to a daughter of each --- paper_title: Marginalized Denoising Autoencoders for Domain Adaptation paper_content: Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we propose marginalized SDA (mSDA) that addresses two crucial limitations of SDAs: high computational cost and lack of scalability to high-dimensional features. In contrast to SDAs, our approach of mSDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters--in fact, they are computed in closed-form. Consequently, mSDA, which can be implemented in only 20 lines of MATLAB™, significantly speeds up SDAs by two orders of magnitude. Furthermore, the representations learnt by mSDA are as effective as the traditional SDAs, attaining almost identical accuracies in benchmark tasks. --- paper_title: Multimodal Deep Learning paper_content: Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. Our models are validated on the CUAVE and AVLetters datasets on audio-visual speech classification, demonstrating best published visual speech classification on AVLetters and effective shared representation learning. --- paper_title: Hybrid heterogeneous transfer learning through deep learning paper_content: Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between heterogeneous feature spaces based on a few cross-domain instance-correspondences, and these corresponding instances are assumed to be representative in the source and target domains respectively. However, in many real-world scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be precise due to the bias issue of the correspondences in the target or (and) source domain(s). In this case, a classifier trained on the labeled transformed-source-domain data may not be useful for the target domain. In this paper, we present a new transfer learning framework called Hybrid Heterogeneous Transfer Learning (HHTL), which allows the corresponding instances across domains to be biased in either the source or target domain. Specifically, we propose a deep learning approach to learn a feature mapping between cross-domain heterogeneous features as well as a better feature representation for mapped data to reduce the bias issue caused by the cross-domain correspondences. Extensive experiments on several multilingual sentiment classification tasks verify the effectiveness of our proposed approach compared with some baseline methods. --- paper_title: Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis paper_content: The problem of learning a semantic representation of a text document from data is addressed, in the situation where a corpus of unlabeled paired documents is available, each pair being formed by a short English document and its French translation. This representation can then be used for any retrieval, categorization or clustering task, both in a standard and in a cross-lingual setting. By using kernel functions, in this case simple bag-of-words inner products, each part of the corpus is mapped to a high-dimensional space. The correlations between the two spaces are then learnt by using kernel Canonical Correlation Analysis. A set of directions is found in the first and in the second space that are maximally correlated. Since we assume the two representations are completely independent apart from the semantic content, any correlation between them should reflect some semantic similarity. Certain patterns of English words that relate to a specific meaning should correlate with certain patterns of French words corresponding to the same meaning, across the corpus. Using the semantic representation obtained in this way we first demonstrate that the correlations detected between the two versions of the corpus are significantly higher than random, and hence that a representation based on such features does capture statistical patterns that should reflect semantic information. Then we use such representation both in cross-language and in single-language retrieval tasks, observing performance that is consistently and significantly superior to LSI on the same data. --- paper_title: Deep learning paper_content: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. --- paper_title: Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation paper_content: Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task. --- paper_title: Extracting and composing robust features with denoising autoencoders paper_content: Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite. --- paper_title: Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy paper_content: This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy. --- paper_title: Heterogeneous domain adaptation method for video annotation paper_content: In this study, the authors study the video annotation problem over heterogeneous domains, in which data from the image source domain and the video target domain is represented by heterogeneous features with different dimensions and physical meanings. A novel feature learning method, called heterogeneous discriminative analysis of canonical correlation (HDCC), is proposed to discover a common feature subspace in which heterogeneous features can be compared. The HDCC utilises discriminative information from the source domain as well as topology information from the target domain to learn two different projection matrices. By using these two matrices, heterogeneous data can be projected onto a common subspace and different features can be compared. They additionally design a group weighting learning framework for multi-domain adaptation to effectively leverage knowledge learned from the source domain. Under this framework, source domain images are organised in groups according to their semantic meanings, and different weights are assigned to these groups according to their relevancies to the target domain videos. Extensive experiments on the Columbia Consumer Video and Kodak datasets demonstrate the effectiveness of their HDCC and group weighting methods. --- paper_title: Domain Adaptation With Structural Correspondence Learning paper_content: Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For many NLP tasks, however, we are confronted with new domains in which labeled data is scarce or non-existent. In such cases, we seek to adapt existing models from a resource-rich source domain to a resource-poor target domain. We introduce structural correspondence learning to automatically induce correspondences among features from different domains. We test our technique on part of speech tagging and show performance gains for varying amounts of source and target training data, as well as improvements in target domain parsing accuracy using our improved tagger. --- paper_title: Cross-Language Text Classification Using Structural Correspondence Learning paper_content: We present a new approach to cross-language text classification that builds on structural correspondence learning, a recently proposed theory for domain adaptation. The approach uses unlabeled documents, along with a simple word translation oracle, in order to induce task-specific, cross-lingual word correspondences. We report on analyses that reveal quantitative insights about the use of unlabeled data and the complexity of inter-language correspondence modeling. ::: ::: We conduct experiments in the field of cross-language sentiment classification, employing English as source language, and German, French, and Japanese as target languages. The results are convincing; they demonstrate both the robustness and the competitiveness of the presented ideas. --- paper_title: Feature Selection with High-Dimensional Imbalanced Data paper_content: Feature selection is an important topic in data mining, especially for high dimensional datasets. Filtering techniques in particular have received much attention, but detailed comparisons of their performance is lacking. This work considers three filters using classifier performance metrics and six commonly-used filters. All nine filtering techniques are compared and contrasted using five different microarray expression datasets. In addition, given that these datasets exhibit an imbalance between the number of positive and negative examples, the utilization of sampling techniques in the context of feature selection is examined. --- paper_title: Heterogeneous defect prediction paper_content: Software defect prediction is one of the most active research areas in software engineering. We can build a prediction model with defect data collected from a software project and predict defects in the same project, i.e. within-project defect prediction (WPDP). Researchers also proposed cross-project defect prediction (CPDP) to predict defects for new projects lacking in defect data by using prediction models built by other projects. In recent studies, CPDP is proved to be feasible. However, CPDP requires projects that have the same metric set, meaning the metric sets should be identical between projects. As a result, current techniques for CPDP are difficult to apply across projects with heterogeneous metric sets. To address the limitation, we propose heterogeneous defect prediction (HDP) to predict defects across projects with heterogeneous metric sets. Our HDP approach conducts metric selection and metric matching to build a prediction model between projects with heterogeneous metric sets. Our empirical study on 28 subjects shows that about 68% of predictions using our approach outperform or are comparable to WPDP with statistical significance. --- paper_title: Reducing Features to Improve Code Change-Based Bug Prediction paper_content: Machine learning classifiers have recently emerged as a way to predict the introduction of bugs in changes made to source code files. The classifier is first trained on software history, and then used to predict if an impending change causes a bug. Drawbacks of existing classifier-based bug prediction techniques are insufficient performance for practical use and slow prediction times due to a large number of machine learned features. This paper investigates multiple feature selection techniques that are generally applicable to classification-based bug prediction methods. The techniques discard less important features until optimal classification performance is reached. The total number of features used for training is substantially reduced, often to less than 10 percent of the original. The performance of Naive Bayes and Support Vector Machine (SVM) classifiers when using this technique is characterized on 11 software projects. Naive Bayes using feature selection provides significant improvement in buggy F-measure (21 percent improvement) over prior change classification bug prediction results (by the second and fourth authors [28]). The SVM's improvement in buggy F-measure is 9 percent. Interestingly, an analysis of performance for varying numbers of features shows that strong performance is achieved at even 1 percent of the original number of features. --- paper_title: Choosing software metrics for defect prediction: an investigation on feature selection techniques paper_content: The selection of software metrics for building software quality prediction models is a search-based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault-proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real-world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85were eliminated. Copyright © 2011 John Wiley & Sons, Ltd. --- paper_title: Understanding and using linear programming paper_content: What Is It, and What For?.- Examples.- Integer Programming and LP Relaxation.- Theory of Linear Programming: First Steps.- The Simplex Method.- Duality of Linear Programming.- Not Only the Simplex Method.- More Applications.- Software and Further Reading. --- paper_title: A survey of transfer learning paper_content: Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. --- paper_title: The WM method completed: a flexible fuzzy system approach to data mining paper_content: In this paper, the so-called Wang-Mendel (WM) method for generating fuzzy rules from data is enhanced to make it a comprehensive and flexible fuzzy system approach to data description and prediction. In the description part, the core ideas of the WM method are used to develop three methods to extract fuzzy IF-THEN rules from data. The first method shows how to extract rules for the user-specified cases, the second method generates all the rules that can be generated directly from the data, and the third method extrapolates the rules generated by the second method over the entire domain of interest. In the prediction part, two fuzzy predictive models are constructed based on the fuzzy IF-THEN rules extracted by the methods of the description part. The first model gives a continuous output and is suitable for predicting continuous variables, and the second model gives a piecewise constant output and is suitable for predicting categorical variables. We show that by comparing the prediction accuracy of the fuzzy predictive models with different numbers of fuzzy sets covering the input variables, we can rank the importance of the input variables. We also propose an algorithm to optimize the fuzzy predictive models, and show how to use the models to solve pattern recognition problems. Throughout this paper, we use a set of real data from a steel rolling plant to demonstrate the ideas and test the models. --- paper_title: Towards fuzzy transfer learning for intelligent environments paper_content: By their very nature, Intelligent Environments (IE’s) are infused with complexity, unreliability and uncertainty due to a combination of sensor noise and the human element. The quantity, type and availability of data to model these applications can be a major issue. Each situation is contextually different and constantly changing. The dynamic nature of the implementations present a challenging problem when attempting to model or learn a model of the environment. Training data to construct the model must be within the same feature space and have the same distribution as the target task data, however this is often highly costly and time consuming. There can even be occurrences were a complete lack of labelled target data occurs. It is within these situations that our study is focussed. In this paper we propose a framework to dynamically model IE’s through the use of data sets from differing feature spaces and domains. The framework is constructed using a novel Fuzzy Transfer Learning (FuzzyTL) process. --- paper_title: Generating fuzzy rules by learning from examples paper_content: A general method is developed for generating fuzzy rules from numerical data. The method consists of five steps: dividing the input and output spaces of the given numerical data into fuzzy regions; generating fuzzy rules from the given data; assigning a degree to each of the generated rules for the purpose of resolving conflicts among the generated rules; creating a combined fuzzy-associative-memory (FAM) bank based on both the generated rules and linguistic rules of human experts; and determining a mapping from input space to output space based on the combined FAM bank using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. The method is applied to predicting a chaotic time series. > --- paper_title: Stacked Generalization paper_content: This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second space whose inputs are (for example) the guesses of the original generalizers when taught with part of the learning set and trying to guess the rest of it, and whose output is (for example) the correct guess. When used with multiple generalizers, stacked generalization can be seen as a more sophisticated version of cross-validation, exploiting a strategy more sophisticated than cross-validation's crude winner-takes-all for combining the individual generalizers. When used with a single generalizer, stacked generalization is a scheme for estimating (and then correcting for) the error of a generalizer which has been trained on a particular learning set and then asked a particular question. After introducing stacked generalization and justifying its use, this paper presents two numerical experiments. The first demonstrates how stacked generalization improves upon a set of separate generalizers for the NETtalk task of translating text to phonemes. The second demonstrates how stacked generalization improves the performance of a single surface-fitter. With the other experimental evidence in the literature, the usual arguments supporting cross-validation, and the abstract justifications presented in this paper, the conclusion is that for almost any real-world generalization problem one should use some version of stacked generalization to minimize the generalization error rate. This paper ends by discussing some of the variations of stacked generalization, and how it touches on other fields like chaos theory. --- paper_title: Heterogeneous transfer learning for activity recognition using heuristic search techniques paper_content: Purpose – The purpose of this paper is to study heterogeneous transfer learning for activity recognition using heuristic search techniques. Many pervasive computing applications require information about the activities currently being performed, but activity recognition algorithms typically require substantial amounts of labeled training data for each setting. One solution to this problem is to leverage transfer learning techniques to reuse available labeled data in new situations. Design/methodology/approach – This paper introduces three novel heterogeneous transfer learning techniques that reverse the typical transfer model and map the target feature space to the source feature space and apply them to activity recognition in a smart apartment. This paper evaluates the techniques on data from 18 different smart apartments located in an assisted-care facility and compares the results against several baselines. Findings – The three transfer learning techniques are all able to outperform the baseline compar... --- paper_title: Transfer Learning across Feature-Rich Heterogeneous Feature Spaces via Feature-Space Remapping (FSR) paper_content: Transfer learning aims to improve performance on a target task by utilizing previous knowledge learned from source tasks. In this paper we introduce a novel heterogeneous transfer learning technique, Feature-Space Remapping (FSR), which transfers knowledge between domains with different feature spaces. This is accomplished without requiring typical feature-feature, feature instance, or instance-instance co-occurrence data. Instead we relate features in different feature-spaces through the construction of metafeatures. We show how these techniques can utilize multiple source datasets to construct an ensemble learner which further improves performance. We apply FSR to an activity recognition problem and a document classification problem. The ensemble technique is able to outperform all other baselines and even performs better than a classifier trained using a large amount of labeled data in the target domain. These problems are especially difficult because, in addition to having different feature-spaces, the marginal probability distributions and the class labels are also different. This work extends the state of the art in transfer learning by considering large transfer across dramatically different spaces. --- paper_title: Towards fuzzy transfer learning for intelligent environments paper_content: By their very nature, Intelligent Environments (IE’s) are infused with complexity, unreliability and uncertainty due to a combination of sensor noise and the human element. The quantity, type and availability of data to model these applications can be a major issue. Each situation is contextually different and constantly changing. The dynamic nature of the implementations present a challenging problem when attempting to model or learn a model of the environment. Training data to construct the model must be within the same feature space and have the same distribution as the target task data, however this is often highly costly and time consuming. There can even be occurrences were a complete lack of labelled target data occurs. It is within these situations that our study is focussed. In this paper we propose a framework to dynamically model IE’s through the use of data sets from differing feature spaces and domains. The framework is constructed using a novel Fuzzy Transfer Learning (FuzzyTL) process. --- paper_title: Heterogeneous Domain Adaptation and Classification by Exploiting the Correlation Subspace paper_content: We present a novel domain adaptation approach for solving cross-domain pattern recognition problems, i.e., the data or features to be processed and recognized are collected from different domains of interest. Inspired by canonical correlation analysis (CCA), we utilize the derived correlation subspace as a joint representation for associating data across different domains, and we advance reduced kernel techniques for kernel CCA (KCCA) if nonlinear correlation subspace are desirable. Such techniques not only makes KCCA computationally more efficient, potential over-fitting problems can be alleviated as well. Instead of directly performing recognition in the derived CCA subspace (as prior CCA-based domain adaptation methods did), we advocate the exploitation of domain transfer ability in this subspace, in which each dimension has a unique capability in associating cross-domain data. In particular, we propose a novel support vector machine (SVM) with a correlation regularizer, named correlation-transfer SVM, which incorporates the domain adaptation ability into classifier design for cross-domain recognition. We show that our proposed domain adaptation and classification approach can be successfully applied to a variety of cross-domain recognition tasks such as cross-view action recognition, handwritten digit recognition with different features, and image-to-text or text-to-image classification. From our empirical results, we verify that our proposed method outperforms state-of-the-art domain adaptation approaches in terms of recognition performance. --- paper_title: Heterogeneous Unsupervised Cross-domain Transfer Learning. paper_content: Transfer learning addresses the problem of how to leverage previously acquired knowledge (a source domain) to improve the efficiency of learning in a new domain (the target domain). Although transfer learning has been widely researched in the last decade, existing research still has two restrictions: 1) the feature spaces of the domains must be homogeneous; and 2) the target domain must have at least a few labeled instances. These restrictions significantly limit transfer learning models when transferring knowledge across domains, especially in the big data era. To completely break through both of these bottlenecks, a theorem for reliable unsupervised knowledge transfer is proposed to avoid negative transfers, and a Grassmann manifold is applied to measure the distance between heterogeneous feature spaces. Based on this theorem and the Grassmann manifold, this study proposes two heterogeneous unsupervised knowledge transfer (HeUKT) models - known as RLG and GLG. The RLG uses a linear monotonic map (LMM) to reliably project two heterogeneous feature spaces onto a latent feature space and applies geodesic flow kernel (GFK) model to transfers knowledge between two the projected domains. The GLG optimizes the LMM to achieve the highest possible accuracy and guarantees that the geometric properties of the domains remain unchanged during the transfer process. To test the overall effectiveness of two models, this paper reorganizes five public datasets into ten heterogeneous cross-domain tasks across three application fields: credit assessment, text classification, and cancer detection. Extensive experiments demonstrate that the proposed models deliver superior performance over current benchmarks, and that these HeUKT models are a promising way to give computers the associative ability to judge unknown things using related known knowledge. --- paper_title: Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition paper_content: Domain adaptation aims to correct the mismatch in statistical properties between the source domain on which a classifier is trained and the target domain to which the classifier is to be applied. In this paper, we address the challenging scenario of unsupervised domain adaptation, where the target domain does not provide any annotated data to assist in adapting the classifier. Our strategy is to learn robust features which are resilient to the mismatch across domains and then use them to construct classifiers that will perform well on the target domain. To this end, we propose novel kernel learning approaches to infer such features for adaptation. Concretely, we explore two closely related directions. In the first direction, we propose unsupervised learning of a geodesic flow kernel (GFK). The GFK summarizes the inner products in an infinite sequence of feature subspaces that smoothly interpolates between the source and target domains. In the second direction, we propose supervised learning of a kernel that discriminatively combines multiple base GFKs. Those base kernels model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks--the most useful data instances that reveal the similarity between the source and the target domains, thus bridging them to achieve adaptation. Our approaches are computationally convenient, automatically infer important hyper-parameters, and are capable of learning features and classifiers discriminatively without demanding labeled data from the target domain. In extensive empirical studies on standard benchmark recognition datasets, our appraches yield state-of-the-art results compared to a variety of competing methods. --- paper_title: Geodesic flow kernel for unsupervised domain adaptation paper_content: In real-world applications of visual recognition, many factors — such as pose, illumination, or image quality — can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, the classifiers often perform poorly on the target domain. Domain adaptation techniques aim to correct the mismatch. Existing approaches have concentrated on learning feature representations that are invariant across domains, and they often do not directly exploit low-dimensional structures that are intrinsic to many vision datasets. In this paper, we propose a new kernel-based method that takes advantage of such structures. Our geodesic flow kernel models domain shift by integrating an infinite number of subspaces that characterize changes in geometric and statistical properties from the source to the target domain. Our approach is computationally advantageous, automatically inferring important algorithmic parameters without requiring extensive cross-validation or labeled data from either domain. We also introduce a metric that reliably measures the adaptability between a pair of source and target domains. For a given target domain and several source domains, the metric can be used to automatically select the optimal source domain to adapt and avoid less desirable ones. Empirical studies on standard datasets demonstrate the advantages of our approach over competing methods. --- paper_title: Heterogeneous transfer learning for image classification paper_content: Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset. --- paper_title: Distinctive Image Features from Scale-Invariant Keypoints paper_content: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade. --- paper_title: Indexing by Latent Semantic Analysis paper_content: A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising. --- paper_title: Relational learning via collective matrix factorization paper_content: Relational learning is concerned with predicting unknown values of a relation, given a database of entities and observed relations among entities. An example of relational learning is movie rating prediction, where entities could include users, movies, genres, and actors. Relations encode users' ratings of movies, movies' genres, and actors' roles in movies. A common prediction technique given one pairwise relation, for example a #users x #movies ratings matrix, is low-rank matrix factorization. In domains with multiple relations, represented as multiple matrices, we may improve predictive accuracy by exploiting information from one relation while predicting another. To this end, we propose a collective matrix factorization model: we simultaneously factor several matrices, sharing parameters among factors when an entity participates in multiple relations. Each relation can have a different value type and error distribution; so, we allow nonlinear relationships between the parameters and outputs, using Bregman divergences to measure error. We extend standard alternating projection algorithms to our model, and derive an efficient Newton update for the projection. Furthermore, we propose stochastic optimization methods to deal with large, sparse matrices. Our model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems. Our model can handle any pairwise relational schema and a wide variety of error models. We demonstrate its efficiency, as well as the benefit of sharing parameters among relations. --- paper_title: Building text features for object image classification paper_content: We introduce a text-based image feature and demonstrate that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. We do not inspect or correct the tags and expect that they are noisy. We obtain the text feature of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. Our text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. We test the performance of this feature using PASCAL VOC 2006 and 2007 datasets. Our feature performs well, consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. --- paper_title: Heterogeneous Transfer Learning for Image Clustering via the SocialWeb paper_content: In this paper, we present a new learning scenario, heterogeneous transfer learning, which improves learning performance when the data can be in different feature spaces and where no correspondence between data instances in these spaces is provided. In the past, we have classified Chinese text documents using English training data under the heterogeneous transfer learning framework. In this paper, we present image clustering as an example to illustrate how unsupervised learning can be improved by transferring knowledge from auxiliary heterogeneous data obtained from the social Web. Image clustering is useful for image sense disambiguation in query-based image search, but its quality is often low due to imagedata sparsity problem. We extend PLSA to help transfer the knowledge from social Web data, which have mixed feature representations. Experiments on image-object clustering and scene clustering tasks show that our approach in heterogeneous transfer learning based on the auxiliary data is indeed effective and promising. --- paper_title: CLUSTERING-BASED NETWORK INTRUSION DETECTION paper_content: Recently data mining methods have gained importance in addressing network security issues, including network intrusion detection — a challenging task in network security. Intrusion detection systems aim to identify attacks with a high detection rate and a low false alarm rate. Classification-based data mining models for intrusion detection are often ineffective in dealing with dynamic changes in intrusion patterns and characteristics. Consequently, unsupervised learning methods have been given a closer look for network intrusion detection. We investigate multiple centroid-based unsupervised clustering algorithms for intrusion detection, and propose a simple yet effective self-labeling heuristic for detecting attack and normal clusters of network traffic audit data. The clustering algorithms investigated include, k-means, Mixture-Of-Spherical Gaussians, Self-Organizing Map, and Neural-Gas. The network traffic datasets provided by the DARPA 1998 offline intrusion detection project are used in our empirical investigation, which demonstrates the feasibility and promise of unsupervised learning methods for network intrusion detection. In addition, a comparative analysis shows the advantage of clustering-based methods over supervised classification techniques in identifying new or unseen attack types. --- paper_title: Some methods for classification and analysis of multivariate observations paper_content: The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * , Sk} is a partition of EN, and ui, i = 1, 2, , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special --- paper_title: Self-taught clustering paper_content: This paper focuses on a new clustering task, called self-taught clustering. Self-taught clustering is an instance of unsupervised transfer learning, which aims at clustering a small collection of target unlabeled data with the help of a large amount of auxiliary unlabeled data. The target and auxiliary data can be different in topic distribution. We show that even when the target data are not sufficient to allow effective learning of a high quality feature representation, it is possible to learn the useful features with the help of the auxiliary data on which the target data can be clustered effectively. We propose a co-clustering based self-taught clustering algorithm to tackle this problem, by clustering the target and auxiliary data simultaneously to allow the feature representation from the auxiliary data to influence the target data through a common set of features. Under the new data representation, clustering on the target data can be improved. Our experiments on image clustering show that our algorithm can greatly outperform several state-of-the-art clustering methods when utilizing irrelevant unlabeled auxiliary data. --- paper_title: Probabilistic Latent Semantic Analysis paper_content: Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments. --- paper_title: Unsupervised Learning by Probabilistic Latent Semantic Analysis paper_content: This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed technique uses a generative latent class model to perform a probabilistic mixture decomposition. This results in a more principled approach with a solid foundation in statistical inference. More precisely, we propose to make use of a temperature controlled version of the Expectation Maximization algorithm for model fitting, which has shown excellent performance in practice. Probabilistic Latent Semantic Analysis has many applications, most prominently in information retrieval, natural language processing, machine learning from text, and in related areas. The paper presents perplexity results for different types of text and linguistic data collections and discusses an application in automated document indexing. The experiments indicate substantial and consistent improvements of the probabilistic method over standard Latent Semantic Analysis. --- paper_title: Heterogeneous Transfer Learning for Image Clustering via the SocialWeb paper_content: In this paper, we present a new learning scenario, heterogeneous transfer learning, which improves learning performance when the data can be in different feature spaces and where no correspondence between data instances in these spaces is provided. In the past, we have classified Chinese text documents using English training data under the heterogeneous transfer learning framework. In this paper, we present image clustering as an example to illustrate how unsupervised learning can be improved by transferring knowledge from auxiliary heterogeneous data obtained from the social Web. Image clustering is useful for image sense disambiguation in query-based image search, but its quality is often low due to imagedata sparsity problem. We extend PLSA to help transfer the knowledge from social Web data, which have mixed feature representations. Experiments on image-object clustering and scene clustering tasks show that our approach in heterogeneous transfer learning based on the auxiliary data is indeed effective and promising. --- paper_title: Heterogeneous transfer learning for image classification paper_content: Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset. --- paper_title: Learning Transferred Weights From Co-Occurrence Data for Heterogeneous Transfer Learning paper_content: One of the main research problems in heterogeneous transfer learning is to determine whether a given source domain is effective in transferring knowledge to a target domain, and then to determine how much of the knowledge should be transferred from a source domain to a target domain. The main objective of this paper is to solve this problem by evaluating the relatedness among given domains through transferred weights. We propose a novel method to learn such transferred weights with the aid of co-occurrence data, which contain the same set of instances but in different feature spaces. Because instances with the same category should have similar features, our method is to compute their principal components in each feature space such that co-occurrence data can be rerepresented by these principal components. The principal component coefficients from different feature spaces for the same instance in the co-occurrence data have the same order of significance for describing the category information. By using these principal component coefficients, the Markov Chain Monte Carlo method is employed to construct a directed cyclic network where each node is a domain and each edge weight is the conditional dependence from one domain to another domain. Here, the edge weight of the network can be employed as the transferred weight from a source domain to a target domain. The weight values can be taken as a prior for setting parameters in the existing heterogeneous transfer learning methods to control the amount of knowledge transferred from a source domain to a target domain. The experimental results on synthetic and real-world data sets are reported to illustrate the effectiveness of the proposed method that can capture strong or weak relations among feature spaces, and enhance the learning performance of heterogeneous transfer learning. --- paper_title: Co-transfer learning via joint transition probability graph based method paper_content: This paper studies a new machine learning strategy called co-transfer learning. Unlike many previous learning problems, we focus on how to use labeled data of different feature spaces to enhance the classification of different learning spaces simultaneously. For instance, we make use of both labeled images and labeled text data to help learn models for classifying image data and text data together. An important component of co-transfer learning is to build different relations to link different feature spaces, thus knowledge can be co-transferred across different spaces. Our idea is to model the problem as a joint transition probability graph. The transition probabilities can be constructed by using the intra-relationships based on affinity metric among instances and the inter-relationships based on co-occurrence information among instances from different spaces. The proposed algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the random walk with restart. The main contribution of this paper is to (i) propose a co-transfer learning (CT-Learn) framework that can perform learning simultaneously by co-transferring knowledge across different spaces; (ii) show the theoretical properties of the random walk for such joint transition probability graph so that the proposed learning model can be used effectively; (iii) develop an efficient algorithm to compute ranking scores and generate the possible labels for a given instance. Experimental results on benchmark data (image-text and English-Chinese-French classification data sets) have shown that the proposed algorithm is computationally efficient, and effective in learning across different spaces. In the comparison, we find that the classification performance of the CT-Learn algorithm is better than those of the other tested transfer learning algorithms. --- paper_title: Improving Markov Chain Monte Carlo Model Search for Data Mining paper_content: The motivation of this paper is the application of MCMC model scoring procedures to data mining problems, involving a large number of competing models and other relevant model choice aspects. --- paper_title: Heterogeneous Transfer Learning for Image Clustering via the SocialWeb paper_content: In this paper, we present a new learning scenario, heterogeneous transfer learning, which improves learning performance when the data can be in different feature spaces and where no correspondence between data instances in these spaces is provided. In the past, we have classified Chinese text documents using English training data under the heterogeneous transfer learning framework. In this paper, we present image clustering as an example to illustrate how unsupervised learning can be improved by transferring knowledge from auxiliary heterogeneous data obtained from the social Web. Image clustering is useful for image sense disambiguation in query-based image search, but its quality is often low due to imagedata sparsity problem. We extend PLSA to help transfer the knowledge from social Web data, which have mixed feature representations. Experiments on image-object clustering and scene clustering tasks show that our approach in heterogeneous transfer learning based on the auxiliary data is indeed effective and promising. --- paper_title: Heterogeneous Domain Adaptation and Classification by Exploiting the Correlation Subspace paper_content: We present a novel domain adaptation approach for solving cross-domain pattern recognition problems, i.e., the data or features to be processed and recognized are collected from different domains of interest. Inspired by canonical correlation analysis (CCA), we utilize the derived correlation subspace as a joint representation for associating data across different domains, and we advance reduced kernel techniques for kernel CCA (KCCA) if nonlinear correlation subspace are desirable. Such techniques not only makes KCCA computationally more efficient, potential over-fitting problems can be alleviated as well. Instead of directly performing recognition in the derived CCA subspace (as prior CCA-based domain adaptation methods did), we advocate the exploitation of domain transfer ability in this subspace, in which each dimension has a unique capability in associating cross-domain data. In particular, we propose a novel support vector machine (SVM) with a correlation regularizer, named correlation-transfer SVM, which incorporates the domain adaptation ability into classifier design for cross-domain recognition. We show that our proposed domain adaptation and classification approach can be successfully applied to a variety of cross-domain recognition tasks such as cross-view action recognition, handwritten digit recognition with different features, and image-to-text or text-to-image classification. From our empirical results, we verify that our proposed method outperforms state-of-the-art domain adaptation approaches in terms of recognition performance. --- paper_title: Heterogeneous transfer learning for image classification paper_content: Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset. --- paper_title: Learning Transferred Weights From Co-Occurrence Data for Heterogeneous Transfer Learning paper_content: One of the main research problems in heterogeneous transfer learning is to determine whether a given source domain is effective in transferring knowledge to a target domain, and then to determine how much of the knowledge should be transferred from a source domain to a target domain. The main objective of this paper is to solve this problem by evaluating the relatedness among given domains through transferred weights. We propose a novel method to learn such transferred weights with the aid of co-occurrence data, which contain the same set of instances but in different feature spaces. Because instances with the same category should have similar features, our method is to compute their principal components in each feature space such that co-occurrence data can be rerepresented by these principal components. The principal component coefficients from different feature spaces for the same instance in the co-occurrence data have the same order of significance for describing the category information. By using these principal component coefficients, the Markov Chain Monte Carlo method is employed to construct a directed cyclic network where each node is a domain and each edge weight is the conditional dependence from one domain to another domain. Here, the edge weight of the network can be employed as the transferred weight from a source domain to a target domain. The weight values can be taken as a prior for setting parameters in the existing heterogeneous transfer learning methods to control the amount of knowledge transferred from a source domain to a target domain. The experimental results on synthetic and real-world data sets are reported to illustrate the effectiveness of the proposed method that can capture strong or weak relations among feature spaces, and enhance the learning performance of heterogeneous transfer learning. --- paper_title: Cross-Language Text Classification Using Structural Correspondence Learning paper_content: We present a new approach to cross-language text classification that builds on structural correspondence learning, a recently proposed theory for domain adaptation. The approach uses unlabeled documents, along with a simple word translation oracle, in order to induce task-specific, cross-lingual word correspondences. We report on analyses that reveal quantitative insights about the use of unlabeled data and the complexity of inter-language correspondence modeling. ::: ::: We conduct experiments in the field of cross-language sentiment classification, employing English as source language, and German, French, and Japanese as target languages. The results are convincing; they demonstrate both the robustness and the competitiveness of the presented ideas. --- paper_title: A Probabilistic Programming Approach for Outlier Detection in Healthcare Claims paper_content: Healthcare is an integral component in people's lives, especially for the rising elderly population. Medicare is one such healthcare program that provides for the needs of the elderly. It is imperative that these healthcare programs are affordable, but this is not always the case. Out of the many possible factors for the rising cost of healthcare, claims fraud is a major contributor, but its impact can be lessened through effective fraud detection. We propose a general outlier detection model, based on Bayesian inference, using probabilistic programming. Our model provides probability distributions rather than just point values, as with most common outlier detection methods. Credible intervals are also generated to further enhance confidence that the detected outliers should in fact be considered outliers. Two case studies are presented demonstrating our model's effectiveness in detecting outliers. The first case study uses temperature data in order to provide a clear comparison of several outlier detection techniques. The second case study uses a Medicare dataset to showcase our proposed outlier detection model. Our results show that the successful detection of outliers, which indicate possible fraudulent activities, can provide effective and meaningful results for further investigation within medical specialties or by using real-world, medical provider fraud investigation cases. --- paper_title: Analysis of Transfer Learning Performance Measures paper_content: In machine learning applications, there are scenarios of having no labeled training data, due to the data being rare or too expensive to obtain. In these cases, it is desirable to use readily available labeled data, that is similar to, but not the same as, the domain application of interest. Transfer learning algorithms are used to build high-performance classifiers, when the training data has different distribution characteristics from the testing data. For a transfer learning environment, it is not possible to use validation techniques (such as cross validation or data splitting) to set the desired performance of a classifier, due to the lack of labeled training data from the test domain. As a result, the area under the receiver operating characteristic curve (AUC) performance measure may not be predictive of the actual classifier performance. In an environment where validation techniques are not possible, the relationship between AUC and classification accuracy is needed to better characterize transfer learning algorithm performance. This paper provides relative performance analysis of state-of-the-art transfer learning algorithms and traditional machine learning algorithms, addressing the correlation between AUC and classification accuracy under domain class imbalance conditions with statistical analysis provided. --- paper_title: Towards Semantic Knowledge Propagation from Text Corpus to Web Images paper_content: In this paper, we study the problem of transfer learning from text to images in the context of network data in which link based bridges are available to transfer the knowledge between the different domains. The problem of classification of image data is often much more challenging than text data because of the following two reasons: (a) Labeled text data is very widely available for classification purposes. On the other hand, this is often not the case for image data, in which a lot of images are available from many sources, but many of them are often not labeled. (b) The image features are not directly related to semantic concepts inherent in class labels. On the other hand, since text data tends to have natural semantic interpretability (because of their human origins), they are often more directly related to class labels. Therefore, the relationships between the images and text features also provide additional hints for the classification process in terms of the image feature transformations which provide the most effective results. The semantic challenges of image features are glaringly evident, when we attempt to recognize complex abstract concepts, and the visual features often fail to discriminate such concepts. However, the copious availability of bridging relationships between text and images in the context of web and social network data can be used in order to design for effective classifiers for image data. One of our goals in this paper is to develop a mathematical model for the functional relationships between text and image features, so as indirectly transfer semantic knowledge through feature transformations. This feature transformation is accomplished by mapping instances from different domains into a common space of unspecific topics. This is used as a bridge to semantically connect the two heterogeneous spaces. This is also helpful for the cases where little image data is available for the classification process. We evaluate our knowledge transfer techniques on an image classification task with labeled text corpora and show the effectiveness with respect to competing algorithms. --- paper_title: An Investigation of Transfer Learning and Traditional Machine Learning Algorithms paper_content: Previous research focusing on the evaluation of transfer learning algorithms has predominantly used real-world datasets to measure an algorithm's performance. A test with a real-world dataset exposes an algorithm to a single instance of distribution difference between the training (source) and test (target) datasets. These previous works have not measured performance over a wide-range of source and target distribution differences. We propose to use a test framework that creates many source and target datasets from a single base dataset, representing a diverse-range of distribution differences. These datasets will be used as a stress test to measure an algorithm's performance. The stress test process will measure and compare different transfer learning algorithms and traditional learning algorithms. The unique contributions of this paper, with respect to transfer learning, are defining a test framework, defining multiple distortion profiles, defining a stress test suite, and the evaluation and comparison of different transfer learning and traditional machine learning algorithms over a wide-range of distributions. --- paper_title: Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data paper_content: This paper compares the performance of several boosting and bagging techniques in the context of learning from imbalanced and noisy binary-class data. Noise and class imbalance are two well-established data characteristics encountered in a wide range of data mining and machine learning initiatives. The learning algorithms studied in this paper, which include SMOTEBoost, RUSBoost, Exactly Balanced Bagging, and Roughly Balanced Bagging, combine boosting or bagging with data sampling to make them more effective when data are imbalanced. These techniques are evaluated in a comprehensive suite of experiments, for which nearly four million classification models were trained. All classifiers are assessed using seven different performance metrics, providing a complete perspective on the performance of these techniques, and results are tested for statistical significance via analysis-of-variance modeling. The experiments show that the bagging techniques generally outperform boosting, and hence in noisy data environments, bagging is the preferred method for handling class imbalance. --- paper_title: Transfer Learning across Feature-Rich Heterogeneous Feature Spaces via Feature-Space Remapping (FSR) paper_content: Transfer learning aims to improve performance on a target task by utilizing previous knowledge learned from source tasks. In this paper we introduce a novel heterogeneous transfer learning technique, Feature-Space Remapping (FSR), which transfers knowledge between domains with different feature spaces. This is accomplished without requiring typical feature-feature, feature instance, or instance-instance co-occurrence data. Instead we relate features in different feature-spaces through the construction of metafeatures. We show how these techniques can utilize multiple source datasets to construct an ensemble learner which further improves performance. We apply FSR to an activity recognition problem and a document classification problem. The ensemble technique is able to outperform all other baselines and even performs better than a classifier trained using a large amount of labeled data in the target domain. These problems are especially difficult because, in addition to having different feature-spaces, the marginal probability distributions and the class labels are also different. This work extends the state of the art in transfer learning by considering large transfer across dramatically different spaces. --- paper_title: Online Heterogeneous Transfer Learning by Weighted Offline and Online Classifiers paper_content: In this paper, we study online heterogeneous transfer learning (HTL) problems where offline labeled data from a source domain is transferred to enhance the online classification performance in a target domain. The main idea of our proposed algorithm is to build an offline classifier based on heterogeneous similarity constructed by using labeled data from a source domain and unlabeled co-occurrence data which can be easily collected from web pages and social networks. We also construct an online classifier based on data from a target domain, and combine the offline and online classifiers by using the Hedge weighting strategy to update their weights for ensemble prediction. The theoretical analysis of error bound of the proposed algorithm is provided. Experiments on a real-world data set demonstrate the effectiveness of the proposed algorithm. --- paper_title: Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification paper_content: In this paper, we present the supervised multi-view canonical correlation analysis ensemble (SMVCCAE) and its semi-supervised version (SSMVCCAE), which are novel techniques designed to address heterogeneous domain adaptation problems, i.e., situations in which the data to be processed and recognized are collected from different heterogeneous domains. Specifically, the multi-view canonical correlation analysis scheme is utilized to extract multiple correlation subspaces that are useful for joint representations for data association across domains. This scheme makes homogeneous domain adaption algorithms suitable for heterogeneous domain adaptation problems. Additionally, inspired by fusion methods such as Ensemble Learning (EL), this work proposes a weighted voting scheme based on canonical correlation coefficients to combine classification results in multiple correlation subspaces. Finally, the semi-supervised MVCCAE extends the original procedure by incorporating multiple speed-up spectral regression kernel discriminant analysis (SRKDA). To validate the performances of the proposed supervised procedure, a single-view canonical analysis (SVCCA) with the same base classifier (Random Forests) is used. Similarly, to evaluate the performance of the semi-supervised approach, a comparison is made with other techniques such as Logistic label propagation (LLP) and the Laplacian support vector machine (LapSVM). All of the approaches are tested on two real hyperspectral images, which are considered the target domain, with a classifier trained from synthetic low-dimensional multispectral images, which are considered the original source domain. The experimental results confirm that multi-view canonical correlation can overcome the limitations of SVCCA. Both of the proposed procedures outperform the ones used in the comparison with respect to not only the classification accuracy but also the computational efficiency. Moreover, this research shows that canonical correlation weighted voting (CCWV) is a valid option with respect to other ensemble schemes and that because of their ability to balance diversity and accuracy, canonical views extracted using partially joint random view generation are more effective than those obtained by exploiting disjoint random view generation. --- paper_title: Heterogeneous domain adaptation method for video annotation paper_content: In this study, the authors study the video annotation problem over heterogeneous domains, in which data from the image source domain and the video target domain is represented by heterogeneous features with different dimensions and physical meanings. A novel feature learning method, called heterogeneous discriminative analysis of canonical correlation (HDCC), is proposed to discover a common feature subspace in which heterogeneous features can be compared. The HDCC utilises discriminative information from the source domain as well as topology information from the target domain to learn two different projection matrices. By using these two matrices, heterogeneous data can be projected onto a common subspace and different features can be compared. They additionally design a group weighting learning framework for multi-domain adaptation to effectively leverage knowledge learned from the source domain. Under this framework, source domain images are organised in groups according to their semantic meanings, and different weights are assigned to these groups according to their relevancies to the target domain videos. Extensive experiments on the Columbia Consumer Video and Kodak datasets demonstrate the effectiveness of their HDCC and group weighting methods. --- paper_title: Co-transfer learning via joint transition probability graph based method paper_content: This paper studies a new machine learning strategy called co-transfer learning. Unlike many previous learning problems, we focus on how to use labeled data of different feature spaces to enhance the classification of different learning spaces simultaneously. For instance, we make use of both labeled images and labeled text data to help learn models for classifying image data and text data together. An important component of co-transfer learning is to build different relations to link different feature spaces, thus knowledge can be co-transferred across different spaces. Our idea is to model the problem as a joint transition probability graph. The transition probabilities can be constructed by using the intra-relationships based on affinity metric among instances and the inter-relationships based on co-occurrence information among instances from different spaces. The proposed algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the random walk with restart. The main contribution of this paper is to (i) propose a co-transfer learning (CT-Learn) framework that can perform learning simultaneously by co-transferring knowledge across different spaces; (ii) show the theoretical properties of the random walk for such joint transition probability graph so that the proposed learning model can be used effectively; (iii) develop an efficient algorithm to compute ranking scores and generate the possible labels for a given instance. Experimental results on benchmark data (image-text and English-Chinese-French classification data sets) have shown that the proposed algorithm is computationally efficient, and effective in learning across different spaces. In the comparison, we find that the classification performance of the CT-Learn algorithm is better than those of the other tested transfer learning algorithms. --- paper_title: Towards fuzzy transfer learning for intelligent environments paper_content: By their very nature, Intelligent Environments (IE’s) are infused with complexity, unreliability and uncertainty due to a combination of sensor noise and the human element. The quantity, type and availability of data to model these applications can be a major issue. Each situation is contextually different and constantly changing. The dynamic nature of the implementations present a challenging problem when attempting to model or learn a model of the environment. Training data to construct the model must be within the same feature space and have the same distribution as the target task data, however this is often highly costly and time consuming. There can even be occurrences were a complete lack of labelled target data occurs. It is within these situations that our study is focussed. In this paper we propose a framework to dynamically model IE’s through the use of data sets from differing feature spaces and domains. The framework is constructed using a novel Fuzzy Transfer Learning (FuzzyTL) process. --- paper_title: The pairwise attribute noise detection algorithm paper_content: Analyzing the quality of data prior to constructing data mining models is emerging as an important issue. Algorithms for identifying noise in a given data set can provide a good measure of data quality. Considerable attention has been devoted to detecting class noise or labeling errors. In contrast, limited research work has been devoted to detecting instances with attribute noise, in part due to the difficulty of the problem. We present a novel approach for detecting instances with attribute noise and demonstrate its usefulness with case studies using two different real-world software measurement data sets. Our approach, called Pairwise Attribute Noise Detection Algorithm (PANDA), is compared with a nearest neighbor, distance-based outlier detection technique (denoted DM) investigated in related literature. Since what constitutes noise is domain specific, our case studies uses a software engineering expert to inspect the instances identified by the two approaches to determine whether they actually contain noise. It is shown that PANDA provides better noise detection performance than the DM algorithm. --- paper_title: Translated Learning : Transfer Learning across Different Feature Spaces † paper_content: This paper investigates a new machine learning strategy called translated learning. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled text data to help learn a model for classifying image data, when the labeled images are difficult to obtain. An important aspect of translated learning is to build a "bridge" to link one feature space (known as the "source space") to another space (known as the "target space") through a translator in order to migrate the knowledge from source to target. The translated learning solution uses a language model to link the class labels to the features in the source spaces, which in turn is translated to the features in the target spaces. Finally, this chain of linkages is completed by tracing back to the instances in the target spaces. We show that this path of linkage can be modeled using a Markov chain and risk minimization. Through experiments on the text-aided image classification and cross-language classification tasks, we demonstrate that our translated learning framework can greatly outperform many state-of-the-art baseline methods. --- paper_title: Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation paper_content: Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task. --- paper_title: Designing a Testing Framework for Transfer Learning Algorithms (Application Paper) paper_content: Most works covering the topic of transfer learning propose an algorithm to solve a given domain adaptation problem, then test the algorithm using real-world datasets. A test with a real-world dataset represents a single transfer learning test condition, which partially measures an algorithm's performance. Previous research has placed little emphasis on developing a comprehensive and uniform test for transfer learning algorithms. With this in mind, a test framework is proposed, comprising of distortion profiles which define a comprehensive test suite. The unique contribution of this paper is the definition of a test framework that measures a more complete profile of a transfer learning algorithm's capability, facilitating the identification of relative poor and good performance areas. As a proof of concept, the test framework is used to test a homogeneous transfer learning algorithm. The test framework will be the basis for a number of future applications. ---	Title: A survey on heterogeneous transfer learning Section 1: Introduction Description 1: Write an introduction about traditional machine learning assumptions and the necessity for transfer learning in real-world scenarios. Section 2: Transfer learning Description 2: Describe what transfer learning (TL) is, its goals, and the distinction between homogeneous and heterogeneous transfer learning. Section 3: Homogeneous transfer learning Description 3: Explain homogeneous transfer learning, its characteristics, and various approaches to bridge the data distribution gap between domains. Section 4: Heterogeneous transfer learning Description 4: Discuss heterogeneous transfer learning, its complexities, and how it addresses non-equivalent and non-overlapping feature spaces. Section 5: Negative transfer Description 5: Describe the concept of negative transfer, its implications on TL, and why it occurs when the source domain is not sufficiently related to the target domain. Section 6: Big data application Description 6: Explain how transfer learning can be applied to big data environments, leveraging available datasets to enhance target tasks while avoiding costly data collection efforts. Section 7: Paper overview/contributions Description 7: Provide an overview of the paper’s objectives, including the survey of 38 heterogeneous transfer learning methods, their discussion, analysis, and future research directions. Section 8: Methods which require limited target labels Description 8: Survey techniques that require labeled source data and limited labeled target data, enhancing performance using supplementary labeled data from a related source domain. Section 9: Methods which require limited target labels and accept unlabeled target instances Description 9: Survey semi-supervised techniques that incorporate unlabeled target instances to enhance model performance using both labeled source data and limited labeled target data. Section 10: Methods which require no target labels Description 10: Survey techniques that require labeled source data but do not rely on labeled target instances, utilizing unlabeled target data for training. Section 11: Methods which require limited target labels and no source labels Description 11: Discuss techniques that use limited labeled target data and only unlabeled source data for enhancing target classifier performance. Section 12: Methods which require no target or source labels Description 12: Present unsupervised HTL methods which operate without any labeled data, leveraging auxiliary domains for tasks like clustering. Section 13: Methods for HTL preprocessing Description 13: Discuss preprocessing methods used before employing HTL algorithms to select optimal parameters and improve overall performance. Section 14: Discussion Description 14: Compare and analyze characteristics and empirical studies of the surveyed HTL methods, identifying patterns, differences, and shortcomings. Section 15: Comparative analysis Description 15: Conduct a comparative analysis of all surveyed HTL methods, summarizing commonalities, application specifics, and research gaps. Section 16: Performance analysis Description 16: Analyze the performance of HTL methods based on empirical studies, comparing them with common baseline algorithms. Section 17: Conclusion Description 17: Write a conclusion summarizing the importance of HTL, the challenges it addresses, and the insights gained from surveyed methods. Section 18: Future work Description 18: Discuss potential directions for future research in HTL, focusing on areas like scalability, negative transfer prevention, and addressing label space differences.
A Survey of Continuous-Time Computation Theory	4	--- paper_title: Some mathematical limitations of the general-purpose analog computer paper_content: We prove that the Dirichlet problem on the disc cannot be solved by the general-purpose analog computer, by constructing, on the boundary, a function u"0 that does satisfy an algebraic differential equation, but whose Poisson integral u satisfies no algebraic differential equation on some line segment inside the disc. --- paper_title: DNA solution of hard computational problems paper_content: DNA experiments are proposed to solve the famous "SAT" problem of computer science. This is a special case of a more general method that can solve NP-complete problems. The advantage of these results is the huge parallelism inherent in DNA-based computing. It has the potential to yield vast speedups over conventional electronic-based computers for such search problems. --- paper_title: Algorithms for quantum computation: discrete logarithms and factoring paper_content: A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a cost in computation time of at most a polynomial factor: It is not clear whether this is still true when quantum mechanics is taken into consideration. Several researchers, starting with David Deutsch, have developed models for quantum mechanical computers and have investigated their computational properties. This paper gives Las Vegas algorithms for finding discrete logarithms and factoring integers on a quantum computer that take a number of steps which is polynomial in the input size, e.g., the number of digits of the integer to be factored. These two problems are generally considered hard on a classical computer and have been used as the basis of several proposed cryptosystems. We thus give the first examples of quantum cryptanalysis. > --- paper_title: On the computational power of neural nets paper_content: This paper deals with finite networks which consist of interconnections of synchronously evolving processors. Each processor updates its state by applying a “sigmoidal” scalar nonlinearity to a linear combination of the previous states of all units. We prove that one may simulate all Turing Machines by rational nets. In particular, one can do this in linear time, and there is a net made up of about 1,000 processors which computes a universal partial-recursive function. Products (high order nets) are not required, contrary to what had been stated in the literature. Furthermore, we assert a similar theorem about non-deterministic Turing Machines. Consequences for undecidability and complexity issues about nets are discussed too. --- paper_title: Universal Computation and Other Capabilities of Hybrid and Continuous Dynamical Systems paper_content: We explore the simulation and computational capabilities of hybrid and continuous dynamical systems. The continuous dynamical systems considered are ordinary differential equations (ODEs). For hybrid systems we concentrate on models that combine ODEs and discrete dynamics (e.g., finite automata). We review and compare four such models from the literature. Notions of simulation of a discrete dynamical system by a continuous one are developed. We show that hybrid systems whose equations can describe a precise binary timing pulse (exact clock) can simulate arbitrary reversible discrete dynamical systems defined on closed subsets of R n . The simulations require continuous ODEs in IR2n with the exact clock as input. All four hybrid systems models studied here can implement exact clocks. We also prove that any discrete dynamical system in rn can be simulated by continuous ODEs in Rt2n+1. We use this to show that smooth ODEs in RI3 can simulate arbitrary Turing machines, and hence possess the power of universal computation. We use the famous asynchronous arbiter problem to distinguish between hybrid and continuous dynamical systems. We prove that one cannot build an arbiter with devices described by a system of Lipschitz ODEs. On the other hand, all four hybrid systems models considered can implement arbiters even if their ODEs are Lipschitz. --- paper_title: On some Relations between Dynamical Systems and Transition Systems paper_content: In this paper we define a precise notion of abstraction relation between continuous dynamical systems and discrete state-transition systems. Our main result states that every Turing Machine can be realized by a dynamical system with piecewise-constant derivatives in a 3-dimensional space and thus the reachability problem for such systems is undecidable for 3 dimensions. A decision procedure for 2-dimensional systems has been recently reported by Maler and Pnueli. On the other hand we show that some non-deterministic finite automata cannot be realized by any continuous dynamical system with less than 3 dimensions. --- paper_title: The computability and complexity of optical beam tracing paper_content: The ray-tracing problem is considered for optical systems consisting of a set of refractive or reflective surfaces. It is assumed that the position and the tangent of the incident angle of the initial light ray are rational. The computability and complexity of the ray-tracing problems are investigated for various optical models. The results show that, depending on the optical model, ray tracing is sometimes undecidable, sometimes PSPACE-hard, and sometimes in PSPACE. > --- paper_title: On a theory of computation over the real numbers; NP completeness, recursive functions and universal machines paper_content: A model for computation over an arbitrary (ordered) ring R is presented. In this general setting, universal machines, partial recursive functions, and NP-complete problems are obtained. While the theory reflects of classical over Z (e.g. the computable functions are the recursive functions), it also reflects the special mathematical character of the underlying ring R (e.g. complements of Julia sets provide natural examples of recursively enumerable undecidable sets over the reals) and provides a natural setting for studying foundational issues concerning algorithms in numerical analysis. > --- paper_title: Analog computation with continuous ODEs paper_content: Demonstrates simple, low-dimensional systems of ODEs that can simulate arbitrary finite automata, push-down automata, and Turing machines. We conclude that there are systems of ODEs in R/sup 3/ with continuous vector fields possessing the power of universal computation. Further, such computations can be made robust to small errors in coding of the input or measurement of the output. As such, they represent physically realizable computation. We make precise what we mean by "simulation" of digital machines by continuous dynamical systems. We also discuss elements that a more comprehensive ODE-based model of analog computation should contain. The "axioms" of such a model are based on considerations from physics. > --- paper_title: The Computational Power of Continuous Time Neural Networks paper_content: We investigate the computational power of continuous-time neural networks with Hopfield-type units. We prove that polynomial-size networks with saturated-linear response functions are at least as powerful as polynomially space-bounded Turing machines. --- paper_title: Dynamical systems that sort lists, diagonalize matrices and solve linear programming problems paper_content: The author establishes a number of properties associated with the dynamical system H=(H,(H,N)), where H and N are symmetric n-by-n matrices and (A,B)=AB-BA. The most important of these come from the fact that this equation is equivalent to a certain gradient flow on the space of orthogonal matrices. Particular emphasis is placed on the role of this equation as an analog computer. For example, it is shown how to map the data associated with a linear programming problem into H(0) and N in such a way as to have H=(H(H,N)) evolve to a solution of the linear programming problem. This result can be applied to find systems that solve a variety of generic combinatorial optimization problems, and it also provides an algorithm for diagonalizing symmetric matrices. > --- paper_title: Computability with low-dimensional dynamical systems paper_content: It has been known for a short time that a class of recurrent neural networks has universal computational abilities. These networks can be viewed as iterated piecewise-linear maps in a high-dimensional space. In this paper, we show that similar systems in dimension two are also capable of universal computations. On the contrary, it is necessary to resort to more complex systems (e.g., iterated piecewise-monotone maps) in order to retain this capability in dimension one. --- paper_title: Complexity theory and genetics paper_content: We introduce a population genetics model in which the operators are effectively computable-computable in polynomial time on probabilistic Turing machines. We shall show that in this model a population can encode easily large amount of information from environment into genetic code. Then it can process the information as a parallel computer. More precisely, we show that it can stimulate polynomial space computations in polynomially many steps, even if the recombination rules are very simple. > --- paper_title: Optical Computing: A Survey for Computer Scientists paper_content: Optical Computers provides the first in-depth review of the possibilities and limitations of optical data processing. --- paper_title: On the computational power of neural nets paper_content: This paper deals with finite networks which consist of interconnections of synchronously evolving processors. Each processor updates its state by applying a “sigmoidal” scalar nonlinearity to a linear combination of the previous states of all units. We prove that one may simulate all Turing Machines by rational nets. In particular, one can do this in linear time, and there is a net made up of about 1,000 processors which computes a universal partial-recursive function. Products (high order nets) are not required, contrary to what had been stated in the literature. Furthermore, we assert a similar theorem about non-deterministic Turing Machines. Consequences for undecidability and complexity issues about nets are discussed too. --- paper_title: Universal Computation and Other Capabilities of Hybrid and Continuous Dynamical Systems paper_content: We explore the simulation and computational capabilities of hybrid and continuous dynamical systems. The continuous dynamical systems considered are ordinary differential equations (ODEs). For hybrid systems we concentrate on models that combine ODEs and discrete dynamics (e.g., finite automata). We review and compare four such models from the literature. Notions of simulation of a discrete dynamical system by a continuous one are developed. We show that hybrid systems whose equations can describe a precise binary timing pulse (exact clock) can simulate arbitrary reversible discrete dynamical systems defined on closed subsets of R n . The simulations require continuous ODEs in IR2n with the exact clock as input. All four hybrid systems models studied here can implement exact clocks. We also prove that any discrete dynamical system in rn can be simulated by continuous ODEs in Rt2n+1. We use this to show that smooth ODEs in RI3 can simulate arbitrary Turing machines, and hence possess the power of universal computation. We use the famous asynchronous arbiter problem to distinguish between hybrid and continuous dynamical systems. We prove that one cannot build an arbiter with devices described by a system of Lipschitz ODEs. On the other hand, all four hybrid systems models considered can implement arbiters even if their ODEs are Lipschitz. --- paper_title: Analog computation with continuous ODEs paper_content: Demonstrates simple, low-dimensional systems of ODEs that can simulate arbitrary finite automata, push-down automata, and Turing machines. We conclude that there are systems of ODEs in R/sup 3/ with continuous vector fields possessing the power of universal computation. Further, such computations can be made robust to small errors in coding of the input or measurement of the output. As such, they represent physically realizable computation. We make precise what we mean by "simulation" of digital machines by continuous dynamical systems. We also discuss elements that a more comprehensive ODE-based model of analog computation should contain. The "axioms" of such a model are based on considerations from physics. > --- paper_title: Dynamical systems that sort lists, diagonalize matrices and solve linear programming problems paper_content: The author establishes a number of properties associated with the dynamical system H=(H,(H,N)), where H and N are symmetric n-by-n matrices and (A,B)=AB-BA. The most important of these come from the fact that this equation is equivalent to a certain gradient flow on the space of orthogonal matrices. Particular emphasis is placed on the role of this equation as an analog computer. For example, it is shown how to map the data associated with a linear programming problem into H(0) and N in such a way as to have H=(H(H,N)) evolve to a solution of the linear programming problem. This result can be applied to find systems that solve a variety of generic combinatorial optimization problems, and it also provides an algorithm for diagonalizing symmetric matrices. > --- paper_title: Computability with low-dimensional dynamical systems paper_content: It has been known for a short time that a class of recurrent neural networks has universal computational abilities. These networks can be viewed as iterated piecewise-linear maps in a high-dimensional space. In this paper, we show that similar systems in dimension two are also capable of universal computations. On the contrary, it is necessary to resort to more complex systems (e.g., iterated piecewise-monotone maps) in order to retain this capability in dimension one. --- paper_title: Logical reversibility of computation paper_content: The usual general-purpose computing automaton (e.g.. a Turing machine) is logically irreversible- its transition function lacks a single-valued inverse. Here it is shown that such machines may he made logically reversible at every step, while retainillg their simplicity and their ability to do general computations. This result is of great physical interest because it makes plausible the existence of thermodynamically reversible computers which could perform useful computations at useful speed while dissipating considerably less than kT of energy per logical step. In the first stage of its computation the logically reversible automaton parallels the corresponding irreversible automaton, except that it saves all intermediate results, there by avoiding the irreversible operation of erasure. The second stage consists of printing out the desired output. The third stage then reversibly disposes of all the undesired intermediate results by retracing the steps of the first stage in backward order (a process which is only possible because the first stage has been carried out reversibly), there by restoring the machine (except for the now-written output tape) to its original condition. The final machine configuration thus contains the desired output and a reconstructed copy of the input, but no other undesired data. The foregoing results are demonstrated explicitly using a type of three-tape Turing machine. The biosynthesis of messenger RNA is discussed as a physical example of reversible computation. --- paper_title: Universal Computation and Other Capabilities of Hybrid and Continuous Dynamical Systems paper_content: We explore the simulation and computational capabilities of hybrid and continuous dynamical systems. The continuous dynamical systems considered are ordinary differential equations (ODEs). For hybrid systems we concentrate on models that combine ODEs and discrete dynamics (e.g., finite automata). We review and compare four such models from the literature. Notions of simulation of a discrete dynamical system by a continuous one are developed. We show that hybrid systems whose equations can describe a precise binary timing pulse (exact clock) can simulate arbitrary reversible discrete dynamical systems defined on closed subsets of R n . The simulations require continuous ODEs in IR2n with the exact clock as input. All four hybrid systems models studied here can implement exact clocks. We also prove that any discrete dynamical system in rn can be simulated by continuous ODEs in Rt2n+1. We use this to show that smooth ODEs in RI3 can simulate arbitrary Turing machines, and hence possess the power of universal computation. We use the famous asynchronous arbiter problem to distinguish between hybrid and continuous dynamical systems. We prove that one cannot build an arbiter with devices described by a system of Lipschitz ODEs. On the other hand, all four hybrid systems models considered can implement arbiters even if their ODEs are Lipschitz. --- paper_title: Analog computation with continuous ODEs paper_content: Demonstrates simple, low-dimensional systems of ODEs that can simulate arbitrary finite automata, push-down automata, and Turing machines. We conclude that there are systems of ODEs in R/sup 3/ with continuous vector fields possessing the power of universal computation. Further, such computations can be made robust to small errors in coding of the input or measurement of the output. As such, they represent physically realizable computation. We make precise what we mean by "simulation" of digital machines by continuous dynamical systems. We also discuss elements that a more comprehensive ODE-based model of analog computation should contain. The "axioms" of such a model are based on considerations from physics. > ---	Title: A Survey of Continuous-Time Computation Theory Section 1: Introduction Description 1: Introduce the renewed interest in analog computation, the advancements prompting this interest, and set the stage for the theoretical discussions to follow. Section 2: Unconstrained Models Description 2: Discuss mathematically-based continuous-time computation models that are implementation-wise unconstrained. Section 3: Constrained Models Description 3: Detail the models of analog computation that correspond to idealized versions of existing devices, such as mechanical and electronic differential analyzers and neural networks. Section 4: Computational Complexity Description 4: Examine the limited existing work on computational complexity in continuous-time systems and suggest possible directions for defining and understanding complexity in these systems. Section 5: Conclusion and Open Problems Description 5: Summarize the current state of research in continuous-time computation theory, highlight open problems, and suggest directions for future work.
Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey	7	--- paper_title: Particle swarm optimization paper_content: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described. --- paper_title: The Design Space of Wireless Sensor Networks ∗ paper_content: In the recent past, wireless sensor networks have found their way into a wide variety of applications and systems with vastly varying requirements and characteristics. As a consequence, it is becoming increasingly difficult to discuss typical requirements regarding hardware issues and software support. This is particularly problematic in a multidisciplinary research area such as wireless sensor networks, where close collaboration between users, application domain experts, hardware designers, and software developers is needed to implement efficient systems. In this article we discuss the consequences of this fact with regard to the design space of wireless sensor networks by considering its various dimensions. We justify our view by demonstrating that specific existing applications occupy different points in the design space. --- paper_title: Wireless sensor network survey paper_content: A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application's design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges. --- paper_title: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems paper_content: Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance in several real-world applications. In this paper, we evaluate the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization techniques. The comparison is performed on a suite of 34 widely used benchmark problems. The results from our study show that DE generally outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA. --- paper_title: Foundations of Global Genetic Optimization paper_content: This book is devoted to the application of genetic algorithms in continuous global optimization. Some of their properties and behavior are highlighted and formally justified. Various optimization techniques and their taxonomy are the background for detailed discussion. The nature of continuous genetic search is explained by studying the dynamics of probabilistic measure, which is utilized to create subsequent populations. This approach shows that genetic algorithms can be used to extract some areas of the search domain more effectively than to find isolated local minima. The biological metaphor of such behavior is the whole population surviving by rapid exploration of new regions of feeding rather than caring for a single individual. One group of strategies that can make use of this property are two-phase global optimization methods. In the first phase the central parts of the basins of attraction are distinguished by genetic population analysis. Afterwards, the minimizers are found by convex optimization methods executed in parallel. --- paper_title: Biomimicry of bacterial foraging for distributed optimization and control paper_content: We explain the biology and physics underlying the chemotactic (foraging) behavior of E. coli bacteria. We explain a variety of bacterial swarming and social foraging behaviors and discuss the control system on the E. coli that dictates how foraging should proceed. Next, a computer program that emulates the distributed optimization process represented by the activity of social bacterial foraging is presented. To illustrate its operation, we apply it to a simple multiple-extremum function minimization problem and briefly discuss its relationship to some existing optimization algorithms. The article closes with a brief discussion on the potential uses of biomimicry of social foraging to develop adaptive controllers and cooperative control strategies for autonomous vehicles. For this, we provide some basic ideas and invite the reader to explore the concepts further. --- paper_title: Bio-inspired node localization in wireless sensor networks paper_content: Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. A common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. In this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. In this paper, the ranging-based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. An investigation on distributed iterative localization is presented in this paper. Here, the nodes that get localized in an iteration act as references for remaining nodes to localize. The problem has been addressed using particle swarm optimization (PSO) and bacterial foraging algorithm (BFA). A comparison of the performances of PSO and BFA in terms of the number of nodes localized, localization accuracy and computation time is presented. --- paper_title: Performance Comparison of Optimization Algorithms for Clustering in Wireless Sensor Networks paper_content: Clustering in wireless sensor networks (WSNs) is one of the techniques that can expand the lifetime of the whole network through data aggregation at the cluster head. This paper presents performance comparison between particle swarm optimization (PSO) and genetic algorithms (GA) with a new cost function that has the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. Furthermore, a comparison is made with the well known cluster-based protocols developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH, as well as the traditional K-means clustering algorithm. Simulation results demonstrate that the proposed protocol using PSO algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station over its comparatives. --- paper_title: A Survey on Wireless Sensor Networks Deployment paper_content: In recent years extensive research has opened challenging issues for wireless sensor networks (WSNs) deployment. Among numerous challenges faced while designing architectures and protocols, maintaining connectivity and maximizing the network lifetime stand out as critical considerations. WSNs are formed by a large number of resource-constrained and inexpensive nodes, which has an impact on protocol design and network scalability. Sensor networks have enabled a range of applications where the objective is to observe an environment and collect information about the observed phenomena or events. This has lead to the emergence of a new generation sensor networks called sensor actuator networks. Approaches developed to query sensor-actuator networks (SANETs) are either application-specific or generic. Application-specific SANETs provide limited reusability, are net cost effective and may require extensive programming efforts to make the network able to serve new applications. A WSNs should be able to operate for long time with little or no external management. The sensor nodes must be able to configurate themselves in the presence of adverse situations. In this work, dealing with challenges for WSNs deployment, we start with mobility-based communication in WSNs. Then, we introduce service-oriented SANETs (SOSANETs) as an approach to build customizable SANETs. In the second part, we describe localization systems and analyze self configurability, situation awareness and intrusion detection system. In the third part, we present wireless distributed detection as well as a model for WSN simulation. Finally, conclusions and proposals for future research are given. --- paper_title: Improving sensing coverage of wireless sensor networks by employing mobile robots paper_content: To provide proper coverage of their random deployment regions, wireless sensor networks (WSN) should employ abundant nodes. In this paper we correct such situations by employing some mobile robots as mobile nodes in WSN which can actively move to desired locations for repairing the broken networks. According to the pre-research work we know that the number of nodes employed by WSN is closely relevant to quality of service (QoS) in sensing coverage when the nodes are randomly deployed in the target region. A modified particle swarm optimization (PSO) named particle swarm genetic optimization (PSGO), which imports selection and mutation operators in PSO to overcome the premature fault of classical PSO, is proposed to redeploy the mobile robots according to the node density for repairing the sensing coverage hole after their initial random deployment. It is suggested by the simulated experiment results that the WSN employing the mobile robots can improve the QoS in sensing coverage than the stationary WSN by redeploying mobile robots according to the node density. --- paper_title: An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment paper_content: The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms. --- paper_title: Allocating Multiple Base Stations under General Power Consumption by the Particle Swarm Optimization paper_content: In this paper, a two-tiered wireless sensor networks consisting of small sensor nodes, application nodes and base-stations is considered. An algorithm based on particle swarm optimization (PSO) is proposed for multiple base stations under general power-consumption constraints. The proposed approach can search for nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results also show the good performance of the proposed PSO approach and the effects of the parameters on the results --- paper_title: A Theory of Network Localization paper_content: In this paper, we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigidity theory to test the conditions for unique localizability and to construct uniquely localizable networks. We further study the computational complexity of network localization and investigate a subclass of grounded graphs where localization can be computed efficiently. We conclude with a discussion of localization in sensor networks where the sensors are placed randomly --- paper_title: Wireless Sensor Network Localization Techniques paper_content: Wireless sensor network localization is an important area that attracted significant research interest. This interest is expected to grow further with the proliferation of wireless sensor network applications. This paper provides an overview of the measurement techniques in sensor network localization and the one-hop localization algorithms based on these measurements. A detailed investigation on multi-hop connectivity-based and distance-based localization algorithms are presented. A list of open research problems in the area of distance-based sensor network localization is provided with discussion on possible approaches to them. --- paper_title: Optimizing the Localization of a Wireless Sensor Network in Real Time Based on a Low-Cost Microcontroller paper_content: In this paper, a low-cost microcontroller-based system that uses the pedometer measurement and communication ranging between neighboring nodes of a wireless sensor network for localization is presented. Unlike most of the existing methods that require good network connectivity, the proposed system works well in a sparse network. As the localization requires solving of nonlinear equations in real time, two optimization approaches, namely, the Gauss-Newton algorithm and the particle swarm optimization have been studied. The localization and optimization algorithms have been implemented with a microcontroller. The performance has been evaluated with experimental results. --- paper_title: Localization in wireless sensor networks using particle swarm optimization paper_content: This paper proposes a novel and computationally efficient global optimization method based on swarm intelligence for locating nodes in a WSN environment. The mean squared range error of all neighbouring anchor nodes is taken as the objective function for this non linear optimization problem. The Particle Swarm Optimization (PSO) is a high performance stochastic global optimization tool that ensures the minimization of the objective function, without being trapped into local optima. The easy implementation and low memory requirement features of PSO make it suitable for highly resource constrained WSN environments. Computational experiments on data drawn from simulated WSNs show better convergence characteristics than the existing Simulated Annealing based WSN localization. --- paper_title: A particle swarm optimization approach for the localization of a wireless sensor network paper_content: For many wireless sensor network applications, the localization of sensor nodes is an essential requirement. In this paper, a low cost localization system based on the measurements from a pedometer and communication ranging between neighboring nodes is presented. Unlike most of the existing methods that require good network connectivity, the proposed system works well in a sparse network. The localization information is obtained through a probability based algorithm that requires the solving of a nonlinear optimization problem. To obtain the optimum location of the sensor nodes, the particle swarm optimization (PSO) scheme that can be realized with a microcontroller for real time application is investigated in this paper. Experimental results show that the proposed approach yields good performance. --- paper_title: Bio-inspired node localization in wireless sensor networks paper_content: Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. A common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. In this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. In this paper, the ranging-based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. An investigation on distributed iterative localization is presented in this paper. Here, the nodes that get localized in an iteration act as references for remaining nodes to localize. The problem has been addressed using particle swarm optimization (PSO) and bacterial foraging algorithm (BFA). A comparison of the performances of PSO and BFA in terms of the number of nodes localized, localization accuracy and computation time is presented. --- paper_title: Optimization of Sensor Node Locations in a Wireless Sensor Network paper_content: In this paper, a localization system for unknown emitter nodes in a wireless sensor network (WSN) system is presented. For this system, it is assumed that there are four anchor nodes with known locations and one or more unknown nodes which transmit RF signals that can be received by the four anchor nodes. The only available information to the system is the received signal strength indicator which is in general not very accurate. To obtain better estimated location of the sensor nodes, the particle swarm optimization (PSO) scheme that can be realized in real time is investigated in this paper. Both simulation and experimental results of the proposed approach are presented. --- paper_title: Energy-efficient communication protocol for wireless microsensor networks paper_content: Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated. --- paper_title: Particle Swarm Optimisers for Cluster formation in Wireless Sensor Networks paper_content: We describe the results of a performance evaluation of four extensions of Particle Swarm Optimisation (PSO) to reduce energy consumption in wireless sensor networks. Communication distances are an important factor to be reduced in sensor networks. By using clustering in a sensor network we can reduce the total communication distance, thus increasing the life of a network. We adopt a distance based clustering criterion for sensor network optimisation. From PSO perspective, we study the suitability of four different PSO algorithms for our application and propose modifications. An important modification proposed is to use a boundary checking routine for re-initialisation of a particle which moves outside the set boundary. --- paper_title: An application-specific protocol architecture for wireless microsensor networks paper_content: Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches. --- paper_title: Cluster Heads Election Analysis for Multi-hop Wireless Sensor Networks Based on Weighted Graph and Particle Swarm Optimization paper_content: Continued advances of wireless communication technologies have enabled the deployment of large scale wireless sensor networks. The sensors' limited power makes energy consumption a critical issue. In single-hop wireless sensor networks, cluster heads election method based on residual energy can obtain better energy efficiency than the method in which cluster heads are elected in turns or by probabilities. Is it the same in multi-hop wireless sensor networks? In this paper we proposed and evaluated a routing optimization scheme based on graph theory and particle swarm optimization algorithm for multi-hop wireless sensor network. Our algorithm synthesized the intuitionist advantages of graph theory and optimal search capability of PSO. The result in multi-hop networks is completely different from that in single-hop wireless sensor networks. The result shows that there is very little difference from these methods. The reason is discussed in detail. --- paper_title: Performance Comparison of Optimization Algorithms for Clustering in Wireless Sensor Networks paper_content: Clustering in wireless sensor networks (WSNs) is one of the techniques that can expand the lifetime of the whole network through data aggregation at the cluster head. This paper presents performance comparison between particle swarm optimization (PSO) and genetic algorithms (GA) with a new cost function that has the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. Furthermore, a comparison is made with the well known cluster-based protocols developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH, as well as the traditional K-means clustering algorithm. Simulation results demonstrate that the proposed protocol using PSO algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station over its comparatives. --- paper_title: Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization paper_content: Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the need for energy efficient infrastructure is becoming increasingly more important since it impacts upon the network operational lifetime. Sensor node clustering is one of the techniques that can expand the lifespan of the whole network through data aggregation at the cluster head. In this paper, we present an energy-aware clustering for wireless sensor networks using particle swarm optimization (PSO) algorithm which is implemented at the base station. We define a new cost function, with the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. The performance of our protocol is compared with the well known cluster-based protocol developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH. Simulation results demonstrate that our proposed protocol can achieve better network lifetime and data delivery at the base station over its comparatives. --- paper_title: Data-aggregation techniques in sensor networks: a survey paper_content: Wireless sensor networks consist of sensor nodes with sensing and com- munication capabilities. We focus on data-aggregation problems in energy- constrained sensor networks. The main goal of data-aggregation algorithms is to gather and aggregate data in an energy efficient manner so that net- work lifetime is enhanced. In this article we present a survey of data-aggre- gation algorithms in wireless sensor networks. We compare and contrast different algorithms on the basis of performance measures such as lifetime, latency, and data accuracy. We conclude with possible future research directions. --- paper_title: Optimal Power Scheduling for Correlated Data Fusion in Wireless Sensor Networks via Constrained PSO paper_content: Optimal power scheduling for distributed detection in a Gaussian sensor network is addressed for both independent and correlated observations. We assume amplify-and-forward local processing at each node. The wireless link between sensors and the fusion center is assumed to undergo fading and coefficients are assumed to be available at the transmitting sensors. The objective is to minimize the total network power to achieve a desired fusion error probability at the fusion center. For i.i.d. observations, the optimal power allocation is derived analytically in closed form. When observations are correlated, first, an easy to optimize upper bound is derived for sufficiently small correlations and the power allocation scheme is derived accordingly. Next, an evolutionary computation technique based on particle swarm optimization is developed to find the optimal power allocation for arbitrary correlations. The optimal power scheduling scheme suggests that the sensors with poor observation quality and bad channels should be inactive to save the total power expenditure of the system. It is shown that the probability of fusion error performance based on the optimal power allocation scheme outperforms the uniform power allocation scheme especially when either the number of sensors is large or the local observation quality is good. --- paper_title: Swarm intelligence based optimization and control of decentralized serial sensor networks paper_content: In this paper threshold design and hierarchy management of serial sensor networks employed for distributed detection is accomplished using a hybrid of ant colony optimization and particle swarm optimization. The particle swarm optimization determines the optimal thresholds, decision rules for the sensors. The ant colony optimization algorithm determines the hierarchy of sensor decision communication, affecting the accuracy. The problem of hierarchy management is known as ldquowho reports to whom?rdquo problem in sensor networks. The new algorithm is tested on a suite of 10 heterogeneous sensors. Probabilistic measures including probability of error and Bayesian risk are adopted to evaluate the performance of the sensor network. The new sensor management methodology is compared to (a) static hierarchy network, (b) a network with the best sensor at the top of the hierarchy and (c) incrementally best hierarchy. Results show 40% performance improvements in terms of Bayesian risk value. --- paper_title: Dynamic sensor management using multi-objective particle swarm optimizer paper_content: This paper presents a Swarm Intelligence based approach for sensor management of a multi sensor network. Alternate sensor configurations and fusion strategies are evaluated by swarm agents, and an optimum configuration and fusion strategy evolves. An evolutionary algorithm, particle swarm optimization, is modified to optimize two objectives: accuracy and time. The output of the algorithm is the choice of sensors, individual sensor’s thresholds and the optimal decision fusion rule. The results achieved show the capability of the algorithm in selecting optimal configuration for a given requirement consisting of multiple objectives. ---	Title: Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey Section 1: PSO Algorithm Description 1: This section briefly outlines the Particle Swarm Optimization (PSO) algorithm, including its principles, implementation, and the parameters typically considered in PSO research. Section 2: Other Optimization Algorithms Description 2: This section provides an overview of traditional optimization methods and other heuristic algorithms, comparing their computational complexities and advantages against PSO. Section 3: Optimal WSN Deployment Description 3: This section discusses the applications of PSO in optimal Wireless Sensor Network (WSN) deployment, covering strategies for stationary and mobile node positioning and base station positioning. Section 4: Node Localization in WSNs Description 4: This section explores how PSO is used for node localization in WSNs, detailing various approaches and their effectiveness in different conditions, including stationary and dynamic scenarios. Section 5: Energy-Aware Clustering (EAC) in WSNs Description 5: This section delves into the role of PSO in energy-aware clustering to enhance the network's lifespan by optimizing cluster-head selection and clustering strategies. Section 6: Data Aggregation in WSNs Description 6: This section discusses how PSO contributes to data aggregation, optimizing transmission power allocation, defining local thresholds, and configuring sensors for improved data fusion and reduced communication overhead. Section 7: Conclusion Description 7: This section summarizes the advantages and limitations of PSO in solving various WSN issues, projects future research directions, and provides concluding remarks on the applicability of PSO in WSN environments.
COMPUTATIONAL ASPECTS OF MONOTONE DUALIZATION: A BRIEF SURVEY	9	--- paper_title: An O(nm)-time algorithm for computing the dual of a regular Boolean function paper_content: Abstract We consider the problem of dualizing a positive Boolean function ƒ: Bn → B given in irredundant disjunctive normal form (DNF), that is, obtaining the irredundant DNF form of its dual ƒ d (x) = ƒ ( x ) . The function f is said to be regular if there is a linear order ≳ on {1,…,n} such that i≳j, xi = 0, and xj = 1 imply ƒ(x) ⩽ ƒ(x + u i − u j ) , where uk denote unit vectors. A previous algorithm of the authors, the Hop-Skip-and-Jump algorithm, dualizes a regular function in polynomial time. We use this algorithm to give an explicit expression for the irredundant DNF of ƒd in terms of the one for ƒ. We show that if the irredundant DNF for ƒ has m ⩾ 2 terms, then the one for ƒd has at most (n − 2)m + 1, and can be computed in O(nm) time. This can be applied to solve regular set-covering problems in O(nm) time. --- paper_title: Average Case Self-Duality of Monotone Boolean Functions paper_content: The problem of determining whether a monotone boolean function is self-dual has numerous applications in Logic and AI. The applications include theory revision, model-based diagnosis, abductive explanations and learning monotone boolean functions. It is not known whether self-duality of monotone boolean functions can be tested in polynomial time, though a quasi-polynomial time algorithm exists. We describe another quasi-polynomial time algorithm for solving the self-duality problem of monotone boolean functions and analyze its average-case behaviour on a set of randomly generated instances. --- paper_title: Abduction and the Dualization Problem paper_content: Computing abductive explanations is an important problem, which has been studied extensively in Artificial Intelligence (AI) and related disciplines. While computing some abductive explanation for a literal χ with respect to a set of abducibles A from a Horn propositional theory Σ is intractable under the traditional representation of Σ by a set of Horn clauses, the problem is polynomial under model-based theory representation, where Σ is represented by its characteristic models. Furthermore, computing all the (possibly exponentially) many explanations is polynomial-time equivalent to the problem of dualizing a positive CNF, which is a well-known problem whose precise complexity in terms of the theory of NP-completeness is not known yet. In this paper, we first review the monotone dualization problem and its connection to computing all abductive explanations for a query literal and some related problems in knowledge discovery. We then investigate possible generalizations of this connection to abductive queries beyond literals. Among other results, we find that the equivalence for generating all explanations for a clause query (resp., term query) χ to the monotone dualization problem holds if χ contains at most k positive (resp., negative) literals for constant k, while the problem is not solvable in polynomial total-time, i.e., in time polynomial in the combined size of the input and the output, unless P=NP for general clause resp. term queries. Our results shed new light on the computational nature of abduction and Horn theories in particular, and might be interesting also for related problems, which remains to be explored. --- paper_title: Generating all maximal independent sets of bounded-degree hypergraphs paper_content: We show that any monotone function with a read-k CNF representation can be learned in terms of its DNF representation with membership queries alone in time polynomial in the DNF size and n (the number of variables) assuming k is some fixed constant. The problem is motivated by the well-studied open problem of enumerating all maximal independent sets of a given hypergraph. Our algorithm gives a solution for the bounded degree case and works even if the hypergraph is not input, but rather only queries are available as to which sets are independent. --- paper_title: Generating All Maximal Independent Sets: NP-Hardness and Polynomial-Time Algorithms paper_content: Suppose that an independence system $(E,\mathcal {I})$ is characterized by a subroutine which indicates in unit time whether or not a given subset of E is independent. It is shown that there is no algorithm for generating all the K maximal independent sets of such an independence system in time polynomial in $\|E\|$ and K, unless $\mathcal {P} = \mathcal {NP}$. However, it is possible to apply ideas of Paull and Unger and of Tsukiyama et al. to obtain polynomial-time algorithms for a number of special cases, e.g. the efficient generation of all maximal feasible solutions to a knapsack problem. The algorithmic techniques bear an interesting relationship with those of Read for the enumeration of graphs and other combinatorial configurations. --- paper_title: Polynomial-time algorithms for regular set-covering and threshold synthesis paper_content: Abstract A set-covering problem is called regular if a cover always remains a cover when any column in it is replaced by an earlier column. From the input of the problem - the coefficient matrix of the set-covering inequalities - it is possible to check in polynomial time whether the problem is regular or can be made regular by permuting the columns. If it is, then all the minimal covers are generated in polynomial time, and one of them is an optimal solution. The algorithm also yields an explicit bound for the number of minimal covers. These results can be used to check in polynomial time whether a given set-covering problem is equivalent to some knapsack problem without additional variables, or equivalently to recognize positive threshold functions in polynomial time. However, the problem of recognizing when an arbitrary Boolean function is threshold is NP-complete. It is also shown that the list of maximal non-covers is essentially the most compact input possible, even if it is known in advance that the problem is regular. --- paper_title: Dualization of regular Boolean functions paper_content: Abstract A monotonic Boolean function is regular if its variables are naturally ordered by decreasing ‘strength’, so that shifting to the right the non-zero entries of any binary false point always yields another false point. Peled and Simeone recently published a polynomial algorithm to generate the maximal false points (MFP's) of a regular function from a list of its minimal true points (MTP's). Another efficient algorithm for this problem is presented here, based on characterization of the MFP's of a regular function in terms of its MTP's. This result is also used to derive a new upper bound on the number of MFP's of a regular function. --- paper_title: On the Complexity of Dualization of Monotone Disjunctive Normal Forms paper_content: We show that the duality of a pair of monotone disjunctive normal forms of sizencan be tested inno(logn)time. --- paper_title: A New Algorithm for Generating Prime Implicants paper_content: This paper describes an algorithm which will generate all the prime implicants of a Boolean function. The algorithm is different from those previously given in the literature, and in many cases it is more efficient. It is proved that the algorithm will find all the prime implicants. The algorithm may possibly generate some nonprime implicants. However, using frequency orderings on literals, the experiments with the algorithm show that it usually generates very few ( possibly none) nonprime implicants. Furthermore, the algorithm may be used to find the minimal sums of a Boolean function. The algorithm is implemented by a computer program in the LISP language. --- paper_title: An incremental method for generating prime implicants/implicates paper_content: Given the recent investigation of Clause Management Systems (CMSs) for ArtificialIntelligence applications, there is an urgent need for an efficient incremental method for generating prime implicants. Given a set of clauses F, a set of prime implicants II of F and a clause C"1 the problem can be formulated as finding the set of prime implicants for II U {C}. Intuitively, the property of implicants, being prime implies that any effort to generate prime implicants from a set of prime implicants will not yield any new prime implicants but themselves. In this paper, we exploit the properties of prime implicants and propose an incremental method for generating prime implicants from a set of existing prime implicants plus a new clause. The correctness proof and complexity analysis of the incremental method are presented, and the intricacy of subsumptions in the incremental method is also examined. Additionally, the role of prime implicants in the CMS is also mentioned. --- paper_title: Learning conjunctions of Horn clauses paper_content: An algorithm for learning the class of Boolean formulas that are expressible as conjunctions of Horn clauses is presented. (A Horn clause is a disjunction of literals, all but at most one of which is a negated variable). The algorithm uses equivalence queries and membership queries to produce a formula that is logically equivalent to the unknown formula to be learned. The amount of time used by the algorithm is polynomial in the number of variables and the number of clauses in the unknown formula. > --- paper_title: Hypergraph Transversal Computation and Related Problems in Logic and AI paper_content: Generating minimal transversals of a hypergraph is an important problem which has many applications in Computer Science. In the present paper, we address this problem and its decisional variant, i.e., the recognition of the transversal hypergraph for another hypergraph. We survey some results on problems which are known to be related to computing the transversal hypergraph, where we focus on problems in propositional Logic and AI. Some of the results have been established already some time ago, and were announced but their derivation was not widely disseminated. We then address recent developments on the computational complexity of computing resp. recognizing the transversal hypergraph. The precise complexity of these problems is not known to date, and is in fact open for more than 20 years now. --- paper_title: Queries and Concept Learning paper_content: We consider the problem of using queries to learn an unknown concept. Several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries. Examples are given of efficient learning methods using various subsets of these queries for formal domains, including the regular languages, restricted classes of context-free languages, the pattern languages, and restricted types of prepositional formulas. Some general lower bound techniques are given. Equivalence queries are compared with Valiant's criterion of probably approximately correct identification under random sampling. --- paper_title: An efficient incremental algorithm for generating all maximal independent sets in hypergraphs of bounded dimension paper_content: We show that for hypergraphs of bounded edge size, the problem of extending a given list of maximal independent sets is NC-reducible to the computation of an arbitrary maximal independent set for an induced sub-hypergraph. The latter problem is known to be in RNC. In particular, our reduction yields an incremental RNC dualization algorithm for hypergraphs of bounded edge size, a problem previously known to be solvable in polynomial incremental time. We also give a similar parallel algorithm for the dualization problem on the product of arbitrary lattices which have a bounded number of immediate predecessors for each element. --- paper_title: Almost all monotone Boolean functions are polynomially learnable using membership queries paper_content: We consider exact learning or identification of monotone Boolean functions by only using membership queries. It is shown that almost all monotone Boolean functions are polynomially identifiable in the input number of variables as well as the output being the sum of the sizes of the CNF and DNF representations. --- paper_title: Exact Transversal Hypergraphs and Application to Boolean µ-Functions paper_content: Call an hypergraph, that is a family of subsets (edges) from a finite vertex set, an exact transversal hypergraph iff each of its minimal transversals, i.e., minimal vertex subsets that intersect each edge, meets each edge in a singleton. We show that such hypergraphs are recognizable in polynomial time and that their minimal transversals as well as their maximal independent sets can be generated in lexicographic order with polynomial delay between subsequent outputs, which is impossible in the general case unless P= NP. The results obtained are applied to monotone Boolean ?-functions, that are Boolean functions defined by a monotone Boolean expression (that is, built with ?, ? only) in which no variable occurs repeatedly. We also show that recognizing such functions from monotone Boolean expressions is co-NP-hard, thus complementing Mundici's result that this problem is in co-NP. --- paper_title: Dual-Bounded Generating Problems: All Minimal Integer Solutions for a Monotone System of Linear Inequalities paper_content: We consider the problem of enumerating all minimal integer solutions of a monotone system of linear inequalities. We first show that, for any monotone system of r linear inequalities in n variables, the number of maximal infeasible integer vectors is at most rn times the number of minimal integer solutions to the system. This bound is accurate up to a polylog(r) factor and leads to a polynomial-time reduction of the enumeration problem to a natural generalization of the well-known dualization problem for hypergraphs, in which dual pairs of hypergraphs are replaced by dual collections of integer vectors in a box. We provide a quasi-polynomial algorithm for the latter dualization problem. These results imply, in particular, that the problem of incrementally generating all minimal integer solutions to a monotone system of linear inequalities can be done in quasi-polynomial time. --- paper_title: An O(mn) algorithm for regular set-covering problems paper_content: A clutter L is a collection of m subsets of a ground set E(L) = {x1,…, xn} with the property that, for every pair Ai, Aj ϵ L, Ai is neither contained nor contains Aj, A transversal of L is a subset of E(L) intersecting every member of L. ::: ::: If we associate with each element xj ϵ E(L) a weight cj, the problem of finding a transversal having minimum weight is equivalent to the following set-covering problem ::: min{cTx\|MLx ⩾ 1m, xj ϵ {0, 1}, j = 1,…, n} ::: where ML is the matrix whose rows are the incidence vectors of the subsets Ai ϵ L and 1m denotes the vector with m ones. ::: ::: A set-covering problem is regular if there exists an ordering of the variables σ = (x1,…, xn) such that, for every feasible solution x with xi = 1, xj = 0, j < i, the vector x + ej − ei is also a feasible solution, where ei is the ith unit vector. The matrix M of a regular set-covering problem is said to be regular. ::: ::: A regular clutter is any clutter whose incidence matrix is regular. In this paper we describe some properties of regular clutters and propose an algorithm which, in O(mn) steps, generates all the minimal transversals of a regular clutter L and produces the transversal having minimum weight. --- paper_title: Efficient dualization of O(log n)-term monotone disjunctive normal forms paper_content: This paper shows that O(logn)-term monotone disjunctive normal forms (DNFs) φ can be dualized in incremental polynomial time, where n is the number of variables in φ. This improves upon the trivial result that k-term monotone DNFs can be dualized in polynomial time, where k is bounded by some constant. --- paper_title: Generating Dual-Bounded Hypergraphs paper_content: This article surveys some recent results on the generation of implicitly given hypergraphs and their applications in Boolean and integer programming, data mining, reliability theory, and combinatorics. Given a monotone property ~ over the subsets of a finite set V, we consider the problem of incrementally generating the family F π of all minimal subsets satisfying property ~ , when ~ is given by a polynomial-time satisfiability oracle. For a number of interesting monotone properties, the family F π turns out to be uniformly dual-bounded , allowing for the incrementally efficient enumeration of the members of F π. Important applications include the efficient generation of minimal infrequent sets of a database (data mining), minimal connectivity ensuring collections of subgraphs from a given list (reliability theory), minimal feasible solutions to a system of monotone inequalities in integer variables (integer programming), minimal spanning collections of subspaces from a given list (linear algebra) and max... --- paper_title: Polynomial Time Recognition Of 2-Monotonic Positive Boolean Functions Given By An Oracle paper_content: We consider the problem of identifying an unknown Boolean function $f$ by asking an oracle the functional values $f(a)$ for a selected set of test vectors $a \in \{0,1\}^{n}$. Furthermore, we assume that $f$ is a positive (or monotone) function of $n$ variables. It is not yet known whether or not the whole task of generating test vectors and checking if the identification is completed can be carried out in polynomial time in $n$ and $m$, where $m=\|\min T(f)\| + \|\max F(f)\|$ and $\min T(f)$ (respectively, $\max F(f))$ denotes the set of minimal true (respectively, maximal false) vectors of $f$. To partially answer this question, we propose here two polynomial-time algorithms that, given an unknown positive function $f$ of $n$ variables, decide whether or not $f$ is 2-monotonic and, if $f$ is 2-monotonic, output both sets $\min T(f)$ and $\max F(f)$. The first algorithm uses $O(nm^{2} + n^{2}m)$ time and $O(nm)$ queries, while the second one uses $O(n^{3}m)$ time and $O(n^{3}m)$ queries. --- paper_title: A Fast and Simple Algorithm for Identifying 2-Monotonic Positive Boolean Functions paper_content: Consider the problem of identifying minT(f) and maxF(f) of a positive (i.e., monotone) Boolean functionf, by using membership queries only, where minT(f) (maxF(f)) denotes the set of minimal true vectors (maximum false vectors) off. Moreover, as the existence of a polynomial total time algorithm (i.e., polynomial time in the length of input and output) for this problem is still open, we consider here a restricted problem: given an unknown positive functionfofnvariables, decide whetherfis 2-monotonic or not, and iffis 2-monotonic, output both minT(f) and maxF(f). For this problem, we propose a simple algorithm, which is based on the concept of maximum latency, and we show that it usesO(n2m) time andO(n2m) queries, wherem=\|minT(f)\|+\|maxF(f)\|. This answers affirmatively the conjecture raised in Boroset al.Lecture Notes in Comput. Sci.557(1991), 104?115, Boroset al.SIAM J. Comput.26(1997), 93?109, and is an improvement over the two algorithms discussed therein: one usesO(n3m) time andO(n3m) queries, and the other usesO(nm2+n2m) time andO(nm) queries. --- paper_title: New results on monotone dualization and generating hypergraph transversals paper_content: We consider the problem of dualizing a monotone CNF (equivalently, computing all minimal transversals of a hypergraph), whose associated decision problem is a prominent open problem in NP-completeness. We present a number of new polynomial time resp. output-polynomial time results for significant cases, which largely advance the tractability frontier and improve on previous results. Furthermore, we show that duality of two monotone CNFs can be disproved with limited nondeterminism. More precisely, this is feasible in polynomial time with O(chi(n) * log n) suitably guessed bits, where chi(n) is given by \chi(n)^chi(n) = n; note that chi(n) = o(log n). This result sheds new light on the complexity of this important problem. --- paper_title: The Maximum Latency and Identification of Positive Boolean Functions paper_content: Consider the problem of identifying min T(f) and max F(f) of a positive (i.e., monotone) Boolean function f, by using membership queries only, where min T(f) (maxF(f)) denotes the set of minimal true vectors (maximal false vectors) of f. It is known that an incrementally polynomial algorithm exists if and only if there is a polynomial time algorithm to check the existence of an unknown vector for given sets MT\(\subseteq \) min T(f) and MF\(\subseteq \) max F(f). Unfortunately, however, the complexity of this problem is still unknown. To answer this question partially, we introduce in this paper a measure for the difficulty of finding an unknown vector, which is called the maximum latency. If the maximum latency is constant, then an unknown vector can be found in polynomial time and there is an incrementally polynomial algorithm for identification. Several subclasses of positive functions are shown to have constant maximum latency, e.g., 2-monotonic positive functions, Δ-partial positive threshold functions and matroid functions, while the class of general positive functions has maximum latency not smaller than [n/4]+1 and the class of positive k-DNF functions has Ω(√n) maximum latency. --- paper_title: Generating Maximal Independent Sets for Hypergraphs with Bounded Edge-Intersections paper_content: Given a finite set V, and integers k > 1 and r > 0, denote by A(k, r) the class of hypergraphs A ⊆ 2 V with (k, r)-bounded intersections, i.e. in which the intersection of any k distinct hyperedges has size at most r. We consider the problem MIS(A,I): given a hypergraph A and a subfamily I C I(A), of its maximal independent sets (MIS) I(A), either extend this subfamily by constructing a new MIS I ∈ I(A) \ I or prove that there are no more MIS, that is I = I(A). We show that for hypergraphs A E A(k,r) with k + r ≤ const, problem MIS(A,I) is NC-reducible to problem MIS(A',O) of generating a single MIS for a partial subhypergraph A' of A. In particular, for this class of hypergraphs, we get an incremental polynomial algorithm for generating all MIS. Furthermore, combining this result with the currently known algorithms for finding a single maximal independent set of a hypergraph, we obtain efficient parallel algorithms for incrementally generating all MIS for hypergraphs in the classes A(1, c), A(c, 0), and A(2,1), where c is a constant. We also show that, for A ∈ A(k,r), where k + r < const, the problem of generating all MIS of A can be solved in incremental polynomial-time with space polynomial only in the size of A. --- paper_title: NP-Completeness: A Retrospective paper_content: For a quarter of a century now, NP-completeness has been computer science's favorite paradigm, fad, punching bag, buzzword, alibi, and intellectual export. This paper is a fragmentary commentary on its origins, its nature, its impact, and on the attributes that have made it so pervasive and contagious. --- paper_title: An SE-tree-based prime implicant generation algorithm paper_content: Prime implicants/implicates (PIs) have been shown to be a useful tool in several problem domains. In model-based diagnosis (MBD), de Kleer et al. (Proc. AAAI-90) have used PIs to characterize diagnoses. We present a PI generation algorithm which, although based on thegeneral SE-tree-based search framework, is effectively an improvement of aparticular PI generation algorithm proposed by Slagle et al. (IEEE Trans. Comput. 19(4) (1970)). The improvement is achieved via adecomposition tactic which is boosted by the SE-tree-based framework. The new algorithm is also more flexible in a number of ways. We present empirical results comparing the new algorithm to the old one, as well as to current PI generation algorithms. --- paper_title: On Generating the Irredundant Conjunctive and Disjunctive Normal Forms of Monotone Boolean Functions paper_content: Let f : {0,1} n → {0,1} be a monotone Boolean function whose value at any point x ∈ {0,1} n can be determined in time t. Denote by c=Λ I ∈ C ∨ i ∈ I x i the irredundant CNF off, where C is the set of the prime implicates of f. Similarly, let d=∨ J ∈ D Λ j ∈ J x j be the irredundant DNF of the same function, where D is the set of the prime implicants of f. We show that given subsets C' ⊆ C and D' ⊆ D such that (C',D') ¬= (C,D), a new term in (C\C')∪(D\D') can be found in time O(n(t+n))+m o(log m) , where m=\|C'\|+\|D'\|. In particular, if f(x) can be evaluated for every x ∈ {0,1} n in polynomial time, then the forms c and d can be jointly generated in incremental quasi-polynomial time. On the other hand, even for the class of Λ, ∨-formulae f of depth 2, i.e., for CNFs or DNFs, it is unlikely that uniform sampling from within the set of the prime implicates and implicants of f can be carried out in time bounded by a quasi-polynomial 2 polylog(.) in the input size of f. We also show that for some classes of polynomial-time computable monotone Boolean functions it is NP-hard to test either of the conditions D' =D or C' =C. This provides evidence that for each of these classes neither conjunctive nor disjunctive irredundant normal forms can be generated in total (or incremental) quasi-polynomial time. Such classes of monotone Boolean functions naturally arise in game theory, networks and relay contact circuits, convex programming, and include a subset of Λ, ∨-formulae of depth 3. --- paper_title: Self-Duality of Bounded Monotone Boolean Functions and Related Problems paper_content: In this paper we show the equivalence between the problem of determining self-duality of a boolean function in DNF and a special type of satisfiability problem called NAESPI. Eiter and Gottlob [8] use a result from [2] to show that self-duality of monotone boolean functions which have bounded clause sizes (by some constant) can be determined in polynomial time. We show that the self-duality of instances in the class studied by Eiter and Gottlob can be determined in time linear in the number of clauses in the input, thereby strengthening their result. Domingo [7] recently showed that self-duality of boolean functions where each clause is bounded by (√log n) can be solved in polynomial time. Our linear time algorithm for solving the clauses with bounded size infact solves the (√log n) bounded self-duality problem in O(n2 √log n) time, which is better bound then the algorithm of Domingo [7], O(n3). ::: ::: Another class of self-dual functions arising naturally in application domain has the property that every pair of terms in f intersect in at most constant number of variables. The equivalent subclass of NAESPI is the c-bounded NAESPI. We also show that c-bounded NAESPI can be solved in polynomial time when c is some constant. We also give an alternative characterization of almost self-dual functions proposed by Bioch and Ibaraki [5] in terms of NAESPI instances which admit solutions of a 'particular' type. --- paper_title: Complexity of Identification and Dualization of Positive Boolean Functions paper_content: Abstract We consider in this paper the problem of identifying min T(ƒ) and max F(ƒ) of a positive (i.e., monotone) Boolean function ƒ, by using membership queries only, where min T(ƒ) (max F(ƒ)) denotes the set of minimal true vectors (maximal false vectors) of ƒ. It is shown that the existence of an incrementally polynomial algorithm for this problem is equivalent to the existence of the following algorithms, where ƒ and g are positive Boolean functions: • An incrementally polynomial algorithm to dualize ƒ; • An incrementally polynomial algorithm to self-dualize ƒ; • A polynomial algorithm to decide if ƒ and are mutually dual; • A polynomial algorithm to decide if ƒ is self-dual; • A polynomial algorithm to decide if ƒ is saturated; • A polynomial algorithm in \|min (ƒ)\| + \|max (ƒ)\| to identify min (ƒ) only. Some of these are already well known open problems in the respective fields. Other related topics, including various equivalent problems encountered in hypergraph theory and theory of coteries (used in distributed systems), are also discussed. --- paper_title: Identifying the Minimal Transversals of a Hypergraph and Related Problems paper_content: The paper considers two decision problems on hypergraphs, hypergraph saturation and recognition of the transversal hypergraph, and discusses their significance for several search problems in applied computer science. Hypergraph saturation (i.e., given a hypergraph $\cal H$, decide if every subset of vertices is contained in or contains some edge of $\cal H$) is shown to be co-NP-complete. A certain subproblem of hypergraph saturation, the saturation of simple hypergraphs (i.e., Sperner families), is shown to be under polynomial transformation equivalent to transversal hypergraph recognition; i.e., given two hypergraphs ${\cal H}_{1}, {\cal H}_{2}$, decide if the sets in ${\cal H}_{2}$ are all the minimal transversals of ${\cal H}_{1}$. The complexity of the search problem related to the recognition of the transversal hypergraph, the computation of the transversal hypergraph, is an open problem. This task needs time exponential in the input size; it is unknown whether an output-polynomial algorithm exists. For several important subcases, for instance if an upper or lower bound is imposed on the edge size or for acyclic hypergraphs, output-polynomial algorithms are presented. Computing or recognizing the minimal transversals of a hypergraph is a frequent problem in practice, which is pointed out by identifying important applications in database theory, Boolean switching theory, logic, and artificial intelligence (AI), particularly in model-based diagnosis. --- paper_title: Efficient Read-Restricted Monotone CNF/DNF Dualization by Learning with Membership Queries paper_content: We consider exact learning monotone CNF formulas in which each variable appears at most some constant k times (“read-k” monotone CNF). Let f : l0,1r^n → l0,1r be expressible as a read-k monotone CNF formula for some natural number k. We give an incremental output polynomial time algorithm for exact learning both the read-k CNF and (not necessarily read restricted) DNF descriptions of f. The algorithm‘s only method of obtaining information about f is through membership queries, i.e., by inquiring about the value f(x) for points x ∈ l0,1r^n. The algorithm yields an incremental polynomial output time solution to the (read-k) monotone CNF/DNF dualization problem. The unrestricted versions remain open problems of importance. --- paper_title: A New Algorithm for Generating Prime Implicants paper_content: This paper describes an algorithm which will generate all the prime implicants of a Boolean function. The algorithm is different from those previously given in the literature, and in many cases it is more efficient. It is proved that the algorithm will find all the prime implicants. The algorithm may possibly generate some nonprime implicants. However, using frequency orderings on literals, the experiments with the algorithm show that it usually generates very few ( possibly none) nonprime implicants. Furthermore, the algorithm may be used to find the minimal sums of a Boolean function. The algorithm is implemented by a computer program in the LISP language. --- paper_title: Evaluation of an Algorithm for the Transversal Hypergraph Problem paper_content: The Transversal Hypergraph Problem is the problem of computing, given a hypergraph, the set of its minimal transversals, i.e. the hypergraph whose hyperedges are all minimal hitting sets of the given one. This problem turns out to be central in various fields of Computer Science. We present and experimentally evaluate a heuristic algorithm for the problem, which seems able to handle large instances and also possesses some nice features especially desirable in problems with large output such as the Transversal Hypergraph Problem. --- paper_title: Algorithms for inferring functional dependencies from relations paper_content: Abstract The dependency inference problem is to find a cover of the set of functional dependencies that hold in a given relation. The problem has applications in relational database design, in query optimization, and in artificial intelligence. The problem is exponential in the number of attributes. We develop two algorithms with better best case behavior than the simple one. One algorithm reduces the problem to computing the transversal of a hypergraph. The other is based on repeatedly sorting the relation with respect to a set of attributes. --- paper_title: New results on monotone dualization and generating hypergraph transversals paper_content: We consider the problem of dualizing a monotone CNF (equivalently, computing all minimal transversals of a hypergraph), whose associated decision problem is a prominent open problem in NP-completeness. We present a number of new polynomial time resp. output-polynomial time results for significant cases, which largely advance the tractability frontier and improve on previous results. Furthermore, we show that duality of two monotone CNFs can be disproved with limited nondeterminism. More precisely, this is feasible in polynomial time with O(chi(n) * log n) suitably guessed bits, where chi(n) is given by \chi(n)^chi(n) = n; note that chi(n) = o(log n). This result sheds new light on the complexity of this important problem. --- paper_title: An SE-tree-based prime implicant generation algorithm paper_content: Prime implicants/implicates (PIs) have been shown to be a useful tool in several problem domains. In model-based diagnosis (MBD), de Kleer et al. (Proc. AAAI-90) have used PIs to characterize diagnoses. We present a PI generation algorithm which, although based on thegeneral SE-tree-based search framework, is effectively an improvement of aparticular PI generation algorithm proposed by Slagle et al. (IEEE Trans. Comput. 19(4) (1970)). The improvement is achieved via adecomposition tactic which is boosted by the SE-tree-based framework. The new algorithm is also more flexible in a number of ways. We present empirical results comparing the new algorithm to the old one, as well as to current PI generation algorithms. --- paper_title: A fast algorithm for computing hypergraph transversals and its application in mining emerging patterns paper_content: Computing the minimal transversals of a hypergraph is an important problem in computer science that has significant applications in data mining. We present a new algorithm for computing hypergraph transversals and highlight their close connection to an important class of patterns known as emerging patterns. We evaluate our technique on a number of large datasets and show that it outperforms previous approaches by a factor of 9-29 times. --- paper_title: A Practical Fast Algorithm for Enumerating Minimal SetCoverings paper_content: For a set family F defined on a grand set E, a subset of F covering all the elements of E is called a set covering. The enumeration problem of minimal set covering is equal to the enumeration problem of hypergraph dualization, minimal hitting sets, and other other many problems, and have been studied intensively. However, the existence of an output polynomial algorithm for this problem is still open. However, there proposed an algorithm whose average computation time on computational experiments for random generated instances is output polynomial. In this paper, we propose a practical fast algorithm obtained by improving this algorithm, and by computational experiments, show that the average computation time for randomly generated instances is O(\|E\|) per output, by computational experiments. --- paper_title: On the Complexity of Dualization of Monotone Disjunctive Normal Forms paper_content: We show that the duality of a pair of monotone disjunctive normal forms of sizencan be tested inno(logn)time. --- paper_title: Learning conjunctions of Horn clauses paper_content: An algorithm for learning the class of Boolean formulas that are expressible as conjunctions of Horn clauses is presented. (A Horn clause is a disjunction of literals, all but at most one of which is a negated variable). The algorithm uses equivalence queries and membership queries to produce a formula that is logically equivalent to the unknown formula to be learned. The amount of time used by the algorithm is polynomial in the number of variables and the number of clauses in the unknown formula. > --- paper_title: Exact Transversal Hypergraphs and Application to Boolean µ-Functions paper_content: Call an hypergraph, that is a family of subsets (edges) from a finite vertex set, an exact transversal hypergraph iff each of its minimal transversals, i.e., minimal vertex subsets that intersect each edge, meets each edge in a singleton. We show that such hypergraphs are recognizable in polynomial time and that their minimal transversals as well as their maximal independent sets can be generated in lexicographic order with polynomial delay between subsequent outputs, which is impossible in the general case unless P= NP. The results obtained are applied to monotone Boolean ?-functions, that are Boolean functions defined by a monotone Boolean expression (that is, built with ?, ? only) in which no variable occurs repeatedly. We also show that recognizing such functions from monotone Boolean expressions is co-NP-hard, thus complementing Mundici's result that this problem is in co-NP. --- paper_title: On Generating the Irredundant Conjunctive and Disjunctive Normal Forms of Monotone Boolean Functions paper_content: Let f : {0,1} n → {0,1} be a monotone Boolean function whose value at any point x ∈ {0,1} n can be determined in time t. Denote by c=Λ I ∈ C ∨ i ∈ I x i the irredundant CNF off, where C is the set of the prime implicates of f. Similarly, let d=∨ J ∈ D Λ j ∈ J x j be the irredundant DNF of the same function, where D is the set of the prime implicants of f. We show that given subsets C' ⊆ C and D' ⊆ D such that (C',D') ¬= (C,D), a new term in (C\C')∪(D\D') can be found in time O(n(t+n))+m o(log m) , where m=\|C'\|+\|D'\|. In particular, if f(x) can be evaluated for every x ∈ {0,1} n in polynomial time, then the forms c and d can be jointly generated in incremental quasi-polynomial time. On the other hand, even for the class of Λ, ∨-formulae f of depth 2, i.e., for CNFs or DNFs, it is unlikely that uniform sampling from within the set of the prime implicates and implicants of f can be carried out in time bounded by a quasi-polynomial 2 polylog(.) in the input size of f. We also show that for some classes of polynomial-time computable monotone Boolean functions it is NP-hard to test either of the conditions D' =D or C' =C. This provides evidence that for each of these classes neither conjunctive nor disjunctive irredundant normal forms can be generated in total (or incremental) quasi-polynomial time. Such classes of monotone Boolean functions naturally arise in game theory, networks and relay contact circuits, convex programming, and include a subset of Λ, ∨-formulae of depth 3. --- paper_title: On the frequency of the most frequently occurring variable in dual monotone DNFs paper_content: Abstract Let f ( X l ,…, X N )=⋁ I ∈ F ⋀ i ∈ I X i and g ( X l ,…, X N )=⋁ I ∈ G ⋀ i ∈ I X i be a pair of dual monotone irredundant disjunctive normal forms, where F and G are the sets of the prime implicants of tf and g , respectively. For a variable x i , i = 1, …, n , let μ i = # { I ∈ F \| i ∈ I }/\| F \| and v i = # { I ∈ G \| i ∈ I }/\| G \| be the frequencies with which x i occurs in f and g . It is easily seen that max { μ 1 , v 1 , …, μ n , v n } ⩾ 1/log(\| F \| + \| G \|). We give examples of arbitrarily large F and G for which the above bound is tight up to a factor of 2. --- paper_title: Dual-Bounded Generating Problems: Partial And Multiple Transversals Of A Hypergraph paper_content: We consider two generalizations of the notion of transversal to a finite hypergraph, the so-called multiple and partial transversals. Multiple transversals naturally arise in 0-1 programming, while partial transversals are related to data mining and machine learning. We show that for an arbitrary hypergraph the families of multiple and partial transversals are both dual-bounded in the sense that the size of the corresponding dual hypergraph is bounded by a polynomial in the cardinality and the length of description of the input hypergraph. Our bounds are based on new inequalities of extremal set theory and threshold Boolean logic, which may be of independent interest. We also show that the problems of generating all multiple and all partial transversals for a given hypergraph are polynomial-time reducible to the generation of all ordinary transversals for another hypergraph, i.e., to the well-known dualization problem for hypergraphs. As a corollary, we obtain incremental quasi-polynomial-time algorithms for both of the above problems, as well as for the generation of all the minimal binary solutions for an arbitrary monotone system of linear inequalities. --- paper_title: An intersection inequality for discrete distributions and related generation problems paper_content: Given two finite sets of points X, Y in Rn which can be separated by a nonnegative linear function, and such that the componentwise minimum of any two distinct points in X is dominated by some point in Y, we show that \|X\| = n\|Y\|. As a consequence of this result, we obtain quasi-polynomial time algorithms for generating all maximal integer feasible solutions for a given monotone system of separable inequalities, for generating all p-inefficient points of a given discrete probability distribution, and for generating all maximal empty hyper-rectangles for a given set of points in Rn. This provides a substantial improvement over previously known exponential algorithms for these generation problems related to Integer and Stochastic Programming, and Data Mining. Furthermore, we give an incremental polynomial time generation algorithm for monotone systems with fixed number of separable inequalities, which, for the very special case of one inequality, implies that for discrete probability distributions with independent coordinates, both p-efficient and p-inefficient points can be separately generated in incremental polynomial time. --- paper_title: Abduction and the Dualization Problem paper_content: Computing abductive explanations is an important problem, which has been studied extensively in Artificial Intelligence (AI) and related disciplines. While computing some abductive explanation for a literal χ with respect to a set of abducibles A from a Horn propositional theory Σ is intractable under the traditional representation of Σ by a set of Horn clauses, the problem is polynomial under model-based theory representation, where Σ is represented by its characteristic models. Furthermore, computing all the (possibly exponentially) many explanations is polynomial-time equivalent to the problem of dualizing a positive CNF, which is a well-known problem whose precise complexity in terms of the theory of NP-completeness is not known yet. In this paper, we first review the monotone dualization problem and its connection to computing all abductive explanations for a query literal and some related problems in knowledge discovery. We then investigate possible generalizations of this connection to abductive queries beyond literals. Among other results, we find that the equivalence for generating all explanations for a clause query (resp., term query) χ to the monotone dualization problem holds if χ contains at most k positive (resp., negative) literals for constant k, while the problem is not solvable in polynomial total-time, i.e., in time polynomial in the combined size of the input and the output, unless P=NP for general clause resp. term queries. Our results shed new light on the computational nature of abduction and Horn theories in particular, and might be interesting also for related problems, which remains to be explored. --- paper_title: On the Complexity of Dualization of Monotone Disjunctive Normal Forms paper_content: We show that the duality of a pair of monotone disjunctive normal forms of sizencan be tested inno(logn)time. --- paper_title: Dual-Bounded Generating Problems: Weighted Transversals of a Hypergraph paper_content: Abstract We consider a generalization of the notion of transversal to a finite hypergraph, the so-called weighted transversals. Given a non-negative weight vector assigned to each hyperedge of an input hypergraph A and a non-negative threshold vector, we define a weighted transversal as a minimal vertex set which intersects all the hyperedges of A except for a sub-family of total weight not exceeding the given threshold vector. Weighted transversals generalize partial and multiple transversals introduced in Boros et al. (SIAM J. Comput. 30 (6) (2001)) and also include minimal binary solutions to non-negative systems of linear inequalities and minimal weighted infrequent sets in databases. We show that the hypergraph of all weighted transversals is dual-bounded, i.e., the size of its transversal hypergraph is polynomial in the number of weighted transversals and the size of the input hypergraph. Our bounds are based on new inequalities of extremal set theory and threshold Boolean logic, which may be of independent interest. For instance, we show that for any row-weighted m×n binary matrix and any threshold weight t, the number of maximal sets of columns whose row support has weight above t is at most m times the number of minimal sets of columns with row support of total weight below t. We also prove that the problem of generating all weighted transversals for a given hypergraph is polynomial-time reducible to the generation of all ordinary transversals for another hypergraph, i.e., to the well-known hypergraph dualization problem. As a corollary, we obtain an incremental quasi-polynomial-time algorithm for generating all weighted transversals for a given hypergraph. This result includes as special cases the generation of all the minimal Boolean solutions to a given system of non-negative linear inequalities and the generation of all minimal weighted infrequent sets of columns for a given binary matrix. --- paper_title: Dual-Bounded Generating Problems: All Minimal Integer Solutions for a Monotone System of Linear Inequalities paper_content: We consider the problem of enumerating all minimal integer solutions of a monotone system of linear inequalities. We first show that, for any monotone system of r linear inequalities in n variables, the number of maximal infeasible integer vectors is at most rn times the number of minimal integer solutions to the system. This bound is accurate up to a polylog(r) factor and leads to a polynomial-time reduction of the enumeration problem to a natural generalization of the well-known dualization problem for hypergraphs, in which dual pairs of hypergraphs are replaced by dual collections of integer vectors in a box. We provide a quasi-polynomial algorithm for the latter dualization problem. These results imply, in particular, that the problem of incrementally generating all minimal integer solutions to a monotone system of linear inequalities can be done in quasi-polynomial time. --- paper_title: An inequality for polymatroid functions and its applications paper_content: An integral-valued set function f:2v ↦ Z is called polymatroid if it is submodular, nondecreasing, and f(φ) = 0. Given a polymatroid function f and an integer threshold t ≥ 1, let α = α(f,t) denote the number of maximal sets X ⊆ V satisfying f(X) < t, let β = β(f,t) be the number of minimal sets X ⊆ V for which f(X) ≥ t, and let n = \|V\|. We show that if β ≥ 2 then α ≤ β(log t)/c, where c = c(n,β) is the unique positive root of the equation 1 = 2c(nc/log β - 1). In particular, our bound implies that α ≤ (nβ)log t for all β ≥ 1. We also give examples of polymatroid functions with arbitrarily large t, n, α and β for which α ≥ β(0.551 log t)/c. More generally, given a polymatroid function f : 2v ↦ Z and an integral threshold t ≥ 1, consider an arbitrary hypergraph H' such that \|H'\| ≥ 2 and f(H) ≥ t for all H ∈ H'. Let f' be the family of all maximal independent sets X of H' for which f(X) < t. Then \|f'\| ≤ \|H'\|(log t)/c(n,\|H'\|). As an application, we show that given a system of polymatroid inequalities f1(X) ≥ t1,..., fm(X) ≥ tm with quasi-polynomially bounded right-hand sides t1,....,tm, all minimal feasible solutions to this system can be generated in incremental quasi-polynomial time. In contrast to this result, the generation of all maximal infeasible sets is an NP-hard problem for many polymatroid inequalities of small range. --- paper_title: On Maximal Frequent and Minimal Infrequent Sets in Binary Matrices paper_content: Given an m×n binary matrix A, a subset C of the columns is called t-frequent if there are at least t rows in A in which all entries belonging to C are non-zero. Let us denote by α the number of maximal t-frequent sets of A, and let β denote the number of those minimal column subsets of A which are not t-frequent (so called t-infrequent sets). We prove that the inequality α≤(m−t+1)β holds for any binary matrix A in which not all column subsets are t-frequent. This inequality is sharp, and allows for an incremental quasi-polynomial algorithm for generating all minimal t-infrequent sets. We also prove that the analogous generation problem for maximal t-frequent sets is NP-hard. Finally, we discuss the complexity of generating closed frequent sets and some other related problems. --- paper_title: Generating Dual-Bounded Hypergraphs paper_content: This article surveys some recent results on the generation of implicitly given hypergraphs and their applications in Boolean and integer programming, data mining, reliability theory, and combinatorics. Given a monotone property ~ over the subsets of a finite set V, we consider the problem of incrementally generating the family F π of all minimal subsets satisfying property ~ , when ~ is given by a polynomial-time satisfiability oracle. For a number of interesting monotone properties, the family F π turns out to be uniformly dual-bounded , allowing for the incrementally efficient enumeration of the members of F π. Important applications include the efficient generation of minimal infrequent sets of a database (data mining), minimal connectivity ensuring collections of subgraphs from a given list (reliability theory), minimal feasible solutions to a system of monotone inequalities in integer variables (integer programming), minimal spanning collections of subspaces from a given list (linear algebra) and max... --- paper_title: Dual-Bounded Generating Problems: Partial And Multiple Transversals Of A Hypergraph paper_content: We consider two generalizations of the notion of transversal to a finite hypergraph, the so-called multiple and partial transversals. Multiple transversals naturally arise in 0-1 programming, while partial transversals are related to data mining and machine learning. We show that for an arbitrary hypergraph the families of multiple and partial transversals are both dual-bounded in the sense that the size of the corresponding dual hypergraph is bounded by a polynomial in the cardinality and the length of description of the input hypergraph. Our bounds are based on new inequalities of extremal set theory and threshold Boolean logic, which may be of independent interest. We also show that the problems of generating all multiple and all partial transversals for a given hypergraph are polynomial-time reducible to the generation of all ordinary transversals for another hypergraph, i.e., to the well-known dualization problem for hypergraphs. As a corollary, we obtain incremental quasi-polynomial-time algorithms for both of the above problems, as well as for the generation of all the minimal binary solutions for an arbitrary monotone system of linear inequalities. --- paper_title: On the Complexity of Dualization of Monotone Disjunctive Normal Forms paper_content: We show that the duality of a pair of monotone disjunctive normal forms of sizencan be tested inno(logn)time. --- paper_title: Dual-Bounded Generating Problems: Weighted Transversals of a Hypergraph paper_content: Abstract We consider a generalization of the notion of transversal to a finite hypergraph, the so-called weighted transversals. Given a non-negative weight vector assigned to each hyperedge of an input hypergraph A and a non-negative threshold vector, we define a weighted transversal as a minimal vertex set which intersects all the hyperedges of A except for a sub-family of total weight not exceeding the given threshold vector. Weighted transversals generalize partial and multiple transversals introduced in Boros et al. (SIAM J. Comput. 30 (6) (2001)) and also include minimal binary solutions to non-negative systems of linear inequalities and minimal weighted infrequent sets in databases. We show that the hypergraph of all weighted transversals is dual-bounded, i.e., the size of its transversal hypergraph is polynomial in the number of weighted transversals and the size of the input hypergraph. Our bounds are based on new inequalities of extremal set theory and threshold Boolean logic, which may be of independent interest. For instance, we show that for any row-weighted m×n binary matrix and any threshold weight t, the number of maximal sets of columns whose row support has weight above t is at most m times the number of minimal sets of columns with row support of total weight below t. We also prove that the problem of generating all weighted transversals for a given hypergraph is polynomial-time reducible to the generation of all ordinary transversals for another hypergraph, i.e., to the well-known hypergraph dualization problem. As a corollary, we obtain an incremental quasi-polynomial-time algorithm for generating all weighted transversals for a given hypergraph. This result includes as special cases the generation of all the minimal Boolean solutions to a given system of non-negative linear inequalities and the generation of all minimal weighted infrequent sets of columns for a given binary matrix. --- paper_title: Dual-Bounded Generating Problems: All Minimal Integer Solutions for a Monotone System of Linear Inequalities paper_content: We consider the problem of enumerating all minimal integer solutions of a monotone system of linear inequalities. We first show that, for any monotone system of r linear inequalities in n variables, the number of maximal infeasible integer vectors is at most rn times the number of minimal integer solutions to the system. This bound is accurate up to a polylog(r) factor and leads to a polynomial-time reduction of the enumeration problem to a natural generalization of the well-known dualization problem for hypergraphs, in which dual pairs of hypergraphs are replaced by dual collections of integer vectors in a box. We provide a quasi-polynomial algorithm for the latter dualization problem. These results imply, in particular, that the problem of incrementally generating all minimal integer solutions to a monotone system of linear inequalities can be done in quasi-polynomial time. --- paper_title: An inequality for polymatroid functions and its applications paper_content: An integral-valued set function f:2v ↦ Z is called polymatroid if it is submodular, nondecreasing, and f(φ) = 0. Given a polymatroid function f and an integer threshold t ≥ 1, let α = α(f,t) denote the number of maximal sets X ⊆ V satisfying f(X) < t, let β = β(f,t) be the number of minimal sets X ⊆ V for which f(X) ≥ t, and let n = \|V\|. We show that if β ≥ 2 then α ≤ β(log t)/c, where c = c(n,β) is the unique positive root of the equation 1 = 2c(nc/log β - 1). In particular, our bound implies that α ≤ (nβ)log t for all β ≥ 1. We also give examples of polymatroid functions with arbitrarily large t, n, α and β for which α ≥ β(0.551 log t)/c. More generally, given a polymatroid function f : 2v ↦ Z and an integral threshold t ≥ 1, consider an arbitrary hypergraph H' such that \|H'\| ≥ 2 and f(H) ≥ t for all H ∈ H'. Let f' be the family of all maximal independent sets X of H' for which f(X) < t. Then \|f'\| ≤ \|H'\|(log t)/c(n,\|H'\|). As an application, we show that given a system of polymatroid inequalities f1(X) ≥ t1,..., fm(X) ≥ tm with quasi-polynomially bounded right-hand sides t1,....,tm, all minimal feasible solutions to this system can be generated in incremental quasi-polynomial time. In contrast to this result, the generation of all maximal infeasible sets is an NP-hard problem for many polymatroid inequalities of small range. --- paper_title: On Maximal Frequent and Minimal Infrequent Sets in Binary Matrices paper_content: Given an m×n binary matrix A, a subset C of the columns is called t-frequent if there are at least t rows in A in which all entries belonging to C are non-zero. Let us denote by α the number of maximal t-frequent sets of A, and let β denote the number of those minimal column subsets of A which are not t-frequent (so called t-infrequent sets). We prove that the inequality α≤(m−t+1)β holds for any binary matrix A in which not all column subsets are t-frequent. This inequality is sharp, and allows for an incremental quasi-polynomial algorithm for generating all minimal t-infrequent sets. We also prove that the analogous generation problem for maximal t-frequent sets is NP-hard. Finally, we discuss the complexity of generating closed frequent sets and some other related problems. --- paper_title: Generating Dual-Bounded Hypergraphs paper_content: This article surveys some recent results on the generation of implicitly given hypergraphs and their applications in Boolean and integer programming, data mining, reliability theory, and combinatorics. Given a monotone property ~ over the subsets of a finite set V, we consider the problem of incrementally generating the family F π of all minimal subsets satisfying property ~ , when ~ is given by a polynomial-time satisfiability oracle. For a number of interesting monotone properties, the family F π turns out to be uniformly dual-bounded , allowing for the incrementally efficient enumeration of the members of F π. Important applications include the efficient generation of minimal infrequent sets of a database (data mining), minimal connectivity ensuring collections of subgraphs from a given list (reliability theory), minimal feasible solutions to a system of monotone inequalities in integer variables (integer programming), minimal spanning collections of subspaces from a given list (linear algebra) and max... --- paper_title: New results on monotone dualization and generating hypergraph transversals paper_content: We consider the problem of dualizing a monotone CNF (equivalently, computing all minimal transversals of a hypergraph), whose associated decision problem is a prominent open problem in NP-completeness. We present a number of new polynomial time resp. output-polynomial time results for significant cases, which largely advance the tractability frontier and improve on previous results. Furthermore, we show that duality of two monotone CNFs can be disproved with limited nondeterminism. More precisely, this is feasible in polynomial time with O(chi(n) * log n) suitably guessed bits, where chi(n) is given by \chi(n)^chi(n) = n; note that chi(n) = o(log n). This result sheds new light on the complexity of this important problem. --- paper_title: On Generating the Irredundant Conjunctive and Disjunctive Normal Forms of Monotone Boolean Functions paper_content: Let f : {0,1} n → {0,1} be a monotone Boolean function whose value at any point x ∈ {0,1} n can be determined in time t. Denote by c=Λ I ∈ C ∨ i ∈ I x i the irredundant CNF off, where C is the set of the prime implicates of f. Similarly, let d=∨ J ∈ D Λ j ∈ J x j be the irredundant DNF of the same function, where D is the set of the prime implicants of f. We show that given subsets C' ⊆ C and D' ⊆ D such that (C',D') ¬= (C,D), a new term in (C\C')∪(D\D') can be found in time O(n(t+n))+m o(log m) , where m=\|C'\|+\|D'\|. In particular, if f(x) can be evaluated for every x ∈ {0,1} n in polynomial time, then the forms c and d can be jointly generated in incremental quasi-polynomial time. On the other hand, even for the class of Λ, ∨-formulae f of depth 2, i.e., for CNFs or DNFs, it is unlikely that uniform sampling from within the set of the prime implicates and implicants of f can be carried out in time bounded by a quasi-polynomial 2 polylog(.) in the input size of f. We also show that for some classes of polynomial-time computable monotone Boolean functions it is NP-hard to test either of the conditions D' =D or C' =C. This provides evidence that for each of these classes neither conjunctive nor disjunctive irredundant normal forms can be generated in total (or incremental) quasi-polynomial time. Such classes of monotone Boolean functions naturally arise in game theory, networks and relay contact circuits, convex programming, and include a subset of Λ, ∨-formulae of depth 3. --- paper_title: Complexity of Identification and Dualization of Positive Boolean Functions paper_content: Abstract We consider in this paper the problem of identifying min T(ƒ) and max F(ƒ) of a positive (i.e., monotone) Boolean function ƒ, by using membership queries only, where min T(ƒ) (max F(ƒ)) denotes the set of minimal true vectors (maximal false vectors) of ƒ. It is shown that the existence of an incrementally polynomial algorithm for this problem is equivalent to the existence of the following algorithms, where ƒ and g are positive Boolean functions: • An incrementally polynomial algorithm to dualize ƒ; • An incrementally polynomial algorithm to self-dualize ƒ; • A polynomial algorithm to decide if ƒ and are mutually dual; • A polynomial algorithm to decide if ƒ is self-dual; • A polynomial algorithm to decide if ƒ is saturated; • A polynomial algorithm in \|min (ƒ)\| + \|max (ƒ)\| to identify min (ƒ) only. Some of these are already well known open problems in the respective fields. Other related topics, including various equivalent problems encountered in hypergraph theory and theory of coteries (used in distributed systems), are also discussed. --- paper_title: Identifying the Minimal Transversals of a Hypergraph and Related Problems paper_content: The paper considers two decision problems on hypergraphs, hypergraph saturation and recognition of the transversal hypergraph, and discusses their significance for several search problems in applied computer science. Hypergraph saturation (i.e., given a hypergraph $\cal H$, decide if every subset of vertices is contained in or contains some edge of $\cal H$) is shown to be co-NP-complete. A certain subproblem of hypergraph saturation, the saturation of simple hypergraphs (i.e., Sperner families), is shown to be under polynomial transformation equivalent to transversal hypergraph recognition; i.e., given two hypergraphs ${\cal H}_{1}, {\cal H}_{2}$, decide if the sets in ${\cal H}_{2}$ are all the minimal transversals of ${\cal H}_{1}$. The complexity of the search problem related to the recognition of the transversal hypergraph, the computation of the transversal hypergraph, is an open problem. This task needs time exponential in the input size; it is unknown whether an output-polynomial algorithm exists. For several important subcases, for instance if an upper or lower bound is imposed on the edge size or for acyclic hypergraphs, output-polynomial algorithms are presented. Computing or recognizing the minimal transversals of a hypergraph is a frequent problem in practice, which is pointed out by identifying important applications in database theory, Boolean switching theory, logic, and artificial intelligence (AI), particularly in model-based diagnosis. --- paper_title: An Efficient Implementation of a Quasi-polynomial Algorithm for Generating Hypergraph Transversals paper_content: Given a finite set V , and a hypergraph H⊆ 2 V , the hyper- graph transversal problem calls for enumerating all minimal hitting sets (transversals) for H. This problem plays an important role in practi- cal applications as many other problems were shown to be polynomially equivalent to it. Fredman and Khachiyan (1996) gave an incremental quasi-polynomial time algorithm for solving the hypergraph transversal problem (9). In this paper, we present an efficient implementation of this algorithm. While we show that our implementation achieves the same bound on the running time as in (9), practical experience with this im- plementation shows that it can be substantially faster. We also show that a slight modification of the algorithm in (9) can be used to give a stronger bound on the running time. --- paper_title: Average Case Self-Duality of Monotone Boolean Functions paper_content: The problem of determining whether a monotone boolean function is self-dual has numerous applications in Logic and AI. The applications include theory revision, model-based diagnosis, abductive explanations and learning monotone boolean functions. It is not known whether self-duality of monotone boolean functions can be tested in polynomial time, though a quasi-polynomial time algorithm exists. We describe another quasi-polynomial time algorithm for solving the self-duality problem of monotone boolean functions and analyze its average-case behaviour on a set of randomly generated instances. --- paper_title: On the Complexity of Dualization of Monotone Disjunctive Normal Forms paper_content: We show that the duality of a pair of monotone disjunctive normal forms of sizencan be tested inno(logn)time. --- paper_title: Almost all monotone Boolean functions are polynomially learnable using membership queries paper_content: We consider exact learning or identification of monotone Boolean functions by only using membership queries. It is shown that almost all monotone Boolean functions are polynomially identifiable in the input number of variables as well as the output being the sum of the sizes of the CNF and DNF representations. --- paper_title: Complexity of Identification and Dualization of Positive Boolean Functions paper_content: Abstract We consider in this paper the problem of identifying min T(ƒ) and max F(ƒ) of a positive (i.e., monotone) Boolean function ƒ, by using membership queries only, where min T(ƒ) (max F(ƒ)) denotes the set of minimal true vectors (maximal false vectors) of ƒ. It is shown that the existence of an incrementally polynomial algorithm for this problem is equivalent to the existence of the following algorithms, where ƒ and g are positive Boolean functions: • An incrementally polynomial algorithm to dualize ƒ; • An incrementally polynomial algorithm to self-dualize ƒ; • A polynomial algorithm to decide if ƒ and are mutually dual; • A polynomial algorithm to decide if ƒ is self-dual; • A polynomial algorithm to decide if ƒ is saturated; • A polynomial algorithm in \|min (ƒ)\| + \|max (ƒ)\| to identify min (ƒ) only. Some of these are already well known open problems in the respective fields. Other related topics, including various equivalent problems encountered in hypergraph theory and theory of coteries (used in distributed systems), are also discussed. --- paper_title: An intersection inequality for discrete distributions and related generation problems paper_content: Given two finite sets of points X, Y in Rn which can be separated by a nonnegative linear function, and such that the componentwise minimum of any two distinct points in X is dominated by some point in Y, we show that \|X\| = n\|Y\|. As a consequence of this result, we obtain quasi-polynomial time algorithms for generating all maximal integer feasible solutions for a given monotone system of separable inequalities, for generating all p-inefficient points of a given discrete probability distribution, and for generating all maximal empty hyper-rectangles for a given set of points in Rn. This provides a substantial improvement over previously known exponential algorithms for these generation problems related to Integer and Stochastic Programming, and Data Mining. Furthermore, we give an incremental polynomial time generation algorithm for monotone systems with fixed number of separable inequalities, which, for the very special case of one inequality, implies that for discrete probability distributions with independent coordinates, both p-efficient and p-inefficient points can be separately generated in incremental polynomial time. --- paper_title: Dual-Bounded Generating Problems: All Minimal Integer Solutions for a Monotone System of Linear Inequalities paper_content: We consider the problem of enumerating all minimal integer solutions of a monotone system of linear inequalities. We first show that, for any monotone system of r linear inequalities in n variables, the number of maximal infeasible integer vectors is at most rn times the number of minimal integer solutions to the system. This bound is accurate up to a polylog(r) factor and leads to a polynomial-time reduction of the enumeration problem to a natural generalization of the well-known dualization problem for hypergraphs, in which dual pairs of hypergraphs are replaced by dual collections of integer vectors in a box. We provide a quasi-polynomial algorithm for the latter dualization problem. These results imply, in particular, that the problem of incrementally generating all minimal integer solutions to a monotone system of linear inequalities can be done in quasi-polynomial time. --- paper_title: Generating Dual-Bounded Hypergraphs paper_content: This article surveys some recent results on the generation of implicitly given hypergraphs and their applications in Boolean and integer programming, data mining, reliability theory, and combinatorics. Given a monotone property ~ over the subsets of a finite set V, we consider the problem of incrementally generating the family F π of all minimal subsets satisfying property ~ , when ~ is given by a polynomial-time satisfiability oracle. For a number of interesting monotone properties, the family F π turns out to be uniformly dual-bounded , allowing for the incrementally efficient enumeration of the members of F π. Important applications include the efficient generation of minimal infrequent sets of a database (data mining), minimal connectivity ensuring collections of subgraphs from a given list (reliability theory), minimal feasible solutions to a system of monotone inequalities in integer variables (integer programming), minimal spanning collections of subspaces from a given list (linear algebra) and max... --- paper_title: On dualization in products of forests, in paper_content: Let P = P 1 × … x P n be the product of n partially ordered sets, each with an acyclic precedence graph in which either the in-degree or the out-degree of each element is bounded. Given a subset A C P, it is shown that the set of maximal independent elements of A in P can be incrementally generated in quasi-polynomial time. We discuss some applications in data mining related to this dualization problem. --- paper_title: On one criterion of the optimality of an algorithm for evaluating monotonic Boolean functions paper_content: Abstract A criterion of the optimality of an algorithm for evaluating monotonic Boolean functions, which is different from the Shannon criterion, is considered and its practical significance is proved. Upper and lower estimates are obtained for the efficiency of the algorithm for evaluating monotonic Boolean functions which is optimal with respect to the criterion which has been introduced. An algorithm is constructed for evaluating the class of monotonic Boolean functions which are generated by imcompatible systems of linear ineaqualities. This algorithm is optimal with respect to the criterion introduced in this paper, the Shannon criterion, and a number of other criteria subject to certain additional conditions. --- paper_title: A fast parallel algorithm for the maximal independent set problem paper_content: A parallel algorithm is presented which accepts as input a graph G and produces a maximal independent set of vertices in G. On a P-RAM without the concurrent write or concurrent read features, the algorithm executes in O((log n) 4 ) time and uses O((n/log n) 3 ) processors, where n is the number of vertices in G. The algorithm has several novel features that may find other applications. These include the use of balanced incomplete block designs to replace random sampling by deterministic sampling, and the use of a “dynamic pigeonhole principle” that generalizes the conventional pigeonhole principle. --- paper_title: Generating All Maximal Independent Sets: NP-Hardness and Polynomial-Time Algorithms paper_content: Suppose that an independence system $(E,\mathcal {I})$ is characterized by a subroutine which indicates in unit time whether or not a given subset of E is independent. It is shown that there is no algorithm for generating all the K maximal independent sets of such an independence system in time polynomial in $\|E\|$ and K, unless $\mathcal {P} = \mathcal {NP}$. However, it is possible to apply ideas of Paull and Unger and of Tsukiyama et al. to obtain polynomial-time algorithms for a number of special cases, e.g. the efficient generation of all maximal feasible solutions to a knapsack problem. The algorithmic techniques bear an interesting relationship with those of Read for the enumeration of graphs and other combinatorial configurations. --- paper_title: A New Algorithm for the Hypergraph Transversal Problem paper_content: We consider the problem of finding all minimal transversals of a hypergraph ${\mathcal H}\subseteq 2^V$, given by an explicit list of its hyperedges. We give a new decomposition technique for solving the problem with the following advantages: (i) Global parallelism: for certain classes of hypergraphs, e.g. hypergraphs of bounded edge size, and any given integer k, the algorithm outputs k minimal transversals of ${\mathcal H}$ in time bounded by ${\rm polylog}(\|V\|,\|{\mathcal H}\|,k)$ assuming ${\rm poly}(\|V\|,\|{\mathcal H}\|,k)$ number of processors. Except for the case of graphs, none of the previously known algorithms for solving the same problem exhibit this feature. (ii) Using this technique, we also obtain new results on the complexity of generating minimal transversals for new classes of hypergraphs, namely hypergraphs of bounded dual-conformality, and hypergraphs in which every edge intersects every minimal transversal in a bounded number of vertices. --- paper_title: A variant of Reiter's hitting-set algorithm paper_content: In this paper we introduce a variant of Reiter's hitting-set algorithm. This variant produces a hitting-set tree instead of an acyclic directed graph. As an advantage some subset checks necessary for reducing the graph during search can be avoided. --- paper_title: Hypergraph Transversal Computation and Related Problems in Logic and AI paper_content: Generating minimal transversals of a hypergraph is an important problem which has many applications in Computer Science. In the present paper, we address this problem and its decisional variant, i.e., the recognition of the transversal hypergraph for another hypergraph. We survey some results on problems which are known to be related to computing the transversal hypergraph, where we focus on problems in propositional Logic and AI. Some of the results have been established already some time ago, and were announced but their derivation was not widely disseminated. We then address recent developments on the computational complexity of computing resp. recognizing the transversal hypergraph. The precise complexity of these problems is not known to date, and is in fact open for more than 20 years now. --- paper_title: Queries and Concept Learning paper_content: We consider the problem of using queries to learn an unknown concept. Several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries. Examples are given of efficient learning methods using various subsets of these queries for formal domains, including the regular languages, restricted classes of context-free languages, the pattern languages, and restricted types of prepositional formulas. Some general lower bound techniques are given. Equivalence queries are compared with Valiant's criterion of probably approximately correct identification under random sampling. --- paper_title: An efficient incremental algorithm for generating all maximal independent sets in hypergraphs of bounded dimension paper_content: We show that for hypergraphs of bounded edge size, the problem of extending a given list of maximal independent sets is NC-reducible to the computation of an arbitrary maximal independent set for an induced sub-hypergraph. The latter problem is known to be in RNC. In particular, our reduction yields an incremental RNC dualization algorithm for hypergraphs of bounded edge size, a problem previously known to be solvable in polynomial incremental time. We also give a similar parallel algorithm for the dualization problem on the product of arbitrary lattices which have a bounded number of immediate predecessors for each element. --- paper_title: Almost all monotone Boolean functions are polynomially learnable using membership queries paper_content: We consider exact learning or identification of monotone Boolean functions by only using membership queries. It is shown that almost all monotone Boolean functions are polynomially identifiable in the input number of variables as well as the output being the sum of the sizes of the CNF and DNF representations. --- paper_title: The computation of hitting sets: review and new algorithms paper_content: In model-based diagnosis or other research fields, the hitting sets of a set cluster are usually used. In this paper we introduce some algorithms, including the new BHS-tree and Boolean algebraic algorithms. In the BHS-tree algorithm, a binary-tree is used for the computation of hitting sets, and in the Boolean algebraic algorithm, components are represented by Boolean variables. It runs just for one time to catch the minimal hitting sets. We implemented the algorithms and present empirical results in order to show their superiority over other algorithms for computing hitting sets. --- paper_title: A Correction to the Algorithm in Reiter's Theory of Diagnosis paper_content: Reiter [3] has developed a general theory of diagnosis based on first principles. His algorithm computes all diagnoses which explain the differences between the predicted and observed behavior of a given system. Unfortunately, Reiter's description of the algorithm is incorrect in that some diagnoses can be missed under certain conditions. This note presents a revised algorithm and a proof of its correctness. --- paper_title: Polynomial Time Recognition Of 2-Monotonic Positive Boolean Functions Given By An Oracle paper_content: We consider the problem of identifying an unknown Boolean function $f$ by asking an oracle the functional values $f(a)$ for a selected set of test vectors $a \in \{0,1\}^{n}$. Furthermore, we assume that $f$ is a positive (or monotone) function of $n$ variables. It is not yet known whether or not the whole task of generating test vectors and checking if the identification is completed can be carried out in polynomial time in $n$ and $m$, where $m=\|\min T(f)\| + \|\max F(f)\|$ and $\min T(f)$ (respectively, $\max F(f))$ denotes the set of minimal true (respectively, maximal false) vectors of $f$. To partially answer this question, we propose here two polynomial-time algorithms that, given an unknown positive function $f$ of $n$ variables, decide whether or not $f$ is 2-monotonic and, if $f$ is 2-monotonic, output both sets $\min T(f)$ and $\max F(f)$. The first algorithm uses $O(nm^{2} + n^{2}m)$ time and $O(nm)$ queries, while the second one uses $O(n^{3}m)$ time and $O(n^{3}m)$ queries. --- paper_title: A Fast and Simple Algorithm for Identifying 2-Monotonic Positive Boolean Functions paper_content: Consider the problem of identifying minT(f) and maxF(f) of a positive (i.e., monotone) Boolean functionf, by using membership queries only, where minT(f) (maxF(f)) denotes the set of minimal true vectors (maximum false vectors) off. Moreover, as the existence of a polynomial total time algorithm (i.e., polynomial time in the length of input and output) for this problem is still open, we consider here a restricted problem: given an unknown positive functionfofnvariables, decide whetherfis 2-monotonic or not, and iffis 2-monotonic, output both minT(f) and maxF(f). For this problem, we propose a simple algorithm, which is based on the concept of maximum latency, and we show that it usesO(n2m) time andO(n2m) queries, wherem=\|minT(f)\|+\|maxF(f)\|. This answers affirmatively the conjecture raised in Boroset al.Lecture Notes in Comput. Sci.557(1991), 104?115, Boroset al.SIAM J. Comput.26(1997), 93?109, and is an improvement over the two algorithms discussed therein: one usesO(n3m) time andO(n3m) queries, and the other usesO(nm2+n2m) time andO(nm) queries. --- paper_title: Data mining, hypergraph transversals, and machine learning paper_content: Several data mining problems can be formulated as problems of finding maximally specific sentences that are interesting in a database. We first show that this problem has a close relationship with the hypergraph transversal problem. We then analyze two algorithms that have been previously used in data mining, proving upper bounds on their complexity. The first algorithm is useful when the maximally specific interesting sentences are “small”. We show that this algorithm can also be used to efficiently solve a special case of the hypergraph transversal problem, improving on previous results. The second algorithm utilizes a subroutine for hypergraph transversals, and is applicable in more general situations, with complexity close to a lower bound for the problem. We also relate these problems to the model of exact learning in computational learning theory, and use the correspondence to derive some corollaries. --- paper_title: Algorithms for inferring functional dependencies from relations paper_content: Abstract The dependency inference problem is to find a cover of the set of functional dependencies that hold in a given relation. The problem has applications in relational database design, in query optimization, and in artificial intelligence. The problem is exponential in the number of attributes. We develop two algorithms with better best case behavior than the simple one. One algorithm reduces the problem to computing the transversal of a hypergraph. The other is based on repeatedly sorting the relation with respect to a set of attributes. --- paper_title: New results on monotone dualization and generating hypergraph transversals paper_content: We consider the problem of dualizing a monotone CNF (equivalently, computing all minimal transversals of a hypergraph), whose associated decision problem is a prominent open problem in NP-completeness. We present a number of new polynomial time resp. output-polynomial time results for significant cases, which largely advance the tractability frontier and improve on previous results. Furthermore, we show that duality of two monotone CNFs can be disproved with limited nondeterminism. More precisely, this is feasible in polynomial time with O(chi(n) * log n) suitably guessed bits, where chi(n) is given by \chi(n)^chi(n) = n; note that chi(n) = o(log n). This result sheds new light on the complexity of this important problem. --- paper_title: The Maximum Latency and Identification of Positive Boolean Functions paper_content: Consider the problem of identifying min T(f) and max F(f) of a positive (i.e., monotone) Boolean function f, by using membership queries only, where min T(f) (maxF(f)) denotes the set of minimal true vectors (maximal false vectors) of f. It is known that an incrementally polynomial algorithm exists if and only if there is a polynomial time algorithm to check the existence of an unknown vector for given sets MT\(\subseteq \) min T(f) and MF\(\subseteq \) max F(f). Unfortunately, however, the complexity of this problem is still unknown. To answer this question partially, we introduce in this paper a measure for the difficulty of finding an unknown vector, which is called the maximum latency. If the maximum latency is constant, then an unknown vector can be found in polynomial time and there is an incrementally polynomial algorithm for identification. Several subclasses of positive functions are shown to have constant maximum latency, e.g., 2-monotonic positive functions, Δ-partial positive threshold functions and matroid functions, while the class of general positive functions has maximum latency not smaller than [n/4]+1 and the class of positive k-DNF functions has Ω(√n) maximum latency. --- paper_title: Generating Maximal Independent Sets for Hypergraphs with Bounded Edge-Intersections paper_content: Given a finite set V, and integers k > 1 and r > 0, denote by A(k, r) the class of hypergraphs A ⊆ 2 V with (k, r)-bounded intersections, i.e. in which the intersection of any k distinct hyperedges has size at most r. We consider the problem MIS(A,I): given a hypergraph A and a subfamily I C I(A), of its maximal independent sets (MIS) I(A), either extend this subfamily by constructing a new MIS I ∈ I(A) \ I or prove that there are no more MIS, that is I = I(A). We show that for hypergraphs A E A(k,r) with k + r ≤ const, problem MIS(A,I) is NC-reducible to problem MIS(A',O) of generating a single MIS for a partial subhypergraph A' of A. In particular, for this class of hypergraphs, we get an incremental polynomial algorithm for generating all MIS. Furthermore, combining this result with the currently known algorithms for finding a single maximal independent set of a hypergraph, we obtain efficient parallel algorithms for incrementally generating all MIS for hypergraphs in the classes A(1, c), A(c, 0), and A(2,1), where c is a constant. We also show that, for A ∈ A(k,r), where k + r < const, the problem of generating all MIS of A can be solved in incremental polynomial-time with space polynomial only in the size of A. --- paper_title: Exact Learning of Subclasses of CDNF Formulas with Membership Queries paper_content: We consider the exact, learuability of subclasses of Boolean formulas from membership queries alone. We show how to combine known learning algorithms that use membership and equivalence queries to obtain new learning results only with memberships. In particular we show the exact learuability of read-k monotone formulas, Sat-k\(\mathcal{O}\)(log n)-CDNF, and \(\mathcal{O}(\sqrt {\log n} )\)-size CDNF from membership queries only. --- paper_title: A theory of the learnable paper_content: Humans appear to be able to learn new concepts without needing to be programmed explicitly in any conventional sense. In this paper we regard learning as the phenomenon of knowledge acquisition in the absence of explicit programming. We give a precise methodology for studying this phenomenon from a computational viewpoint. It consists of choosing an appropriate information gathering mechanism, the learning protocol, and exploring the class of concepts that can be learnt using it in a reasonable (polynomial) number of steps. We find that inherent algorithmic complexity appears to set serious limits to the range of concepts that can be so learnt. The methodology and results suggest concrete principles for designing realistic learning systems. --- paper_title: Complexity of Identification and Dualization of Positive Boolean Functions paper_content: Abstract We consider in this paper the problem of identifying min T(ƒ) and max F(ƒ) of a positive (i.e., monotone) Boolean function ƒ, by using membership queries only, where min T(ƒ) (max F(ƒ)) denotes the set of minimal true vectors (maximal false vectors) of ƒ. It is shown that the existence of an incrementally polynomial algorithm for this problem is equivalent to the existence of the following algorithms, where ƒ and g are positive Boolean functions: • An incrementally polynomial algorithm to dualize ƒ; • An incrementally polynomial algorithm to self-dualize ƒ; • A polynomial algorithm to decide if ƒ and are mutually dual; • A polynomial algorithm to decide if ƒ is self-dual; • A polynomial algorithm to decide if ƒ is saturated; • A polynomial algorithm in \|min (ƒ)\| + \|max (ƒ)\| to identify min (ƒ) only. Some of these are already well known open problems in the respective fields. Other related topics, including various equivalent problems encountered in hypergraph theory and theory of coteries (used in distributed systems), are also discussed. --- paper_title: Efficient Read-Restricted Monotone CNF/DNF Dualization by Learning with Membership Queries paper_content: We consider exact learning monotone CNF formulas in which each variable appears at most some constant k times (“read-k” monotone CNF). Let f : l0,1r^n → l0,1r be expressible as a read-k monotone CNF formula for some natural number k. We give an incremental output polynomial time algorithm for exact learning both the read-k CNF and (not necessarily read restricted) DNF descriptions of f. The algorithm‘s only method of obtaining information about f is through membership queries, i.e., by inquiring about the value f(x) for points x ∈ l0,1r^n. The algorithm yields an incremental polynomial output time solution to the (read-k) monotone CNF/DNF dualization problem. The unrestricted versions remain open problems of importance. --- paper_title: On one criterion of the optimality of an algorithm for evaluating monotonic Boolean functions paper_content: Abstract A criterion of the optimality of an algorithm for evaluating monotonic Boolean functions, which is different from the Shannon criterion, is considered and its practical significance is proved. Upper and lower estimates are obtained for the efficiency of the algorithm for evaluating monotonic Boolean functions which is optimal with respect to the criterion which has been introduced. An algorithm is constructed for evaluating the class of monotonic Boolean functions which are generated by imcompatible systems of linear ineaqualities. This algorithm is optimal with respect to the criterion introduced in this paper, the Shannon criterion, and a number of other criteria subject to certain additional conditions. --- paper_title: An Efficient Implementation of a Quasi-polynomial Algorithm for Generating Hypergraph Transversals paper_content: Given a finite set V , and a hypergraph H⊆ 2 V , the hyper- graph transversal problem calls for enumerating all minimal hitting sets (transversals) for H. This problem plays an important role in practi- cal applications as many other problems were shown to be polynomially equivalent to it. Fredman and Khachiyan (1996) gave an incremental quasi-polynomial time algorithm for solving the hypergraph transversal problem (9). In this paper, we present an efficient implementation of this algorithm. While we show that our implementation achieves the same bound on the running time as in (9), practical experience with this im- plementation shows that it can be substantially faster. We also show that a slight modification of the algorithm in (9) can be used to give a stronger bound on the running time. --- paper_title: On the Complexity of Dualization of Monotone Disjunctive Normal Forms paper_content: We show that the duality of a pair of monotone disjunctive normal forms of sizencan be tested inno(logn)time. --- paper_title: Detailed Description of an Algorithm for Enumeration of Maximal Frequent Sets with Irredundant Dualization paper_content: We describe an implementation of an algorithm for enumerating all maximal frequent sets using irredundant dualization, which is an improved version of that of Gunopulos et al. The algorithm of Gunopulos et al. solves many dualization problems, and takes long computation time. We interleaves dualization with the main algorithm, and reduce the computation time for dualization by as long as one dualization. This also reduces the space complexity. Moreover, we accelerate the computation by using sparseness. --- paper_title: An Efficient Algorithm for the Transversal Hypergraph Generation paper_content: The Transversal Hypergraph Generation is the problem of generating, given a hypergraph, the set of its minimal transversals, i.e., the hypergraph whose hyperedges are the minimal hitting sets of the given one. The purpose of this paper is to present an efficient and practical algorithm for solving this problem. We show that the proposed algorithm operates in a way that rules out regeneration and, thus, its memory requirements are polynomially bounded to the size of the input hypergraph. Although no time bound for the algorithm is given, experimental evaluation and comparison with other approaches have shown that it behaves well in practice and it can successfully handle large problem instances. --- paper_title: Evaluation of an Algorithm for the Transversal Hypergraph Problem paper_content: The Transversal Hypergraph Problem is the problem of computing, given a hypergraph, the set of its minimal transversals, i.e. the hypergraph whose hyperedges are all minimal hitting sets of the given one. This problem turns out to be central in various fields of Computer Science. We present and experimentally evaluate a heuristic algorithm for the problem, which seems able to handle large instances and also possesses some nice features especially desirable in problems with large output such as the Transversal Hypergraph Problem. --- paper_title: Minimizing the Average Query Complexity of Learning Monotone Boolean Functions paper_content: This paper addresses the problem of completely reconstructing deterministic monotone Boolean functions via membership queries. The minimum average query complexity is guaranteed via recursion, where partially ordered sets (posets) make up the overlapping subproblems. For problems with up to 4 variables, the posets' optimality conditions are summarized in the form of an evaluative criterion. The evaluative criterion extends then computational feasibility to problems involving up to about 20 variables. A frameworkfor unbiased average case comparison of monotone Boolean function inference algorithms is developed using unequal probability sampling. The unbiased empirical results show that an implementation of the subroutine considered as a standard in the literature performs almost twice as many queries as the evaluative criterion on the average. It should also be noted that the first algorithm ever designed for this problem performed consistently within two percentage points of the evaluative criterion. As such, it prevails, by far, as the most efficient of the many preexisting algorithms. --- paper_title: The computation of hitting sets: review and new algorithms paper_content: In model-based diagnosis or other research fields, the hitting sets of a set cluster are usually used. In this paper we introduce some algorithms, including the new BHS-tree and Boolean algebraic algorithms. In the BHS-tree algorithm, a binary-tree is used for the computation of hitting sets, and in the Boolean algebraic algorithm, components are represented by Boolean variables. It runs just for one time to catch the minimal hitting sets. We implemented the algorithms and present empirical results in order to show their superiority over other algorithms for computing hitting sets. --- paper_title: A fast algorithm for computing hypergraph transversals and its application in mining emerging patterns paper_content: Computing the minimal transversals of a hypergraph is an important problem in computer science that has significant applications in data mining. We present a new algorithm for computing hypergraph transversals and highlight their close connection to an important class of patterns known as emerging patterns. We evaluate our technique on a number of large datasets and show that it outperforms previous approaches by a factor of 9-29 times. --- paper_title: A Practical Fast Algorithm for Enumerating Minimal SetCoverings paper_content: For a set family F defined on a grand set E, a subset of F covering all the elements of E is called a set covering. The enumeration problem of minimal set covering is equal to the enumeration problem of hypergraph dualization, minimal hitting sets, and other other many problems, and have been studied intensively. However, the existence of an output polynomial algorithm for this problem is still open. However, there proposed an algorithm whose average computation time on computational experiments for random generated instances is output polynomial. In this paper, we propose a practical fast algorithm obtained by improving this algorithm, and by computational experiments, show that the average computation time for randomly generated instances is O(\|E\|) per output, by computational experiments. --- paper_title: Bidual Horn Functions and Extensions paper_content: Partially defined Boolean functions (pdBf) , where are disjoint sets of true and false vectors, generalize total Boolean functions by allowing that the function values on some input vectors are unknown. The main issue with pdBfs is the extension problem, which is deciding, given a pdBf, whether it is interpolated by a function from a given class of total Boolean functions, and computing a formula for . In this paper, we consider extensions of bidual Horn functions, which are the Boolean functions such that both and its dual function are Horn. They are intuitively appealing for considering extensions because they give a symmetric role to positive and negative information (i.e., true and false vectors) of a pdBf, which is not possible with arbitrary Horn functions. Bidual Horn functions turn out to constitute an intermediate class between positive and Horn functions which retains several benign properties of positive functions. Besides the extension problem, we study recognition of bidual Horn functions from Boolean formulas and properties of normal form expressions. We show that finding a bidual Horn extension and checking biduality of a Horn DNF is feasible in polynomial time, and that the latter is intractable from arbitrary formulas. We also give characterizations of shortest DNF expressions of a bidual Horn function and show how to compute such an expression from a Horn DNF for in polynomial time; for arbitrary Horn functions, this is NP-hard. Furthermore, we show that a polynomial total algorithm for dualizing a bidual Horn function exists if and only if there is such an algorithm for dualizing a positive function. --- paper_title: Translating between Horn Representations and their Characteristic Models paper_content: Characteristic models are an alternative, model based, representation for Horn expressions. It has been shown that these two representations are incomparable and each has its advantages over the other. It is therefore natural to ask what is the cost of translating, back and forth, between these representations. Interestingly, the same translation questions arise in database theory, where it has applications to the design of relational databases. This paper studies the computational complexity of these problems. ::: ::: Our main result is that the two translation problems are equivalent under polynomial reductions, and that they are equivalent to the corresponding decision problem. Namely, translating is equivalent to deciding whether a given set of models is the set of characteristic models for a given Horn expression. ::: ::: We also relate these problems to the hypergraph transversal problem, a well known problem which is related to other applications in AI and for which no polynomial time algorithm is known. It is shown that in general our translation problems are at least as hard as the hypergraph transversal problem, and in a special case they are equivalent to it. --- paper_title: An Efficient Implementation of a Quasi-polynomial Algorithm for Generating Hypergraph Transversals paper_content: Given a finite set V , and a hypergraph H⊆ 2 V , the hyper- graph transversal problem calls for enumerating all minimal hitting sets (transversals) for H. This problem plays an important role in practi- cal applications as many other problems were shown to be polynomially equivalent to it. Fredman and Khachiyan (1996) gave an incremental quasi-polynomial time algorithm for solving the hypergraph transversal problem (9). In this paper, we present an efficient implementation of this algorithm. While we show that our implementation achieves the same bound on the running time as in (9), practical experience with this im- plementation shows that it can be substantially faster. We also show that a slight modification of the algorithm in (9) can be used to give a stronger bound on the running time. --- paper_title: On the Complexity of Dualization of Monotone Disjunctive Normal Forms paper_content: We show that the duality of a pair of monotone disjunctive normal forms of sizencan be tested inno(logn)time. --- paper_title: A New Algorithm for the Hypergraph Transversal Problem paper_content: We consider the problem of finding all minimal transversals of a hypergraph ${\mathcal H}\subseteq 2^V$, given by an explicit list of its hyperedges. We give a new decomposition technique for solving the problem with the following advantages: (i) Global parallelism: for certain classes of hypergraphs, e.g. hypergraphs of bounded edge size, and any given integer k, the algorithm outputs k minimal transversals of ${\mathcal H}$ in time bounded by ${\rm polylog}(\|V\|,\|{\mathcal H}\|,k)$ assuming ${\rm poly}(\|V\|,\|{\mathcal H}\|,k)$ number of processors. Except for the case of graphs, none of the previously known algorithms for solving the same problem exhibit this feature. (ii) Using this technique, we also obtain new results on the complexity of generating minimal transversals for new classes of hypergraphs, namely hypergraphs of bounded dual-conformality, and hypergraphs in which every edge intersects every minimal transversal in a bounded number of vertices. --- paper_title: Detailed Description of an Algorithm for Enumeration of Maximal Frequent Sets with Irredundant Dualization paper_content: We describe an implementation of an algorithm for enumerating all maximal frequent sets using irredundant dualization, which is an improved version of that of Gunopulos et al. The algorithm of Gunopulos et al. solves many dualization problems, and takes long computation time. We interleaves dualization with the main algorithm, and reduce the computation time for dualization by as long as one dualization. This also reduces the space complexity. Moreover, we accelerate the computation by using sparseness. --- paper_title: Parameterized enumeration, transversals, and imperfect phylogeny reconstruction paper_content: We study parameterized enumeration problems where we are interested in all solutions of limited size rather than just some solution of minimum cardinality. (Actually, we have to enumerate the inclusion-minimal solutions in order to get fixed-parameter tractable (FPT) results.) Two novel concepts are the notion of a full kernel that contains all small solutions and implicit enumeration of solutions in form of compressed descriptions. In particular, we study combinatorial and computational bounds for the transversal hypergraph (vertex covers in graphs is a special case), restricted to hyperedges with at most k elements. As an example, we apply the results and further special-purpose techniques to almost-perfect phylogeny reconstruction, a problem in computational biology. --- paper_title: Evaluation of an Algorithm for the Transversal Hypergraph Problem paper_content: The Transversal Hypergraph Problem is the problem of computing, given a hypergraph, the set of its minimal transversals, i.e. the hypergraph whose hyperedges are all minimal hitting sets of the given one. This problem turns out to be central in various fields of Computer Science. We present and experimentally evaluate a heuristic algorithm for the problem, which seems able to handle large instances and also possesses some nice features especially desirable in problems with large output such as the Transversal Hypergraph Problem. --- paper_title: Minimizing the Average Query Complexity of Learning Monotone Boolean Functions paper_content: This paper addresses the problem of completely reconstructing deterministic monotone Boolean functions via membership queries. The minimum average query complexity is guaranteed via recursion, where partially ordered sets (posets) make up the overlapping subproblems. For problems with up to 4 variables, the posets' optimality conditions are summarized in the form of an evaluative criterion. The evaluative criterion extends then computational feasibility to problems involving up to about 20 variables. A frameworkfor unbiased average case comparison of monotone Boolean function inference algorithms is developed using unequal probability sampling. The unbiased empirical results show that an implementation of the subroutine considered as a standard in the literature performs almost twice as many queries as the evaluative criterion on the average. It should also be noted that the first algorithm ever designed for this problem performed consistently within two percentage points of the evaluative criterion. As such, it prevails, by far, as the most efficient of the many preexisting algorithms. --- paper_title: The computation of hitting sets: review and new algorithms paper_content: In model-based diagnosis or other research fields, the hitting sets of a set cluster are usually used. In this paper we introduce some algorithms, including the new BHS-tree and Boolean algebraic algorithms. In the BHS-tree algorithm, a binary-tree is used for the computation of hitting sets, and in the Boolean algebraic algorithm, components are represented by Boolean variables. It runs just for one time to catch the minimal hitting sets. We implemented the algorithms and present empirical results in order to show their superiority over other algorithms for computing hitting sets. --- paper_title: New Algorithms for Enumerating All Maximal Cliques paper_content: In this paper, we consider the problems of generating all maximal (bipartite) cliques in a given (bipartite) graph G=(V,E) with n vertices and m edges. We propose two algorithms for enumerating all maximal cliques. One runs with O(M(n)) time delay and in O(n 2) space and the other runs with O(Δ4) time delay and in O(n+m) space, where Δ denotes the maximum degree of G, M(n) denotes the time needed to multiply two n × n matrices, and the latter one requires O(nm) time as a preprocessing. --- paper_title: A fast algorithm for computing hypergraph transversals and its application in mining emerging patterns paper_content: Computing the minimal transversals of a hypergraph is an important problem in computer science that has significant applications in data mining. We present a new algorithm for computing hypergraph transversals and highlight their close connection to an important class of patterns known as emerging patterns. We evaluate our technique on a number of large datasets and show that it outperforms previous approaches by a factor of 9-29 times. ---	Title: COMPUTATIONAL ASPECTS OF MONOTONE DUALIZATION: A BRIEF SURVEY Section 1: Introduction Description 1: Introduce the monotone dualization problem, its significance, historical context, and application areas. Section 2: Preliminaries Description 2: Provide basic definitions and concepts related to Boolean functions, CNF, DNF, and their properties necessary for understanding monotone dualization. Section 3: Simple Algorithms Description 3: Discuss basic algorithms for dualization, including clause-based and variable-based decomposition methods, and their limitations. Section 4: Fredman and Khachiyan's Results Description 4: Detail the seminal results by Fredman and Khachiyan, their algorithms A and B, and the theoretical implications of their work on the complexity of the dualization problem. Section 5: Follow-Up Work Description 5: Summarize the subsequent research influenced by Fredman and Khachiyan's work, focusing on different computational aspects, limited nondeterminism, and probabilistic approaches. Section 6: Generalizations to Posets Description 6: Explain extensions of the dualization problem to partially ordered sets (posets) and the associated computational challenges and results. Section 7: Other Algorithms Description 7: Cover various alternative algorithms for dualization, including those based on learning theory, partitioning methods, and parallel computation. Section 8: Implementations and Experiments Description 8: Review practical implementations and experimental evaluations of dualization algorithms, discussing performance aspects and comparative studies. Section 9: Discussion and Conclusion Description 9: Conclude with a discussion on open problems, future research directions, and a summary of the state of the art in the computational aspects of monotone dualization.
Tools for simulating humanoid robot dynamics: A survey based on user feedback	11	--- paper_title: Extensive analysis of Linear Complementarity Problem (LCP) solver performance on randomly generated rigid body contact problems paper_content: The Linear Complementarity Problem (LCP) is a key problem in robot dynamics, optimization, and simulation. Common experience with dynamic robotic simulations suggests that the numerical robustness of the LCP solver often determines simulation usability: if the solver fails to find a solution or finds a solution with significant residual error, interpenetration can result, the simulation can gain energy, or both. This paper undertakes the first comprehensive evaluation of LCP solvers across the space of multi-rigid body contact problems. We evaluate the performance of these solvers along the dimensions of solubility, solution quality, and running time. --- paper_title: MIRA - middleware for robotic applications paper_content: In this paper, we present MIRA, a new middleware for robotic applications. It is designed for use in real-world applications and for research and teaching. In comparison to many other existing middlewares, MIRA employs novel techniques for communication that are described in this paper. Moreover, we present benchmarks that analyze the performance of the most commonly used middlewares ROS, Yarp, LCM, Player, Urbi, and MOOS. Using these benchmarks, we can show that MIRA outperforms the other middlewares in terms of latency and computation time. --- paper_title: An evaluation of methods for modeling contact in multibody simulation paper_content: Modeling contact in multibody simulation is a difficult problem frequently characterized by numerically brittle algorithms, long running times, and inaccurate (with respect to theory) models. We present a comprehensive evaluation of four methods for contact modeling on seven benchmark scenarios in order to quantify the performance of these methods with respect to robustness and speed. We also assess the accuracy of these methods where possible. We conclude the paper with a prescriptive description in order to guide the user of multibody simulation. --- paper_title: Three-dimensional impact: energy-based modeling of tangential compliance paper_content: Impact is indispensable in robotic manipulation tasks in which objects and/or manipulators move at high speeds. Applied research using impact has been hindered by underdeveloped computational foundations for rigid-body collision. This paper studies the computation of tangential impulse as two rigid bodies in the space collide at a point with both tangential compliance and friction. It extends Stronge's spring-based planar contact structure to three dimensions by modeling the contact point as a massless particle able to move tangentially on one body while connected to an infinitesimal region on the other body via three orthogonal springs. Slip or stick is indicated by whether the particle is still or moving. Impact analysis is carried out using normal impulse rather than time as the only independent variable, unlike in previous work on tangential compliance. This is due to the ability to update the energies stored in the three springs. Collision is governed by a system of differential equations that are solvable numerically. Modularity of the impact model makes it easy to be integrated into a multibody system, with one copy at each contact, in combination with a model for multiple impacts that governs normal impulses at different contacts. --- paper_title: Control of elastic soft robots based on real-time finite element method paper_content: In this paper, we present a new method for the control of soft robots with elastic behavior, piloted by several actuators. The central contribution of this work is the use of the Finite Element Method (FEM), computed in real-time, in the control algorithm. The FEM based simulation computes the nonlinear deformations of the robots at interactive rates. The model is completed by Lagrange multipliers at the actuation zones and at the end-effector position. A reduced compliance matrix is built in order to deal with the necessary inversion of the model. Then, an iterative algorithm uses this compliance matrix to find the contribution of the actuators (force and/or position) that will deform the structure so that the terminal end of the robot follows a given position. Additional constraints, like rigid or deformable obstacles, or the internal characteristics of the actuators are integrated in the control algorithm. We illustrate our method using simulated examples of both serial and parallel structures and we validate it on a real 3D soft robot made of silicone. --- paper_title: Realistic Haptic Rendering of Interacting Deformable Objects in Virtual Environments paper_content: A new computer haptics algorithm to be used in general interactive manipulations of deformable virtual objects is presented. In multimodal interactive simulations, haptic feedback computation often comes from contact forces. Subsequently, the fidelity of haptic rendering depends significantly on contact space modeling. Contact and friction laws between deformable models are often simplified in up to date methods. They do not allow a "realistic" rendering of the subtleties of contact space physical phenomena (such as slip and stick effects due to friction or mechanical coupling between contacts). In this paper, we use Signorini's contact law and Coulomb's friction law as a computer haptics basis. Real-time performance is made possible thanks to a linearization of the behavior in the contact space, formulated as the so-called Delassus operator, and iteratively solved by a Gauss-Seidel type algorithm. Dynamic deformation uses corotational global formulation to obtain the Delassus operator in which the mass and stiffness ratio are dissociated from the simulation time step. This last point is crucial to keep stable haptic feedback. This global approach has been packaged, implemented, and tested. Stable and realistic 6D haptic feedback is demonstrated through a clipping task experiment. --- paper_title: Convex and analytically-invertible dynamics with contacts and constraints: Theory and implementation in MuJoCo paper_content: We describe a full-featured simulation pipeline implemented in the MuJoCo physics engine. It includes multi-joint dynamics in generalized coordinates, holonomic constraints, dry joint friction, joint and tendon limits, frictionless and frictional contacts that can have sliding, torsional and rolling friction. The forward dynamics of a 27-dof humanoid with 10 contacts are evaluated in 0.1 msec. Since the simulation is stable at 10 msec timesteps, it can run 100 times faster than real-time on a single core of a desktop processor. Furthermore the entire simulation pipeline can be inverted analytically, an order-ofmagnitude faster than the corresponding forward dynamics. We soften all constraints, in a way that avoids instabilities and unrealistic penetrations associated with earlier spring-damper methods and yet is sufficient to allow inversion. Constraints are imposed via impulses, using an extended version of the velocitystepping approach. For holomonic constraints the extension involves a soft version of the Gauss principle. For all other constraints we extend our earlier work on complementarity-free contact dynamics – which were already known to be invertible via an iterative solver – and develop a new formulation allowing analytical inversion. --- paper_title: Robot and Multibody Dynamics: Analysis and Algorithms paper_content: Part I Basics: Serial Chain Dynamics.- Spatial vectors.- Single rigid body dynamics.- Differential kinematics for a serial-chain system.- The mass matrix.- Equations of motion for a serial chain system.- Articulated body models for serial chains.- Operator factorization and inversion of the mass matrix.- Forward dynamics.- Part II General Multibody Systems.- Tree topology systems.- The operational space inertia.- Closed chain system dynamics.- Multi-arm manipulators.- Systems with hinge flexibility.- Systems with link flexibility.- Part III Advanced Topics and Applications.- Under-actuated systems.- Free-flying space manipulators.- Mass matrix sensitives.- Linearized dynamics models.- Sensitivity of innovations factors.- Diagnolized lagrangian dynamics.- Overview of optimal linear estimation theory. --- paper_title: Extensive analysis of Linear Complementarity Problem (LCP) solver performance on randomly generated rigid body contact problems paper_content: The Linear Complementarity Problem (LCP) is a key problem in robot dynamics, optimization, and simulation. Common experience with dynamic robotic simulations suggests that the numerical robustness of the LCP solver often determines simulation usability: if the solver fails to find a solution or finds a solution with significant residual error, interpenetration can result, the simulation can gain energy, or both. This paper undertakes the first comprehensive evaluation of LCP solvers across the space of multi-rigid body contact problems. We evaluate the performance of these solvers along the dimensions of solubility, solution quality, and running time. --- paper_title: A convex, smooth and invertible contact model for trajectory optimization paper_content: Trajectory optimization is done most efficiently when an inverse dynamics model is available. Here we develop the first model of contact dynamics defined in both the forward and inverse directions. The contact impulse is the solution to a convex optimization problem: minimize kinetic energy in contact space subject to non-penetration and friction-cone constraints. We use a custom interior-point method to make the optimization problem unconstrained; this is key to defining the forward and inverse dynamics in a consistent way. The resulting model has a parameter which sets the amount of contact smoothing, facilitating continuation methods for optimization. We implemented the proposed contact solver in our new physics engine (MuJoCo). A full Newton step of trajectory optimization for a 3D walking gait takes only 160 msec, on a 12-core PC. --- paper_title: MuJoCo: A physics engine for model-based control paper_content: We describe a new physics engine tailored to model-based control. Multi-joint dynamics are represented in generalized coordinates and computed via recursive algorithms. Contact responses are computed via efficient new algorithms we have developed, based on the modern velocity-stepping approach which avoids the difficulties with spring-dampers. Models are specified using either a high-level C++ API or an intuitive XML file format. A built-in compiler transforms the user model into an optimized data structure used for runtime computation. The engine can compute both forward and inverse dynamics. The latter are well-defined even in the presence of contacts and equality constraints. The model can include tendon wrapping as well as actuator activation states (e.g. pneumatic cylinders or muscles). To facilitate optimal control applications and in particular sampling and finite differencing, the dynamics can be evaluated for different states and controls in parallel. Around 400,000 dynamics evaluations per second are possible on a 12-core machine, for a 3D homanoid with 18 dofs and 6 active contacts. We have already used the engine in a number of control applications. It will soon be made publicly available. --- paper_title: An evaluation of methods for modeling contact in multibody simulation paper_content: Modeling contact in multibody simulation is a difficult problem frequently characterized by numerically brittle algorithms, long running times, and inaccurate (with respect to theory) models. We present a comprehensive evaluation of four methods for contact modeling on seven benchmark scenarios in order to quantify the performance of these methods with respect to robustness and speed. We also assess the accuracy of these methods where possible. We conclude the paper with a prescriptive description in order to guide the user of multibody simulation. --- paper_title: Extensive analysis of Linear Complementarity Problem (LCP) solver performance on randomly generated rigid body contact problems paper_content: The Linear Complementarity Problem (LCP) is a key problem in robot dynamics, optimization, and simulation. Common experience with dynamic robotic simulations suggests that the numerical robustness of the LCP solver often determines simulation usability: if the solver fails to find a solution or finds a solution with significant residual error, interpenetration can result, the simulation can gain energy, or both. This paper undertakes the first comprehensive evaluation of LCP solvers across the space of multi-rigid body contact problems. We evaluate the performance of these solvers along the dimensions of solubility, solution quality, and running time. --- paper_title: An opensource simulator for cognitive robotics research: The prototype of the icub humanoid robot simulator paper_content: This paper presents the prototype of a new computer simulator for the humanoid robot iCub. The iCub is a new open-source humanoid robot developed as a result of the "RobotCub" project, a collaborative European project aiming at developing a new open-source cognitive robotics platform. The iCub simulator has been developed as part of a joint effort with the European project "ITALK" on the integration and transfer of action and language knowledge in cognitive robots. This is available open-source to all researchers interested in cognitive robotics experiments with the iCub humanoid platform. --- paper_title: MuJoCo: A physics engine for model-based control paper_content: We describe a new physics engine tailored to model-based control. Multi-joint dynamics are represented in generalized coordinates and computed via recursive algorithms. Contact responses are computed via efficient new algorithms we have developed, based on the modern velocity-stepping approach which avoids the difficulties with spring-dampers. Models are specified using either a high-level C++ API or an intuitive XML file format. A built-in compiler transforms the user model into an optimized data structure used for runtime computation. The engine can compute both forward and inverse dynamics. The latter are well-defined even in the presence of contacts and equality constraints. The model can include tendon wrapping as well as actuator activation states (e.g. pneumatic cylinders or muscles). To facilitate optimal control applications and in particular sampling and finite differencing, the dynamics can be evaluated for different states and controls in parallel. Around 400,000 dynamics evaluations per second are possible on a 12-core machine, for a 3D homanoid with 18 dofs and 6 active contacts. We have already used the engine in a number of control applications. It will soon be made publicly available. --- paper_title: Yarp Based Plugins for Gazebo Simulator paper_content: This paper presents a set of plugins for the Gazebo simulator that enables the interoperability between a robot, controlled using the YARP framework, and Gazebo itself. Gazebo is an open-source simulator that can handle different Dynamic Engines developed by the Open Source Robotics Foundation. Since our plugins conform with the YARP layer used on the real robot, applications written for our robots, COMAN and iCub, can be run on the simulator with no changes. Our plugins have two main components: a YARP interface with the same API as the real robot interface, and a Gazebo plugin which handles simulated joints, encoders, IMUs, force/torque sensors and synchronization. Different modules and tasks for COMAN and iCub have been developed using Gazebo and our plugins as a testbed before moving to the real robots. --- paper_title: Convex and analytically-invertible dynamics with contacts and constraints: Theory and implementation in MuJoCo paper_content: We describe a full-featured simulation pipeline implemented in the MuJoCo physics engine. It includes multi-joint dynamics in generalized coordinates, holonomic constraints, dry joint friction, joint and tendon limits, frictionless and frictional contacts that can have sliding, torsional and rolling friction. The forward dynamics of a 27-dof humanoid with 10 contacts are evaluated in 0.1 msec. Since the simulation is stable at 10 msec timesteps, it can run 100 times faster than real-time on a single core of a desktop processor. Furthermore the entire simulation pipeline can be inverted analytically, an order-ofmagnitude faster than the corresponding forward dynamics. We soften all constraints, in a way that avoids instabilities and unrealistic penetrations associated with earlier spring-damper methods and yet is sufficient to allow inversion. Constraints are imposed via impulses, using an extended version of the velocitystepping approach. For holomonic constraints the extension involves a soft version of the Gauss principle. For all other constraints we extend our earlier work on complementarity-free contact dynamics – which were already known to be invertible via an iterative solver – and develop a new formulation allowing analytical inversion. --- paper_title: Convex and analytically-invertible dynamics with contacts and constraints: Theory and implementation in MuJoCo paper_content: We describe a full-featured simulation pipeline implemented in the MuJoCo physics engine. It includes multi-joint dynamics in generalized coordinates, holonomic constraints, dry joint friction, joint and tendon limits, frictionless and frictional contacts that can have sliding, torsional and rolling friction. The forward dynamics of a 27-dof humanoid with 10 contacts are evaluated in 0.1 msec. Since the simulation is stable at 10 msec timesteps, it can run 100 times faster than real-time on a single core of a desktop processor. Furthermore the entire simulation pipeline can be inverted analytically, an order-ofmagnitude faster than the corresponding forward dynamics. We soften all constraints, in a way that avoids instabilities and unrealistic penetrations associated with earlier spring-damper methods and yet is sufficient to allow inversion. Constraints are imposed via impulses, using an extended version of the velocitystepping approach. For holomonic constraints the extension involves a soft version of the Gauss principle. For all other constraints we extend our earlier work on complementarity-free contact dynamics – which were already known to be invertible via an iterative solver – and develop a new formulation allowing analytical inversion. --- paper_title: Robot and Multibody Dynamics: Analysis and Algorithms paper_content: Part I Basics: Serial Chain Dynamics.- Spatial vectors.- Single rigid body dynamics.- Differential kinematics for a serial-chain system.- The mass matrix.- Equations of motion for a serial chain system.- Articulated body models for serial chains.- Operator factorization and inversion of the mass matrix.- Forward dynamics.- Part II General Multibody Systems.- Tree topology systems.- The operational space inertia.- Closed chain system dynamics.- Multi-arm manipulators.- Systems with hinge flexibility.- Systems with link flexibility.- Part III Advanced Topics and Applications.- Under-actuated systems.- Free-flying space manipulators.- Mass matrix sensitives.- Linearized dynamics models.- Sensitivity of innovations factors.- Diagnolized lagrangian dynamics.- Overview of optimal linear estimation theory. --- paper_title: Extensive analysis of Linear Complementarity Problem (LCP) solver performance on randomly generated rigid body contact problems paper_content: The Linear Complementarity Problem (LCP) is a key problem in robot dynamics, optimization, and simulation. Common experience with dynamic robotic simulations suggests that the numerical robustness of the LCP solver often determines simulation usability: if the solver fails to find a solution or finds a solution with significant residual error, interpenetration can result, the simulation can gain energy, or both. This paper undertakes the first comprehensive evaluation of LCP solvers across the space of multi-rigid body contact problems. We evaluate the performance of these solvers along the dimensions of solubility, solution quality, and running time. --- paper_title: Tools for dynamics simulation of robots: a survey based on user feedback paper_content: The number of tools for dynamics simulation has grown in the last years. It is necessary for the robotics community to have elements to ponder which of the available tools is the best for their research. As a complement to an objective and quantitative comparison, difficult to obtain since not all the tools are open-source, an element of evaluation is user feedback. With this goal in mind, we created an online survey about the use of dynamical simulation in robotics. This paper reports the analysis of the participants' answers and a descriptive information fiche for the most relevant tools. We believe this report will be helpful for roboticists to choose the best simulation tool for their researches. --- paper_title: A convex, smooth and invertible contact model for trajectory optimization paper_content: Trajectory optimization is done most efficiently when an inverse dynamics model is available. Here we develop the first model of contact dynamics defined in both the forward and inverse directions. The contact impulse is the solution to a convex optimization problem: minimize kinetic energy in contact space subject to non-penetration and friction-cone constraints. We use a custom interior-point method to make the optimization problem unconstrained; this is key to defining the forward and inverse dynamics in a consistent way. The resulting model has a parameter which sets the amount of contact smoothing, facilitating continuation methods for optimization. We implemented the proposed contact solver in our new physics engine (MuJoCo). A full Newton step of trajectory optimization for a 3D walking gait takes only 160 msec, on a 12-core PC. --- paper_title: MuJoCo: A physics engine for model-based control paper_content: We describe a new physics engine tailored to model-based control. Multi-joint dynamics are represented in generalized coordinates and computed via recursive algorithms. Contact responses are computed via efficient new algorithms we have developed, based on the modern velocity-stepping approach which avoids the difficulties with spring-dampers. Models are specified using either a high-level C++ API or an intuitive XML file format. A built-in compiler transforms the user model into an optimized data structure used for runtime computation. The engine can compute both forward and inverse dynamics. The latter are well-defined even in the presence of contacts and equality constraints. The model can include tendon wrapping as well as actuator activation states (e.g. pneumatic cylinders or muscles). To facilitate optimal control applications and in particular sampling and finite differencing, the dynamics can be evaluated for different states and controls in parallel. Around 400,000 dynamics evaluations per second are possible on a 12-core machine, for a 3D homanoid with 18 dofs and 6 active contacts. We have already used the engine in a number of control applications. It will soon be made publicly available. --- paper_title: An evaluation of methods for modeling contact in multibody simulation paper_content: Modeling contact in multibody simulation is a difficult problem frequently characterized by numerically brittle algorithms, long running times, and inaccurate (with respect to theory) models. We present a comprehensive evaluation of four methods for contact modeling on seven benchmark scenarios in order to quantify the performance of these methods with respect to robustness and speed. We also assess the accuracy of these methods where possible. We conclude the paper with a prescriptive description in order to guide the user of multibody simulation. --- paper_title: Three-dimensional impact: energy-based modeling of tangential compliance paper_content: Impact is indispensable in robotic manipulation tasks in which objects and/or manipulators move at high speeds. Applied research using impact has been hindered by underdeveloped computational foundations for rigid-body collision. This paper studies the computation of tangential impulse as two rigid bodies in the space collide at a point with both tangential compliance and friction. It extends Stronge's spring-based planar contact structure to three dimensions by modeling the contact point as a massless particle able to move tangentially on one body while connected to an infinitesimal region on the other body via three orthogonal springs. Slip or stick is indicated by whether the particle is still or moving. Impact analysis is carried out using normal impulse rather than time as the only independent variable, unlike in previous work on tangential compliance. This is due to the ability to update the energies stored in the three springs. Collision is governed by a system of differential equations that are solvable numerically. Modularity of the impact model makes it easy to be integrated into a multibody system, with one copy at each contact, in combination with a model for multiple impacts that governs normal impulses at different contacts. --- paper_title: Realistic Haptic Rendering of Interacting Deformable Objects in Virtual Environments paper_content: A new computer haptics algorithm to be used in general interactive manipulations of deformable virtual objects is presented. In multimodal interactive simulations, haptic feedback computation often comes from contact forces. Subsequently, the fidelity of haptic rendering depends significantly on contact space modeling. Contact and friction laws between deformable models are often simplified in up to date methods. They do not allow a "realistic" rendering of the subtleties of contact space physical phenomena (such as slip and stick effects due to friction or mechanical coupling between contacts). In this paper, we use Signorini's contact law and Coulomb's friction law as a computer haptics basis. Real-time performance is made possible thanks to a linearization of the behavior in the contact space, formulated as the so-called Delassus operator, and iteratively solved by a Gauss-Seidel type algorithm. Dynamic deformation uses corotational global formulation to obtain the Delassus operator in which the mass and stiffness ratio are dissociated from the simulation time step. This last point is crucial to keep stable haptic feedback. This global approach has been packaged, implemented, and tested. Stable and realistic 6D haptic feedback is demonstrated through a clipping task experiment. --- paper_title: Three-dimensional impact: energy-based modeling of tangential compliance paper_content: Impact is indispensable in robotic manipulation tasks in which objects and/or manipulators move at high speeds. Applied research using impact has been hindered by underdeveloped computational foundations for rigid-body collision. This paper studies the computation of tangential impulse as two rigid bodies in the space collide at a point with both tangential compliance and friction. It extends Stronge's spring-based planar contact structure to three dimensions by modeling the contact point as a massless particle able to move tangentially on one body while connected to an infinitesimal region on the other body via three orthogonal springs. Slip or stick is indicated by whether the particle is still or moving. Impact analysis is carried out using normal impulse rather than time as the only independent variable, unlike in previous work on tangential compliance. This is due to the ability to update the energies stored in the three springs. Collision is governed by a system of differential equations that are solvable numerically. Modularity of the impact model makes it easy to be integrated into a multibody system, with one copy at each contact, in combination with a model for multiple impacts that governs normal impulses at different contacts. --- paper_title: Control of elastic soft robots based on real-time finite element method paper_content: In this paper, we present a new method for the control of soft robots with elastic behavior, piloted by several actuators. The central contribution of this work is the use of the Finite Element Method (FEM), computed in real-time, in the control algorithm. The FEM based simulation computes the nonlinear deformations of the robots at interactive rates. The model is completed by Lagrange multipliers at the actuation zones and at the end-effector position. A reduced compliance matrix is built in order to deal with the necessary inversion of the model. Then, an iterative algorithm uses this compliance matrix to find the contribution of the actuators (force and/or position) that will deform the structure so that the terminal end of the robot follows a given position. Additional constraints, like rigid or deformable obstacles, or the internal characteristics of the actuators are integrated in the control algorithm. We illustrate our method using simulated examples of both serial and parallel structures and we validate it on a real 3D soft robot made of silicone. --- paper_title: Realistic Haptic Rendering of Interacting Deformable Objects in Virtual Environments paper_content: A new computer haptics algorithm to be used in general interactive manipulations of deformable virtual objects is presented. In multimodal interactive simulations, haptic feedback computation often comes from contact forces. Subsequently, the fidelity of haptic rendering depends significantly on contact space modeling. Contact and friction laws between deformable models are often simplified in up to date methods. They do not allow a "realistic" rendering of the subtleties of contact space physical phenomena (such as slip and stick effects due to friction or mechanical coupling between contacts). In this paper, we use Signorini's contact law and Coulomb's friction law as a computer haptics basis. Real-time performance is made possible thanks to a linearization of the behavior in the contact space, formulated as the so-called Delassus operator, and iteratively solved by a Gauss-Seidel type algorithm. Dynamic deformation uses corotational global formulation to obtain the Delassus operator in which the mass and stiffness ratio are dissociated from the simulation time step. This last point is crucial to keep stable haptic feedback. This global approach has been packaged, implemented, and tested. Stable and realistic 6D haptic feedback is demonstrated through a clipping task experiment. --- paper_title: Tools for dynamics simulation of robots: a survey based on user feedback paper_content: The number of tools for dynamics simulation has grown in the last years. It is necessary for the robotics community to have elements to ponder which of the available tools is the best for their research. As a complement to an objective and quantitative comparison, difficult to obtain since not all the tools are open-source, an element of evaluation is user feedback. With this goal in mind, we created an online survey about the use of dynamical simulation in robotics. This paper reports the analysis of the participants' answers and a descriptive information fiche for the most relevant tools. We believe this report will be helpful for roboticists to choose the best simulation tool for their researches. --- paper_title: An opensource simulator for cognitive robotics research: The prototype of the icub humanoid robot simulator paper_content: This paper presents the prototype of a new computer simulator for the humanoid robot iCub. The iCub is a new open-source humanoid robot developed as a result of the "RobotCub" project, a collaborative European project aiming at developing a new open-source cognitive robotics platform. The iCub simulator has been developed as part of a joint effort with the European project "ITALK" on the integration and transfer of action and language knowledge in cognitive robots. This is available open-source to all researchers interested in cognitive robotics experiments with the iCub humanoid platform. --- paper_title: Yarp Based Plugins for Gazebo Simulator paper_content: This paper presents a set of plugins for the Gazebo simulator that enables the interoperability between a robot, controlled using the YARP framework, and Gazebo itself. Gazebo is an open-source simulator that can handle different Dynamic Engines developed by the Open Source Robotics Foundation. Since our plugins conform with the YARP layer used on the real robot, applications written for our robots, COMAN and iCub, can be run on the simulator with no changes. Our plugins have two main components: a YARP interface with the same API as the real robot interface, and a Gazebo plugin which handles simulated joints, encoders, IMUs, force/torque sensors and synchronization. Different modules and tasks for COMAN and iCub have been developed using Gazebo and our plugins as a testbed before moving to the real robots. ---	Title: Tools for simulating humanoid robot dynamics: A survey based on user feedback Section 1: INTRODUCTION Description 1: Introduce the importance of dynamics simulation in humanoid robotics, the purpose of the survey, and the lack of existing comparisons among simulation tools. Section 2: SIMULATION TECHNOLOGIES Description 2: Discuss the origin of the technologies used for robot dynamics simulation, challenges for robotics simulation, and introduce the user experience based survey. Section 3: Challenges for robotics simulation Description 3: Explore the stricter requirements for robotics simulation compared to virtual character animation, including numerical stability, contact forces, and actuation systems. Section 4: Physics engines and system simulators Description 4: Differentiate between physics engines and system simulators, and elaborate on their specific characteristics and use cases in robot simulation. Section 5: Assorted software tools Description 5: List and briefly describe a variety of simulation tools, highlighting their main features and intended applications. Section 6: THE USERS POINT OF VIEW Description 6: Present the results of the online survey, detailing the demographics of participants, their preferences, and their evaluation criteria for simulation tools. Section 7: A diversity of tools Description 7: Examine the abundance of available dynamics simulators and the familiarity of researchers with these tools, as well as their experiences in abandoning or adopting them. Section 8: The main currently used tools Description 8: Identify the most currently used simulation tools and provide user ratings and satisfaction levels for specific aspects of these tools. Section 9: A diversity of tools: too many? Description 9: Highlight the lack of a dominant simulator and the significant variants used by researchers for different types of robotics applications. Section 10: A use-case: the iCub simulator Description 10: Detail the decision process and justification for choosing a new simulation platform for the iCub project based on the survey results. Section 11: CONCLUSIONS Description 11: Summarize the key findings of the survey, stressing the importance of open-source projects and community efforts towards improving simulation tools for robotics applications.
A Systematic Literature Review of Flexible E-Procurement Marketplace	7	--- paper_title: Business strategy, human resources, labour market flexibility and competitive advantage paper_content: N.B. Professor Michie was based at Birmingham Business School when this article was first published. The full-text of this article is not available in ORA at this time. Citation: Michie, J. & Sheehan, M. (2005). 'Business strategy, human resources, labour market flexibility and competitive advantage', International Journal of Human Resource Management, 16(3), 445-464. [Available at http://www.informaworld.com/openurl?genre=article&issn=0958%2d5192&volume=16&issue=3&spage=445]. --- paper_title: Manufacturing flexibility and business strategy: An empirical study of small and medium sized firms paper_content: Abstract This study investigates the practice of manufacturing flexibility in small and medium sized firms. Using the data collected from 87 firms from machinery and machine tool industries in Taiwan, we analyzed and prescribed the alignment of various manufacturing flexibility dimensions with business strategies. Several practical approaches to developing manufacturing flexibility in small and medium sized firms were discussed. In addition, statistical results indicate that the compatibility between business strategy and manufacturing flexibility is critical to business performance. The one-to-one relationship between business strategy and manufacturing flexibility is established to enable managers to set clear priorities in investing and developing necessary manufacturing flexibility. --- paper_title: A strategic analysis of electronic marketplaces paper_content: Information systems can serve as intermediaries between the buyers and the sellers in a vertical market, thus creating an "electronic marketplace" A major impact of these electronic market systems is that they typically reduce the search costs buyers must pay to obtain information about the prices and product offerings available in the market. Economic theory suggests that this reduction in search costs plays a major role in determining the implications of these systems for market efficiency and competitive behavior. This article draws on economic models of search and examines how prices, seller profits, and buyer welfare are affected by reducing search costs in commodity and differentiated markets. This reduction results in direct efficiency gains from reduced intermediation costs and in indirect but possibly larger gains in allocational efficiency from better-informed buyers. Because electronic market systems generally reduce buyers' search costs, they ultimately increase the efficiency of interorganizational transactions, in the process affecting the market power of buyers and sellers. The economic characteristics of electronic marketplaces, in addition to their ability to reduce search costs, create numerous possibilities for the strategic use of these systems. --- paper_title: Migrating Agile Methods to Standardized Development Practice paper_content: Situated process and quality frameworks offer a way to resolve the tensions that arise when introducing agile methods into standardized software development engineering. For these to be successful, however, organizations must grasp the opportunity to reintegrate software development management, theory, and practice. --- paper_title: Motives for e-marketplace Participation: Differences and Similarities between Buyers and Suppliers paper_content: The motivation of suppliers as well as buyers for e-marketplace participation is closely linked to the perceived outcome of participation, not only in terms of the benefits of joining an e-marketplace, but also in terms of the possible consequences of not joining. The key issue, therefore, is why organizations decide to buy and/or sell goods or services in e-marketplaces. We develop a theoretical framework for the categorization of motivational factors, resulting in four different types of motives. We then apply the framework to a dataset consisting of 41 case studies covering 20 industries in 12 countries. We conclude that buyers and suppliers have different motives for engaging in e-marketplace activities. Although e-marketplaces are a way of increasing the efficiency of supply chain activities, this is not necessarily done with the sole aim of exploiting suppliers: buyers also use e-marketplaces to find new or alternative suppliers. Similarly, even though demands from existing customers have spurred th... --- paper_title: Electronic marketplaces and price transparency: strategy, information technology, and success paper_content: Electronic marketplaces (EMPs) are widely assumed to increase price transparency and hence lower product prices. Results of empirical studies have been mixed, with several studies showing that product prices have not decreased and others showing that prices have increased in some cases. One explanation is that sellers prefer not to join EMPs with high price transparency, leading highly price transparent EMPs to fail. Therefore, in order to be successful, EMPs might be expected to avoid high price transparency. But that strategy creates a catch-22 for EMPs on the buy side: Why would buyers want to join EMPs in the absence of price transparency and the benefit of lower prices? We argue that successful EMPs must provide compensatory benefits for sellers in the case of high price transparency and for buyers in the case of low price transparency. ::: ::: To understand how EMPs could succeed, regardless of price transparency, we examined the relationships among EMP strategy, price transparency, and performance by analyzing all 19 EMPs that compete by selling a broad range of standard electronics components. We found that all EMPs pursuing a low cost strategy had high price transparency and performed poorly. All EMPs that performed well pursued strategies of differentiation, but, interestingly, not all successful EMPs avoided price transparency: Some EMPs succeeded despite enabling high price transparency. We therefore examined two differentiated EMPs in greater depth-one with high price transparency, the other with low price transparency-to show how they achieved strategic alignment of activities and resources and provided compensatory benefits for their customers. --- paper_title: Electronic marketplaces: A literature review and a call for supply chain management research paper_content: Abstract These days, Internet-based electronic marketplaces (EMs) are getting more and more popular. They emerge in different industries, supporting the exchange of goods and services of different kinds, with and for different types of actors, and are following different architectural principles. Most observers have assumed that EM would come to dominate the e-business landscape. Once you look beyond the publicity, however, you quickly see that most EMs are struggling. The supply chain dimension of an EM is largely neglected and poorly managed, while basic logistics operation is currently hampering turnover and revenues. The Paper at hand examines, based on a critical literature review, the actual EM discussion and calls for more supply chain management research within this field. --- paper_title: A Study of the Value and Impact of B2B E-Commerce: The Case of Web-Based Procurement paper_content: Web-enabled business-to-business (B2B) e-commerce enhances interorganizational coordination, resulting in transaction cost savings and competitive sourcing opportunities for the buyer organization. However, organizations are uncertain whether this is an improvement over existing information technology, such as EDI. In particular, what is the value of B2B e-commerce to a buyer organization, and how can it be measured? What factors most affect the realization of the value of B2B e-commerce? Using the case of Web-based B2B procurement systems, a framework is proposed to quantify and measure the value of B2B e-commerce systems and identify the factors that determine it. The methodology is applied to help a major heavy-equipment manufacturer located in the midwestern United States evaluate the potential of its Web-based procurement system. The preliminary results indicate that, even though all stages of B2B procurement are affected by the Web, the value of Web-based procurement is most determined by the process characteristics, organization of business units, and the "extended enterprise." --- paper_title: Multi-Criteria Markets: An Exploratory Study of Market Process Design paper_content: This paper explores market process design for multi-criteria markets using the electronic market process design work of Ribbers et al. (2002) and Kambil and van Heck (1998). The study utilizes a case study of a market intermediary in the utilities sector to examine how multi-criteria markets differ from price-only alternatives. The study reveals significant differences in the role of the intermediary in the operation of multi-criteria markets, as well as marked differences in market process design in the areas of authentication, product representation and communications/computing. We conclude that these differences represent a fundamental shift in B2B procurement relationships from those described by Kaplan and Sawhney (2000) towards strategic sourcing. --- paper_title: A Descriptive Content Analysis of Trust-Building Measures in B2B Electronic Marketplaces paper_content: Because business-to-business (B2B) electronic marketplaces (e-marketplaces) facilitate transactions between buyers and sellers, they strive to foster a trustworthy trading environment with a variety of trust-building measures. However, little research has been undertaken to explore trust-building measures used in B2B e-marketplaces, or to determine to what extent these measures are applied in B2B e-marketplaces and how they are applied. Based on reviews of the scholarly, trade, and professional literature on trust in electronic commerce, we identified 11 trustbuilding measures used to create trust in B2B e-marketplaces. Zucker’s trust production theory [1986] was applied to understand how these trust-building measures will enhance participants’ trust in buyers and sellers in B2B e-marketplaces or in B2B e-marketplace providers. A descriptive content analysis of 100 B2B e-marketplaces was conducted to survey the current usage of the 11 trust-building measures. Many of the trust-building measures were found to be widely used in the B2B e-marketplaces. However, although they were proven to be effective in building trust-related beliefs in online business environments, several institutional-based trustbuilding measures, such as escrow services, insurance and third-party assurance seals, are not widely used in B2B e-marketplaces. --- paper_title: Online reverse auctions and their role in buyer–supplier relationships paper_content: Abstract Despite the move in recent years towards supplier partnerships, buying firms need at times to make use of competitive procurement strategies for certain purchases. This study examines the impact of reverse auctions on buyer–supplier relationships through six case studies, analysing primarily the supplier perspective through participant interviews. The authors identify that there are potential benefits for both parties in a reverse auction, which can offer tendering and transactional cost advantages. For buyers, it offers a competitive procurement process. The effect on relationships will depend on the extent to which buyers employ the auction as a price weapon, or whether it is used primarily as a process improvement tool. --- paper_title: Adoption of e-procurement and participation of e-marketplace on firm performance: Trust as a moderator paper_content: Today, IT has a major influence on commercial activities, accelerating the adoption of e-procurement and e-marketplace participation in many industries. We examined firm motivations for adopting e-procurement for their operations in thee-marketplace and measured their performance to assess its benefits. Trust was considered as a moderating variable between the relationship of e-procurement adoption and e-marketplace participation. A two-stage analysis, including both a qualitative and quantitative approach, was applied. Hypotheses were developed and a model constructed. A research questionnaire was developed and distributed followed by data analysis and testing. The results showed that firms that adopted e-procurement were more likely to participate in the e-marketplace and that the firm's performance was enhanced after such participation. Trust was shown to have a moderating effect upon firm willingness to adopt e-procurement when it was considering participation in the e-marketplace. --- paper_title: Organizational Buyers' Adoption and Use of B2B Electronic Marketplaces: Efficiency-and Legitimacy-Oriented Perspectives paper_content: Despite the significant opportunities to transform the way that organizations conduct trading activities, few studies have investigated the impetus for organizational strategic moves toward business-to-business (B2B) electronic marketplaces. Drawing on transaction cost theory and institutional theory, this paper identifies two groups of factors-efficiency-and legitimacy-oriented factors, respectively-that can influence organizational buyers' initial adoption of, and the level of participation in, B2B e-marketplaces. The effects of these factors on initial adoption of and participation level in B2B e-marketplaces are empirically tested with data collected, respectively, from 98 potential adopter and 85 current adopter organizations. The results of a partial least squares analysis of the data indicate that the two groups of factors exhibit different patterns in explaining initial adoption in the preadoption period and participation level in the postadoption period. Specifically, all three of the efficiency-oriented factors investigated in this study-product characteristics, demand uncertainty, and market volatility-and their subconstructs exhibit a significant influence on adoption intent or participation level, or both. The results demonstrate that two legitimacy-oriented factors-mimetic pressures and normative pressures-and their subconstructs have a significant impact on adoption intent, but not on participation level. Our findings also indicate that clearly different patterns exist between the two groups of factors in explaining adoption intent and participation level. --- paper_title: Configurable offers and winner determination in multi-attribute auctions paper_content: Abstract The theory of procurement auctions traditionally assumes that the offered quantity and quality is fixed prior to source selection. Multi-attribute reverse auctions allow negotiation over price and qualitative attributes such as color, weight, or delivery time. They promise higher market efficiency through a more effective information exchange of buyer’s preferences and supplier’s offerings. This paper focuses on a number of winner determination problems in multi-attribute auctions. Previous work assumes that multi-attribute bids are described as attribute value pairs and that the entire demand is purchased from a single supplier. Our contribution is twofold: First, we will analyze the winner determination problem in case of multiple sourcing. Second, we will extend the concept of multi-attribute auctions to allow for configurable offers. Configurable offers enable suppliers to specify multiple values and price markups for each attribute. In addition, suppliers can define configuration and discount rules in form of propositional logic statements. These extensions provide suppliers with more flexibility in the specification of their bids and allow for an efficient information exchange among market participants. We will present MIP formulations for the resulting allocation problems and an implementation. --- paper_title: A framework for analyzing flexibility of generic objects paper_content: Flexibility is a loosely defined term used in a number of application areas with different and frequently contradicting views. In this paper, adopting as a starting point the use of the term in the manufacturing and information systems domains, we present a framework for the examination of generic objects utilizing cloud diagrams. We first portray the potential flexibility of a designed object and then its actual flexibility expressing its ability to adapt to changes. Examples of its use illustrate the ideas and their application. We believe that this research will be helpful in offering guidelines to designers of new systems where flexibility is important. --- paper_title: Manufacturing flexibility and business strategy: An empirical study of small and medium sized firms paper_content: Abstract This study investigates the practice of manufacturing flexibility in small and medium sized firms. Using the data collected from 87 firms from machinery and machine tool industries in Taiwan, we analyzed and prescribed the alignment of various manufacturing flexibility dimensions with business strategies. Several practical approaches to developing manufacturing flexibility in small and medium sized firms were discussed. In addition, statistical results indicate that the compatibility between business strategy and manufacturing flexibility is critical to business performance. The one-to-one relationship between business strategy and manufacturing flexibility is established to enable managers to set clear priorities in investing and developing necessary manufacturing flexibility. --- paper_title: A framework for the selection of electronic marketplaces : a content analysis approach paper_content: Although there has recently been an increased practitioner and media focus on electronic marketplaces, there still remains confusion over the advantages of participation. As a consequence organisations are finding difficulty in developing strategies, policies and procedures in relation to the e‐marketplace selection process. In some cases due to the dynamic and evolving environment of electronic trading, there can be a reluctance on the part of buyers and suppliers to participate in e‐marketplaces. Several classification models offer assessments of which type of marketplace are most suitable for different procurement purposes, but they fail to remain relevant in this dynamically changing environment. In this paper a content analysis of research and practitioner articles is carried out to evaluate the issues that prospective participants, seeking to purchase goods and services online, need to address in their selection process. A framework to support electronic marketplace related decision making is proposed, which is based within the contexts of business drivers, internal company issues and e‐marketplace facilitators. --- paper_title: A framework for the sustainability of e‐marketplaces paper_content: Electronic marketplaces have promised many benefits to participants, and hence have aroused considerable interest in the business community. However, the failure of some marketplaces and the success of others have led business managers to question which marketplaces will be successful in the future, and even whether the entire idea is viable. This question is particularly pressing for those considering sponsoring or participating in a marketplace. This exploratory study seeks to address these issues by proposing a framework of the factors that help explain the sustainability of e‐marketplaces. The framework proposed is based upon the findings of interviews carried out with 14 managers based in 11 companies active in the field of e‐marketplaces, and findings from the current literature from this domain. The framework proposed identifies seven factors that can be categorised according to three levels of influence, i.e. the macroeconomic/regulatory level, the industry level, and the firm level. Further work to validate the proposed framework would provide practitioners with additional insight to apply to their e‐marketplace strategies. --- paper_title: A strategic analysis of electronic marketplaces paper_content: Information systems can serve as intermediaries between the buyers and the sellers in a vertical market, thus creating an "electronic marketplace" A major impact of these electronic market systems is that they typically reduce the search costs buyers must pay to obtain information about the prices and product offerings available in the market. Economic theory suggests that this reduction in search costs plays a major role in determining the implications of these systems for market efficiency and competitive behavior. This article draws on economic models of search and examines how prices, seller profits, and buyer welfare are affected by reducing search costs in commodity and differentiated markets. This reduction results in direct efficiency gains from reduced intermediation costs and in indirect but possibly larger gains in allocational efficiency from better-informed buyers. Because electronic market systems generally reduce buyers' search costs, they ultimately increase the efficiency of interorganizational transactions, in the process affecting the market power of buyers and sellers. The economic characteristics of electronic marketplaces, in addition to their ability to reduce search costs, create numerous possibilities for the strategic use of these systems. --- paper_title: Business models for Internet-based e-procurement systems and B2B electronic markets: an exploratory assessment paper_content: Information technology (IT) has long been applied to support the exchange of goods, services and information between organizations. It is with the advent of Internet-based e-procurement systems and business-to-business (B2B) electronic markets that the real opportunities for online transactions have opened up across space and over time. The authors draw on IS and economics theory to investigate the motivation for the various online business models, and the adoption requirements of purchasing firms, through the examination of a set of mini-cases. Our exploratory study finds that private aggregating and negotiating mechanisms are being adopted for large quantity business supply purchases, while public market mechanisms are more often adopted when firms face uncertain and high variance demand. Moreover, market facilitation, expertise sharing and collaboration are gradually attracting more attention, and call for future investigation. --- paper_title: Reshaping It for Business Flexibility paper_content: From the Publisher: ::: Dealing with the issues surrounding the growing need for IT and business integration, this text takes a detailed look at the concepts, developments and "waves of change" in each area. These ideas are then drawn together in a practical synthesis, enabling the reader to create an IT architecture. The emphasis is on practical details and guidelines, reflecting the authors' experience of large organizations. --- paper_title: On the critical success factors for B2B e-marketplace paper_content: B2B e-marketplaces have a profound influence on the traditional market and on the way business is conducted. All kinds of e-marketplaces are emerging and developing now, and we have witnessed both successes and failures among these e-marketplaces. However, the failures are far outnumber the successes. Under this background, the paper discusses the critical success factors for operating e-marketplaces from different perspectives. It is shown that the core of e-marketplaces is to build liquidity and capture value. Based on these analyses, comprehensive critical factors including functional factors, strategic factors and technical Factors are discussed for the success of electronic marketplaces; then a tentative framework for the analysis on the critical success factors is proposed. --- paper_title: Assessing the benefits from e-business transformation through effective enterprise management paper_content: This paper reports on research carried out in 1999-2001 on the use of e-business applications in enterprise resource planning (ERR)-based organisations. Multiple structured interviews were used to collect data on 11 established organisations from a diverse range of industries. The findings are analysed according to the level of sophistication of e-business models and their transformational impact on the organisation. Early adopters of e-business show a trend towards cost reductions and administrative efficiencies from e-procurement and self-service applications used by customers and employees. More mature users focus on strategic advantage and generate this through an evolutionary model of organisational change. Two complex case studies of e-business integration with global suppliers and their corporate customers are analysed to identify specific stages of benefits accrual through the e-business transformation process. Collectively, the set of case studies is used to demonstrate the increased benefits derived from an e-business architecture based on a network of ERP-enabled organisations. --- paper_title: Online reverse auctions and their role in buyer–supplier relationships paper_content: Abstract Despite the move in recent years towards supplier partnerships, buying firms need at times to make use of competitive procurement strategies for certain purchases. This study examines the impact of reverse auctions on buyer–supplier relationships through six case studies, analysing primarily the supplier perspective through participant interviews. The authors identify that there are potential benefits for both parties in a reverse auction, which can offer tendering and transactional cost advantages. For buyers, it offers a competitive procurement process. The effect on relationships will depend on the extent to which buyers employ the auction as a price weapon, or whether it is used primarily as a process improvement tool. --- paper_title: A cross-industry review of B2B critical success factors paper_content: Business‐to‐business international Internet marketing (B2B IIM) has emerged as one of the key drivers in sustaining an organisation’s competitive advantage. However, market entry and communication via the Internet have affected the dynamics and traditional process in B2B commerce. Difficulties resulting from these new trends have been cited in the literature. Research into identifying what are the critical success factors for global market entry is rare. This research presents a comprehensive review in this field. The study identified 21 critical success factors applicable to most of the B2B IIM. These factors were classified into five categories: marketing strategy, Web site, global, internal and external related factors. The significance, importance and implications for each category are discussed and then recommendations are made. --- paper_title: A conceptual model of supply chain flexibility paper_content: This paper presents an integrated conceptual model of supply chain flexibility. It examines flexibility classification schemes and the commonalities of flexibility typologies published in the literature to create a theoretical foundation for analyzing the components of supply chain flexibility. Even though there has been a tremendous amount of research on the topic of flexibility, most of it has been confined to intra‐firm flexibility concerns. As supply chain management goes beyond a firm’s boundaries, the flexibility strategies must also extend beyond the firm. This paper identifies the cross‐enterprise nature of supply chain flexibility and the need to improve flexibility measures across firms. Opportunities are identified for future cross‐functional research that builds on this theoretical foundation and leads to more effective formulation of supply chain strategies. --- paper_title: Organizational Buyers' Adoption and Use of B2B Electronic Marketplaces: Efficiency-and Legitimacy-Oriented Perspectives paper_content: Despite the significant opportunities to transform the way that organizations conduct trading activities, few studies have investigated the impetus for organizational strategic moves toward business-to-business (B2B) electronic marketplaces. Drawing on transaction cost theory and institutional theory, this paper identifies two groups of factors-efficiency-and legitimacy-oriented factors, respectively-that can influence organizational buyers' initial adoption of, and the level of participation in, B2B e-marketplaces. The effects of these factors on initial adoption of and participation level in B2B e-marketplaces are empirically tested with data collected, respectively, from 98 potential adopter and 85 current adopter organizations. The results of a partial least squares analysis of the data indicate that the two groups of factors exhibit different patterns in explaining initial adoption in the preadoption period and participation level in the postadoption period. Specifically, all three of the efficiency-oriented factors investigated in this study-product characteristics, demand uncertainty, and market volatility-and their subconstructs exhibit a significant influence on adoption intent or participation level, or both. The results demonstrate that two legitimacy-oriented factors-mimetic pressures and normative pressures-and their subconstructs have a significant impact on adoption intent, but not on participation level. Our findings also indicate that clearly different patterns exist between the two groups of factors in explaining adoption intent and participation level. --- paper_title: Building trust in business-to-business electronic marketplaces paper_content: Although the concept of trust has been studied in many areas, the trust building strategies of business to business electronic marketplaces are not well studied. This paper analyzed the impact of B2B EMs on trust, and proposed a three dimensional trust building strategy for EMs, including inter-organizational trust between intermediaries and participants, between buyers and sellers, and man machines trust between precipitants and servers. This paper has important managerial implications for B2B EM operators. --- paper_title: A framework for analyzing flexibility of generic objects paper_content: Flexibility is a loosely defined term used in a number of application areas with different and frequently contradicting views. In this paper, adopting as a starting point the use of the term in the manufacturing and information systems domains, we present a framework for the examination of generic objects utilizing cloud diagrams. We first portray the potential flexibility of a designed object and then its actual flexibility expressing its ability to adapt to changes. Examples of its use illustrate the ideas and their application. We believe that this research will be helpful in offering guidelines to designers of new systems where flexibility is important. --- paper_title: Relating strategy and structure to flexible automation: A test of fit and performance implications paper_content: This study analyzed various strategy and structure choices to determine their fit relationship with flexible automation (FA). 1Using the moderator hypothesis, we proposed that the more strategy and structure choices complemented FA's competences, the higher would be the performance impact of FA. Data from 87 FA users indicate that quality and flexibility strategies, described as complementary to FA's strengths, interact positively with FA. Low cost strategy, described as conflicting with FA, interacts negatively. Organic structure, viewed as complementary to FA, has only main effects whereas a mechanistic structure interacts negatively. At the manufacturing level, skill diversity and team approaches, considered as complementary to FA, interact positively. While a subgroup analysis of high-low performers lends additional support to these relationships, analysis of industry subgroups indicates that some relationships are industry specific. We discuss the implications of these findings for research and practice. --- paper_title: Flexibility as Process Mobility: The Management of Plant Capabilities for Quick Response Manufacturing paper_content: This paper examines the relationship between one form of manufacturing flexibility - operational mobility (or the ability to change quickly between products) - and structure, infrastructure and managerial policy at the plant level. Using data from a broader study aimed at exploring the general sources of manufacturing flexibility, the paper provides evidence of the strength of the links between manufacturing flexibility and such factors as scale, technology vintage, computer integration and workforce management. There has been little empirical work on this subject, partly as a result of the difficulties of defining and measuring flexibility. The type of flexibility explored in this paper is specifically the capability of a plant to change between process states quickly. I find that most of the variance in flexibility across plants can be explained by a combination of structural factors, such as the scale of the plant; infrastructural factors, such as the length of service of the operators; and measures of managerial emphasis, such as the perceived importance of making quick changeovers. Of these factors, I find that the scale of a plant does not strongly inhibit its flexibility and that computer integration is either insignificant or detrimental to the flexibility of a plant. Workforce characteristics are also important determinants of flexibility, and the results suggest that less experienced operators may be more flexible in their ability to make certain types of change quickly between products. A strong determinant of the flexibility of a plant is the extent to which operators perceive managers to have emphasized the importance of various performance dimensions. Finally, I show that different forms of flexibility are not necessarily related to each other. This emphasizes the importance of identifying precisely the particular type of flexibility that is to be developed when building manufacturing capabilities. --- paper_title: The flexibility of manufacturing systems paper_content: Many of the new pressures from today's manufacturing environment are turning manufacturing managers' attention to the virtues of developing a flexible manufacturing function. Flexibility, however, has different meanings for different managers and several perfectly legitimate alternative paths exist towards flexible manufacturing. How managers in ten companies view manufacturing flexibility in terms of how they see the contribution of manufacturing flexibility to overall company performance; what types of flexibility they regard as important; and what their desired degree of flexibility is. The results of the investigations in these ten companies are summarised in the form of ten empirical “observations”. Based on these “observations” a check‐list of prescriptions is presented and a hierarchical framework developed into which the various issues raised by the “observations” can be incorporated. --- paper_title: A theoretical framework for analyzing the dimensions of manufacturing flexibility paper_content: Abstract The competitive environment of today has generated an increased interest in flexibility as a response mechanism. While the potential benefits of flexibility are familiar, the concept of flexibility itself is not well-understood. Neither practitioners nor academics agree upon, or know, how flexibility can be gauged or measured in its totality. Consequently, this study seeks to provide a framework for understanding this complex concept and to create a theoretical foundation for the development of generalizable measures for manufacturing flexibility. With this objective in mind, we first critically examine diverse streams of literature to define four constituent elements of flexibility: range-number (R-N), range-heterogeneity (R-H), mobility (M), and uniformity (U). The R-H element is new, and has not been proposed before in prior literature. These four elements can be applied to consistently define different types or dimensions of flexibility. Definitions for 10 flexibility dimensions pertaining to manufacturing are thus obtained. These definitions serve a dual purpose. First, they capture the domain of flexibility. Second, we show in this study how these definitions can be used to generate scale items, thereby facilitating the development of generalizable manufacturing flexibility measures. Several research avenues that can be explored once such measures are developed are also highlighted. --- paper_title: Making manufacturing flexibility operational – part 2 : distinctions and an example paper_content: Structures the concept of flexibility by making clear distinctions in three generic dimensions, describes the use of the framework for manufacturing flexibility by working through a concrete example. The framework was presented in “Making manufacturing flexibility operational – part 1: a framework”, IMS, Vol. 6 No. 2. Makes distinctions between the concept of flexibility in the three generic dimensions: utilized flexibility versus potential flexibility, external flexibility versus internal flexibility, and requested flexibility versus replied flexibility. The framework makes a clear distinction between the internal and the external factors impinging on the company, and brings together the market demand for flexibility, the characteristics of the production system, and the flexibility of the suppliers. Furthermore, pursues the connection from the strategic level to the single resource characteristics in the production system. Using the framework as a systematization for handling flexibility related issues in companies, can be especially useful for managers. --- paper_title: Process range in manufacturing: an empirical study of flexibility paper_content: This paper examines the relationship between one form of manufacturing flexibility---process range---and structure, infrastructure, and managerial policy at the plant level. The paper provides evidence of the strength of the links between manufacturing flexibility and such factors as scale, technology vintage, computer integration, and workforce management. Data from 54 plants in the fine-paper industry are presented, and a model of the determinants of short-term flexibility is developed. The plants examined differed by a factor of 20 in their ability to accommodate large process variation. The evidence suggests that flexibility is strongly negatively related to scale and degree of computer integration, yet positively related to newer vintages of mechanical technology and workforce experience. Some results differ significantly from the prevailing view of the industry, in particular, that newer plants are less flexible. The paper shows that newer machine technology is more flexible once other factors are controlled for. In the longer term, the results show that management has a significant impact on the improvement of flexibility in operations, regardless of the technology and infrastructure in place. Plant network managers' views of flexibility are important. The data suggest that inflexible plants may be inflexible partly as a result of their being considered inflexible by network managers, and never being assigned the product range needed to improve the capability. --- paper_title: Flexibility in Manufacturing: A Survey paper_content: This article is an attempt to survey the vast literature on flexibility in manufacturing that has accumulated over the last 10 to 20 years. The survey begins with a brief review of the classical literature on flexibility in economics and organization theory, which provides a background for manufacturing flexibility. Several kinds of flexibilities in manufacturing are then defined carefully along with their purposes, the means to obtain them, and some suggested measurements and valuations. Then we examine the interrelationships among the several flexibilities. Various empirical studies and analytical/optimization models dealing with these flexibilities are reported and discussed. The article concludes with suggestions for some possible future research directions. --- paper_title: The flexibility of production processes: a general framework paper_content: Various models have been developed over the years to analyze the many facets of the flexibility of production and operations systems. This paper proposes a general framework for the modeling and analysis of flexibility. The argument hinges upon the distinction between flexibility---a property of the technology---and diversity---a property of the environment in which the technology is operated. Flexibility is characterized as a hedge against diversity. Intuitive strategic properties that are conventionally attributed to flexibility are shown to follow directly from this framework. As illustrated by the different examples that are discussed, many existing models can be naturally interpreted in this context. As an application, the effect of load imbalance on a set of parallel machines is analyzed. The problem sheds light on the role of flexibility in queuing network and lot-sizing models of production. --- paper_title: Managing the Flexibility of Flexible Manufacturing Systems for Competitive Edge paper_content: Flexible Manufacturing Systems (FMS) are believed to be a major means for improving both production flexibility and productivity. If well managed, FMS can be a formidable competitive weapon for manufacturing firms. However, there exists confusion concerning how to define the concept of flexibility. One result is the misconception that flexibility may cause the decline of productivity. This paper reviews the existing studies on flexibility and presents a “flexibility map” which clarifies the relationship between various flexibility concepts and measures. This map also provides a foundation for exploring the issue of how flexibility can contribute to the firm’s competitiveness. We further point out the importance of a Total System Flexibility (TSF) concept which considers two important flexibility factors: quickness of response to a change and economic response to the change. A numerical example of routing flexibility is used to demonstrate how flexibility can enhance the competitive or strategic value of FMS. Indeed, total system flexibility can increase rather than reduce productivity, and therefore enhance the firm’s competitiveness. --- paper_title: On measurement and valuation of manufacturing flexibility paper_content: Abstract The concept of flexibility has been a recurring theme in recent manufacturing literature. Manufacturing flexibility is widely acclaimed as a formidable competitive weapon in the arsenal of any manufacturing firm. Yet critical underpinnings of this concept are not well understood despite efforts by various researchers to classify different flexibility types in order to facilitate its measurement and valuation. This paper examines reasons why measurement of flexibility has remained difficult. It proposes that any measure must inevitably depend on factors such as the degree of uncertainty in the environment, management objectives, machine capabilities and configuration (control). Therefore it advocates surrogate measures such as the value of flexibility. A model illustrating how flexibility might be measured and its appropriate level chosen, in a specific scenario, is presented. Numerical examples reveal additional insights that might be useful to firms with characteristics that match those of the m... --- paper_title: Manufacturing flexibility: a strategic perspective paper_content: To help meet competitive realities operations managers need to know more about the strategic aspects of manufacturing flexibility. This paper takes steps toward meeting that need by critically reviewing the literature and establishing a research agenda for the area. A conceptual model, which places flexibility within a broad context, helps to identify certain assumptions of theoretical studies which need to be challenged. The model also provides a basis for identifying specific flexibility dimensions. The manner in which these dimensions may limit the effectiveness of a manufacturing process, and the problems in operationalizing them are discussed. Focusing next on the neglected area of applied work, concepts are presented for analyzing whether desired amounts of flexibility are being achieved and whether the potential for flexibility built into a manufacturing process is being tapped. Once more, a procedure is outlined for altering a plant's types and amounts of flexibility over time. The research agenda, which grows out of the appraisal of theoretical and applied work, indicates the value in studying generic flexibility strategies, the flexibility dimensions, methods of delivery, ways of evaluating and changing a process's flexibility, and above all measurement problems. The conclusions indicate principles for strategic research, some of which have relevance for the development of mathematical models. --- paper_title: Flexibility configurations for the supply chain management paper_content: Abstract Facing a dynamic and complex environment, networked companies often require the coordination of many plants, which produce and deliver goods to customers located in different places, and suppliers, which provide each plant with the required components. A way to optimize the product flows in supply chains (SCs) is to adopt the concept of limited flexibility , that is a particular configuration of product assignments to plants and components to suppliers which can yield many benefits without dramatically increasing the flexibility costs. In this paper, a simulation model is proposed to evaluate the performance of different configurations of a SC. In particular, based on a work-in-process and time performance analysis, the different configurations are analyzed in order to support the selection of suitable flexibility degrees of the operations network. --- paper_title: A survey and critical review of flexibility measures in manufacturing systems paper_content: Abstract Flexibility of manufacturing systems is currently under intensive study, and the need for objective measurement of this important characteristic is widely expressed. Measurement of manufacturing flexibility is being increasingly discussed in the literature on manufacturing systems. This paper surveys the literature dealing with quantification of certain types of flexibility, analyses the proposed measures, and presents critical views. Approaches to developing flexibility measures are compiled and classified. Discussion on the suitability of specific measures is provided. Requirements of flexibility measures and recommendations for future research in this area are also provided. --- paper_title: A conceptual model of supply chain flexibility paper_content: This paper presents an integrated conceptual model of supply chain flexibility. It examines flexibility classification schemes and the commonalities of flexibility typologies published in the literature to create a theoretical foundation for analyzing the components of supply chain flexibility. Even though there has been a tremendous amount of research on the topic of flexibility, most of it has been confined to intra‐firm flexibility concerns. As supply chain management goes beyond a firm’s boundaries, the flexibility strategies must also extend beyond the firm. This paper identifies the cross‐enterprise nature of supply chain flexibility and the need to improve flexibility measures across firms. Opportunities are identified for future cross‐functional research that builds on this theoretical foundation and leads to more effective formulation of supply chain strategies. --- paper_title: Flexibility in logistic systems--modeling and performance evaluation paper_content: Abstract This paper examines potential benefits of flexibility in logistic systems. It briefly reviews flexibility concepts and existing flexibility frameworks before suggesting a bottom-up framework for flexibility in logistic systems. Then, one among the proposed system logistic flexibility types, denoted here trans-routing flexibility, is quantitatively investigated. The research focuses on flexibility's benefits exclusive of cost considerations. Logistics dependability, a customer oriented logistic performance measure is introduced. The analysis is framed in a multi-factor design of experiments (DOE) that considers factors representing changes in operational and environmental conditions of a logistic system and design factors, such as trans-routing flexibility, acting as countermeasures to changes. The model stems from a military logistics scenario easily adaptable to an industrial or service environment. Using DOE to examine the important interactions between the factor effects contributes to a better understanding of the logistics decision problems and their eventual solutions. --- paper_title: Business Process Reengineering and Flexibility: A Case for Unification paper_content: Business process reengineering (BPR) offers a radical approach to improving the performance of an organization. However, although there have been successes BPR is recognized as a high-risk activity, prone to failure. There are a variety of reasons for this and this paper highlights one which is argued to be the lack of attention that BPR pays to flexibility and its inability to cope with a changing environment. The purpose of this article is to raise the issue of flexibility within BPR and an approach is taken that examines flexibility in other business functional areas, such as manufacturing, architecture, information systems, and organizational strategy, where there is an extensive literature that indicates the importance of flexibility. The lessons from these other areas are identified and some of the implications for BPR are highlighted. A number of proposals are made including the suggestion that a form of “flexibility analysis” be adopted as a stage in BPR projects. It is argued that this would help to move the focus of a BPR project away from the current requirements toward a longer term, more flexible, enduring set of requirements. Flexibility analysis also ensures analysis of the kinds of changes that might be required over time, and how such change could be accommodated in the reengineered processes. --- paper_title: A framework for analyzing flexibility of generic objects paper_content: Flexibility is a loosely defined term used in a number of application areas with different and frequently contradicting views. In this paper, adopting as a starting point the use of the term in the manufacturing and information systems domains, we present a framework for the examination of generic objects utilizing cloud diagrams. We first portray the potential flexibility of a designed object and then its actual flexibility expressing its ability to adapt to changes. Examples of its use illustrate the ideas and their application. We believe that this research will be helpful in offering guidelines to designers of new systems where flexibility is important. --- paper_title: BUSINESS STRATEGY, MANUFACTURING FLEXIBILITY, AND ORGANIZATIONAL PERFORMANCE RELATIONSHIPS: A PATH ANALYSIS APPROACH paper_content: It has been argued in the literature that business strategy and manufacturing flexibility independently affect the performance of an organization. However, no empirical examination of the interrelationship among these three constructs has been performed. In this paper, based on a field study of 269 firms in the manufacturing industry, the identified constructs have been used to test a theoretical model using path analysis techniques. Our results indicate that business strategy contributes both directly and indirectly to organizational performance. The findings provide evidence of direct effects of (i) business strategy on manufacturing flexibility and (ii) manufacturing flexibility on organizational performance. --- paper_title: The role of flexibility in online business paper_content: Abstract Companies are trying to be more competitive by establishing a presence on the fast-growing Net. At the same time, the Net's business environment is growing more complex and uncertain. Management literature argues that flexibility can enhance corporate responsiveness to such an environment. However, research on e-businesses has focused mainly on the idea of customizing online offerings. Here is a holistic view of flexibility, investigating how online companies can become flexible through the different functional aspects of their business—technology, human resources, operations, marketing, finance, and management. What are the related opportunities and pitfalls? And what guidelines can managers follow? --- paper_title: THE NEED FOR STRATEGIC FLEXIBILITY paper_content: How can today's business cope with increasing uncertainty? The answer lies in opening up avenues of strategic flexibility. The authors tell how. --- paper_title: Manufacturing flexibility: a strategic perspective paper_content: To help meet competitive realities operations managers need to know more about the strategic aspects of manufacturing flexibility. This paper takes steps toward meeting that need by critically reviewing the literature and establishing a research agenda for the area. A conceptual model, which places flexibility within a broad context, helps to identify certain assumptions of theoretical studies which need to be challenged. The model also provides a basis for identifying specific flexibility dimensions. The manner in which these dimensions may limit the effectiveness of a manufacturing process, and the problems in operationalizing them are discussed. Focusing next on the neglected area of applied work, concepts are presented for analyzing whether desired amounts of flexibility are being achieved and whether the potential for flexibility built into a manufacturing process is being tapped. Once more, a procedure is outlined for altering a plant's types and amounts of flexibility over time. The research agenda, which grows out of the appraisal of theoretical and applied work, indicates the value in studying generic flexibility strategies, the flexibility dimensions, methods of delivery, ways of evaluating and changing a process's flexibility, and above all measurement problems. The conclusions indicate principles for strategic research, some of which have relevance for the development of mathematical models. --- paper_title: A framework for analyzing flexibility of generic objects paper_content: Flexibility is a loosely defined term used in a number of application areas with different and frequently contradicting views. In this paper, adopting as a starting point the use of the term in the manufacturing and information systems domains, we present a framework for the examination of generic objects utilizing cloud diagrams. We first portray the potential flexibility of a designed object and then its actual flexibility expressing its ability to adapt to changes. Examples of its use illustrate the ideas and their application. We believe that this research will be helpful in offering guidelines to designers of new systems where flexibility is important. --- paper_title: E-Supply Chain: Using the Internet to Revolutionize Your Business - How Market Leaders Focus Their Entire Organization on Driving Value to Customers paper_content: Supply chain has emerged as a major focus of business improvement efforts. Unfortunately, not all firms have taken advantage of the concept, resulting in a large gap between successful e-commerce companies and failures. E-Supply Chain explains how the progress of e-commerce is dovetailing with the final stages of the supply chain evolution, and how to take advantage of it in business. The authors show how the convergence of supply chain and e-commerce can catalyze the forging of advanced-level networks that will dominate future markets. In the first wave, some part of virtually every business will transfer to a cyber channel of distribution. In the second wave, networks targeting specific consumers will create new alliances across the full spectrum of supply. In the third wave, advanced networks will form global "value chain constellations" that will become the norm for most future industries. --- paper_title: Global strategies for SMe‐business: applying the SMALL framework paper_content: The World Wide Web (WWW) offers exciting new opportunities for small and medium‐sized enterprises (SMEs) to extend their customer base into the global marketplace. However, in order to exploit these advantages in a global strategy, the SME needs to adopt an entirely different approach to strategic planning and management which can enable it to deploy an extensive infrastructure network based on shared resources with other firms. This paper presents a framework for the analysis and design of global strategies within the organisational context of SMEs using Internet‐based information technologies. Central to the framework – SMALL – is the transformation of the key attributes of an SME environment through a virtual organising perspective. The framework is supported by a number of case examples of SMEs operating in a global context and a detailed analysis of three Australian SMEs. It provides a new perspective to strategies for e‐business in SMEs and to e‐business research. --- paper_title: On the critical success factors for B2B e-marketplace paper_content: B2B e-marketplaces have a profound influence on the traditional market and on the way business is conducted. All kinds of e-marketplaces are emerging and developing now, and we have witnessed both successes and failures among these e-marketplaces. However, the failures are far outnumber the successes. Under this background, the paper discusses the critical success factors for operating e-marketplaces from different perspectives. It is shown that the core of e-marketplaces is to build liquidity and capture value. Based on these analyses, comprehensive critical factors including functional factors, strategic factors and technical Factors are discussed for the success of electronic marketplaces; then a tentative framework for the analysis on the critical success factors is proposed. --- paper_title: Implementation of e-procurement and e-fulfillment processes: A comparison of cases in the motorcycle industry paper_content: Abstract Electronic business is the process which uses Internet technology to simplify certain company processes, improve productivity and increase efficiency. It allows companies to easily communicate with their suppliers, buyers and customers, to integrate “back-office” systems with those used for transactions, to accurately transmit information and to carry out data analysis in order to increase their competitiveness. The aim of this work is to define the parameters which can be used to define the performance of companies which use e-business. Particular attention is given to procurement and fulfillment in order to compare the companies studied and measure their efficiency. Fulfillment means controlling and managing transactions, warehouses, transportations and reverse logistics. This analysis is followed by case studies of two large Italian companies in the field of motorcycles. The market strategy they use and the role of Information and Communication Technologies (ICT) in their procurement and distribution processes is analyzed. This comparison provides useful information regarding the way in which Internet can be used by two companies which operate in the same market. The paper ends with the presentation of an evolutionary model for e-business strategy. The stages of the model go from the use of ICT simply as instruments of communication to the improvement of coordination processes. --- paper_title: Evolution of e-commerce Web sites: A conceptual framework and a longitudinal study paper_content: Before the 1990s, the digital exchange of information between companies was achieved using electronic data interchange (EDI) and needed agreement between the organizations. The early 1990s saw the commercialization of the Internet and the advent of open computer technology and connectivity became affordable for individuals as well as businesses. The consequence was the World Wide Web. As e-commerce activities extended across businesses, enterprises, and industries, a genre of Web sites emerged allowing the integrative management of business operations. Here, we provide an evolutionary perspective of e-commerce Web sites. We posited that there have been four eras. To chart the evolution of e-commerce Web sites, a conceptual framework was developed to characterize such sites. Based on the framework, we conducted a longitudinal study between 1993 and 2001. The result showed that the proposed four eras were clearly discernible. --- paper_title: Achieving flexible information systems: the case for improved analysis paper_content: This paper examines the problems of change that continue to thwart the development of successful information systems. The symptom of maintenance is discussed and a variety of techniques and methodologies that seek to provide solutions are discussed. Changing business needs and user requirements are identified as enduring problems and the technique of Flexibility Analysis is proposed. Finally some preliminary results from a research study are discussed. The study looked at a number of information systems in organizations and examined the changes and enhancements that were subsequently made to those systems and the reasons for them. The results indicate the benefits and practicality of the technique of Flexibility Analysis. --- paper_title: Supply management and e-procurement: creating value added in the supply chain paper_content: Abstract The increasing emphasis on supply chain management is creating a greater focus on the supply management link in the supply chain. This focus will become even more intense as firms continue to adopt e-procurement strategies to leverage the competitive advantages of the Internet. Supply managers need to understand the impact of technology and gain competency in making a business case for e-procurement. The implications are profound for the industrial marketer. --- paper_title: Adapting to changing user requirements paper_content: Abstract Systems which have the capability of adapting to changing user requirements must be founded on accurate and perceptive models of the organisation in which they have to function. Design methods based on active user participation and the use of experimental and prototyping methods help to ensure accurate models. But because systems are expected to survive even when circumstances change, the designers have to have a view of what the future world will look like. A technique which helps to provide such a view is called “ future analysis ”. However, even designs based on the best prediction technique cannot guarantee a fit between the currently designed system and future needs. Hence it is important to design systems with built-in flexibility. A number of methods have been developed which reduce the disruption caused by the amendment or even replacement of a system or system's component. --- paper_title: The role of flexibility in online business paper_content: Abstract Companies are trying to be more competitive by establishing a presence on the fast-growing Net. At the same time, the Net's business environment is growing more complex and uncertain. Management literature argues that flexibility can enhance corporate responsiveness to such an environment. However, research on e-businesses has focused mainly on the idea of customizing online offerings. Here is a holistic view of flexibility, investigating how online companies can become flexible through the different functional aspects of their business—technology, human resources, operations, marketing, finance, and management. What are the related opportunities and pitfalls? And what guidelines can managers follow? --- paper_title: Flexibility as Process Mobility: The Management of Plant Capabilities for Quick Response Manufacturing paper_content: This paper examines the relationship between one form of manufacturing flexibility - operational mobility (or the ability to change quickly between products) - and structure, infrastructure and managerial policy at the plant level. Using data from a broader study aimed at exploring the general sources of manufacturing flexibility, the paper provides evidence of the strength of the links between manufacturing flexibility and such factors as scale, technology vintage, computer integration and workforce management. There has been little empirical work on this subject, partly as a result of the difficulties of defining and measuring flexibility. The type of flexibility explored in this paper is specifically the capability of a plant to change between process states quickly. I find that most of the variance in flexibility across plants can be explained by a combination of structural factors, such as the scale of the plant; infrastructural factors, such as the length of service of the operators; and measures of managerial emphasis, such as the perceived importance of making quick changeovers. Of these factors, I find that the scale of a plant does not strongly inhibit its flexibility and that computer integration is either insignificant or detrimental to the flexibility of a plant. Workforce characteristics are also important determinants of flexibility, and the results suggest that less experienced operators may be more flexible in their ability to make certain types of change quickly between products. A strong determinant of the flexibility of a plant is the extent to which operators perceive managers to have emphasized the importance of various performance dimensions. Finally, I show that different forms of flexibility are not necessarily related to each other. This emphasizes the importance of identifying precisely the particular type of flexibility that is to be developed when building manufacturing capabilities. --- paper_title: Technology flexibility: conceptualization, validation, and measurement paper_content: This research investigates technology flexibility, which is the technology characteristic that allows or enables adjustments and other changes to the business process. Technology flexibility has two dimensions, structural and process flexibility, encompassing both the actual technology application and the people and processes that support it. The flexibility of technology that supports business processes can greatly influence the organization's capacity for change. Existing technology can present opportunities for or barriers to business process flexibility through structural characteristics such as language, platform and design. Technology can also indirectly affect flexibility through the relationship between the technology maintenance organization and the business process owners, change request processing, and other response characteristics. These indirect effects reflect a more organizational perspective of flexibility. This paper asks the question, "what makes technology flexible?" This question is addressed by developing and validating a measurement model of technology flexibility. Constructs and definitions of technology flexibility are developed by examining the concept of flexibility in other disciplines, and the demands imposed on technology by business processes. The purpose of building a measurement model is to show validity for the constructs of technology flexibility. This paper discusses the theory of technology flexibility, develops constructs and determinants of this phenomenon, and proposes a methodology for the validation and study of the flexibility of emerging technologies. --- paper_title: Measuring technology flexibility paper_content: This research investigates technology flexibility, which is the technology characteristic that allows or enables adjustments and other changes to the business process. We develop dimensions and determinants of this phenomenon and demonstrate a methodology for the validation and the study of flexibility. The results of a test of software system flexibility are reported. Technology flexibility has two dimensions, structural and process flexibility, encompassing both the actual technology application and the people and processes that support and use it. The flexibility of technology that supports business processes can greatly influence the organization's capacity for change. Existing technology can present opportunities for, or barriers to, business process flexibility through structural characteristics such as language, platform, and design. Technology can also indirectly affect flexibility through the relationship between the technology maintenance organization and the business process owners, change request processing, and other response characteristics. These indirect effects reflect a more organizational perspective of flexibility. --- paper_title: Process range in manufacturing: an empirical study of flexibility paper_content: This paper examines the relationship between one form of manufacturing flexibility---process range---and structure, infrastructure, and managerial policy at the plant level. The paper provides evidence of the strength of the links between manufacturing flexibility and such factors as scale, technology vintage, computer integration, and workforce management. Data from 54 plants in the fine-paper industry are presented, and a model of the determinants of short-term flexibility is developed. The plants examined differed by a factor of 20 in their ability to accommodate large process variation. The evidence suggests that flexibility is strongly negatively related to scale and degree of computer integration, yet positively related to newer vintages of mechanical technology and workforce experience. Some results differ significantly from the prevailing view of the industry, in particular, that newer plants are less flexible. The paper shows that newer machine technology is more flexible once other factors are controlled for. In the longer term, the results show that management has a significant impact on the improvement of flexibility in operations, regardless of the technology and infrastructure in place. Plant network managers' views of flexibility are important. The data suggest that inflexible plants may be inflexible partly as a result of their being considered inflexible by network managers, and never being assigned the product range needed to improve the capability. --- paper_title: Flexibility as Process Mobility: The Management of Plant Capabilities for Quick Response Manufacturing paper_content: This paper examines the relationship between one form of manufacturing flexibility - operational mobility (or the ability to change quickly between products) - and structure, infrastructure and managerial policy at the plant level. Using data from a broader study aimed at exploring the general sources of manufacturing flexibility, the paper provides evidence of the strength of the links between manufacturing flexibility and such factors as scale, technology vintage, computer integration and workforce management. There has been little empirical work on this subject, partly as a result of the difficulties of defining and measuring flexibility. The type of flexibility explored in this paper is specifically the capability of a plant to change between process states quickly. I find that most of the variance in flexibility across plants can be explained by a combination of structural factors, such as the scale of the plant; infrastructural factors, such as the length of service of the operators; and measures of managerial emphasis, such as the perceived importance of making quick changeovers. Of these factors, I find that the scale of a plant does not strongly inhibit its flexibility and that computer integration is either insignificant or detrimental to the flexibility of a plant. Workforce characteristics are also important determinants of flexibility, and the results suggest that less experienced operators may be more flexible in their ability to make certain types of change quickly between products. A strong determinant of the flexibility of a plant is the extent to which operators perceive managers to have emphasized the importance of various performance dimensions. Finally, I show that different forms of flexibility are not necessarily related to each other. This emphasizes the importance of identifying precisely the particular type of flexibility that is to be developed when building manufacturing capabilities. --- paper_title: Building the Flexible Firm: How to Remain Competitive paper_content: Bespreking van: H.W. Volberda,Building the Flexible Firm: How to Remain Competitive Oxford:Oxford University Press ,1999 019829090X --- paper_title: Process range in manufacturing: an empirical study of flexibility paper_content: This paper examines the relationship between one form of manufacturing flexibility---process range---and structure, infrastructure, and managerial policy at the plant level. The paper provides evidence of the strength of the links between manufacturing flexibility and such factors as scale, technology vintage, computer integration, and workforce management. Data from 54 plants in the fine-paper industry are presented, and a model of the determinants of short-term flexibility is developed. The plants examined differed by a factor of 20 in their ability to accommodate large process variation. The evidence suggests that flexibility is strongly negatively related to scale and degree of computer integration, yet positively related to newer vintages of mechanical technology and workforce experience. Some results differ significantly from the prevailing view of the industry, in particular, that newer plants are less flexible. The paper shows that newer machine technology is more flexible once other factors are controlled for. In the longer term, the results show that management has a significant impact on the improvement of flexibility in operations, regardless of the technology and infrastructure in place. Plant network managers' views of flexibility are important. The data suggest that inflexible plants may be inflexible partly as a result of their being considered inflexible by network managers, and never being assigned the product range needed to improve the capability. --- paper_title: The Decision-Making Paradigm of Organizational Design paper_content: This paper introduces and explicates the decision-making paradigm of organizational design. We argue that the domains of existing design paradigms are declining in scope, and that the nature of current and future organizational environments requires use of a design paradigm that responds to the increasing frequency and criticality of the decision-making process. In particular, we argue that the decision-making paradigm is applicable when the organizational environments are hostile, complex, and turbulent. ::: ::: The focal concept of the decision-making paradigm is that organizations should be designed primarily to facilitate the making of organizational decisions. The paper sets forth the paradigm's six major concepts and discusses the principal domains of its application. The paper also examines the relationships between the decision-making paradigm and the literatures on 1 organizational decision making, 2 the information processing view of organizations, and 3 the need for compatibility between the organization's design and the design of its technologically supported information systems. The paper concludes by identifying ten organizational design guidelines that follow from the decision-making paradigm. --- paper_title: An Empirical Study of Manufacturing Flexibility in Printed Circuit Board Assembly paper_content: This paper addresses the empirical verification of hypotheses that relate to the strategic use and implementation of manufacturing flexibility. We begin with a literature review and framework for analyzing different types of flexibility in manufacturing. Next, we examine some of the propositions in the framework using data from 31 printed circuit-board plants in Europe, Japan, and the United States. Based on our analysis and findings, we then suggest several new strategic insights related to the management of flexibility and some potentially fruitful areas for further theoretical and empirical research. Our findings include: more automation is associated empirically with less flexibility, as found in other studies; nontechnology factors, such as high involvement of workers in problem-solving activities, close relationships with suppliers, and flexible wage schemes, are associated with greater mix, volume, and new-product flexibility; component reusability is significantly correlated with mix and new-product flexibility; achieving high-mix or new-product flexibility does not seem to involve a cost or quality penalty; mix and new-product flexibility are mutually reinforcing and tend to be supported by similar factors; and mix flexibility may reduce volume fluctuations, which could theoretically reduce the need for volume flexibility. --- paper_title: Building the Flexible Firm: How to Remain Competitive paper_content: Bespreking van: H.W. Volberda,Building the Flexible Firm: How to Remain Competitive Oxford:Oxford University Press ,1999 019829090X --- paper_title: Flexibility in Manufacturing: A Survey paper_content: This article is an attempt to survey the vast literature on flexibility in manufacturing that has accumulated over the last 10 to 20 years. The survey begins with a brief review of the classical literature on flexibility in economics and organization theory, which provides a background for manufacturing flexibility. Several kinds of flexibilities in manufacturing are then defined carefully along with their purposes, the means to obtain them, and some suggested measurements and valuations. Then we examine the interrelationships among the several flexibilities. Various empirical studies and analytical/optimization models dealing with these flexibilities are reported and discussed. The article concludes with suggestions for some possible future research directions. --- paper_title: THE NEED FOR STRATEGIC FLEXIBILITY paper_content: How can today's business cope with increasing uncertainty? The answer lies in opening up avenues of strategic flexibility. The authors tell how. --- paper_title: Strategy and Environment: A Conceptual Integration paper_content: An elaboration of the concepts of strategy and environment can be achieved by categorizing environment into its objective and perceived states, and by subdividing strategy according to content (outcomes) or process. The objective environment can be further categorized into “task” and “general.” An alternative subdivision of strategy is primary (domain selection) and secondary (competitive approach). The concepts of strategy and environment are integrated in that primary strategy concerns opportunities in the general environment and secondary strategy involves navigating within a task environment. --- paper_title: Business Process Reengineering and Flexibility: A Case for Unification paper_content: Business process reengineering (BPR) offers a radical approach to improving the performance of an organization. However, although there have been successes BPR is recognized as a high-risk activity, prone to failure. There are a variety of reasons for this and this paper highlights one which is argued to be the lack of attention that BPR pays to flexibility and its inability to cope with a changing environment. The purpose of this article is to raise the issue of flexibility within BPR and an approach is taken that examines flexibility in other business functional areas, such as manufacturing, architecture, information systems, and organizational strategy, where there is an extensive literature that indicates the importance of flexibility. The lessons from these other areas are identified and some of the implications for BPR are highlighted. A number of proposals are made including the suggestion that a form of “flexibility analysis” be adopted as a stage in BPR projects. It is argued that this would help to move the focus of a BPR project away from the current requirements toward a longer term, more flexible, enduring set of requirements. Flexibility analysis also ensures analysis of the kinds of changes that might be required over time, and how such change could be accommodated in the reengineered processes. --- paper_title: Competitive Strategy Under Uncertainty. paper_content: Competitive strategy under uncertainty involves a trade-off between acting early and acting later after the uncertainty is resolved, and another trade-off between focusing resources on one scenario and spreading resources on several scenarios thus maintaining flexibility. This paper analyzes both these trade-offs taking into consideration the nature of uncertainty, industry economics, intensity of competition, and the position of a firm relative to its competitors. --- paper_title: Flexibility Ratios and Manufacturing Strategy paper_content: In this exploratory, empirical study of modernizing durable goods plants, it was found that typical measures of flexibility e.g., number of unique parts and part families are independent. More importantly, plants and firms with greater strategic manufacturing focus, regardless of specific emphasis e.g., cost or quality, scheduled fewer part numbers on new flexible automation systems. This suggests that product focus and strategic focus are related in plants producing discrete parts. When flexibility is emphasized as a strategic manufacturing focus, new automation systems are significantly more likely to have shorter change-over times per part family. In general, part family-changeover time ratios appear to have the greatest potential of measures evaluated for building a useful theory of flexibility in discrete parts manufacturing. An evaluation of changes made in part types and part families during the implementation period showed that product flexibility is pursued as a way to reduce high labor costs in manufacturing. These plants accomplished this end by increasing the number of parts scheduled on new systems. Implications for strategic management of flexibility and scope are presented. --- paper_title: Implementation and management framework for supply chain flexibility paper_content: Purpose – The purpose of this research is to develop a conceptual framework for implementing and managing supply chain flexibility in supply chain organizations. The framework suggests that supply chain flexibility should be implemented and managed using a three‐stage approach: required flexibility identification, implementation and shared responsibility, and feedback and control.Design/methodology/approach – The major components of the proposed framework are based on a review of research in the manufacturing flexibility literature as well as the limited research in supply chain flexibility. The strengths and weaknesses of these frameworks, combined with a published empirical study were analyzed to identify the important issues that must be considered when implementing and managing supply chain flexibility, and those components that need to be incorporated into a new integrated framework.Findings – This framework was constructed by synthesizing the strengths of other conceptual frameworks. As a result, th... --- paper_title: A framework for the sustainability of e‐marketplaces paper_content: Electronic marketplaces have promised many benefits to participants, and hence have aroused considerable interest in the business community. However, the failure of some marketplaces and the success of others have led business managers to question which marketplaces will be successful in the future, and even whether the entire idea is viable. This question is particularly pressing for those considering sponsoring or participating in a marketplace. This exploratory study seeks to address these issues by proposing a framework of the factors that help explain the sustainability of e‐marketplaces. The framework proposed is based upon the findings of interviews carried out with 14 managers based in 11 companies active in the field of e‐marketplaces, and findings from the current literature from this domain. The framework proposed identifies seven factors that can be categorised according to three levels of influence, i.e. the macroeconomic/regulatory level, the industry level, and the firm level. Further work to validate the proposed framework would provide practitioners with additional insight to apply to their e‐marketplace strategies. --- paper_title: A review of manufacturing flexibility paper_content: Abstract In the field of operations management, manufacturing flexibility has been the subject of much academic enquiry. Moreover, the need for this fundamental characteristic has never been more urgent. However, a comprehensive understanding of the subject remains elusive. An extensive review of the literature is used to examine the issues surrounding the concept of manufacturing flexibility. Specifically: the use of manufacturing flexibility as a strategic objective, the relationship flexibility has with environmental uncertainty, the use of taxonomies as a vehicle for furthering understanding of the types of flexibility, the nature of flexibility, and its measurement. Through this process of synthesis, the paper attempts to establish the extent to which knowledge of manufacturing flexibility has now progressed. Suggestions for future research topics in flexibility are also presented. --- paper_title: THE NEED FOR STRATEGIC FLEXIBILITY paper_content: How can today's business cope with increasing uncertainty? The answer lies in opening up avenues of strategic flexibility. The authors tell how. --- paper_title: Manufacturing flexibility: a strategic perspective paper_content: To help meet competitive realities operations managers need to know more about the strategic aspects of manufacturing flexibility. This paper takes steps toward meeting that need by critically reviewing the literature and establishing a research agenda for the area. A conceptual model, which places flexibility within a broad context, helps to identify certain assumptions of theoretical studies which need to be challenged. The model also provides a basis for identifying specific flexibility dimensions. The manner in which these dimensions may limit the effectiveness of a manufacturing process, and the problems in operationalizing them are discussed. Focusing next on the neglected area of applied work, concepts are presented for analyzing whether desired amounts of flexibility are being achieved and whether the potential for flexibility built into a manufacturing process is being tapped. Once more, a procedure is outlined for altering a plant's types and amounts of flexibility over time. The research agenda, which grows out of the appraisal of theoretical and applied work, indicates the value in studying generic flexibility strategies, the flexibility dimensions, methods of delivery, ways of evaluating and changing a process's flexibility, and above all measurement problems. The conclusions indicate principles for strategic research, some of which have relevance for the development of mathematical models. --- paper_title: STRATEGIC FLEXIBILITY FOR HIGH TECHNOLOGY MANOEUVRES: A CONCEPTUAL FRAMEWORK paper_content: Strategic flexibility is proposed as an expedient capability for managing capricious settings, such as those confronted in technology‐intensive arenas. This article examines the historical evolution of the concept of flexibility and analyses its different senses by relating it to other concepts with a ‘family resemblance’. A conceptual framework is subsequently developed, which integrates the temporal and intentional dimensions of flexibility. Four archetypal manoeuvres, derived from the framework, are proposed as a means of attaining strategic flexibility. The deployment of these manoeuvres is exemplified by means of selected strategic engagements of firms in the computer peripherals arena. The article concludes with a discussion of the theoretical and practical implications of the research. --- paper_title: Business Process Reengineering and Flexibility: A Case for Unification paper_content: Business process reengineering (BPR) offers a radical approach to improving the performance of an organization. However, although there have been successes BPR is recognized as a high-risk activity, prone to failure. There are a variety of reasons for this and this paper highlights one which is argued to be the lack of attention that BPR pays to flexibility and its inability to cope with a changing environment. The purpose of this article is to raise the issue of flexibility within BPR and an approach is taken that examines flexibility in other business functional areas, such as manufacturing, architecture, information systems, and organizational strategy, where there is an extensive literature that indicates the importance of flexibility. The lessons from these other areas are identified and some of the implications for BPR are highlighted. A number of proposals are made including the suggestion that a form of “flexibility analysis” be adopted as a stage in BPR projects. It is argued that this would help to move the focus of a BPR project away from the current requirements toward a longer term, more flexible, enduring set of requirements. Flexibility analysis also ensures analysis of the kinds of changes that might be required over time, and how such change could be accommodated in the reengineered processes. --- paper_title: Engineering supply chains to match customer requirements paper_content: Modern day market places are highly varied and cannot be serviced effectively by a single supply chain paradigm. Consequently products and services must be provided to the end consumer via tailored supply chain strategies. This article categorises consumer products and details the specific supply chain management tools and techniques required to service each. A comparison of lean and agile strategies is provided along with a detailed explanation of the integration of the two within a Leagile supply chain. The application of such a strategy for electronic products is provided via a four stage case study. A route map for engineering supply chains to match customer requirements is developed in order to avoid costly and ineffective mismatches of supply chain strategy to product characteristics. --- paper_title: Systematic Review in Software Engineering paper_content: A kit of assemblable components for implantation into the bone of a mammal for use in distraction osteogenesis. The kit comprises a fixture, a footing and a distracter, the fixture including a longitudinally extending body portion with a proximal end and a distal end, the body portion having an exterior surface adapted for contact and integration with bone tissue, the body portion having a generally longitudinally extending bore extending from a proximal opening adjacent the proximal end to a distal opening adjacent the distal end. The footing includes a proximal surface and a distal surface. The distracter comprises a generally rod-shaped body including a distal end and a proximal end, and the proximal end of the distracter is adapted to bear against the footing. There are first and second engaging means on the fixture and the distracter respectively for adjustably locating the fixture relative to the distracter. --- paper_title: Factors influencing clients in the selection of offshore software outsourcing vendors: An exploratory study using a systematic literature review paper_content: Context: Offshore software development outsourcing is a modern business strategy for developing high quality software at low cost. Objective: The objective of this research paper is to identify and analyse factors that are important in terms of the competitiveness of vendor organisations in attracting outsourcing projects. Method: We performed a systematic literature review (SLR) by applying our customised search strings which were derived from our research questions. We performed all the SLR steps, such as the protocol development, initial selection, final selection, quality assessment, data extraction and data synthesis. Results: We have identified factors such as cost-saving, skilled human resource, appropriate infrastructure, quality of product and services, efficient outsourcing relationships management, and an organisation's track record of successful projects which are generally considered important by the outsourcing clients. Our results indicate that appropriate infrastructure, cost-saving, and skilled human resource are common in three continents, namely Asia, North America and Europe. We identified appropriate infrastructure, cost-saving, and quality of products and services as being common in three types of organisations (small, medium and large). We have also identified four factors-appropriate infrastructure, cost-saving, quality of products and services, and skilled human resource as being common in the two decades (1990-1999 and 2000-mid 2008). Conclusions: Cost-saving should not be considered as the driving factor in the selection process of software development outsourcing vendors. Vendors should rather address other factors in order to compete in the OSDO business, such as skilled human resource, appropriate infrastructure and quality of products and services. --- paper_title: Systematic Review in Software Engineering paper_content: A kit of assemblable components for implantation into the bone of a mammal for use in distraction osteogenesis. The kit comprises a fixture, a footing and a distracter, the fixture including a longitudinally extending body portion with a proximal end and a distal end, the body portion having an exterior surface adapted for contact and integration with bone tissue, the body portion having a generally longitudinally extending bore extending from a proximal opening adjacent the proximal end to a distal opening adjacent the distal end. The footing includes a proximal surface and a distal surface. The distracter comprises a generally rod-shaped body including a distal end and a proximal end, and the proximal end of the distracter is adapted to bear against the footing. There are first and second engaging means on the fixture and the distracter respectively for adjustably locating the fixture relative to the distracter. --- paper_title: Supply management and e-procurement: creating value added in the supply chain paper_content: Abstract The increasing emphasis on supply chain management is creating a greater focus on the supply management link in the supply chain. This focus will become even more intense as firms continue to adopt e-procurement strategies to leverage the competitive advantages of the Internet. Supply managers need to understand the impact of technology and gain competency in making a business case for e-procurement. The implications are profound for the industrial marketer. --- paper_title: Business Process Reengineering and Flexibility: A Case for Unification paper_content: Business process reengineering (BPR) offers a radical approach to improving the performance of an organization. However, although there have been successes BPR is recognized as a high-risk activity, prone to failure. There are a variety of reasons for this and this paper highlights one which is argued to be the lack of attention that BPR pays to flexibility and its inability to cope with a changing environment. The purpose of this article is to raise the issue of flexibility within BPR and an approach is taken that examines flexibility in other business functional areas, such as manufacturing, architecture, information systems, and organizational strategy, where there is an extensive literature that indicates the importance of flexibility. The lessons from these other areas are identified and some of the implications for BPR are highlighted. A number of proposals are made including the suggestion that a form of “flexibility analysis” be adopted as a stage in BPR projects. It is argued that this would help to move the focus of a BPR project away from the current requirements toward a longer term, more flexible, enduring set of requirements. Flexibility analysis also ensures analysis of the kinds of changes that might be required over time, and how such change could be accommodated in the reengineered processes. --- paper_title: The development of e‐procurement within the ICT manufacturing industry in Ireland paper_content: Purpose – The purpose of this paper is to show that e‐procurement provides manufacturing firms with new and efficient solutions to drive significant value into their business, yet generally the use of internet technologies to accommodate e‐procurement systems remains in a formative stage. Previous research tends to focus on larger economies, so this paper provides a new perspective by presenting evidence from the Irish ICT manufacturing industry.Design/methodology/approach – The research locale is justified on the basis that the ICT manufacturing sector has a greater propensity to adopt technologies such as e‐procurement. In addition, by conducting the research in a small peripheral economy, a gap in the knowledge base is being addressed. The exploratory research adopted a quantitative methodology with a questionnaire instrument being employed to investigate various e‐procurement activities within a sample of top performing ICT manufacturing firms.Findings – Findings show that e‐procurement is developing ... ---	Title: A Systematic Literature Review of Flexible E-Procurement Marketplace Section 1: E-Procurement Marketplace Description 1: Discuss the definition and evolution of e-procurement marketplaces (EPM) and their significance in buyer-seller relationships. Section 2: Flexibility Description 2: Define flexibility and its importance, covering various types and aspects such as operational, hierarchical, measurement, strategic and time horizon. Section 3: From Web Evolution to Development of Flexible EPM Description 3: Analyze the phases of web evolution and how they have influenced the flexible nature of EPMs, highlighting key technological advancements and changes in business processes. Section 4: A Synthesis of Flexibility Types Into an EPM Framework Description 4: Synthesize the different flexibility types identified in the literature into a coherent framework applicable to EPMs, detailing each type and its relevance. Section 5: Research Methodology Description 5: Present the systematic literature review methodology applied in this study, including research questions, search process, inclusion and exclusion criteria, and data extraction and analysis procedures. Section 6: Discussion Description 6: Discuss the findings of the systematic literature review, addressing the research questions and providing insights into the temporal aspects and domain-specific applications of EPM flexibility. Section 7: Conclusion Description 7: Summarize the key findings of the study, the importance of flexibility in EPMs, and suggestions for future research directions in the field.
A Survey of Application Layer Techniques for Adaptive Streaming Of Multimedia	22	--- paper_title: Adaptive vector quantization of image sequences using generalized threshold replenishment paper_content: We describe a new adaptive vector quantization (AVQ) algorithm designed for the coding of nonstationary sources. This new algorithm, generalized threshold replenishment (GTR), differs from prior AVQ algorithms in that it features an explicit, online consideration of both rate and distortion. Rate-distortion cost criteria are used in the determination of nearest-neighbor codewords and as well as in the decision to update the codebook. Results presented indicate that, for the coding of an image sequence: (1) most AVQ algorithms achieve distortion much lower than that of nonadaptive VQ for the same rate (about 1.5 bits/pixel), and (2) the GTR algorithm achieves rate-distortion performance substantially superior to that of other AVQ algorithms for low-rate coding, being the only algorithm to achieve a rate below 1.0 bits/pixel. --- paper_title: A rate control mechanism for packet video in the Internet paper_content: Datagram networks such as the Internet do not provide guaranteed resources such as bandwidth or guaranteed performance measures such as maximum delay. One way to support packet video in these networks is to use feedback mechanisms that adapt the output rate of video coders based on the state of the network. The authors present one such mechanism. They describe the feedback information, and how it is used by the coder control algorithm. They also examine how the need to operate in a multicast environment impacts the design of the control mechanism. This mechanism has been implemented in the H.261 video coder of IVS. IVS is a videoconference system for the Internet developed at INRIA. Experiments indicate that the control mechanism is well suited to the Internet environment. In particular, it makes it possible to establish and maintain quality videoconferences even across congested connections in the Internet. Furthermore, it prevents video sources from swamping the resources of the Internet, which could lead to unacceptable service to all users of the network. > --- paper_title: SAVE: an algorithm for smoothed adaptive video over explicit rate networks paper_content: Supporting compressed video efficiently on networks is a challenge because of its burstiness. Although a large number of applications using compressed video are rate adaptive, it is also important to preserve quality as much as possible. We propose a smoothing and rate adaptation algorithm, called SAVE, that the compressed video source uses in conjunction with explicit rate based control in the network. SAVE smoothes the demand from the source to the network, thus helping achieve good multiplexing gains. SAVE maintains the quality of the video and ensures that the delay at the source buffer does not exceed a bound. We examine the effectiveness of SAVE across 28 different traces (entertainment and teleconferencing videos) using different compression algorithms. --- paper_title: A rate control mechanism for packet video in the Internet paper_content: Datagram networks such as the Internet do not provide guaranteed resources such as bandwidth or guaranteed performance measures such as maximum delay. One way to support packet video in these networks is to use feedback mechanisms that adapt the output rate of video coders based on the state of the network. The authors present one such mechanism. They describe the feedback information, and how it is used by the coder control algorithm. They also examine how the need to operate in a multicast environment impacts the design of the control mechanism. This mechanism has been implemented in the H.261 video coder of IVS. IVS is a videoconference system for the Internet developed at INRIA. Experiments indicate that the control mechanism is well suited to the Internet environment. In particular, it makes it possible to establish and maintain quality videoconferences even across congested connections in the Internet. Furthermore, it prevents video sources from swamping the resources of the Internet, which could lead to unacceptable service to all users of the network. > --- paper_title: Control mechanisms for packet audio in the Internet paper_content: The Internet provides a single class best effort service. From an application's point of view, this service amounts in practice to providing channels with time-varying characteristics such as delay and loss distributions. One way to support real time applications such as interactive audio given this service is to use control mechanisms that adapt the audio coding and decoding processes based on the characteristics of the channels, the goal being to maximize the quality of the audio delivered to the destinations. In this paper, we describe and analyze a set of such control mechanisms. They include a jitter control mechanism and a combined error and rate control mechanism. These mechanisms have been implemented and evaluated over the Internet and the MBone. Experiments indicate that they make it possible to establish and maintain reasonable quality audioconferences even across fairly congested connections. --- paper_title: Time Constrained Bandwidth Smoothing For Interactive Video-On-Demand Systems paper_content: The use of a client-side buffer in the delivery of compressed prerecorded video can be an effective tool for removing the burstiness required of the underlying server and network by smoothing the bandwidth requirements for continuous delivery. Given a fixed-size smoothing buffer, several bandwidth smoothing algorithms have been introduced in the literature that are provably optimal under certain constraints, typically requiring large buffer residency times to realize their optimal properties. The large buffer residency times, however, make VCR functions harder to support. In this paper, we introduce the notion of time constrained bandwidth smoothing. Specifically, we introduce two new algorithms that, in addition to the size of the client side buffer, use a time constraint as a parameter in the bandwidth smoothing plan creation, making the plans more amenable to supporting VCR interactivity. Our results show that the buffer residency times can be reduced, while still allowing the bandwidth allocation to be smoothed for continuous video delivery. --- paper_title: Supporting stored video: reducing rate variability and end-to-end resource requirements through optimal smoothing paper_content: VBR compressed video is known to exhibit significant, multiple-time-scale bit rate variability. In this paper, we consider the transmission of stored video from a server to a client across a high speed network, and explore how the client buffer space can be used most effectively toward reducing the variability of the transmitted bit rate.We present two basic results. First, we present an optimal smoothing algorithm for achieving the greatest possible reduction in rate variability when transmitting stored video to a client with given buffer size. We provide a formal proof of optimality, and demonstrate the performance of the algorithm on a set of long MPEG-1 encoded video traces. Second, we evaluate the impact of optimal smoothing on the network resources needed for video transport, under two network service models: Deterministic Guaranteed service [1, 9] and Renegotiated CBR (RCBR) service [8, 7]. Under both models, we find the impact of optimal smoothing to be dramatic. --- paper_title: Rate-constrained bandwidth smoothing for delivery of stored video paper_content: Bandwidth smoothing techniques for the delivery of compressed prerecorded video have been shown effective in removing the burstiness required for the continuous playback of stored video. Given a fixed client-side buffer, several bandwidth smoothing algorithms have been introduced that are provably optimal under certain constraints. These algorithms, however, may be too aggressive in the amount of data that they prefetch, making it more difficult to support VCR functions that are required for interactive video-on- demand systems. In this paper, we introduce a rate- constrained bandwidth smoothing algorithm for the delivery of stored video that, given a fixed maximum bandwidth rate minimizes both the smoothing buffer requirements as well as the buffer residency requirements. By minimizing the buffer residency times, the clients and servers can remain more tightly coupled making VCR functions easier to support. A comparison between the rate-constrained bandwidth smoothing algorithm and other bandwidth smoothing algorithms is presented using a compressed full-length movie.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. --- paper_title: Critical Bandwidth Allocation for the Delivery of Compressed Video paper_content: The transportation of compressed video data without loss of picture quality requires the network to support large fluctuations in bandwidth requirements. These fluctuations can be smoothed, but straightforward approaches to smoothing can still suffer from excessive buffering requirements, poor buffer utilization and an excessive number of bandwidth changes. This paper introduces critical bandwidth allocation, which reduces the number of bandwidth changes to a very small number, and achieves the maximum effectiveness from clientside buffers. A comparison between critical bandwidth allocation algorithms and other smoothing algorithms is presented, the sensitivity of the algorithm to jitter is examined, and implications for the design of network services are discussed. --- paper_title: An optimal bandwidth allocation strategy for the delivery of compressed prerecorded video paper_content: The transportation of prerecorded, compressed video data without loss of picture quality requires the network and video servers to support large fluctuations in bandwidth requirements. Fully utilizing a client-side buffer for smoothing bandwidth requirements can limit the fluctuations in bandwidth required from the underlying network and the video-on-demand servers. This paper shows that, for a fixed-size buffer constraint, the critical bandwidth allocation technique results in plans for continuous playback of stored video that have (1) the minimum number of bandwidth increases, (2) the smallest peak bandwidth requirements, and (3) the largest minimum bandwidth requirements. In addition, this paper introduces an optimal bandwidth allocation algorithm which, in addition to the three critical bandwidth allocation properties, minimizes the total number of bandwidth changes necessary for continuous playback. A comparison between the optimal bandwidth allocation algorithm and other critical bandwidth-based algorithms using 17 full-length movie videos and 3 seminar videos is also presented. ---	Title: A Survey of Application Layer Techniques for Adaptive Streaming Of Multimedia Section 1: Introduction Description 1: Provide an overview of the need for adaptive multimedia streaming and the current challenges in supporting multimedia applications over heterogeneous and variable network conditions. Section 2: Compression Level Methods Description 2: Discuss various video compression algorithms and their features which are useful for adaptation, including discrete cosine transformation (DCT), wavelet transforms, and proprietary methods. Section 3: MPEG Compression Standard Description 3: Describe the MPEG set of standards used for video compression, focusing on MPEG-2, including its encoding techniques, types of frames (I, P, B), and adaptability features. Section 4: Wavelet Encoding Description 4: Explore wavelet compression methods, their approaches for video encoding, and the scalability features they support. Section 5: Proprietary Methods Description 5: Cover commercial applications that use proprietary compression and adaptation methods, such as RealVideo and Intel's Indeo. Section 6: Application Streaming Description 6: Discuss adaptive streaming techniques at the application level, including layered encoding, adaptive error control, adaptive synchronization, and smoothing. Section 7: Layered Encoding Description 7: Explain the methodology and benefits of layered encoding for video streaming, including network feedback mechanisms. Section 8: Receiver Driven Multicast Description 8: Describe receiver-driven layered multicast (RLM) and its approach to adapting the transmission of layered video through network feedback. Section 9: Rate Shaping Description 9: Examine rate shaping techniques which adjust the traffic rate generated by the video encoder according to current network conditions using feedback mechanisms. Section 10: Error Control Description 10: Discuss methods to mitigate errors and packet loss, including Automatic Repeat Request (ARQ) and Forward Error Correction (FEC). Section 11: Adaptive FEC for Internet Audio Description 11: Analyze an adaptive FEC-based error control scheme for interactive audio in the Internet. Section 12: Adaptive FEC for Internet Video Description 12: Discuss an adaptive FEC-based error control scheme specifically designed for Internet video. Section 13: Adaptive Synchronization Description 13: Explain methods for adaptive synchronization to solve both intramedia and intermedia synchronization issues in multimedia applications. Section 14: Smoothing Description 14: Explore techniques for smoothing or shaping the video information transmitted to mitigate rate variations of multimedia applications. Section 15: Smoothing Algorithms Description 15: Discuss various algorithms for smoothing compressed video streams to ensure continuous and optimal playback at the client side. Section 16: Online Smoothing Description 16: Examine online smoothing algorithms for live video applications that reduce the resource variability using window-based approaches. Section 17: Proactive Buffering Description 17: Describe rate-constrained bandwidth smoothing techniques for stored video and how they proactively manage buffers and bandwidth. Section 18: Bridging Bandwidth Smoothing and Adaptation Techniques Description 18: Discuss how combining reactive and passive techniques can optimize video quality over best-effort networks by utilizing a priori information. Section 19: Example Adaptive Applications Description 19: Provide examples of commercial adaptive applications such as Real Network Solutions, highlighting the adaptive streaming techniques they employ. Section 20: Operating System Support for Adaptive Multimedia Description 20: Discuss operating system-level support necessary for adaptive multimedia streaming, such as integrated CPU and network-I/O QoS management and adaptive rate-controlled scheduling. Section 21: Related Work Description 21: Present a summary of related research efforts, focusing on techniques that support multimedia streaming and identifying potential for adaptation. Section 22: Summary Description 22: Summarize key points from the survey, emphasizing the importance of adaptive techniques at different layers and the potential benefits of low-level network feedback.
Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review	16	--- paper_title: Improvements to the Community Land Model and their impact on the hydrological cycle: COMMUNITY LAND MODEL HYDROLOGY paper_content: [1] The Community Land Model version 3 (CLM3) is the land component of the Community Climate System Model (CCSM). CLM3 has energy and water biases resulting from deficiencies in some of its canopy and soil parameterizations related to hydrological processes. Recent research by the community that utilizes CLM3 and the family of CCSM models has indicated several promising approaches to alleviating these biases. This paper describes the implementation of a selected set of these parameterizations and their effects on the simulated hydrological cycle. The modifications consist of surface data sets based on Moderate Resolution Imaging Spectroradiometer products, new parameterizations for canopy integration, canopy interception, frozen soil, soil water availability, and soil evaporation, a TOPMODEL-based model for surface and subsurface runoff, a groundwater model for determining water table depth, and the introduction of a factor to simulate nitrogen limitation on plant productivity. The results from a set of offline simulations were compared with observed data for runoff, river discharge, soil moisture, and total water storage to assess the performance of the new model (referred to as CLM3.5). CLM3.5 exhibits significant improvements in its partitioning of global evapotranspiration (ET) which result in wetter soils, less plant water stress, increased transpiration and photosynthesis, and an improved annual cycle of total water storage. Phase and amplitude of the runoff annual cycle is generally improved. Dramatic improvements in vegetation biogeography result when CLM3.5 is coupled to a dynamic global vegetation model. Lower than observed soil moisture variability in the rooting zone is noted as a remaining deficiency. --- paper_title: Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission paper_content: Microwave radiometry at low frequencies (L-band: 1.4 GHz, 21 cm) is an established technique for estimating surface soil moisture and sea surface salinity with a suitable sensitivity. However, from space, large antennas (several meters) are required to achieve an adequate spatial resolution at L-band. So as to reduce the problem of putting into orbit a large filled antenna, the possibility of using antenna synthesis methods has been investigated. Such a system, relying on a deployable structure, has now proved to be feasible and has led to the Soil Moisture and Ocean Salinity (SMOS) mission, which is described. The main objective of the SMOS mission is to deliver key variables of the land surfaces (soil moisture fields), and of ocean surfaces (sea surface salinity fields). The SMOS mission is based on a dual polarized L-band radiometer using aperture synthesis (two-dimensional [2D] interferometer) so as to achieve a ground resolution of 50 km at the swath edges coupled with multiangular acquisitions. The radiometer will enable frequent and global coverage of the globe and deliver surface soil moisture fields over land and sea surface salinity over the oceans. The SMOS mission was proposed to the European Space Agency (ESA) in the framework of the Earth Explorer Opportunity Missions. It was selected for a tentative launch in 2005. The goal of this paper is to present the main aspects of the baseline mission and describe how soil moisture will be retrieved from SMOS data. --- paper_title: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics paper_content: A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplified closure in the error covariance equation, is completely eliminated. Open boundaries can be handled as long as the ocean model is well posed. Well-known numerical instabilities associated with the error covariance equation are avoided because storage and evolution of the error covariance matrix itself are not needed. The results are also better than what is provided by the extended Kalman filter since there is no closure problem and the quality of the forecast error statistics therefore improves. The method should be feasible also for more sophisticated primitive equation models. The computational load for reasonable accuracy is only a fraction of what is required for the extended Kalman filter and is given by the storage of, say, 100 model states for an ensemble size of 100 and thus CPU requirements of the order of the cost of 100 model integrations. The proposed method can therefore be used with realistic nonlinear ocean models on large domains on existing computers, and it is also well suited for parallel computers and clusters of workstations where each processor integrates a few members of the ensemble. --- paper_title: Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem paper_content: [1] Real-time groundwater flow modeling with filter methods is interesting for dynamical groundwater flow systems, for which measurement data in real-time are available. The Ensemble Kalman Filter (EnKF) approach is used here to update states together with parameters by adopting an augmented state vector approach. The performance of EnKF is investigated in a synthetic study with a two-dimensional transient groundwater flow model where (1) only the recharge rate is spatiotemporally variable, (2) only transmissivity is spatially variable with σlnT2 = 1.0 or (3) with σlnT2 = 2.7, and (4) both recharge rate and transmissivity are uncertain (a combination of (1) and (3)). The performance of EnKF for simultaneous state and parameter estimation in saturated groundwater flow problems is investigated in dependence of the number of stochastic realizations, the updating frequency and updating intensity of log-transmissivity, the amount of measurements in space and time, and the method (iterative versus noniterative EnKF), among others. Satisfactory results were also obtained if both transmissivity and recharge rate were uncertain. However, it was found that filter inbreeding is much more severe if hydraulic heads and transmissivities are jointly updated than if only hydraulic heads are updated. The filter inbreeding problem was investigated in more detail and could be strongly reduced with help of a damping parameter, which limits the intensity of the perturbation of the log-transmissivity field. An additional reduction of filter inbreeding could be achieved by combining two measures: (1) inflating the elements of the predicted state covariance matrix on the basis of a comparison between the model uncertainty and the observed errors at the measurement points and (2) starting the flow simulations with a very large number of realizations and then sampling the desired number of realizations after one simulation time step by minimizing the differences between the local cpdfs (and bivariate cpdfs) of hydraulic head for the large ensemble and the corresponding cpdfs for the reduced ensemble. The two measures, which cause very limited CPU costs, allowed making 100 stochastic realizations for the reproduction of the states as efficient as 200–500 untreated stochastic realizations. --- paper_title: Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data paper_content: Received 21 October 2008; revised 2 September 2009; accepted 16 September 2009; published 13 February 2010. [1] A distributed physically based model incorporating novel approaches for the representation of surface-subsurface processes and interactions is presented. A path-based description of surface flow across the drainage basin is used, with several options for identifying flow directions, for separating channel cells from hillslope cells, and for representing stream channel hydraulic geometry. Lakes and other topographic depressions are identified and specially treated as part of the preprocessing procedures applied to the digital elevation data for the catchment. Threshold-based boundary condition switching is used to partition potential (atmospheric) fluxes into actual fluxes across the land surface and changes in surface storage, thus resolving the exchange fluxes, or coupling, between the surface and subsurface modules. Nested time stepping allows smaller steps to be taken for typically faster and explicitly solved surface runoff routing, while a mesh coarsening option allows larger grid elements to be used for typically slower and more compute-intensive subsurface flow. Sequential data assimilation schemes allow the model predictions to be updated with spatiotemporal observation data of surface and subsurface variables. These approaches are discussed in detail, and the physical and numerical behavior of the model is illustrated over catchment scales ranging from 0.0027 to 356 km 2 , addressing different hydrological processes and highlighting the importance of describing coupled surfacesubsurface flow. --- paper_title: A Network of Terrestrial Environmental Observatories in Germany paper_content: Multicompartment and multiscale long-term observation and research are important prerequisites to tackling the scientific challenges resulting from climate and global change. Long-term monitoring programs are cost intensive and require high analytical standards, however, and the gain of knowledge often requires longer observation times. Nevertheless, several environmental research networks have been established in recent years, focusing on the impact of climate and land use change on terrestrial ecosystems. From 2008 onward, a network of Terrestrial Environmental Observatories (TERENO) has been established in Germany as an interdisciplinary research program that aims to observe and explore the long-term ecological, social, and economic impacts of global change at the regional level. State-of-the-art methods from the field of environmental monitoring, geophysics, and remote sensing will be used to record and analyze states and fluxes for different environmental compartments from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. --- paper_title: Radiance data assimilation for operational snow and streamflow forecasting paper_content: Estimation of seasonal snowpack, in mountainous regions, is crucial for accurate streamflow prediction. This paper examines the ability of data assimilation (DA) of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center (NWSRFC) are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the ensemble Kalman filter (EnKF) and the particle filter (PF), is made using a coupled SNOW17 and the microwave emission model for layered snow pack (MEMLS) model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the advanced microwave scanning radiometer-earth observing system (AMSR-E) at the 36.5 GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting. --- paper_title: An adjoint data assimilation approach for estimating parameters in a three-dimensional ecosystem model paper_content: In this paper an ecosystem model, including phytoplankton, zooplankton, nitrate, ammonium, phosphate and detritus, is described. The model is driven by physical fields derived from a three-dimensional physical transport model. Simulation includes nitrate input from a river. Simulated results are then sampled and the sampled data are used in sequential numerical experiments to assess the ability of using an adjoint data assimilation approach for estimating the poorly known parameters of the ecosystem model, such as growth and death rate, half-saturation constant of nutrients, etc. Data with different spatial and temporal resolution over 1 week are assimilated into the ecosystem model. Assimilation of data at 30 grid stations with a sampling interval of 6 h is proved to be adequate for recovering all the parameters of the ecosystem model. Both the spatial and temporal resolution of the data are mutually complementary in the assimilative model. Thus, improvement of either of them can result in improvement of model parameter recoveries. The assimilation of phytoplankton data is essential to recover the model parameters. Phytoplankton is the core of the food web and without the information on phytoplankton, the structure of the ecosystem cannot be constructed correctly. The adjoint method can work well with the noisy data. In the twin experiments with noisy data, the parameters can be recovered but the error is increased. The results of the model and parameter recovery are sensitive to the initial conditions of state variables, so the determination of the initial condition is as important as that of the model parameter. The spatial and temporal resolution and the data type of the observations in Analysis and Modelling Research of the Ecosystem in the Bohai Sea (AMREB) are suitable for the recovery of the model parameters used in this study. --- paper_title: Calibration of the SUCROS emergence and early growth module for sugar beet using optical remote sensing data assimilation paper_content: Crop models are useful for monitoring crop production on a local scale. Their application to a larger area, such as a region, is hampered by the difficulty in determining the value of some of their parameters, which may differ greatly between fields. The use of optical remote sensing helps to overcome this problem. Coupling a radiation transfer model to a crop model makes it possible to simulate reflectance for those times in crop growth for which remote sensing data are available. The inversion of the combined model on these data then makes it possible to estimate new values for certain sensitive parameters of the crop model. This paper describes the use of such a method on a local scale, for sugar beet, focusing on the parameters describing emergence and early crop growth. These processes vary greatly depending on the soil, climate and seedbed preparation, and affect yield significantly. The SUCROS crop model and the SAIL reflectance model were combined. The resulting model was calibrated under standard conditions and then evaluated under test conditions to which the emergence and early growth parameters of the SUCROS model were adjusted. The test conditions seedbed structure was coarser, and the sowing depth was greater than expected. Consequently, emergence occurred later, and the initial leaf area was smaller. The SUCROS simulation using standard values for emergence and early growth parameters did not accurately predict crop growth under these test conditions. The inversion of the combined model using a set of canopy reflectance measurements during crop establishment provided new parameter values that allowed us to accurately estimate crop yield. Application of this method on a regional scale, for yield prediction or agronomic diagnosis, should be of great value. --- paper_title: FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem–Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities paper_content: FLUXNET is a global network of micrometeorological flux measurement sites that measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere. At present over 140 sites are operating on a long-term and continuous basis. Vegetation under study includes temperate conifer and broadleaved (deciduous and evergreen) forests, tropical and boreal forests, crops, grasslands, chaparral, wetlands, and tundra. Sites exist on five continents and their latitudinal distribution ranges from 70°N to 30°S. FLUXNET has several primary functions. First, it provides infrastructure for compiling, archiving, and distributing carbon, water, and energy flux measurement, and meteorological, plant, and soil data to the science community. (Data and site information are available online at the FLUXNET Web site, http://www-eosdis.ornl.gov/FLUXNET/.) Second, the project supports calibration and flux intercomparison activities. This activity ensures that data from the regional networks are intercomparable. And third, FLUXNET supports the synthesis, discussion, and communication of ideas and data by supporting project scientists, workshops, and visiting scientists. The overarching goal is to provide information for validating computations of net primary productivity, evaporation, and energy absorption that are being generated by sensors mounted on the NASA Terra satellite. Data being compiled by FLUXNET are being used to quantify and compare magnitudes and dynamics of annual ecosystem carbon and water balances, to quantify the response of stand-scale carbon dioxide and water vapor flux densities to controlling biotic and abiotic factors, and to validate a hierarchy of soil–plant–atmosphere trace gas exchange models. Findings so far include 1) net CO 2 exchange of temperate broadleaved forests increases by about 5.7 g C m −2 day −1 for each additional day that the growing season is extended; 2) the sensitivity of net ecosystem CO 2 exchange to sunlight doubles if the sky is cloudy rather than clear; 3) the spectrum of CO 2 flux density exhibits peaks at timescales of days, weeks, and years, and a spectral gap exists at the month timescale; 4) the optimal temperature of net CO 2 exchange varies with mean summer temperature; and 5) stand age affects carbon dioxide and water vapor flux densities. --- paper_title: Automatic state updating for operational streamflow forecasting via variational data assimilation. paper_content: Summary In operational hydrologic forecasting, to account for errors in the initial and boundary conditions, and in parameters and structures of the hydrologic models, the forecasters routinely make adjustments in real-time to the hydrometeorological input, hydrologic model states and, in certain cases, model parameters based on streamflow observations. Though a great deal of effort has been made in recent years to automate such “run-time modifications” (MOD) by human forecasters to a possible extent, automatic state updating of hydrologic models is yet to be widely accepted or routinely practiced in operational hydrology for a range of reasons. In this paper, we describe a state updating procedure intended specifically for operational streamflow forecasting for gauged headwater basins, and compare its performance with human forecaster MOD through a real-time forecasting experiment. The procedure is based on variational assimilation (VAR) of streamflow, precipitation and potential evaporation (PE) data into lumped soil moisture accounting and routing models operating at a 1-h timestep. The procedure has been in experimental operation since 2003 at the National Weather Service’s (NWS) West Gulf River Forecast Center (WGRFC) in Fort Worth, TX. Also described is a novel parameter estimation and optimization tool, the Adjoint-Based OPTimizer (AB_OPT), used for lumped hydrologic modeling at a 1-h timestep necessary for VAR. --- paper_title: Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach paper_content: Abstract. Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown. --- paper_title: Novel approach to nonlinear/non-Gaussian Bayesian state estimation paper_content: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linear- ity or Gaussian noise: it may be applied to any state transition or measurement model. A simula- tion example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter. --- paper_title: Data assimilation methods in the Earth sciences paper_content: Abstract Although remote sensing data are often plentiful, they do not usually satisfy the users’ needs directly. Data assimilation is required to extract information about geophysical fields of interest from the remote sensing observations and to make the data more accessible to users. Remote sensing may provide, for example, measurements of surface soil moisture, snow water equivalent, snow cover, or land surface (skin) temperature. Data assimilation can then be used to estimate variables that are not directly observed from space but are needed for applications, for instance root zone soil moisture or land surface fluxes. The paper provides a brief introduction to modern data assimilation methods in the Earth sciences, their applications, and pertinent research questions. Our general overview is readily accessible to hydrologic remote sensing scientists. Within the general context of Earth science data assimilation, we point to examples of the assimilation of remotely sensed observations in land surface hydrology. --- paper_title: Provision of snow water equivalent from satellite data and the hydrological model PROMET using data assimilation techniques paper_content: Information on snow cover and snow properties is an important factor for hydrology and runoff modelling. Frequent updates of snow cover information can help to improve water balance and discharge calculations. Within the frame of polar view, snow products from multisensoral satellite data are operationally provided to control and update water balance models for large parts of Southern Germany. Optical AVHRR sensors of the NOAA satellite are used for snow mapping and snow line delineation. Although these acquisitions are available several times per day, cloud cover hinders frequent updates of snow cover maps. As an additional remote sensing data source microwave data from ASAR on ENVISAT is used. Since C-band SAR sensors are only sensitive to snow with a high content of liquid water, the application of ASAR is limited to the melting periods. However under these conditions the developed procedure allows not only to delineate the snow cover in a comparable way as from optical data, also the additional information where the snow is melting is provided. In order to demonstrate how the remote sensing products can be used for improved water balance modelling, an application example for the watershed of the Upper Danube will be presented. This testsite is the research area of the integrative research project GLOWA-DANUBE that is conducted by the University of Munich. Model results using the PROMET-model of snow distributions with and without data assimilation of the remote sensing products will be given. Developed data assimilation concepts will be presented. Through data assimilation, the modelled snow cover agrees better with the mapped snow cover information from satellite. The optimised model provides maps of snow water equivalent, that can not directed be assessed by remote sensing. The impact of data assimilation on the modelled runoff will thus further be analysed. --- paper_title: Estimating transpiration and the sensitivity of carbon uptake to water availability in a subalpine forest using a simple ecosystem process model informed by measured net CO2 and H2O fluxes paper_content: Modeling how the role of forests in the carbon cycle will respond to predicted changes in water availability hinges on an understanding of the processes controlling water use in ecosystems. Recent studies in forest ecosystem modeling have employed data-assimilation techniques to generate parameter sets that conform to observations, and predict net ecosystem CO2 exchange (NEE) and its component processes. Since the carbon and water cycles are linked, there should be additional process information available from ecosystem H2O exchange. We coupled SIPNET (Simple Photosynthesis EvapoTranspiration), a simplified model of ecosystem function, with a data-assimilation system to estimate parameters leading to model predictions most closely matching the net CO2 and H2O fluxes measured by eddy covariance in a high-elevation, subalpine forest ecosystem. When optimized using measurements of CO2 exchange, the model matched observed NEE (RMSE = 0.49 g C m−2) but underestimated transpiration calculated independently from sap flow measurements by a factor of 4. Consequently, the carbon-only optimization was insensitive to imposed changes in water availability. Including eddy flux data from both CO2 and H2O exchange to the optimization reduced the model fit to the observed NEE fluxes only slightly (RME = 0.53 g C m−2), however this parameterization also reproduced transpiration calculated from independent sap flow measurements (r2 = 0.67, slope = 0.6). A significant amount of information can be extracted from simultaneous analysis of CO2 and H2O exchange, which improved the accuracy of transpiration estimates from measured evapotranspiration. Conversely, failure to include both CO2 and H2O data streams can generate results that mask the responses of ecosystem carbon cycling to variation in the precipitation. In applying the model conditioned on both CO2 and H2O fluxes to the subalpine forest at the Niwot Ridge AmeriFlux site, we observed that the onset of transpiration is coincident with warm soil temperatures. However, after snow has covered the ground in the fall, we observed significant inter-annual variability in the fraction of evapotranspiration composed of transpiration; evapotranspiration was dominated by transpiration in years when late fall air temperatures were high enough to maintain photosynthesis, but by sublimation from the surface of the snowpack in years when late fall air temperatures were colder and forest photosynthetic activity had ceased. Data-assimilation techniques and simultaneous measurements of carbon and water exchange can be used to quantify the response of net carbon uptake to changes in water availability by using an ecosystem model where the carbon and water cycles are linked. --- paper_title: Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter paper_content: Summary In a synthetic study we explore the potential of using surface soil moisture measurements obtained from different satellite platforms to retrieve soil moisture profiles and soil hydraulic properties using a sequential data assimilation procedure and a 1D mechanistic soil water model. Four different homogeneous soil types were investigated including loamy sand, loam, silt, and clayey soils. The forcing data including precipitation and potential evapotranspiration were taken from the meteorological station of Aachen (Germany). With the aid of the forward model run, a synthetic data set was designed and observations were generated. The virtual top soil moisture observations were then assimilated to update the states and hydraulic parameters of the model by means of a particle filtering data assimilation method. Our analyses include the effect of assimilation strategy, measurement frequency, accuracy in surface soil moisture measurements, and soils differing in textural and hydraulic properties. With this approach we were able to assess the value of periodic spaceborne observations of top soil moisture for soil moisture profile estimation and identify the adequate conditions (e.g. temporal resolution and measurement accuracy) for remotely sensed soil moisture data assimilation. Updating of both hydraulic parameters and state variables allowed better predictions of top soil moisture contents as compared with updating of states only. An important conclusion is that the assimilation of remotely-sensed top soil moisture for soil hydraulic parameter estimation generates a bias depending on the soil type. Results indicate that the ability of a data assimilation system to correct the soil moisture state and estimate hydraulic parameters is driven by the non linearity between soil moisture and pressure head. --- paper_title: Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter paper_content: Abstract Proper estimation of initial state variables and model parameters are vital importance for determining the accuracy of numerical model prediction. In this work, we develop a one-dimensional land data assimilation scheme based on ensemble Kalman filter and Common Land Model version 3.0 (CoLM). This scheme is used to improve the estimation of soil temperature profile. The leaf area index (LAI) is also updated dynamically by MODIS LAI production and the MODIS land surface temperature (LST) products are assimilated into CoLM. The scheme was tested and validated by observations from four automatic weather stations (BTS, DRS, MGS, and DGS) in Mongolian Reference Site of CEOP during the period of October 1, 2002 to September 30, 2003. Results indicate that data assimilation improves the estimation of soil temperature profile about 1 K. In comparison with simulation, the assimilation results of soil heat fluxes also have much improvement about 13 W m− 2 at BTS and DGS and 2 W m− 2 at DRS and MGS, respectively. In addition, assimilation of MODIS land products into land surface model is a practical and effective way to improve the estimation of land surface variables and fluxes. --- paper_title: The importance of the spatial patterns of remotely sensed soil moisture in the improvement of discharge predictions for small-scale basins through data assimilation paper_content: Abstract In this paper, we investigate to which degree information concerning the spatial patterns of remotely sensed soil moisture data are needed in order to improve discharge predictions from hydrological models. For this purpose, we use the TOPMODEL-based Land–Atmosphere Transfer Scheme (TOPLATS). The remotely sensed soil moisture values are determined using C-band backscatter data from the European Space Agency (ESA) European Remote Sensing (ERS) Satellites. A baseline run, without soil moisture assimilation, is established for both the distributed and lumped versions of the land–atmosphere scheme. The modeled discharge matches the observations slightly better for the distributed model than for the lumped model. The remotely sensed soil moisture data are assimilated into the distributed version of the model through the ‘nudging to individual observations’ method, and the ‘statistical correction assimilation’ method. The remotely sensed soil moisture data are also assimilated into the lumped version of the model through the ‘statistical correction assimilation’ method. The statistical correction assimilation method leads to similar, and improved, discharge predictions for both the distributed and lumped models. The nudging to individual observations method leads, for the distributed model, to only slightly better results than the statistical correction assimilation method. As a consequence, it is suggested that it is sufficient to assimilate the statistics (spatial mean and variance) of remotely sensed soil moisture data into lumped hydrological models when one wants to improve hydrological model-based discharge predictions. --- paper_title: The representation of soil moisture freezing and its impact on the stable boundary layer paper_content: The 1993 to 1996 version of the European Centre for Medium-Range Weather Forecasts model had a pronounced near-surface cold bias in winter over continental areas. the problem is illustrated in detail with help of tower observations. It is shown that a positive feedback exists in the land surface boundary-layer coupling that has the potential to amplify model biases. If the surface is cooled too much the boundary layer becomes too stable, reducing the downward heat flux and making the surface even colder. This positive feedback is believed to be stronger in the model than in the real atmosphere, resulting in diurnal temperature cycles that are too large and in excessive soil cooling on a seasonal time-scale in winter. ::: ::: ::: ::: An important contributor to the excessive winter cooling turns out to be the lack of soil moisture freezing in the model. the importance of this process is obvious from soil temperature observations. the seasonal soil temperature evolution shows a clear ‘barrier’ at 0°C due to the thermal inertia of freezing and thawing. A more quantitative illustration is the result of a simple calculation. This shows that the amount of energy necessary to freeze/thaw 1 m3 of wet soil, would cool/warm this soil by about 50 K if the phase transition was not taken into account. ::: ::: ::: ::: To reduce the winter cold bias in the model, three model changes have been tested and are described: (i) the introduction of the process of soil moisture freezing; (ii) revised stability functions to increase the turbulent diffusion of heat in stable situations; and (iii) an increase of the skin-layer conductivity. the effect of these changes on the seasonal evolution of soil and 2 m temperatures is investigated with long runs that have terms that relax towards the operational analysis above the boundary layer. In this way the impact can be studied on the temperature forecasts for the winter of 1995/1996, during which the operational model showed considerable soil temperature drift over Europe. Also, short periods of data assimilation (including 10-day forecasts) have been carried out to study the diurnal time-scales and the impact on model performance. ::: ::: ::: ::: The model changes eliminate to a large extent the systematic 2 m temperature biases for the winter of 1995/1996 over Europe and make the soil temperature evolution much more realistic. the soil moisture freezing, in particular, plays a crucial role by introducing thermal inertia near the freezing point, thereby reducing the annual temperature cycle in the soil. the process of soil moisture freezing leads to a considerable warming of the model's near-surface winter climate over continental areas. --- paper_title: The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN) paper_content: Abstract Surface soil moisture plays an important role in the dynamics of land–atmosphere interactions and many current and upcoming models and satellite sensors. In situ data will be required to provide calibration and validation datasets. Therefore, there is a need for sensor networks at a variety of scales that provide near-real-time soil moisture and temperature data combined with other climate information for use in natural resource planning, drought assessment, water resource management, and resource inventory. The U.S. Department of Agriculture (USDA)–Natural Resources Conservation Service (NRCS)–National Water and Climate Center has established a continental-scale network to address this need, called the Soil Climate Analysis Network (SCAN). This ever-growing network has more than 116 stations located in 39 states, most of which have been installed since 1999. The stations are remotely located and collect hourly atmospheric, soil moisture, and soil temperature data that are available to the public... --- paper_title: Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting paper_content: Abstract Operational flood forecasting requires that accurate estimates of the uncertainty associated with model-generated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimization and data assimilation method (SODA), uses an ensemble Kalman filter (EnKF) for recursive state estimation allowing for treatment of streamflow data error, model structural error, and parameter uncertainty, while enabling implementation of the Sacramento model without major modification to its current structural form. Model parameters are estimated in batch using the shuffled complex evolution metropolis stochastic-ensemble optimization approach (SCEM-UA). The SODA approach was implemented using parallel computing to handle the increased computational requirements. Studies using data from the Leaf River... --- paper_title: Dual state-parameter estimation of hydrological models using ensemble Kalman filter paper_content: Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state–parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. 2004 Elsevier Ltd. All rights reserved. --- paper_title: Global Products Of Vegetation Leaf Area And Fraction Absorbed Par From Year One Of Modis Data paper_content: An algorithm based on the physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from surface reflectances was developed and implemented for operational processing prior to the launch of the moderate resolution imaging spectroradiometer (MODIS) aboard the TERRA platform in December of 1999. The performance of the algorithm has been extensively tested in prototyping activities prior to operational production. Considerable attention was paid to characterizing the quality of the product and this information is available to the users as quality assessment (QA) accompanying the product. The MODIS LAI/FPAR product has been operationally produced from day one of science data processing from MODIS and is available free of charge to the users from the Earth Resources Observation System (EROS) Data Center Distributed Active Archive Center. Current and planned validation activities are aimed at evaluating the product at several field sites representative of the six structural biomes. Example results illustrating the physics and performance of the algorithm are presented together with initial QA and validation results. Potential users of the product are advised of the provisional nature of the product in view of changes to calibration, geolocation, cloud screening, atmospheric correction and ongoing validation activities. D 2002 Published by Elsevier Science Inc. --- paper_title: A data assimilation technique applied to a predator-prey model paper_content: A new approach for data assimilation, which is based on the adjoint method, but allows the computer code for the adjoint to be constructed directly from the model computer code, is described. This technique is straightforward and reduces the chance of introducing errors in the construction of the adjoint code. Implementation of the technique is illustrated by applying it to a simple predator-prey model in a model fitting mode. A series of identical twin numerical experiments are used to show that this data assimilation approach can successfully recover model parameters as well as initial conditions. However, the ease with which these values are recovered is dependent on the form of the model equations as well as on the type and amount of data that are available. Additional numerical experiments show that sufficient coefficient and parameter recoveries are possible even when the assimilated data contain significant random noise. Thus, for biological systems that can be described by ecosystem models, the adjoint method represents a powerful approach for estimating values for little-known biological parameters, such as initial conditions, growth rates, and mortality rates. --- paper_title: Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model paper_content: Abstract This paper presents a method to monitor the dynamics of herbaceous vegetation in the Sahel. The approach is based on the assimilation of Normalized Difference Vegetation Index (NDVI) data acquired by the VEGETATION instrument on board SPOT 4/5 into a simple sahelian vegetation dynamics model. The study region is located in the Gourma region of Mali. The vegetation dynamics model is coupled with a radiative transfer model (the SAIL model). First, it is checked that the coupled models allow for a realistic simulation of the seasonal and interannual variability of NDVI over three sampling sites from 1999 to 2004. The data assimilation scheme relies on a parameter identification technique based on an Evolution Strategies algorithm. The simulated above-ground herbage mass resulting from NDVI assimilation is then compared to ground measurements performed over 13 study sites during the period 1999–2004. The assimilation scheme performs well with 404 kg DM/ha of average error ( n = 126 points) and a correlation coefficient of r = 0.80 (to be compared to the 463 kg DM/ha and r = 0.60 of the model performance without data assimilation). Finally, the sensitivity of the herbage mass model estimates to the quality of the meteorological forcing (rainfall and net radiation) is analyzed thanks to a stochastic approach. --- paper_title: Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations paper_content: Abstract The monitoring of snow water equivalent (SWE) and snow depth (SD) in boreal forests is investigated by applying space-borne microwave radiometer data and synoptic snow depth observations. A novel assimilation technique based on (forward) modelling of observed brightness temperatures as a function of snow pack characteristics is introduced. The assimilation technique is a Bayesian approach that weighs the space-borne data and the reference field on SD interpolated from discrete synoptic observations with their estimated statistical accuracy. The results obtained using SSM/I and AMSR-E data for northern Eurasia and Finland indicate that the employment of space-borne data using the assimilation technique improves the SD and SWE retrieval accuracy when compared with the use of values interpolated from synoptic observations. Moreover, the assimilation technique is shown to reduce systematic SWE/SD estimation errors evident in the inversion of space-borne radiometer data. --- paper_title: Surface temperature and water vapour retrieval from MODIS data paper_content: This paper gives operational algorithms for retrieving sea (SST), land surface temperature (LST) and total atmospheric water vapour content (W) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. To this end, the MODTRAN 3.5 radiative transfer program was used to predict radiances for MODIS channels 31, 32, 2, 17, 18 and 19. To analyse atmospheric effects, a simulation with a set of radiosonde observations was used to cover the variability of surface temperature and water vapour concentration on a worldwide scale. These simulated data were split into two sets (DB1 and DB2), the first one (DB1) was used to fit the coefficients of the algorithms, while the second one (DB2) was used to test the fitted coefficients. The results show that the algorithms are capable of producing SST and LST with a standard deviation of 0.3 K and 0.7 K if the satellite data are error free. The LST product has been validated with in situ data from a field campaign carried out in the Mississippi (USA), the results sh... --- paper_title: A strategy for operational implementation of 4D‐Var, using an incremental approach paper_content: SUMMARY An order of magnitude reduction in the cost of fourdimensional variational assimilation (4D-Var) is required before operational implementation is possible. Reconditioning is considered and, although it offers a signi6cant reduction in cost, it seems that it is unlikely to provide a reduction as large as an order of magnitude. An approximation to 4D-Var, namely the incremental approach, is then considered and is shown to produce the same result at the end of the assimilation window as an extended Kalman filter in which no approximations are made in the assimilating model but in which instead a simplitied evolution of the forecast error is introduced. This approach provides the flexibility for a cost-benefit trade-off of 4D-Var to be made. The development of variational four-dimensional assimilation (4D-Var) from the stage of being a theoretical possibility to being a practical reality is progressing at a rapid pace. The first results of four-dimensional variational assimilation using real observations were provided by Thbpaut et al. (1993b) using an adiabatic primitive-equation model at truncations "21 and T42. More recently Andersson ef al. (1994) used 4D-Var with a T63 model to assimilate remotely-sensed data such as infrared and microwave TOVS radiance measurements, while Thdpaut et d. (1993a) used 4D-Var with the same model to assimilate normalized radar backscatter cross-section measurements from the ERS-1 scatterometer. --- paper_title: A mechanistic modelling and data assimilation approach to estimate the carbon / chlorophyll and carbon / nitrogen ratios in a coupled hydrodynamical-biological model paper_content: The principal objective of hydrodynamical-biological models is to provide estimates of the main carbon fluxes such as total and export oceanic production. These models are nitrogen based, that is to say that the variables are expressed in terms of their nitrogen content. Moreover models are calibrated using chlorophyll data sets. Therefore carbon to chlorophyll (C:Chl) and carbon to nitrogen (C:N) ratios have to be assumed. This paper addresses the problem of the representation of these ratios. In a 1D framework at the DYFAMED station (NW Mediterranean Sea) we propose a model which enables the estimation of the basic biogeochemical fluxes and in which the spatio-temporal variability of the C:Chl and C:N ratios is fully represented in a mechanical way. This is achieved through the introduction of new state variables coming from the embedding of a phytoplankton growth model in a more classical Redfieldian NNPZD-DOM model (in which the C:N ratio is assumed to be a constant). Following this modelling step, the parameters of the model are estimated using the adjoint data assimilation method which enables the assimilation of chlorophyll and nitrate data sets collected at DYFAMED in 1997.Comparing the predictions of the new Mechanistic model with those of the classical Redfieldian NNPZD-DOM model which was calibrated with the same data sets, we find that both models reproduce the reference data in a comparable manner. Both fluxes and stocks can be equally well predicted by either model. However if the models are coinciding on an average basis, they are diverging from a variability prediction point of view. In the Mechanistic model biology adapts much faster to its environment giving rise to higher short term variations. Moreover the seasonal variability in total production differs from the Redfieldian NNPZD-DOM model to the Mechanistic model. In summer the Mechanistic model predicts higher production values in carbon unit than the Redfieldian NNPZD-DOM model. In winter the contrary holds. --- paper_title: Comparison of Data Assimilation Techniques for a Coupled Model of Surface and Subsurface Flow paper_content: Data assimilation in the geophysical sciences refers to methodologies to optimally merge model predictions and observations. The ensemble Kalman filter (EnKF) is a statistical sequential data assimilation technique explicitly developed for nonlinear filtering problems. It is based on a Monte Carlo approach that approximates the conditional probability densities of the variables of interest by a finite number of randomly generated model trajectories. In Newtonian relaxation or nudging (NN), which can be viewed as a special case of the classic Kalman filter, model variables are driven toward observations by adding to the model equations a forcing term, or relaxation component, that is proportional to the difference between simulation and observation. The forcing term contains four-dimensional weighting functions that can, ideally, incorporate prior knowledge about the characteristic scales of spatial and temporal variability of the state variable(s) being assimilated. In this study, we examined the EnKF and NN algorithms as implemented for a complex hydrologic model that simulates catchment dynamics, coupling a three-dimensional finite element Richards9 equation solver for variably saturated porous media and a finite difference diffusion wave approximation for surface water flow. We report on the retrieval performance of the two assimilation schemes for a small catchment in Belgium. The results of the comparison show that nudging, while more straightforward and less expensive computationally, is not as effective as the ensemble Kalman filter in retrieving the true system state. We discuss some of the strengths and weaknesses, both physical and numerical, of the NN and EnKF schemes. --- paper_title: Three-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: Simplified Kalman filter covariance forecasting and field application: THREE-DIMENSIONAL SOIL MOISTURE ASSIMILATION paper_content: [1] The Kalman filter data assimilation technique is applied to a distributed three-dimensional soil moisture model for retrieval of the soil moisture profile in a 6 ha catchment using near-surface soil moisture measurements. A simplified Kalman filter covariance forecasting methodology is developed based on forecasting of the state correlations and imposed state variances. This covariance forecasting technique, termed the modified Kalman filter, was then used in a 1 month three-dimensional field application. Two updating scenarios were tested: (1) updating every 2 to 3 days and (2) a single update. The data used were from the Nerrigundah field site, near Newcastle, Australia. This study demonstrates the feasibility of data assimilation in a quasi three-dimensional distributed soil moisture model, provided simplified covariance forecasting techniques are used. It also identifies that (1) the soil moisture profile cannot be retrieved from near-surface soil moisture measurements when the near-surface and deep soil layers become decoupled, such as during extreme drying events; (2) if simulation of the soil moisture profile is already good, the assimilation can result in a slight degradation, but if the simulation is poor, assimilation can yield a significant improvement; (3) soil moisture profile retrieval results are independent of initial conditions; and (4) the required update frequency is a function of the errors in model physics and forcing data. --- paper_title: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics paper_content: A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplified closure in the error covariance equation, is completely eliminated. Open boundaries can be handled as long as the ocean model is well posed. Well-known numerical instabilities associated with the error covariance equation are avoided because storage and evolution of the error covariance matrix itself are not needed. The results are also better than what is provided by the extended Kalman filter since there is no closure problem and the quality of the forecast error statistics therefore improves. The method should be feasible also for more sophisticated primitive equation models. The computational load for reasonable accuracy is only a fraction of what is required for the extended Kalman filter and is given by the storage of, say, 100 model states for an ensemble size of 100 and thus CPU requirements of the order of the cost of 100 model integrations. The proposed method can therefore be used with realistic nonlinear ocean models on large domains on existing computers, and it is also well suited for parallel computers and clusters of workstations where each processor integrates a few members of the ensemble. --- paper_title: Adaptive Ensemble Covariance Localization in Ensemble 4D-VAR State Estimation paper_content: An adaptive ensemble covariance localization technique, previously used in ‘‘local’’ forms of the ensemble Kalman filter, is extended to a global ensemble four-dimensional variational data assimilation (4D-VAR) scheme. The purely adaptive part of the localization matrix considered is given by the element-wise square of the correlation matrix of a smoothed ensemble of streamfunction perturbations. It is found that these purely adaptive localization functions have spurious far-field correlations as large as 0.1 with a 128-member ensemble. To attenuate the spurious features of the purely adaptive localization functions, the authors multiply the adaptive localization functions with very broadscale nonadaptive localization functions. Using the Navy’s operational ensemble forecasting system, it is shown that the covariance localization functions obtained by this approach adapt to spatially anisotropic aspects of the flow, move with the flow, and are free of far-field spurious correlations. The scheme is made computationally feasible by (i) a method for inexpensively generating the square root of an adaptively localized global four-dimensional error covariance model in terms of products or modulations of smoothed ensemble perturbations with themselves and with raw ensemble perturbations, and (ii) utilizing algorithms that speed ensemble covariance localization when localization functions are separable, variable-type independent, and/or large scale. In spite of the apparently useful characteristics of adaptive localization, single analysis/forecast experiments assimilating 583 200 observations over both 6- and 12-h data assimilation windows failed to identify any significant difference in the quality of the analyses and forecasts obtained using nonadaptive localization from that obtained using adaptive localization. --- paper_title: Novel approach to nonlinear/non-Gaussian Bayesian state estimation paper_content: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linear- ity or Gaussian noise: it may be applied to any state transition or measurement model. A simula- tion example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter. --- paper_title: Dual state-parameter estimation of hydrological models using ensemble Kalman filter paper_content: Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state–parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. 2004 Elsevier Ltd. All rights reserved. --- paper_title: Assimilation of Active Microwave Measurements for Soil Moisture Profile Retrieval Under Laboratory Conditions paper_content: The authors discuss the potential of retrieving information on the soil moisture profile from measurements of the surface soil moisture content through active microwave observations. They use active microwave observations of the surface soil moisture content in a data assimilation framework to show that this allows the retrieval of the entire soil moisture profile. The data assimilation procedure demonstrated is based on the Kalman filter technique. Kalman filtering allows reconstruction of the state vector when at least part of the state variables are observed regularly. The dynamic model of the system used is based on the 1D Richards equation. The observation equation is based on the integral equation model of A. K. Fung et al. (1992) and is used to link the radar observations to surface soil moisture content. Recently, M. Mancini et al. (1999) reported about laboratory experiments investigating the use of active microwave observations to estimate surface soil moisture content. The present authors apply the data assimilation scheme to the radar measurements of these experiments to retrieve the entire soil moisture profile in the soil sample used, and compare these results with the soil moisture profile measurements (using TDR). It is shown that with a limited number of radar measurements accurate retrieval of the entire soil moisture profile is possible. --- paper_title: Assessment of local hydraulic properties from electrical resistivity tomography monitoring of a three-dimensional synthetic tracer test experiment paper_content: [1] In recent years geophysical methods have become increasingly popular for hydrological applications. Time-lapse electrical resistivity tomography (ERT) represents a potentially powerful tool for subsurface solute transport characterization since a full picture of the spatiotemporal evolution of the process can be obtained. However, the quantitative interpretation of tracer tests is difficult because of the uncertainty related to the geoelectrical inversion, the constitutive models linking geophysical and hydrological quantities, and the a priori unknown heterogeneous properties of natural formations. Here an approach based on the Lagrangian formulation of transport and the ensemble Kalman filter (EnKF) data assimilation technique is applied to assess the spatial distribution of hydraulic conductivity K by incorporating time-lapse cross-hole ERT data. Electrical data consist of three-dimensional cross-hole ERT images generated for a synthetic tracer test in a heterogeneous aquifer. Under the assumption that the solute spreads as a passive tracer, for high Peclet numbers the spatial moments of the evolving plume are dominated by the spatial distribution of the hydraulic conductivity. The assimilation of the electrical conductivity 4D images allows updating of the hydrological state as well as the spatial distribution of K. Thus, delineation of the tracer plume and estimation of the local aquifer heterogeneity can be achieved at the same time by means of this interpretation of time-lapse electrical images from tracer tests. We assess the impact on the performance of the hydrological inversion of (i) the uncertainty inherently affecting ERT inversions in terms of tracer concentration and (ii) the choice of the prior statistics of K. Our findings show that realistic ERT images can be integrated into a hydrological model even within an uncoupled inverse modeling framework. The reconstruction of the hydraulic conductivity spatial distribution is satisfactory in the portion of the domain directly covered by the passage of the tracer. Aside from the issues commonly affecting inverse models, the proposed approach is subject to the problem of the filter inbreeding and the retrieval performance is sensitive to the choice of K prior geostatistical parameters. --- paper_title: An Ensemble Kalman Smoother for Nonlinear Dynamics paper_content: It is formally proved that the general smoother for nonlinear dynamics can be formulated as a sequential method, that is, observations can be assimilated sequentially during a forward integration. The general filter can be derived from the smoother and it is shown that the general smoother and filter solutions at the final time become identical, as is expected from linear theory. Then, a new smoother algorithm based on ensemble statistics is presented and examined in an example with the Lorenz equations. The new smoother can be computed as a sequential algorithm using only forward-in-time model integrations. It bears a strong resemblance with the ensemble Kalman filter. The difference is that every time a new dataset is available during the forward integration, an analysis is computed for all previous times up to this time. Thus, the first guess for the smoother is the ensemble Kalman filter solution, and the smoother estimate provides an improvement of this, as one would expect a smoother to do. The method is demonstrated in this paper in an intercomparison with the ensemble Kalman filter and the ensemble smoother introduced by van Leeuwen and Evensen, and it is shown to be superior in an application with the Lorenz equations. Finally, a discussion is given regarding the properties of the analysis schemes when strongly non-Gaussian distributions are used. It is shown that in these cases more sophisticated analysis schemes based on Bayesian statistics must be used. --- paper_title: Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation paper_content: Hydrologic models use relatively simple mathematical equations to conceptualize and aggregate the complex, spatially distributed, and highly interrelated water, energy, and vegetation processes in a watershed. A consequence of process aggregation is that the model parameters often do not represent directly measurable entities and must therefore be estimated using measurements of the system inputs and outputs. During this process, known as model calibration, the parameters are adjusted so that the behavior of the model approximates, as closely and consistently as possible, the observed response of the hydrologic system over some historical period of time. In practice, however, because of errors in the model structure and the input (forcing) and output data, this has proven to be difficult, leading to considerable uncertainty in the model predictions. This paper surveys the limitations of current model calibration methodologies, which treat the uncertainty in the input-output relationship as being primarily attributable to uncertainty in the parameters and presents a simultaneous optimization and data assimilation (SODA) method, which improves the treatment of uncertainty in hydrologic modeling. The usefulness and applicability of SODA is demonstrated by means of a pilot study using data from the Leaf River watershed in Mississippi and a simple hydrologic model with typical conceptual components. --- paper_title: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics paper_content: A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplified closure in the error covariance equation, is completely eliminated. Open boundaries can be handled as long as the ocean model is well posed. Well-known numerical instabilities associated with the error covariance equation are avoided because storage and evolution of the error covariance matrix itself are not needed. The results are also better than what is provided by the extended Kalman filter since there is no closure problem and the quality of the forecast error statistics therefore improves. The method should be feasible also for more sophisticated primitive equation models. The computational load for reasonable accuracy is only a fraction of what is required for the extended Kalman filter and is given by the storage of, say, 100 model states for an ensemble size of 100 and thus CPU requirements of the order of the cost of 100 model integrations. The proposed method can therefore be used with realistic nonlinear ocean models on large domains on existing computers, and it is also well suited for parallel computers and clusters of workstations where each processor integrates a few members of the ensemble. --- paper_title: Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data paper_content: Received 21 October 2008; revised 2 September 2009; accepted 16 September 2009; published 13 February 2010. [1] A distributed physically based model incorporating novel approaches for the representation of surface-subsurface processes and interactions is presented. A path-based description of surface flow across the drainage basin is used, with several options for identifying flow directions, for separating channel cells from hillslope cells, and for representing stream channel hydraulic geometry. Lakes and other topographic depressions are identified and specially treated as part of the preprocessing procedures applied to the digital elevation data for the catchment. Threshold-based boundary condition switching is used to partition potential (atmospheric) fluxes into actual fluxes across the land surface and changes in surface storage, thus resolving the exchange fluxes, or coupling, between the surface and subsurface modules. Nested time stepping allows smaller steps to be taken for typically faster and explicitly solved surface runoff routing, while a mesh coarsening option allows larger grid elements to be used for typically slower and more compute-intensive subsurface flow. Sequential data assimilation schemes allow the model predictions to be updated with spatiotemporal observation data of surface and subsurface variables. These approaches are discussed in detail, and the physical and numerical behavior of the model is illustrated over catchment scales ranging from 0.0027 to 356 km 2 , addressing different hydrological processes and highlighting the importance of describing coupled surfacesubsurface flow. --- paper_title: Hydrologic Data Assimilation with the Ensemble Kalman Filter paper_content: Soil moisture controls the partitioning of moisture and energy fluxes at the land surface and is a key variable in weather and climate prediction. The performance of the ensemble Kalman filter (EnKF) for soil moisture estimation is assessed by assimilating L-band (1.4 GHz) microwave radiobrightness observations into a land surface model. An optimal smoother (a dynamic variational method) is used as a benchmark for evaluating the filter’s performance. In a series of synthetic experiments the effect of ensemble size and non-Gaussian forecast errors on the estimation accuracy of the EnKF is investigated. With a state vector dimension of 4608 and a relatively small ensemble size of 30 (or 100; or 500), the actual errors in surface soil moisture at the final update time are reduced by 55% (or 70%; or 80%) from the value obtained without assimilation (as compared to 84% for the optimal smoother). For robust error variance estimates, an ensemble of at least 500 members is needed. The dynamic evolution of the estimation error variances is dominated by wetting and drying events with high variances during drydown and low variances when the soil is either very wet or very dry. Furthermore, the ensemble distribution of soil moisture is typically symmetric except under very dry or wet conditions when the effects of the nonlinearities in the model become significant. As a result, the actual errors are consistently larger than ensemble-derived forecast and analysis error variances. This suggests that the update is suboptimal. However, the degree of suboptimality is relatively small and results presented here indicate that the EnKF is a flexible and robust data assimilation option that gives satisfactory estimates even for moderate ensemble sizes. --- paper_title: Data assimilation for transient flow in geologic formations via ensemble Kalman filter paper_content: Formation properties are one of the key factors in numerical modeling of flow and transport in geologic formations in spite of the fact that they may not be completely characterized. The incomplete knowledge or uncertainty in the description of the formation properties leads to uncertainty in simulation results. In this study, the ensemble Kalman filter (EnKF) approach is used for continuously updating model parameters such as hydraulic conductivity and model variables such as pressure head while simultaneously providing an estimate of the uncertainty through assimilating dynamic and static measurements, without resorting to the explicit computation of the covariance or the Jacobian of the state variables. A two-dimensional example is built to demonstrate the capability of EnKF and to analyze its sensitivity with respect to different factors such as the number of realizations, measurement timings, and initial guesses. An additional example is given to illustrate the applicability of EnKF to three-dimensional problems and to examine the model predictability after dynamic data assimilation. It is found from these examples that EnKF provides an efficient approach for obtaining satisfactory estimation of the hydraulic conductivity field with dynamic measurements. After data assimilation the conductivity field matches the reference field very well, and different kinds of incorrect prior knowledge of the formation properties may also be rectified to a certain extent. --- paper_title: An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation paper_content: Summary The dynamical equations for multiphase flow in porous media are highly non-linear and the number of variables required to characterize the medium is usually large, often two or more variables per simulator gridblock. Neither the extended Kalman filter nor the ensemble Kalman filter is suitable for assimilating data or for characterizing uncertainty for this type of problem. Although the ensemble Kalman filter handles the nonlinear dynamics correctly during the forecast step, it sometimes fails badly in the analysis (or updating) of saturations. This paper focuses on the use of an iterative ensemble Kalman filter for data assimilation in nonlinear problems, especially of the type related to multiphase flow in porous media. Two issues are key: (1) iteration to enforce constraints and (2) ensuring that the resulting ensemble is representative of the conditional pdf (i.e. that the uncertainty quantification is correct). The new algorithm is compared to the ensemble Kalman filter on several highly nonlinear example problems, and shown to be superior in the prediction of uncertainty. --- paper_title: A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals paper_content: A number of recent studies have focused on enhancing runoff prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem from purely a state or parameter estimation perspective in which remotely-sensed soil moisture estimates are assimilated to improve the characterization of pre-storm soil moisture conditions in a hydrologic model, and consequently, its simulation of runoff response to subsequent rainfall. However, recent work has demonstrated that soil moisture retrievals can also be used to filter errors present in satellite-based rainfall accumulation products. This result implies that soil moisture retrievals have potential benefit for characterizing both antecedent moisture conditions (required to estimate sub-surface flow intensities and subsequent surface runoff efficiencies) and storm-scale rainfall totals (required to estimate the total surface runoff volume). In response, this work presents a new sequential data assimilation system that exploits remotely-sensed surface soil moisture retrievals to simultaneously improve estimates of both pre-storm soil moisture conditions and storm-scale rainfall accumulations. Preliminary testing of the system, via a synthetic twin data assimilation experiment based on the Sacramento hydrologic model and data collected from the Model Parameterization Experiment, suggests that the new approach is more efficient at improving stream flow predictions than data assimilation techniques focusing solely on the constraint of antecedent soil moisture conditions. --- paper_title: Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters: ESTIMATION OF SURFACE FLUXES WITH ENKS paper_content: [1] The estimation of surface heat fluxes based on the assimilation of land surface temperature (LST) has been achieved within a variational data assimilation (VDA) framework. Variational approaches require the development of an adjoint model, which is difficult to derive and code in the presence of thresholds and discontinuities. Also, it is computationally expensive to obtain the background error covariance for the variational approaches. Moreover, the variational schemes cannot directly provide statistical information on the accuracy of their estimates. To overcome these shortcomings, we develop an alternative data assimilation (DA) procedure based on ensemble Kalman smoother (EnKS) with the state augmentation method. The unknowns of the assimilation scheme are neutral turbulent heat transfer coefficient (that scales the sum of turbulent heat fluxes) and evaporative fraction, EF (that represents partitioning among the turbulent fluxes). The new methodology is illustrated with an application to the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) that includes areal average hydrometeorological forcings and flux observations. The results indicate that the EnKS model not only provides reasonably accurate estimates of EF and turbulent heat fluxes but also enables us to determine the uncertainty of estimations under various land surface hydrological conditions. The results of the EnKS model are also compared with those of an optimal smoother (a dynamic variational model). It is found that the EnKS model estimates are less than optimal. However, the degree of suboptimality is small, and its outcomes are roughly comparable to those of an optimal smoother. Overall, the results from this test indicate that EnKS is an efficient and flexible data assimilation procedure that is able to extract useful information on the partitioning of available surface energy from LST measurements and eventually provides reliable estimates of turbulent heat fluxes. --- paper_title: Advanced Data Assimilation for Strongly Nonlinear Dynamics paper_content: Advanced data assimilation methods become extremely complicated and challenging when used with strongly nonlinear models. Several previous works have reported various problems when applying existing popular data assimilation techniques with strongly nonlinear dynamics. Common for these techniques is that they can all be considered as extensions to methods that have proved to work well with linear dynamics. This paper examines the properties of three advanced data assimilation methods when used with the highly nonlinear Lorenz equations. The ensemble Kalman filter is used for sequential data assimilation and the recently proposed ensemble smoother method and a gradient descent method are used to minimize two different weak constraint formulations. The problems associated with the use of an approximate tangent linear model when solving the Euler‐Lagrange equations, or when the extended Kalman filter is used, are eliminated when using these methods. All three methods give reasonable consistent results with the data coverage and quality of measurements that are used here and overcome the traditional problems reported in many of the previous papers involving data assimilation with highly nonlinear dynamics. --- paper_title: The ensemble Kalman filter for combined state and parameter estimation: MONTE CARLO TECHNIQUES FOR DATA ASSIMILATION IN LARGE SYSTEMS paper_content: he ensemble Kalman filter (EnKF) [1] is a sequential Monte Carlo method that provides an alternative to the traditional Kalman filter (KF) [2], [3] and adjoint or four-dimensional variational (4DVAR) methods [4]-[6] to better handle large state spaces and nonlinear error evolution. EnKF provides a simple conceptual formulation and ease of implementation, since. --- paper_title: Impact of Multiresolution Active and Passive Microwave Measurements on Soil Moisture Estimation Using the Ensemble Kalman Smoother paper_content: An observing system simulation experiment is developed to test tradeoffs in resolution and accuracy for soil moisture estimation using active and passive L-band remote sensing. Concepts for combined radar and radiometer missions include designs that will provide multiresolution measurements. In this paper, the scientific impacts of instrument performance are analyzed to determine the measurement requirements for the mission concept. The ensemble Kalman smoother (EnKS) is used to merge these multiresolution observations with modeled soil moisture from a land surface model to estimate surface and subsurface soil moisture at 6-km resolution. The model used for assimilation is different from that used to generate "truth." Consequently, this experiment simulates how data assimilation performs in real applications when the model is not a perfect representation of reality. The EnKS is an extension of the ensemble Kalman filter (EnKF) in which observations are used to update states at previous times. Previous work demonstrated that it provides a computationally inexpensive means to improve the results from the EnKF, and that the limited memory in soil moisture can be exploited by employing it as a fixed lag smoother. Here, it is shown that the EnKS can be used in large problems with spatially distributed state vectors and spatially distributed multiresolution observations. The EnKS-based data assimilation framework is used to study the synergy between passive and active observations that have different resolutions and measurement error distributions. The extent to which the design parameters of the EnKS vary depending on the combination of observations assimilated is investigated --- paper_title: Land surface state and flux estimation using the ensemble Kalman smoother during the Southern Great Plains 1997 field experiment paper_content: [1] The ensemble Kalman smoother (EnKS) is employed to estimate surface and subsurface soil moisture and surface energy fluxes during the Southern Great Plains 1997 (SGP97) experiment through the assimilation of observed L band radiobrightness temperatures. Previous work using the ensemble Kalman filter (EnKF) and a simple smoother demonstrated that soil moisture estimation is a reanalysis-type problem. The EnKF uses observations as they become available to update the current state. The EnKS takes the EnKF estimate as its first guess. However, in addition to updating the current state it also updates the best estimate at previous times. The performance of the EnKS is compared to the EnKF and the ensemble open loop in which no measurements are assimilated. Estimated surface soil moisture is compared to gravimetric observations at three locations. Root zone (5–100 cm) soil moisture is evaluated by comparing the resultant latent heat flux to flux tower observations. In a fixed lag smoother, observations are used to update past estimates within a fixed time window. The EnKS can be implemented in a fixed lag formulation in problems with limited memory such as soil moisture estimation. It is shown that there is a trade-off to be made between the improved accuracy with longer lag and the increased computational cost incurred. It is demonstrated that the EnKS is a relatively inexpensive state estimation algorithm suited to operational data assimilation. --- paper_title: Data Assimilation and Inverse Methods in Terms of a Probabilistic Formulation paper_content: Abstract The weak constraint inverse for nonlinear dynamical models is discussed and derived in term of a probabilistic formulation. The well-known result that for Gaussian error statistics the minimum of the weak constraint inverse is equal to the maximum-likelihood estimate is rederived. Then several methods based on ensemble statistics that can be used to find the smoother (as opposed to the filter) solution are introduced and compared to traditional methods. A strong point of the new methods is that they avoid the integration of adjoint equations, which is a complex task for real oceanographic or atmospheric applications. They also avoid iterative searches in a Hilbert space, and error estimates can be obtained without much additional computational effort. The feasibility of the new methods is illustrated in a two-layer quasigeostrophic ocean model. --- paper_title: A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling paper_content: Abstract During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical–empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave. --- paper_title: Assimilating remotely sensed snow observations into a macroscale hydrology model paper_content: Abstract Accurate forecasting of snow properties is important for effective water resources management, especially in mountainous areas like the western United States. Current model-based forecasting approaches are limited by model biases and input data uncertainties. Remote sensing offers an opportunity for observation of snow properties, like areal extent and water equivalent, over larger areas. Data assimilation provides a framework for optimally merging information from remotely sensed observations and hydrologic model predictions. An ensemble Kalman filter (EnKF) was used to assimilate remotely sensed snow observations into the variable infiltration capacity (VIC) macroscale hydrologic model over the Snake River basin. The snow cover extent (SCE) product from the moderate resolution imaging spectroradiometer (MODIS) flown on the NASA Terra satellite was used to update VIC snow water equivalent (SWE), for a period of four consecutive winters (1999–2003). A simple snow depletion curve model was used for the necessary SWE–SCE inversion. The results showed that the EnKF is an effective and operationally feasible solution; the filter successfully updated model SCE predictions to better agree with the MODIS observations and ground surface measurements. Comparisons of the VIC SWE estimates following updating with surface SWE observations (from the NRCS SNOTEL network) indicated that the filter performance was a modest improvement over the open-loop (un-updated) simulations. This improvement was more evident for lower to middle elevations, and during snowmelt, while during accumulation the filter and open-loop estimates were very close on average. Subsequently, a preliminary assessment of the potential for assimilating the SWE product from the advanced microwave scanning radiometer (AMSR-E, flown on board the NASA Aqua satellite) was conducted. The results were not encouraging, and appeared to reflect large errors in the AMSR-E SWE product, which were also apparent in comparisons with SNOTEL data. --- paper_title: Identification of time-variant river bed properties with the ensemble Kalman filter paper_content: [1] An adequate characterization of river bed hydraulic conductivities (L) is crucial for a proper assessment of river-aquifer interactions. However, river bed characteristics may change over time due to dynamic morphological processes like scouring or sedimentation what can lead to erroneous model predictions when static leakage parameters are assumed. Sequential data assimilation with the ensemble Kalman filter (EnKF) allows for an update of model parameters in real-time and may thus be capable of assessing the transient behavior of L. Synthetic experiments with a three-dimensional finite element model of the Limmat aquifer in Zurich were used to assess the performance of data assimilation in capturing time-variant river bed properties. Reference runs were generated where L followed different temporal and/or spatial patterns which should mimic real-world sediment dynamics. Hydraulic head (h) data from these reference runs were then used as input data for EnKF which jointly updated h and L. Results showed that EnKF is able to capture the different spatio-temporal patterns of L in the reference runs well. However, the adaptation time was relatively long which was attributed to the fast decrease of ensemble variance. To improve the performance of EnKF also an adaptive filtering approach with covariance inflation was applied that allowed a faster and more accurate adaptation of model parameters. A sensitivity analysis indicated that even for a low amount of observations a reasonable adaptation of L towards the reference values can be achieved and that EnKF is also able to correct for a biased initial ensemble of L. --- paper_title: Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 Field Experiment paper_content: Remotely sensed microwave measurements provide useful but indirect observations of surface soil moisture. Ground-based measurements are more direct but are very localized and limited in coverage. Model predictions providea more regional perspective but rely on many simplifications and approximations and depend on inputs that are difficult to obtain over extensive areas. The only effective way to achieve soil moisture estimates with the accuracy and coverage required for hydrologic and meteorological applications is to merge information from satellites, ground-based stations, and models. In this paper we describe a convenient data merging (or data assimilation) procedure based on an ensemble Kalman filter. This procedure is illustrated with an application to the Southern Great Plains 1997 (SGP97) field experiment. It uses land surface and radiative transfer models to derive soil moisture estimates from airborne L band microwave observations and ground-based measurements of micrometeorological variables, soil texture, and vegetation type. The ensemble filter approach is appealing because (1) it can accommodate a wide range of models, (2) it can account for input and measurement uncertainties, (3) it provides information on the accuracy of its estimates, and (4) it is relatively efficient, making large-scale applications feasible. Results from our SGP97 application of the ensemble Kalman filter include large-scale maps (∼10,000 km 2 ) of soil moisture estimates and estimation error standard deviations for the entire month long experiment and comparisons of filter soil moisture and latent heat estimates to ground truth measurements (gravimetric and flux tower observations). The ground truth comparisons show that the filter is able to track soil moisture fluctuations. The filter estimates are significantly better than those from an "open loop" simulation that includes the same ground-based data but does not incorporate radio brightness measurements. Overall, the results from this field test indicate that the ensemble Kalman filter is an accurate, efficient, and flexible data assimilation procedure that is able to extract useful information from remote sensing measurements. --- paper_title: Dual state-parameter estimation of hydrological models using ensemble Kalman filter paper_content: Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state–parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. 2004 Elsevier Ltd. All rights reserved. --- paper_title: Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin paper_content: Abstract Assimilation of data from the Gravity Recovery and Climate Experiment (GRACE) system of satellites yielded improved simulation of water storage and fluxes in the Mississippi River basin, as evaluated against independent measurements. The authors assimilated GRACE-derived monthly terrestrial water storage (TWS) anomalies for each of the four major subbasins of the Mississippi into the Catchment Land Surface Model (CLSM) using an ensemble Kalman smoother from January 2003 to May 2006. Compared with the open-loop CLSM simulation, assimilation estimates of groundwater variability exhibited enhanced skill with respect to measured groundwater in all four subbasins. Assimilation also significantly increased the correlation between simulated TWS and gauged river flow for all four subbasins and for the Mississippi River itself. In addition, model performance was evaluated for eight smaller watersheds within the Mississippi basin, all of which are smaller than the scale of GRACE observations. In seven of e... --- paper_title: Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model paper_content: This paper describes an application of the ensemble Kalman filter (EnKF) in which streamflow observations are used to update states in a distributed hydrological model. We demonstrate that the standard implementation of the EnKF is inappropriate because of non-linear relationships between model states and observations. Transforming streamflow into log space before computing error covariances improves filter performance. We also demonstrate that model simulations improve when we use a variant of the EnKF that does not require perturbed observations. Our attempt to propagate information to neighbouring basins was unsuccessful, largely due to inadequacies in modelling the spatial variability of hydrological processes. New methods are needed to produce ensemble simulations that both reflect total model error and adequately simulate the spatial variability of hydrological states and fluxes. --- paper_title: The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: a case study based on ESTAR measurements during SGP97 paper_content: An Ensemble Kalman filter (EnKF) is used to assimilate airborne measurements of 1.4 GHz surface brightness temperature ðTBÞ acquired during the 1997 Southern Great Plains Hydrology Experiment (SGP97) into the TOPMODEL-based Land–Atmosphere Transfer Scheme (TOPLATS). In this way, the potential of using EnKF-assimilated remote measurements of TB to compensate land surface model predictions for errors arising from a climatological description of rainfall is assessed. The use of a real remotely sensed data source allows for a more complete examination of the challenges faced in implementing assimilation strategies than previous studies where observations were synthetically generated. Results demonstrate that the EnKF is an effective and computationally competitive strategy for the assimilation of remotely sensed TB measurements into land surface models. The EnKF is capable of extracting spatial and temporal trends in root-zone (40 cm) soil water content from TB measurements based solely on surface (5 cm) conditions. The accuracy of surface state and flux predictions made with the EnKF, ESTAR TB measurements, and climatological rainfall data within the Central Facility site during SGP97 are shown to be superior to predictions derived from open loop modeling driven by sparse temporal sampling of rainfall at frequencies consistent with expectations of future missions designed to measure rainfall from space (6–10 observations per day). Specific assimilation challenges posed by inadequacies in land surface model physics and spatial support contrasts between model predictions and sensor retrievals are discussed. 2002 Elsevier Science Ltd. All rights reserved. --- paper_title: Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation paper_content: This review discusses recent advances in geophysical data assimilation beyond Gaussian statistical modeling, in the fields of meteorology, oceanography, as well as atmospheric chemistry. The non-Gaussian features are stressed rather than the nonlinearity of the dynamical models, although both aspects are entangled. Ideas recently proposed to deal with these non-Gaussian issues, in order to improve the state or parameter estimation, are emphasized. The general Bayesian solution to the estimation problem and the techniques to solve it are first presented, as well as the obstacles that hinder their use in high-dimensional and complex systems. Approximations to the Bayesian solution relying on Gaussian, or on second-order moment closure, have been wholly adopted in geophysical data assimilation (e.g., Kalman filters and quadratic variational solutions). Yet, nonlinear and non-Gaussian effects remain. They essentially originate in the nonlinear models and in the non-Gaussian priors. How these effects are handled within algorithms based on Gaussian assumptions is then described. Statistical tools that can diagnose them and measure deviations from Gaussianity are recalled. The following advanced techniques that seek to handle the estimation problem beyond Gaussianity are reviewed: maximum entropy filter, Gaussian anamorphosis, non-Gaussian priors, particle filter with an ensemble Kalman filter as a proposal distribution, maximum entropy on the mean, or strictly Bayesian inferences for large linear models, etc. Several ideas are illustrated with recent or original examples that possess some features of high-dimensional systems. Many of the new approaches are well understood only in special cases and have difficulties that remain to be circumvented. Some of the suggested approaches are quite promising, and sometimes already successful for moderately large though specific geophysical applications. Hints are given as to where progress might come from. --- paper_title: Particle Filters for State Estimation of Jump Markov Linear Systems paper_content: Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. In this paper, our aim is to recursively compute optimal state estimates for this class of systems. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem. Our algorithms combine sequential importance sampling, a selection scheme, and Markov chain Monte Carlo methods. They use several variance reduction methods to make the most of the statistical structure of JMLS. Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problems of on-line deconvolution of impulsive processes and of tracking a maneuvering target are considered. It is shown that our algorithms outperform the current methods. --- paper_title: Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation paper_content: Abstract An integrated data assimilation system is implemented over the Red-Arkansas river basin to estimate the regional scale terrestrial water cycle driven by multiple satellite remote sensing data. These satellite products include the Tropical Rainfall Measurement Mission (TRMM), TRMM Microwave Imager (TMI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Also, a number of previously developed assimilation techniques, including the ensemble Kalman filter (EnKF), the particle filter (PF), the water balance constrainer, and the copula error model, and as well as physically based models, including the Variable Infiltration Capacity (VIC), the Land Surface Microwave Emission Model (LSMEM), and the Surface Energy Balance System (SEBS), are tested in the water budget estimation experiments. This remote sensing based water budget estimation study is evaluated using ground observations driven model simulations. It is found that the land surface model driven by the bias-corrected TRMM rainfall produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating TMI 10.67 GHz microwave brightness temperature measurements that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilating evapotranspiration estimated from satellite-based measurements. --- paper_title: The ensemble Kalman filter for combined state and parameter estimation: MONTE CARLO TECHNIQUES FOR DATA ASSIMILATION IN LARGE SYSTEMS paper_content: he ensemble Kalman filter (EnKF) [1] is a sequential Monte Carlo method that provides an alternative to the traditional Kalman filter (KF) [2], [3] and adjoint or four-dimensional variational (4DVAR) methods [4]-[6] to better handle large state spaces and nonlinear error evolution. EnKF provides a simple conceptual formulation and ease of implementation, since. --- paper_title: FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem–Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities paper_content: FLUXNET is a global network of micrometeorological flux measurement sites that measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere. At present over 140 sites are operating on a long-term and continuous basis. Vegetation under study includes temperate conifer and broadleaved (deciduous and evergreen) forests, tropical and boreal forests, crops, grasslands, chaparral, wetlands, and tundra. Sites exist on five continents and their latitudinal distribution ranges from 70°N to 30°S. FLUXNET has several primary functions. First, it provides infrastructure for compiling, archiving, and distributing carbon, water, and energy flux measurement, and meteorological, plant, and soil data to the science community. (Data and site information are available online at the FLUXNET Web site, http://www-eosdis.ornl.gov/FLUXNET/.) Second, the project supports calibration and flux intercomparison activities. This activity ensures that data from the regional networks are intercomparable. And third, FLUXNET supports the synthesis, discussion, and communication of ideas and data by supporting project scientists, workshops, and visiting scientists. The overarching goal is to provide information for validating computations of net primary productivity, evaporation, and energy absorption that are being generated by sensors mounted on the NASA Terra satellite. Data being compiled by FLUXNET are being used to quantify and compare magnitudes and dynamics of annual ecosystem carbon and water balances, to quantify the response of stand-scale carbon dioxide and water vapor flux densities to controlling biotic and abiotic factors, and to validate a hierarchy of soil–plant–atmosphere trace gas exchange models. Findings so far include 1) net CO 2 exchange of temperate broadleaved forests increases by about 5.7 g C m −2 day −1 for each additional day that the growing season is extended; 2) the sensitivity of net ecosystem CO 2 exchange to sunlight doubles if the sky is cloudy rather than clear; 3) the spectrum of CO 2 flux density exhibits peaks at timescales of days, weeks, and years, and a spectral gap exists at the month timescale; 4) the optimal temperature of net CO 2 exchange varies with mean summer temperature; and 5) stand age affects carbon dioxide and water vapor flux densities. --- paper_title: Monte Carlo simulations on adsorptions of benzene and xylenes in sodium-Y zeolites paper_content: Abstract The structures of benzene and C8 isomers ( o -xylene, m -xylene, p -xylene) in NaY simulated by the canonical Monte Carlo method were in good agreement with the experimental data of the available neutron diffraction analysis. All these sorbed compounds are stacked through the aromatic π-electron interaction on the cation of the SII site in the Sodium-Y. The experimental adsorption order ( m -xylene > o -xylene > p -xylene) of xylene isomers in the faujasite was also in good agreement with the order obtained from calculated heats of adsorption. It was shown that the difference in the interaction between the 4-ring sites and the methyl groups of xylene caused the better adsorption of m -xylene compared to those of the others. From these results, our conclusion is that the adsorption of the C8 isomers in the faujasite can be explained by considering only the interaction between the zeolite framework and the adsorbate approximated as a rigid model. Moreover, this type of simulation proved to be very useful to understand the mechanism of the adsorption of aromatic compounds in zeolites like faujasites. --- paper_title: Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach paper_content: Abstract. Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown. --- paper_title: Novel approach to nonlinear/non-Gaussian Bayesian state estimation paper_content: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linear- ity or Gaussian noise: it may be applied to any state transition or measurement model. A simula- tion example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter. --- paper_title: Data assimilation methods in the Earth sciences paper_content: Abstract Although remote sensing data are often plentiful, they do not usually satisfy the users’ needs directly. Data assimilation is required to extract information about geophysical fields of interest from the remote sensing observations and to make the data more accessible to users. Remote sensing may provide, for example, measurements of surface soil moisture, snow water equivalent, snow cover, or land surface (skin) temperature. Data assimilation can then be used to estimate variables that are not directly observed from space but are needed for applications, for instance root zone soil moisture or land surface fluxes. The paper provides a brief introduction to modern data assimilation methods in the Earth sciences, their applications, and pertinent research questions. Our general overview is readily accessible to hydrologic remote sensing scientists. Within the general context of Earth science data assimilation, we point to examples of the assimilation of remotely sensed observations in land surface hydrology. --- paper_title: Sequential Imputations and Bayesian Missing Data Problems paper_content: Abstract For missing data problems, Tanner and Wong have described a data augmentation procedure that approximates the actual posterior distribution of the parameter vector by a mixture of complete data posteriors. Their method of constructing the complete data sets is closely related to the Gibbs sampler. Both required iterations, and, similar to the EM algorithm, convergence can be slow. We introduce in this article an alternative procedure that involves imputing the missing data sequentially and computing appropriate importance sampling weights. In many applications this new procedure works very well without the need for iterations. Sensitivity analysis, influence analysis, and updating with new data can be performed cheaply. Bayesian prediction and model selection can also be incorporated. Examples taken from a wide range of applications are used for illustration. --- paper_title: Simultaneous estimation of both soil moisture and model parameters using particle filtering method through the assimilation of microwave signal paper_content: [1] Soil moisture is a very important variable in land surface processes. Both field moisture measurements and estimates from modeling have their limitations when being used to estimate soil moisture on a large spatial scale. Remote sensing is becoming a practical method to estimate soil moisture globally; however, the quality of current soil surface moisture products needs to be improved in order to meet practical requirements. Data assimilation (DA) is a promising approach to merge model dynamics and remote sensing observations, thus having the potential to estimate soil moisture more accurately. In this study, a data assimilation algorithm, which couples the particle filter and the kernel smoothing technique, is presented to estimate soil moisture and soil parameters from microwave signals. A simple hydrological model with a daily time step is utilized to reduce the computational burden in the process of data assimilation. An observation operator based on the ratio of two microwave brightness temperatures at different frequencies is designed to link surface soil moisture with remote sensing measurements, and a sensitivity analysis of this operator is also conducted. Additionally, a variant of particle filtering method is developed for the joint estimation of soil moisture and soil parameters such as texture and porosity. This assimilation scheme is validated against field moisture measurements at the CEOP/Mongolia experiment site and is found to estimate near-surface soil moisture very well. The retrieved soil texture still contains large uncertainties as the retrieved values cannot converge to fixed points or narrow ranges when using different initial soil texture values, but the retrieved soil porosity has relatively small uncertainties. --- paper_title: Global Products Of Vegetation Leaf Area And Fraction Absorbed Par From Year One Of Modis Data paper_content: An algorithm based on the physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from surface reflectances was developed and implemented for operational processing prior to the launch of the moderate resolution imaging spectroradiometer (MODIS) aboard the TERRA platform in December of 1999. The performance of the algorithm has been extensively tested in prototyping activities prior to operational production. Considerable attention was paid to characterizing the quality of the product and this information is available to the users as quality assessment (QA) accompanying the product. The MODIS LAI/FPAR product has been operationally produced from day one of science data processing from MODIS and is available free of charge to the users from the Earth Resources Observation System (EROS) Data Center Distributed Active Archive Center. Current and planned validation activities are aimed at evaluating the product at several field sites representative of the six structural biomes. Example results illustrating the physics and performance of the algorithm are presented together with initial QA and validation results. Potential users of the product are advised of the provisional nature of the product in view of changes to calibration, geolocation, cloud screening, atmospheric correction and ongoing validation activities. D 2002 Published by Elsevier Science Inc. --- paper_title: Using data assimilation to identify diffuse recharge mechanisms from chemical and physical data in the unsaturated zone: RECHARGE MECHANISMS paper_content: [1] It is difficult to estimate groundwater recharge in semiarid environments, where precipitation and evapotranspiration nearly balance. In such environments, groundwater supplies are sensitive to small changes in the processes that control recharge. Numerical modeling provides the temporal resolution needed to analyze these processes but is highly sensitive to model errors. Natural chloride tracer measurements in the unsaturated zone provide more robust indicators of low recharge rates but yield estimates at coarse time scales that mask most control mechanisms. This study presents a new probabilistic approach for analyzing diffuse recharge in semiarid environments, with an application to study sites in the U.S. southern High Plains. The approach uses data assimilation to combine model predictions and chloride-based recharge estimates. It has the advantage of providing probability distributions rather than point values for uncertain soil and vegetation properties. These can then be used to quantify recharge uncertainty. Estimates of moisture flux time series indicate that percolation (or potential recharge) at the data sites is episodic and exhibits interannual variability. Most percolation occurs during intense rains when crop roots are not fully developed and there is ample antecedent soil moisture. El Nino events can contribute to interannual variability of recharge if they bring rainy winters that provide wet antecedent conditions for spring precipitation. Data assimilation methods that combine modeling and chloride observations provide the high temporal resolution information needed to identify mechanisms controlling diffuse recharge and offer a way to examine the effects of land use change and climatic variability on groundwater resources. --- paper_title: Sequential Monte Carlo Methods for Dynamic Systems paper_content: Abstract We provide a general framework for using Monte Carlo methods in dynamic systems and discuss its wide applications. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling, and Markov chain iterations. We provide guidelines on how they should be used and under what circumstance each method is most suitable. Through the analysis of differences and connections, we consolidate these methods into a generic algorithm by combining desirable features. In addition, we propose a general use of Rao-Blackwellization to improve performance. Examples from econometrics and engineering are presented to demonstrate the importance of Rao–Blackwellization and to compare different Monte Carlo procedures. --- paper_title: The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter paper_content: The Sequential Importance Sampling with Resampling (SISR) particle filter and the SISR with parameter resampling particle filter (SISR-PR) are evaluated for their performance in soil moisture assimilation and the consequent effect on baseflow generation. With respect to the resulting soil moisture time series, both filters perform appropriately. However, the SISR filter has a negative effect on the baseflow due to inconsistency between the parameter values and the states after the assimilation. In order to overcome this inconsistency, parameter resampling is applied along with the SISR filter, to obtain consistent parameter values with the analyzed soil moisture state. Extreme parameter replication, which could lead to a particle collapse, is avoided by the perturbation of the parameters with white noise. Both the modeled soil moisture and baseflow are improved if the complementary parameter resampling is applied. The SISR filter with parameter resampling offers an efficient way to deal with biased observations. The robustness of the methodology is evaluated for 3 model parameter sets and 3 assimilation frequencies. Overall, the results in this paper indicate that the particle filter is a promising tool for hydrologic modeling purposes, but that an additional parameter resampling may be necessary to consistently update all state variables and fluxes within the model. --- paper_title: Variational data assimilation of microwave radiobrightness observations for land surface hydrology applications paper_content: Our ability to accurately describe large-scale variations in soil moisture is severely restricted by process uncertainty and the limited availability of appropriate soil moisture data. Remotely sensed microwave radiobrightness observations can cover large scales but have limited resolution and are only indirectly related to the hydrologic variables of interest. The authors describe a four-dimensional (4D) variational assimilation algorithm that makes best use of available information while accounting for both measurement and model uncertainty. The representer method used is more efficient than a Kalman filter because it avoids explicit propagation of state error covariances. In a synthetic example, which is based on a field experiment, the authors demonstrate estimation performance by examining data residuals. Such tests provide a convenient way to check the statistical assumptions of the approach and to assess its operational feasibility. Internally computed covariances show that the estimation error decreases with increasing soil moisture. An adjoint analysis reveals that trends in model errors in the soil moisture equation can be estimated from daily L-band brightness measurements, whereas model errors in the soil and canopy temperature equations cannot be adequately retrieved from daily data alone. Nonetheless, state estimates obtained from the assimilation algorithm improve significantly on prior model predictions derived without assimilation of radiobrightness data. --- paper_title: A Microwave Satellite Observational Operator for Variational Data Assimilation of Soil Moisture paper_content: An observational operator and its adjoint have been created that are suitable for use within variational data assimilation using polarized 6- and 10-GHz passive microwave satellite observations. When used within a variational data assimilation system, the operator will facilitate NWP soil moisture initialization using existing and future satellite datasets [e.g., NASA Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR-E), Advanced Earth Observing Satellite-II (ADEOS-II), Department of Defense (DoD) WindSat, and the National Polar Orbiter Environmental Satellite System (NPOESS) Conical Microwave Imager Sounder (CMIS)]. Five primary control variables are used within the operator, and surface soil moisture is explicitly included as one of the control variables. In future studies, this operator and its adjoint will be used to perform land surface model data assimilation experiments to better determine important NWP surface characteristics. In the current study, the adjoint model development and analysis of the potential information content of passive microwave measurements sensitive to the land surface and soil parameters are focused on. The multivariate results clarify the effects of masking phenomena upon the soil moisture signal. Also included is a useful transformation of the adjoint between real and complex number spaces. The transformations are necessary when higher-level mathematical operators, such as powers, use complex number arguments. This can be a common occurrence within radiative transfer models. Thus, the adjoint of this particular observational operator serves as an example of this behavior. Various observational operator results and sensitivity analyses are presented, demonstrating significant utility of the operator for NWP soil moisture data assimilation studies. --- paper_title: Constraining a physically based Soil-Vegetation-Atmosphere Transfer model with surface water content and thermal infrared brightness temperature measurements using a multiobjective approach: CONSTRAINING A PHYSICALLY BASED SVAT MODEL paper_content: [1] This article reports on a multiobjective approach which is carried out on the physically based Soil-Vegetation-Atmosphere Transfer (SVAT) model. This approach is designed for (1) analyzing the model sensitivity to its input parameters under various environmental conditions and (2) assessing input parameters through the combined assimilation of the surface water content and the thermal infrared brightness temperature. To reach these goals, a multiobjective calibration iterative procedure (MCIP) is applied on the Simple Soil Plant Atmosphere Transfer–Remote Sensing (SiSPAT-RS) model. This new multiobjective approach consists of performing successive contractions of the feasible parameter space with the multiobjective generalized sensitivity analysis algorithm. Results show that the MCIP is an original and pertinent approach both for improving model calibration (i.e., reducing the a posteriori preferential ranges) and for driving a detailed SVAT model using various calibration data. The usefulness of the water content of the upper 5 cm and the thermal infrared brightness temperature for retrieving quantitative information about the main input surface parameters is also underlined. This study opens perspectives in the combined assimilation of various multispectral remotely sensed observations, such as passive microwaves and thermal infrared signals. --- paper_title: Variational inverse parameter estimation in a cohesive sediment transport model: An adjoint approach: VARIATIONAL INVERSE METHOD IN SEDIMENT TRANSPORT paper_content: [1] Parameter estimation in the sediment deposition and resuspension process is an important issue in numerical modeling of suspended sediment transport. The sediment settling velocity and resuspension rate are two critical parameters controlling the sediment exchange process between the water column and sediment bed. In this paper a variational inverse data assimilation scheme for estimation of the sediment settling velocity and resuspension rate is developed and tested with a three-dimensional cohesive sediment transport model. The sediment settling velocity and resuspension rate are treated as poorly known parameters in the model and are estimated by the variational inverse scheme using an adjoint approach. A limited-memory quasi-Newtonian conjugate gradient algorithm is used in the minimization process. The variational inverse model is tested in the James River estuary in Virginia by identical twin experiments. Numerical experimental results show that variational inverse data assimilation is a useful tool for retrieving poorly known parameters in a cohesive sediment transport model. --- paper_title: Estimation of Surface Turbulent Fluxes through Assimilation of Radiometric Surface Temperature Sequences paper_content: Abstract A model of land surface energy balance is used as a constraint on the estimation of factors characterizing land surface influences on evaporation and turbulent heat transfer from sequences of radiometric surface temperature measurements. The surface moisture control on evaporation is captured by the dimensionless evaporative fraction (ratio of latent heat flux to the sum of the turbulent fluxes), which is nearly constant for near-peak radiation hours on days without precipitation. The dimensionless parameter capturing the turbulent transfer characteristics (bulk heat transfer coefficient) includes the impacts of both forced and free convection. The mean diurnal pattern and seasonal trends are interpreted in the context of expected surface air layer static stability variations. The approach is tested over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site (Kansas) where verification data on surface fluxes are available. It is shown that sequent... --- paper_title: From Near-Surface to Root-Zone Soil Moisture Using Different Assimilation Techniques paper_content: Abstract Root-zone soil moisture constitutes an important variable for hydrological and weather forecast models. Microwave radiometers like the L-band instrument on board the European Space Agency’s (ESA) future Soil Moisture and Ocean Salinity (SMOS) mission are being designed to provide estimates of near-surface soil moisture (0–5 cm). This quantity is physically related to root-zone soil moisture through diffusion processes, and both surface and root-zone soil layers are commonly simulated by land surface models (LSMs). Observed time series of surface soil moisture may be used to analyze the root-zone soil moisture using data assimilation systems. In this paper, various assimilation techniques derived from Kalman filters (KFs) and variational methods (VAR) are implemented and tested. The objective is to correct the modeled root-zone soil moisture deficiencies of the newest version of the Interaction between Soil, Biosphere, and Atmosphere scheme (ISBA) LSM, using the observations of the surface soil mo... --- paper_title: Estimation of Roughness Profile in Trapezoidal Open Channels paper_content: In the well-known de Saint Venant equations, the bed roughness-coefficient cannot be measured directly and therefore needs to be estimated. The estimation process is referred to as “parameter identification,” which is a mathematical process based on using the difference between the solution of the model equations and the measured system response. This paper introduces an approach for solving the parameter identification problem in the de Saint Venant equations. The method proposed herein is widely used in gas dynamics; however, it has not been used before for unsteady problem identification of open channel flow parameters. Although the proposed solution procedure will be applied herein to the bed roughness-coefficient, it can be used for other parameters, e.g., cross-sectional area, bed width, etc. Starting with an initial guess of the roughness coefficient, the algorithm iteratively improves the guesses in the direction of the gradient of the least square criterion. The gradient is obtained by means of a variational approach, while the conditions of the criterion minimum are identified by the general method of indefinite Lagrangian multipliers. --- paper_title: Auto-calibration system developed to assimilate AMSR-E data into a land surface model for estimating soil moisture and the surface energy budget paper_content: Low-frequency microwave brightness temperature is strongly affected by near-surface soil moisture; therefore, it can be assimilated into a land surface model to improve modeling of soil moisture and the surface energy budget. This study presents a new variational land system used to assimilate AMSR-E brightness temperature of vertical polarization of 6.9 GHz and 18.7 GHz. The system consists of a land surface model (LSM) used to calculate surface fluxes and soil moisture, a radiative transfer model (RTM) to estimate the microwave brightness temperature, and an optimization scheme to search for optimal values of soil moisture by minimizing the difference between modeled and observed brightness temperature. The LSM is an improved simple biosphere model for sparse vegetation modeling and the RTM is a Q-h model that can account for the effects of surface roughness and vegetation. Several parameters in the LSM and RTM can significantly affect the outputs of the land data assimilation system but their values are either highly variable or unavailable. To solve this problem, we developed a dual-pass assim --- paper_title: Future directions for advanced evapotranspiration modeling: Assimilation of remote sensing data into crop simulation models and SVAT models paper_content: Soil-Vegetation-Atmosphere Transfer Models (SVAT) and Crop Simulation Models describe physical and physiological processes occurring in crop canopies. Remote sensing data may be used through assimilation procedures for constraining or driving SVAT and crop models. These models provide continuous simulation of processes such as evapotranspiration and, thus, direct means for interpolating evapotranspiration between remote sensing data acquisitions (which is not the case for classical evapotranspiration mapping methods). They also give access to variables other than evapotranspiration, such as soil moisture and crop production. We developed the coupling between crop, SVAT and radiative transfer models in order to implement assimilation procedures in various wavelength domains (solar, thermal and microwave). Such coupling makes it possible to transfer information from one model to another and then to use remote sensing information for retrieving model parameters which are not directly related to remote sensing data (such as soil initial water content, plant growth parameters, physical properties of soil and so on). Simple assimilation tests are presented to illustrate the main techniques that may be used for monitoring crop processes and evapotranspiration. An application to a small agricultural area is also performed showing the potential of such techniques for retrieving evapotranspiration and information on irrigation practices over wheat fields. --- paper_title: Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information paper_content: In this series of three papers a method is presented to estimate the parameters of groundwater flow models under steady and nonsteady state conditions. The parameters include values and directions of principal hydraulic conductivities (or transmissivities) in anisotropic media, specific storage (or storativity), interior and boundary recharge or leakage rates, coefficients of head-dependent interior and boundary sources, and boundary heads. In transient situations, the initial head distribution can also be estimated if the system is originally at a steady state. Paper 1 of the series discusses some of the advantage in treating the inverse problem statistically and in regularizing its solution by means of penalty criteria based on prior estimates of the parameters. The inverse problem is posed in the framework of maximum likelihood theory cast in a manner that accounts for prior information about the parameters. Since not all the factors which contribute to the prior errors can be quantified statistically at the outset, the covariance matrices of these errors are expressed in terms of several parameters which, if unknown, can be estimated jointly with the hydraulic parameters by a stagewise optimization process. When transient head data are separated by a fixed time interval, the temporal structure of these data is approximated by a lag-one autoregressive model with a correlation coefficient that can be treated as another unknown parameter. Estimation errors are analyzed by examining the lower bound of their covariance matrix in the eigenspace. Paper 1 concludes by suggesting that certain model identification criteria developed in the time series literature, all of which are based on the maximum likelihood concept, might be useful for selecting the best groundwater model (or the best method of parameterizing a particular model) among a number of given alternatives. --- paper_title: Using a multiobjective approach to retrieve information on surface properties used in a SVAT model paper_content: Abstract The reliability of model predictions used in meteorology, agronomy or hydrology is partly linked to an adequate representation of the water and energy balances which are described in so-called SVAT (Soil Vegetation Atmosphere Transfer) models. These models require the specification of many surface properties which can generally be obtained from laboratory or field experiments, using time consuming techniques, or can be derived from textural information. The required accuracy of the surface properties depends on the model complexity and their misspecification can affect model performance. At various time and spatial resolutions, remote sensing provides information related to surface parameters in SVAT models or state variables simulated by SVAT models. In this context, the Simple Soil-Plant-Atmosphere Transfer-Remote Sensing (SiSPAT-RS) model was developed for remote sensing data assimilation objectives. This new version of the physically based SiSPAT model simulates the main surface processes (energy fluxes, soil water content profiles, temperatures) and remote sensing data in the visible, infrared and thermal infrared spectral domains. As a preliminary step before data assimilation in the model, the objectives of this study were (1) to apply a multiobjective approach for retrieving quantitative information about the surface properties from different surface measurements and (2) to determine the potential of the SiSPAT-RS model to be applied with ‘little’ a priori information about input parameters. To reach these goals, the ability of the Multiobjective Generalized Sensitivity Analysis (MOGSA) algorithm to determine and quantify the most influential input parameters of the SiSPAT-RS model on several simulated output variables, was investigated. The results revealed the main influential input parameters according to different contrasted environmental conditions, and contributed to the reduction of their a priori uncertainty range. A procedure for specifying surface properties from MOGSA results was tested on the thermal and hydraulic soil parameters, and evaluated through the SiSPAT-RS model performance. Although slightly lower than a reference simulation, the performance were satisfactory and suggested that complex SVAT models can be driven with little a priori information on soil properties, as in a future context of remote sensing data assimilation. Measurement acquired on a winter wheat field of the ReSeDA (Remote Sensing Data Assimilation) experiment were used in this study. --- paper_title: Estimating root zone soil moisture from surface soil moisture data and soil-vegetation-atmosphere transfer modeling paper_content: We studied the possibility of estimating root zone soil moisture through the combined use of a time series of observed surface soil moisture data and soil-vegetation-atmosphere transfer modeling. The analysis was based on the interactions between soil- biosphere-atmosphere surface scheme and two data sets obtained from soybean crops in 1989 and 1990. These data sets included detailed measurements of soil and vegetation characteristics and mass and energy transfer in the soil-plant-atmosphere system. The data measured during the 3-month experiment in 1989 are used to investigate the accuracy of soil reservoir retrievals, as a function of the time period and frequency of measurements of surface soil moisture involved in the retrieval process. This study contributes to better defining the requirements for the use of remotely sensed microwave measurements of surface soil moisture. --- paper_title: A simplified land data assimilation scheme and its application to soil moisture experiments in 2002 (SMEX02) paper_content: [1] The influences of vegetation and its spatial and temporal heterogeneity on the detection of soil moisture can be significant and may limit the applicability of satellite passive microwave sensors. Sensitivity analysis of an applied soil moisture algorithm using ground-based measurements can show where problems can arise and how they may be circumvented. This paper investigates a method of retrieving a one-dimensional soil moisture profile and the surface and canopy temperature, under the influence of different vegetations and dynamics, by integrating numerical models and passive microwave, visible, and near-infrared measurements via a novel application of data assimilation. The land surface scheme (LSS), which is at the heart of the present land data assimilation scheme (LDAS), is a biophysically based model (simplified biosphere model 2: SiB2) of soil, vegetative and atmospheric interactions. The ground-based microwave radiometer (GBMR) measurements, gathered over the soil moisture experiments in 2002 (SMEX02) in Iowa, were assimilated into the LSS via a radiative transfer model (RTM) using LDAS. Compared to open loop simulations, the results of LDAS are in better agreement with observations. --- paper_title: Automatic differentiation : a tool for variational data assimilation and adjoint sensitivity analysis for flood modeling paper_content: Flood modeling involves catchment scale hydrology and river hydraulics. Analysis and reduction of model uncertainties induce sensitivity analysis, reliable initial and boundary conditions, calibration of empirical parameters. A deterministic approach dealing with the aforementioned estimation and sensitivity analysis problems results in the need of computing the derivatives of a function of model output variables with respect to input variables. Modern automatic differentiation (ad) tools such as Tapenade provide an easier and safe way to fulfill this need. Two applications are presented in this paper: variational data assimilation and adjoint sensitivity analysis. --- paper_title: Recent progress of data assimilation methods in meteorology paper_content: Data assimilation is a methodology for estimating accurately the state of a time-evolving complex system like the atmosphere from observational data and a numerical model of the system. It has become an indispensable tool for meteorological researches as well as for numerical weather prediction, as represented by extensive use of reanalysis datasets for research purposes. New advances of data assimilation methods emerged from the 1980s. This review paper presents the theoretical background and implementation of two advanced data assimilation methods: four-dimensional variational assimilation (4D-Var) and ensemble Kalman filtering (EnKF), which currently draw much attention in the meteorological community. Recent research results in Japan on those methods are reviewed, especially on mesoscale applications of 4D-Var and tests of the local ensemble transform Kalman filter (LETKF). Comparison of 4D-Var and EnKF is also briefly discussed. An outline of the mesoscale 4D-Var system of the Japan Meteorological Agency, which is the first operational 4D-Var for a mesoscale model, is given in Appendix. --- paper_title: From Near-Surface to Root-Zone Soil Moisture Using Year-Round Data paper_content: Abstract Passive microwave remote sensing may provide quantitative information about the water content of a shallow near-surface soil layer. However, the variable of interest for applications such as short- and medium-term meteorological modeling and hydrological studies over vegetated areas is the root-zone soil moisture, which controls plant transpiration. Because near-surface soil moisture is related to root-zone soil moisture through diffusion processes, assimilation algorithms may enable one to retrieve the total soil moisture content from observed surface soil moisture. A variational assimilation method is applied to a new dataset obtained over a fallowland in southwestern France in 1997 and 1998. The new database includes continuous automatic measurements of water content within the top 6-cm soil layer during a 17-month period from January 1997 to May 1998. Once calibrated, the Interactions between Soil, Biosphere, and Atmosphere (ISBA) surface scheme is able to simulate properly the measured surfa... --- paper_title: Mantle circulation models with variational data assimilation: inferring past mantle flow and structure from plate motion histories and seismic tomography paper_content: SUMMARY ::: ::: Mantle convection models require an initial condition some time in the past. Because this initial condition is unknown for Earth, we cannot infer the geological evolution of mantle flow from forward mantle convection calculations even for the most recent Mesozoic and Cenozoic geological history of our planet. Here we introduce a fluid dynamic inverse problem to constrain unknown mantle flow back in time from seismic tomographic observations of the mantle and reconstructions of past plate motions using variational data assimilation. We derive the generalized inverse of mantle convection and explore the initial condition problem in high-resolution, 3-D spherical mantle circulation models for a time period of 100 Myr, roughly comparable to half a mantle overturn. We present a synthetic modelling experiment to demonstrate that mid-Cretaceous mantle structure can be inferred accurately from fluid dynamic inverse modelling, assuming present-day mantle structure is well-known, even if an initial first guess assumption about the mid-Cretaceous mantle involved only a simple 1-D radial temperature profile. We also demonstrate that convecting present-day mantle structure back in time by reversing the time-stepping of the energy equation is insufficient to model the mantle structure of the past. The difficulty arises, because such backward convection calculations ignore thermal diffusion effects, and therefore cannot account for the generation of thermal buoyancy in boundary layers as we go back in time. Inverse mantle convection modelling should make it possible to infer a number of flow parameters from observational constraints of the mantle. --- paper_title: What Is an Adjoint Model? paper_content: Adjoint models are powerful tools for many studies that require an estimate of sensitivity of model output (e.g., a forecast) with respect to input. Actual fields of sensitivity are produced directly and efficiently, which can then be used in a variety of applications, including data assimilation, parameter estimation, stability analysis, and synoptic studies. The use of adjoint models as tools for sensitivity analysis is described here using some simple mathematics. An example of sensitivity fields is presented along with a short description of adjoint applications. Limitations of the applications are discussed and some speculations about the future of adjoint models are offered. --- paper_title: Improvements to the Community Land Model and their impact on the hydrological cycle: COMMUNITY LAND MODEL HYDROLOGY paper_content: [1] The Community Land Model version 3 (CLM3) is the land component of the Community Climate System Model (CCSM). CLM3 has energy and water biases resulting from deficiencies in some of its canopy and soil parameterizations related to hydrological processes. Recent research by the community that utilizes CLM3 and the family of CCSM models has indicated several promising approaches to alleviating these biases. This paper describes the implementation of a selected set of these parameterizations and their effects on the simulated hydrological cycle. The modifications consist of surface data sets based on Moderate Resolution Imaging Spectroradiometer products, new parameterizations for canopy integration, canopy interception, frozen soil, soil water availability, and soil evaporation, a TOPMODEL-based model for surface and subsurface runoff, a groundwater model for determining water table depth, and the introduction of a factor to simulate nitrogen limitation on plant productivity. The results from a set of offline simulations were compared with observed data for runoff, river discharge, soil moisture, and total water storage to assess the performance of the new model (referred to as CLM3.5). CLM3.5 exhibits significant improvements in its partitioning of global evapotranspiration (ET) which result in wetter soils, less plant water stress, increased transpiration and photosynthesis, and an improved annual cycle of total water storage. Phase and amplitude of the runoff annual cycle is generally improved. Dramatic improvements in vegetation biogeography result when CLM3.5 is coupled to a dynamic global vegetation model. Lower than observed soil moisture variability in the rooting zone is noted as a remaining deficiency. --- paper_title: Upscaling Hydraulic Properties and Soil Water Flow Processes in Heterogeneous Soils paper_content: This review covers, in a comprehensive manner, the approaches available in the literature to upscale soil water processes and hydraulic parameters in the vadose zone. We distinguish two categories of upscaling methods: forward approaches requiring information about the spatial distribution of hydraulic parameters at a small scale, and inverse modeling approaches requiring information about the spatial and temporal variation of state variables at various scales, including so-called “soft data”. Geostatistical and scaling approaches are crucial to upscale soil water processes and to derive large-scale effective fluxes and parameters from small-scale information. Upscaling approaches include stochastic perturbation methods, the scaleway approach, the stream-tube approach, the aggregation concept, inverse modeling approaches, and data fusion. With all upscaling methods, the estimated effective parameters depend not only on the properties of the heterogeneous flow field but also on boundary conditions. The use of the Richards equation at the field and watershed scale is based more on pragmatism than on a sound physical basis. There are practically no data sets presently available that provide sufficient information to extensively validate existing upscaling approaches. Use of numerical case studies has therefore been most common. More recently and still under development, hydrogeophysical methods combined with ground-based remote sensing techniques promise significant contributions toward providing high-quality data sets. Finally, most of the upscaling literature in vadose zone research has dealt with bare soils or deep vadose zones. There is a need to develop upscaling methods for real world soils, considering root water uptake mechanisms and other soil–plant–atmosphere interactions. --- paper_title: Estimation of Radiative Transfer Parameters from L-Band Passive Microwave Brightness Temperatures Using Advanced Data Assimilation paper_content: ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been designed to extend our knowledge of the Earth's water cycle. Soil Moisture and Ocean Salinity records brightness temperatures at the L-band, which over land are sensitive to soil and vegetation parameters. On the basis of these measurements, soil moisture and vegetation opacity data sets have been derived operationally since 2009 for applications comprising hydrology, numerical weather prediction (NWP), and drought monitoring. We present a method to enhance the knowledge about the temporal evolution of radiative transfer parameters. The radiative transfer model L-Band Microwave Emission of the Biosphere (L-MEB) is used within a data assimilation framework to retrieve vegetation opacity and soil surface roughness. To analyze the ability of the data assimilation approach to track the temporal evolution of these parameters, scenario analyses were performed with increasing complexity. First, the HYDRUS-1D code was used to generate soil moisture and soil temperature time series. On the basis of these data, the L-MEB forward model was run to simulate brightness temperature observations. Finally, the coupled model system HYDRUS-1D and L-MEB were integrated into a data assimilation framework using a particle filter, which is able to update L-MEB states as well as L-MEB parameters. Time invariant and time variable radiative transfer parameters were estimated. Moreover, it was possible to estimate a "bias" term when model simulations show a systematic difference as compared to observations. An application to a USDA-NRCS Soil Climate Analysis Network (SCAN) site showed the good performance of the proposed approach under real conditions. --- paper_title: Assimilation of Disaggregated Microwave Soil Moisture into a Hydrologic Model Using Coarse-Scale Meteorological Data paper_content: Abstract Near-surface soil moisture retrieved from Soil Moisture and Ocean Salinity (SMOS)-type data is downscaled and assimilated into a distributed soil–vegetation–atmosphere transfer (SVAT) model with the ensemble Kalman filter. Because satellite-based meteorological data (notably rainfall) are not currently available at finescale, coarse-scale data are used as forcing in both the disaggregation and the assimilation. Synthetic coarse-scale observations are generated from the Monsoon ‘90 data by aggregating the Push Broom Microwave Radiometer (PBMR) pixels covering the eight meteorological and flux (METFLUX) stations and by averaging the meteorological measurements. The performance of the disaggregation/assimilation coupling scheme is then assessed in terms of surface soil moisture and latent heat flux predictions over the 19-day period of METFLUX measurements. It is found that the disaggregation improves the assimilation results, and vice versa, the assimilation of the disaggregated microwave soil mois... --- paper_title: Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation paper_content: Abstract With well-determined hydraulic parameters in a hydrologic model, a traditional data assimilation method (such as the Kalman filter and its extensions) can be used to retrieve root zone soil moisture under uncertain initial state variables (e.g., initial soil moisture content) and good simulated results can be achieved. However, when the key soil hydraulic parameters are incorrect, the error is non-Gaussian, as the Kalman filter will produce a persistent bias in its predictions. In this paper, we propose a method coupling optimal parameters and extended Kalman filter data assimilation (OP-EKF) by combining optimal parameter estimation, the extended Kalman filter (EKF) assimilation method, a particle swarm optimization (PSO) algorithm, and Richards’ equation. We examine the accuracy of estimating root zone soil moisture through the optimal parameters and extended Kalman filter data assimilation method by using observed in situ data at the Meiling experimental station, China. Results indicate that merely using EKF for assimilating surface soil moisture content to obtain soil moisture content in the root zone will produce a persistent bias between simulated and observed values. Using the OP-EKF assimilation method, estimates were clearly improved. If the soil profile is heterogeneous, soil moisture retrieval is accurate in the 0–50 cm soil profile and is inaccurate at 100 cm depth. Results indicate that the method is useful for retrieving root zone soil moisture over large areas and long timescales even when available soil moisture data are limited to the surface layer, and soil moisture content are uncertain and soil hydraulic parameters are incorrect. --- paper_title: Multi-scale assimilation of root zone soil water predictions paper_content: When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water content). The simulated result can be quite good. However, when a key soil hydraulic property, such as the saturated hydraulic conductivity, is overestimated or underestimated, the traditional soil water assimilation process will produce a persistent bias in its predictions. In this paper, we present and demonstrate a new multi-scale assimilation method by combining the direct insertion assimilation method, particle swarm optimisation (PSO) algorithm and Richards equation. We study the possibility of estimating root zone soil water with a multi-scale assimilation method by using observed in situ data from the Wudaogou experiment station, Huaihe River Basin, China. The results indicate there is a persistent bias between simulated and observed values when the direct insertion assimilation surface soil water content is used to estimate root zone soil water contents. Using a multi-scale assimilation method (PSO algorithm and direct insertion assimilation) and an assumed bottom boundary condition, the results show some obvious improvement, but the root mean square error is still relatively large. When the bottom boundary condition is similar to the actual situation, the multi-scale assimilation method can well represent the root zone soil water content. The results indicate that the method is useful in estimating root zone soil water when available soil water data are limited to the surface layer and the initial soil water content even when the soil hydraulic conductivities are uncertain. Copyright © 2011 John Wiley & Sons, Ltd. --- paper_title: Multi-scale assimilation of surface soil moisture data for robust root zone moisture predictions paper_content: In the presence of uncertain initial conditions and soil hydraulic properties land surface model performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the near surface soil moisture as observed from a remote platform. Recently, Montaldo et al. [Water Resour Res 37 (2001) 2889] derived a framework that uses biases between observed and modeled time rates of change of surface soil moisture to quantify biases between modeled and actual root-zone-average soil moisture contents. For very large errors in the saturated conductivity the soil moisture assimilation procedure is continuously working against the drainage errors, resulting in a persistent bias in its predictions. In this paper, we adopt this persistent (directional) bias in soil moisture as evidence of an error in the saturated conductivity. From manipulations of soil water balance equations we derived an expression that quantitatively relates the persistent bias in soil moisture to the estimated error in the saturated hydraulic conductivity. We combined this result with the approach of Montaldo et al. [Water Resour Res 37 (2001) 2889] to form a multi-scale assimilation approach. The multi-scale assimilation system is shown to provide marked improvements in the prediction of root zone soil moisture for a case study using data taken from an experimental catchment near Cork, Ireland. In effect, the root zone moisture is updated to provide a temporal trajectory of the near surface moisture that follows the trajectory of the observed surface moisture, and the hydraulic conductivity is adjusted on the basis of the time averaged corrections applied to the root zone water content. It is anticipated that this approach would be useful in operational forecasting models over large domains, where system parameters would be uncertain and occasional distributed observations would be limited to the near surface zone. --- paper_title: How much improvement can precipitation data fusion achieve with a Multiscale Kalman Smoother-based framework? paper_content: [1] With advancements in measuring techniques and modeling approaches, more and more precipitation data products, with different spatial resolutions and accuracies, become available. Therefore, there is an increasing need to produce a fused precipitation product that can take advantage of the strengths of each individual precipitation data product. This study systematically and quantitatively evaluates the improvements of the fused precipitation data as a result of using the Mulitscale Kalman Smoother-based (i.e., MKS-based) framework. Impacts of two types of errors, i.e., white noise and bias that are associated with individual precipitation products, are investigated through hypothetical experiments. Two measures, correlation and root-mean-square error, are used to evaluate the improvements of the fused precipitation data. Our study shows that the MKS-based framework can significantly recover the loss of precipitation's spatial patterns and magnitudes that are associated with the white noise and bias when the erroneous data at different spatial scales are fused together. Although the erroneous data at a finer resolution are generally more effective in improving the spatial patterns and magnitudes of the erroneous data at a coarser resolution, data at a coarser resolution can also provide valuable information in improving the quality of the data at a finer resolution when they are fused. This study provides insights on the values of the MKS-based framework and a guideline for determining a potentially optimal spatial scale over which improvements in both the spatial patterns and the magnitudes can be maximized based on given data with different spatial resolutions. --- paper_title: A Multiscale Ensemble Filtering System for Hydrologic Data Assimilation. Part I: Implementation and Synthetic Experiment paper_content: Abstract The multiscale autoregressive (MAR) framework was introduced in the last decade to process signals that exhibit multiscale features. It provides the method for identifying the multiscale structure in signals and a filtering procedure, and thus is an efficient way to solve the optimal estimation problem for many high-dimensional dynamic systems. Later, an ensemble version of this multiscale filtering procedure, the ensemble multiscale filter (EnMSF), was developed for estimation systems that rely on Monte Carlo samples, making this technique suitable for a range of applications in geosciences. Following the prototype study that introduced EnMSF, a strategy is devised here to implement the multiscale method in a hydrologic data assimilation system, which runs a land surface model. Assimilation experiments are carried out over the Arkansas–Red River basin, located in the central United States (∼645 000 km2), using the Variable Infiltration Capacity (VIC) model with a computing grid of 1062 pixels. A... --- paper_title: Effects of uncertainty magnitude and accuracy on assimilation of multiscale measurements for snowpack characterization paper_content: [1] Hydrologists regularly utilize data assimilation (DA) techniques to merge remote sensing measurements with physical models in order to characterize hydrologic reservoirs. DA methods generally require an estimate of the uncertainty of the various inputs to the models; in practice, however, the uncertainty of these quantities is often unknown. This paper explores the effects of the unknown uncertainty on the efficiency of a multifrequency, multiscale hydrologic DA scheme for snowpack characterization. Synthetic passive microwave (PM) measurements at 25 km and near-infrared (NIR) measurements at 1 km were assimilated, and both snow water equivalent (SWE) and grain size were estimated at 1 km resolution. It is found that the uncertainty magnitude had a significant effect on the efficiency of both SWE and grain size estimation, but that the uncertainty magnitude had very different effects on these two variables because of the different PM and NIR measurement scales. Secondly, it was found that the uncertainty accuracy had a very important role in this DA scheme and that the filter may degrade the estimate of SWE and grain size if key model inputs are misspecified. Finally, four metrics were used to assess the difference between the PM and NIR measurement innovations and their expected values. It was shown that these metrics could potentially be used in an adaptive filtering scheme to correct misspecified uncertainty. More investigation will be required before the feasibility of such an adaptive filtering scheme is established. These findings have important ramifications for snowpack estimation since it implies that in the context of DA schemes, better use will be made of remote sensing products when better physical characterization of the uncertainty of modeled estimates of snow states is available. --- paper_title: Multiscale assimilation of Advanced Microwave Scanning Radiometer-EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado: SATELLITE-OBSERVED SNOW DATA ASSIMILATION paper_content: [1] Eight years (2002–2010) of Advanced Microwave Scanning Radiometer–EOS (AMSR-E) snow water equivalent (SWE) retrievals and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule-based update. The satellite data are either left unscaled or are scaled for anomaly assimilation. The results are validated against in situ observations at 14 high-elevation Snowpack Telemetry (SNOTEL) sites with typically deep snow and at 4 lower-elevation Cooperative Observer Program (COOP) sites. Assimilation of coarse-scale AMSR-E SWE and fine-scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with shallow snowpacks, AMSR-E SWE and MODIS SCF data assimilation are beneficial separately, and joint SWE and SCF assimilation yields significantly improved root-mean-square error and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however, AMSR-E retrievals are typically biased low and assimilation without prior scaling leads to degraded SWE estimates. Anomaly SWE assimilation could not improve the interannual SWE variations in the assimilation results because the AMSR-E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near-complete snow cover. Across all sites, SCF assimilation improves the timing of the onset of the snow season but without a net improvement of SWE amounts. --- paper_title: Assimilation of Soil Wetness Index and Leaf Area Index into the ISBA-A-gs land surface model: grassland case study paper_content: Abstract. The performance of the joint assimilation in a land surface model of a Soil Wetness Index (SWI) product provided by an exponential filter together with Leaf Area Index (LAI) is investigated. The data assimilation is evaluated with different setups using the SURFEX modeling platform, for a period of seven years (2001–2007), at the SMOSREX grassland site in southwestern France. The results obtained with a Simplified Extended Kalman Filter demonstrate the effectiveness of a joint data assimilation scheme when both SWI and Leaf Area Index are merged into the ISBA-A-gs land surface model. The assimilation of a retrieved Soil Wetness Index product presents several challenges that are investigated in this study. A significant improvement of around 13 % of the root-zone soil water content is obtained by assimilating dimensionless root-zone SWI data. For comparison, the assimilation of in situ surface soil moisture is considered as well. A lower impact on the root zone is noticed. Under specific conditions, the transfer of the information from the surface to the root zone was found not accurate. Also, our results indicate that the assimilation of in situ LAI data may correct a number of deficiencies in the model, such as low LAI values in the senescence phase by using a seasonal-dependent error definition for background and observations. In order to verify the specification of the errors for SWI and LAI products, a posteriori diagnostics are employed. This approach highlights the importance of the assimilation design on the quality of the analysis. The impact of data assimilation scheme on CO2 fluxes is also quantified by using measurements of net CO2 fluxes gathered at the SMOSREX site from 2005 to 2007. An improvement of about 5 % in terms of rms error is obtained. --- paper_title: Multisensor snow data assimilation at the continental scale: The value of Gravity Recovery and Climate Experiment terrestrial water storage information paper_content: [1] This investigation establishes a multisensor snow data assimilation system over North America (from January 2002 to June 2007), toward the goal of better estimation of snowpack (in particular, snow water equivalent and snow depth) via incorporating both Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) information into the Community Land Model. The different properties associated with the SCF and TWS observations are accommodated through a unified approach using the ensemble Kalman filter and smoother. Results show that this multisensor approach can provide significant improvements over a MODIS-only approach, for example, in the Saint Lawrence, Fraser, Mackenzie, Churchill & Nelson, and Yukon river basins and the southwestern rim of Hudson Bay. At middle latitudes, for example, the North Central and Missouri river basins, the inclusion of GRACE information preserves the advantages (compared with the open loop) shown in the MODIS-only run. However, in some high-latitude areas and given months the open loop run shows a comparable or even better performance, implying considerable room for refinements of the multisensor algorithm. In addition, ensemble-based metrics are calculated and interpreted domainwide. They indicate the potential importance of accurate representation of snow water equivalent autocovariance in assimilating TWS observations and the regional and/or seasonal dependence of GRACE’s capability to reduce ensemble variance. These analyses contribute to clarifying the effects of GRACE’s special features (e.g., a vertical integral of different land water changes, coarse spatial and temporal resolution) in the snow data assimilation system. --- paper_title: Improving Spatial Soil Moisture Representation Through Integration of AMSR-E and MODIS Products paper_content: The use of microwave observations has been highlighted as a complementary tool for evaluating land surface properties. Microwave observations are less affected by clouds, water vapor, and aerosol and also contain valuable soil moisture information. However, a critical limitation in microwave observations is the coarse spatial resolution attributed to the complex retrieval process. The objective of the current study is to develop an independent (from ground observations) downscaling approach that merges information from higher spatial resolution MODerate-resolution Imaging Spectroradiometer (MODIS) (~1 km) with lower spatial resolution AMSR-E (~25 km) to obtain soil moisture estimates at the MODIS scale (~1 km). We compare the developed (UCLA) method against a range of previous published approaches. Various key factors (i.e., surface temperature, vegetation indexes, and albedo) derived from MODIS provide information on relative variations in surface wetness conditions and contribute weighting parameters for downscaling the larger AMSR-E soil moisture footprints. Evaluation of the various downscaled soil moisture products is undertaken at the SMEX04 site in southern Arizona. Results show that the UCLA downscaling technique, as well as the previously published Merlin method, significantly improves the limited spatial variability of the current AMSR-E product. Spatial correlation (R) values improved from -0.08 to 0.34 and 0.27 for the Merlin and UCLA methods, respectively. The evaluated triangle-based methods show poorer performance over the study domain. Results from the current study yield insight on the integration of multiscale remote sensing data in various downscaling methods and the usefulness of MODIS observations in compensating for low-resolution microwave observations. --- paper_title: A Land Data Assimilation System for Soil Moisture and Temperature: An Information Content Study paper_content: Abstract A Canadian Land Data Assimilation System (CaLDAS) for the analysis of land surface prognostic variables is designed and implemented at the Meteorological Service of Canada for the initialization of numerical weather prediction and climate models. The assimilation of different data sources for the production of daily soil moisture and temperature analyses is investigated in a set of observing system simulation experiments over North America. A simplified variational technique is adapted to accommodate different observation types at their appropriate time in a 24-h time window. The screen-level observations of temperature and relative humidity, from conventional synoptic surface observations (SYNOP)/aviation routine weather report (METAR)/surface aviation observation (SA) reports, are considered together with presently available satellite observations provided by the Aqua satellite (microwave C-band), Geostationary Operational Environmental Satellite (GOES) [infrared (IR)], and observations availab... --- paper_title: Novel approach to nonlinear/non-Gaussian Bayesian state estimation paper_content: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linear- ity or Gaussian noise: it may be applied to any state transition or measurement model. A simula- tion example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter. --- paper_title: Three-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: Simplified Kalman filter covariance forecasting and field application: THREE-DIMENSIONAL SOIL MOISTURE ASSIMILATION paper_content: [1] The Kalman filter data assimilation technique is applied to a distributed three-dimensional soil moisture model for retrieval of the soil moisture profile in a 6 ha catchment using near-surface soil moisture measurements. A simplified Kalman filter covariance forecasting methodology is developed based on forecasting of the state correlations and imposed state variances. This covariance forecasting technique, termed the modified Kalman filter, was then used in a 1 month three-dimensional field application. Two updating scenarios were tested: (1) updating every 2 to 3 days and (2) a single update. The data used were from the Nerrigundah field site, near Newcastle, Australia. This study demonstrates the feasibility of data assimilation in a quasi three-dimensional distributed soil moisture model, provided simplified covariance forecasting techniques are used. It also identifies that (1) the soil moisture profile cannot be retrieved from near-surface soil moisture measurements when the near-surface and deep soil layers become decoupled, such as during extreme drying events; (2) if simulation of the soil moisture profile is already good, the assimilation can result in a slight degradation, but if the simulation is poor, assimilation can yield a significant improvement; (3) soil moisture profile retrieval results are independent of initial conditions; and (4) the required update frequency is a function of the errors in model physics and forcing data. --- paper_title: Earth-Viewing L-Band Radiometer Sensing of Sea Surface Scattered Celestial Sky Radiation—Part II: Application to SMOS paper_content: We examine how the rough sea surface scattering of L-band celestial sky radiation might affect the measurements of the future European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. For this purpose, we combined data from several surveys to build a comprehensive all-sky L-band celestial sky brightness temperature map for the SMOS mission that includes the continuum radiation and the hydrogen line emission rescaled for the SMOS bandwidth. We also constructed a separate map of strong and very localized sources that may exhibit L-band brightness temperatures exceeding 1000 K. Scattering by the roughened ocean surface of radiation from even the strongest localized sources is found to reduce the contributions from these localized strong sources to negligible levels, and rough surface scattering solutions may be obtained with a map much coarser than the original continuum maps. In rough ocean surface conditions, the contribution of the scattered celestial noise to the reconstructed brightness temperatures is not significantly modified by the synthetic antenna weighting function, which makes integration over the synthetic beam unnecessary. The contamination of the reconstructed brightness temperatures by celestial noise exhibits a strong annual cycle with the largest contamination occurring in the descending swaths in September and October, when the specular projection of the field of view is aligned with the Galactic equator. Ocean surface roughness may alter the contamination by over 0.1 K in 30% of the SMOS measurements. Given this potentially large impact of surface roughness, an operational method is proposed to account for it in the SMOS level 2 sea surface salinity algorithm. --- paper_title: The Soil Moisture Active Passive (SMAP) Mission paper_content: The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame. --- paper_title: An Algorithm for Merging SMAP Radiometer and Radar Data for High-Resolution Soil-Moisture Retrieval paper_content: A robust and simple algorithm is developed to merge L-band radiometer retrievals and L-band radar observations to obtain high-resolution (9-km) soil-moisture estimates from data of the NASA Soil Moisture Active and Passive (SMAP) mission. The algorithm exploits the established accuracy of coarse-scale radiometer soil-moisture retrievals and blends this with the fine-scale spatial heterogeneity detectable by radar observations to produce a high-resolution optimal soil-moisture estimate at 9 km. The capability of the algorithm is demonstrated by implementing the approach using the airborne Passive and Active L-band System (PALS) instrument data set from Soil Moisture Experiments 2002 (SMEX02) and a four-month synthetic data set in an Observation System Simulation Experiment (OSSE) framework. The results indicate that the algorithm has the potential to obtain better soil-moisture accuracy at a high resolution and show an improvement in root-mean-square error of 0.015-0.02-cm3/cm3 volumetric soil moisture over the minimum performance taken to be retrievals based on radiometer measurements resampled to a finer scale. These results are based on PALS data from SMEX02 and a four-month OSSE data set and need to be further confirmed for different hydroclimatic regions using airborne data sets from prelaunch calibration/validation field campaigns of the SMAP mission. --- paper_title: Model-Based Satellite Image Fusion paper_content: A method is proposed for pixel-level satellite image fusion derived directly from a model of the imaging sensor. By design, the proposed method is spectrally consistent. It is argued that the proposed method needs regularization, as is the case for any method for this problem. A framework for pixel neighborhood regularization is presented. This framework enables the formulation of the regularization in a way that corresponds well with our prior assumptions of the image data. The proposed method is validated and compared with other approaches on several data sets. Lastly, the intensity-hue-saturation method is revisited in order to gain additional insight of what implications the spectral consistency has for an image fusion method. --- paper_title: Image Analysis, Classification, and Change Detection in Remote Sensing paper_content: Images, Arrays, and Vectors. Image Statistics. Transformations. Radiometric Enhancement. Topographic Modeling. Image Registration. Image Sharpening. Change Detection. Unsupervised Classification. Supervised Classification. Hyperspectral Analysis. --- paper_title: Multispectral imagery band sharpening study paper_content: The fusion of multisensor and multiresolution satellite imagery is an effective means of exploiting the complimentary nature of various image types. With band sharpening (a type of imagery fusion), higher spatial resolution panchromatic data is fused with lower resolution multispectral imagery (MSI). This fusion creates a product with the spectral characteristics of the MSI and a spatial resolution approaching that of the panchromatic image (effective ground-sample distance, or GSD). Others have fused 10-m SPOT panchromatic with 20-m SPOT MSI or 30-m Landsat TM (sharpening factors of 2:l and 3:1). In this study, MSI of 10 m to 30 m was sharpened with 5- to 15-m imagery (sharpening factors of 2:1 to 6:l) using four algorithms. The research goals were to (1) determine the validity of the concept of "effective GSD"; (2) determine the relative utility of band sharpening by different factors; and (3) compare the relative effectiveness of different band sharpening algorithms. --- paper_title: Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresol paper_content: This paper compares two general and formal solutions to the problem of fusion of multispectral images with high-resolution panchromatic observations. The former exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and upsampling the wavelet filter bank. The latter relies on the generalized Laplacian pyramid, which is another oversampled structure obtained by recursively subtracting from an image an expanded decimated lowpass version. Both the methods selectively perform spatial-fre- quencies spectrum substitution from an image to another. In both schemes, context dependency is exploited by thresholding the local correlation coefficient between the images to be merged, to avoid injection of spatial details that are not likely to occur in the target image. Unlike other multiscale fusion schemes, both the present decompositions are not critically subsampled, thus avoiding possible impairments in the fused images, due to missing cancellation of aliasing terms. Results are presented and discussed on SPOT data. --- paper_title: Variational data assimilation of microwave radiobrightness observations for land surface hydrology applications paper_content: Our ability to accurately describe large-scale variations in soil moisture is severely restricted by process uncertainty and the limited availability of appropriate soil moisture data. Remotely sensed microwave radiobrightness observations can cover large scales but have limited resolution and are only indirectly related to the hydrologic variables of interest. The authors describe a four-dimensional (4D) variational assimilation algorithm that makes best use of available information while accounting for both measurement and model uncertainty. The representer method used is more efficient than a Kalman filter because it avoids explicit propagation of state error covariances. In a synthetic example, which is based on a field experiment, the authors demonstrate estimation performance by examining data residuals. Such tests provide a convenient way to check the statistical assumptions of the approach and to assess its operational feasibility. Internally computed covariances show that the estimation error decreases with increasing soil moisture. An adjoint analysis reveals that trends in model errors in the soil moisture equation can be estimated from daily L-band brightness measurements, whereas model errors in the soil and canopy temperature equations cannot be adequately retrieved from daily data alone. Nonetheless, state estimates obtained from the assimilation algorithm improve significantly on prior model predictions derived without assimilation of radiobrightness data. --- paper_title: Multiscale Recursive Estimation, Data Fusion, and Regularization paper_content: We describe a framework for modeling stochastic phenomena at multiple scales and for their efficient estimation or reconstruction given partial and/or noisy measurements which may also be at several scales. In particular multiscale signal representations lead naturally to pyramidal or tree-like data structures in which each level in the tree corresponds to a particular scale of representation. A class of multiscale dynamic models evolving on dyadic trees is introduced. The main focus of this paper is on the description, analysis, and application of an extremely efficient optimal estimation algorithm for this class of models. This algorithm consists of a fine-to-coarse filtering sweep, followed by a coarse-to-fine smoothing step, corresponding to the dyadic tree generalization of Kalman filtering and Rauch-Tung-Striebel smoothing. The Kalman filtering sweep consists of the recursive application of three steps: a measurement update step, a fine-to-coarse prediction step, and a fusion step. We illustrate the use of our methodology for the fusion of multiresolution data and for the efficient solution of "fractal regularizations" of ill-posed signal and image processing problems encountered. > --- paper_title: Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission paper_content: Microwave radiometry at low frequencies (L-band: 1.4 GHz, 21 cm) is an established technique for estimating surface soil moisture and sea surface salinity with a suitable sensitivity. However, from space, large antennas (several meters) are required to achieve an adequate spatial resolution at L-band. So as to reduce the problem of putting into orbit a large filled antenna, the possibility of using antenna synthesis methods has been investigated. Such a system, relying on a deployable structure, has now proved to be feasible and has led to the Soil Moisture and Ocean Salinity (SMOS) mission, which is described. The main objective of the SMOS mission is to deliver key variables of the land surfaces (soil moisture fields), and of ocean surfaces (sea surface salinity fields). The SMOS mission is based on a dual polarized L-band radiometer using aperture synthesis (two-dimensional [2D] interferometer) so as to achieve a ground resolution of 50 km at the swath edges coupled with multiangular acquisitions. The radiometer will enable frequent and global coverage of the globe and deliver surface soil moisture fields over land and sea surface salinity over the oceans. The SMOS mission was proposed to the European Space Agency (ESA) in the framework of the Earth Explorer Opportunity Missions. It was selected for a tentative launch in 2005. The goal of this paper is to present the main aspects of the baseline mission and describe how soil moisture will be retrieved from SMOS data. --- paper_title: Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo paper_content: Abstract This study considers advanced statistical approaches for sequential data assimilation. These are explored in the context of nowcasting and forecasting using nonlinear differential equation based marine ecosystem models assimilating sparse and noisy non-Gaussian multivariate observations. The statistical framework uses a state space model with the goal of estimating the time evolving probability distribution of the ecosystem state. Assimilation of observations relies on stochastic dynamic prediction and Bayesian principles. In this study, a new sequential data assimilation approach is introduced based on Markov Chain Monte Carlo (MCMC). The ecosystem state is represented by an ensemble, or sample, from which distributional properties, or summary statistical measures, can be derived. The Metropolis-Hastings based MCMC approach is compared and contrasted with two other sequential data assimilation approaches: sequential importance resampling, and the (approximate) ensemble Kalman filter (including computational comparisons). A simple illustrative application is provided based on a 0-D nonlinear plankton ecosystem model with multivariate non-Gaussian observations of the ecosystem state from a coastal ocean observatory. The MCMC approach is shown to be straightforward to implement and to effectively characterize the non-Gaussian ecosystem state in both nowcast and forecast experiments. Results are reported which illustrate how non-Gaussian information originates, and how it can be used to characterize ecosystem properties. --- paper_title: Using area-average remotely sensed surface soil moisture in multipatch land data assimilation systems paper_content: In coming years, Land Data Assimilation Systems (LDAS) two-dimensional (2-D) arrays of the relevant land-surface model) are likely to become the routine mechanism by which many predictive weather and climate models will be initiated. If this is so, it will be via assimilation into the LDAS that other data relevant to the land surface, such as remotely sensed estimates of soil moisture, will find value. This paper explores the potential for using low-resolution, remotely sensed observations of microwave brightness temperature to infer soil moisture in an LDAS with a "mosaic-patch" representation of land-surface heterogeneity, by coupling the land-surface model in the LDAS to a physically realistic microwave emission model. The past description of soil water movement by the LDAS is proposed as the most appropriate, LDAS-consistent basis for using remotely sensed estimates of surface soil moisture to infer soil moisture at depth, and the plausibility of this proposal is investigated. Three alternative methods are explored for partitioning soil moisture between modeled patches while altering the area-average soil moisture to correspond to the observed, pixel-average microwave brightness temperature, namely, 1) altering the soil moisture by a factor, which is the same for all the patches in the pixel, 2) altering the soil moisture by adding an amount that is the same for all the patches in the pixel, and 3) altering the change in soil moisture since the last assimilation cycle by a factor which is the same for all the patches in the pixel. --- paper_title: Estimation of Radiative Transfer Parameters from L-Band Passive Microwave Brightness Temperatures Using Advanced Data Assimilation paper_content: ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been designed to extend our knowledge of the Earth's water cycle. Soil Moisture and Ocean Salinity records brightness temperatures at the L-band, which over land are sensitive to soil and vegetation parameters. On the basis of these measurements, soil moisture and vegetation opacity data sets have been derived operationally since 2009 for applications comprising hydrology, numerical weather prediction (NWP), and drought monitoring. We present a method to enhance the knowledge about the temporal evolution of radiative transfer parameters. The radiative transfer model L-Band Microwave Emission of the Biosphere (L-MEB) is used within a data assimilation framework to retrieve vegetation opacity and soil surface roughness. To analyze the ability of the data assimilation approach to track the temporal evolution of these parameters, scenario analyses were performed with increasing complexity. First, the HYDRUS-1D code was used to generate soil moisture and soil temperature time series. On the basis of these data, the L-MEB forward model was run to simulate brightness temperature observations. Finally, the coupled model system HYDRUS-1D and L-MEB were integrated into a data assimilation framework using a particle filter, which is able to update L-MEB states as well as L-MEB parameters. Time invariant and time variable radiative transfer parameters were estimated. Moreover, it was possible to estimate a "bias" term when model simulations show a systematic difference as compared to observations. An application to a USDA-NRCS Soil Climate Analysis Network (SCAN) site showed the good performance of the proposed approach under real conditions. --- paper_title: Computationally Efficient Stochastic Realization for Internal Multiscale Autoregressive Models paper_content: In this paper we develop a stochastic realization theory for multiscale autoregressive (MAR) processes that leads to computationally efficient realization algorithms. The utility of MAR processes has been limited by the fact that the previously known general purpose realization algorithm, based on canonical correlations, leads to model inconsistencies and has complexity quartic in problem size. Our realization theory and algorithms addresses these issues by focusing on the estimation-theoretic concept of predictive efficiency and by exploiting the scale-recursive structure of so-called internal MAR processes. Our realization algorithm has complexity quadratic in problem size and with an approximation we also obtain an algorithm that has complexity linear in problem size. --- paper_title: Validation of SMOS Brightness Temperatures During the HOBE Airborne Campaign, Western Denmark paper_content: The Soil Moisture and Ocean Salinity (SMOS) mission delivers global surface soil moisture fields at high temporal resolution which is of major relevance for water management and climate predictions. Between April 26 and May 9, 2010, an airborne campaign with the L-band radiometer EMIRAD-2 was carried out within one SMOS pixel (44 × 44 km) in the Skjern River Catchment, Denmark. Concurrently, ground sampling was conducted within three 2 × 2 km patches (EMIRAD footprint size) of differing land cover. By means of this data set, the objective of this study is to present the validation of SMOS L1C brightness temperatures TB of the selected node. Data is stepwise compared from point via EMIRAD to SMOS scale. From ground soil moisture samples, TB's are pointwise estimated through the L-band microwave emission of the biosphere model using land cover specific model settings. These TB's are patchwise averaged and compared with EMIRAD TB's. A simple uncertainty assessment by means of a set of model runs with the most influencing parameters varied within a most likely interval results in a considerable spread of TB's (5-20 K). However, for each land cover class, a combination of parameters could be selected to bring modeled and EMIRAD data in good agreement. Thereby, replacing the Dobson dielectric mixing model with the Mironov model decreases the overall RMSE from 11.5 K to 3.8 K. Similarly, EMIRAD data averaged at SMOS scale and corresponding SMOS TB 's show good accordance on the single day where comparison is not prevented by strong radio-frequency interference (RFI) (May 2, avg. RMSE = 9.7 K). While the advantages of solid data sets of high spatial coverage and density throughout spatial scales for SMOS validation could be clearly demonstrated, small temporal variability in soil moisture conditions and RFI contamination throughout the campaign limited the extent of the validation work. Further attempts over longer time frames are planned by means of soil moisture network data as well as studies on the impacts of organic layers under natural vegetation and higher open water fractions at surrounding grid nodes. --- paper_title: Assimilation of passive and active microwave soil moisture retrievals paper_content: [1] Near-surface soil moisture observations from the active microwave ASCAT and the passive microwave AMSR-E satellite instruments are assimilated, both separately and together, into the NASA Catchment land surface model over 3.5 years using an ensemble Kalman filter. The impact of each assimilation is evaluated using in situ soil moisture observations from 85 sites in the US and Australia, in terms of the anomaly time series correlation-coefficient, R. The skill gained by assimilating either ASCAT or AMSR-E was very similar, even when separated by land cover type. Over all sites, the mean root-zone R was significantly increased from 0.45 for an open-loop, to 0.55, 0.54, and 0.56 by the assimilation of ASCAT, AMSR-E, and both, respectively. Each assimilation also had a positive impact over each land cover type sampled. For maximum accuracy and coverage it is recommended that active and passive microwave observations be assimilated together. --- paper_title: Brightness Temperature and Soil Moisture Validation at Different Scales During the SMOS Validation Campaign in the Rur and Erft Catchments, Germany paper_content: The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite was launched in November 2009 and delivers now brightness temperature and soil moisture products over terrestrial areas on a regular three-day basis. In 2010, several airborne campaigns were conducted to validate the SMOS products with microwave emission radiometers at L-band (1.4 GHz). In this paper, we present results from measurements performed in the Rur and Erft catchments in May and June 2010. The measurement sites were situated in the very west of Germany close to the borders to Belgium and The Netherlands. We developed an approach to validate spatial and temporal SMOS brightness temperature products. An area-wide brightness temperature reference was generated by using an area-wide modeling of top soil moisture and soil temperature with the WaSiM-ETH model and radiative transfer calculation based on the L-band Microwave Emission of the Biosphere model. Measurements of the airborne L-band sensors EMIRAD and HUT-2D on-board a Skyvan aircraft as well as ground-based mobile measurements performed with the truck mounted JULBARA L-band radiometer were analyzed for calibration of the simulated brightness temperature reference. Radiative transfer parameters were estimated by a data assimilation approach. By this versatile reference data set, it is possible to validate the spaceborne brightness temperature and soil moisture data obtained from SMOS. However, comparisons with SMOS observations for the campaign period indicate severe differences between simulated and observed SMOS data. --- paper_title: An Ensemble Multiscale Filter for Large Nonlinear Data Assimilation Problems paper_content: Abstract Operational data assimilation problems tend to be very large, both in terms of the number of unknowns to be estimated and the number of measurements to be processed. This poses significant computational challenges, especially for ensemble methods, which are critically dependent on the number of replicates used to derive sample covariances and other statistics. Most efforts to deal with the related problems of computational effort and sampling error in ensemble estimation have focused on spatial localization. The ensemble multiscale Kalman filter described here offers an alternative approach that effectively replaces, at each update time, the prior (or background) sample covariance with a multiscale tree. The tree is composed of nodes distributed over a relatively small number of discrete scales. Global correlations between variables at different locations are described in terms of local relationships between nodes at adjacent scales (parents and children). The Kalman updating process can be carri... --- paper_title: Assimilation of Disaggregated Microwave Soil Moisture into a Hydrologic Model Using Coarse-Scale Meteorological Data paper_content: Abstract Near-surface soil moisture retrieved from Soil Moisture and Ocean Salinity (SMOS)-type data is downscaled and assimilated into a distributed soil–vegetation–atmosphere transfer (SVAT) model with the ensemble Kalman filter. Because satellite-based meteorological data (notably rainfall) are not currently available at finescale, coarse-scale data are used as forcing in both the disaggregation and the assimilation. Synthetic coarse-scale observations are generated from the Monsoon ‘90 data by aggregating the Push Broom Microwave Radiometer (PBMR) pixels covering the eight meteorological and flux (METFLUX) stations and by averaging the meteorological measurements. The performance of the disaggregation/assimilation coupling scheme is then assessed in terms of surface soil moisture and latent heat flux predictions over the 19-day period of METFLUX measurements. It is found that the disaggregation improves the assimilation results, and vice versa, the assimilation of the disaggregated microwave soil mois... --- paper_title: Optimal multiscale Kalman filter for assimilation of near-surface soil moisture into land surface models paper_content: [1] We undertake an alternative and novel approach to assimilation of near-surface soil moisture into land surface models by means of an extension of multiscale Kalman filtering (MKF). While most data assimilation studies rely on the assumption of spatially independent near-surface soil moisture observations to attain computational tractability in large-scale problems, MKF allows us to explicitly and very efficiently model the spatial dependence and scaling properties of near-surface soil moisture fields. Furthermore, MKF has the appealing ability to cope with model predictions and observations made at different spatial scales. Yet another essential feature of our approach is that we resort to the use of the expectation maximization (EM) algorithm in conjunction with MKF so that the statistical parameters inherent to MKF may be optimally determined directly from the data at hand and allowed to vary over time. This constitutes a significant advantage since these parameters (e.g., observation and model error noise variances) essentially determine the performance of the assimilation approach and have so far been most commonly prescribed heuristically and not allowed to evolve in time. We test our approach by assimilating the near-surface soil moisture fields derived from electronically scanned thinned array radiometer (ESTAR) during the Southern Great Plains Hydrology experiment of 1997 (SGP97) into the three-layer variable infiltration capacity (VIC-3L) land surface model. The results show that assimilation significantly improves the short-term predictions of soil moisture and energy fluxes from VIC-3L, especially with regard to capturing the spatial structure of these state variables. Additionally, we find that allowing the statistical parameters of the assimilation algorithm to evolve in time allows for an adequate representation of the time-varying uncertainties in land surface model predictions. --- paper_title: Thermal microwave emission depth and soil moisture remote sensing paper_content: This paper presents some additional theoretical and experimental results on the problem of estimating the soil depth to which soil moisture can be directly measured by VHF radiometers. The experimental work was implemented in August 1992 at the Research Farm of the Institute of Hydrotechnique and Amelioration, Sofia, Bulgaria. The soil thermal microwave emission was measured by an L-band Dicke-type radiometer at 1.65GHz at off-nadir angle from 20 to 60. It was found that the value obtained for the effective penetration depth depends on the definition used in the analysis: the definition of penetration depth is based on soil thermal emissivity or soil reflectivity. Further it was found that the depth to which the soil moisture can be directly measured exhibits a negligible dependence on polarization (horizontal, vertical) as well as on off-nadir observation angle. For the purpose of soil brightness temperature and soil reflectivity computing, a fast and robust method and algorithm were proposed which are b... --- paper_title: Modeling transient groundwater flow by coupling ensemble Kalman filtering and upscaling: COUPLING ENKF AND UPSCALING paper_content: [1] The ensemble Kalman filter (EnKF) is coupled with upscaling to build an aquifer model at a coarser scale than the scale at which the conditioning data (conductivity and piezometric head) had been taken for the purpose of inverse modeling. Building an aquifer model at the support scale of observations is most often impractical since this would imply numerical models with many millions of cells. If, in addition, an uncertainty analysis is required involving some kind of Monte Carlo approach, the task becomes impossible. For this reason, a methodology has been developed that will use the conductivity data at the scale at which they were collected to build a model at a (much) coarser scale suitable for the inverse modeling of groundwater flow and mass transport. It proceeds as follows: (1) Generate an ensemble of realizations of conductivities conditioned to the conductivity data at the same scale at which conductivities were collected. (2) Upscale each realization onto a coarse discretization; on these coarse realizations, conductivities will become tensorial in nature with arbitrary orientations of their principal components. (3) Apply the EnKF to the ensemble of coarse conductivity upscaled realizations in order to condition the realizations to the measured piezometric head data. The proposed approach addresses the problem of how to deal with tensorial parameters, at a coarse scale, in ensemble Kalman filtering while maintaining the conditioning to the fine-scale hydraulic conductivity measurements. We demonstrate our approach in the framework of a synthetic worth-of-data exercise, in which the relevance of conditioning to conductivities, piezometric heads, or both is analyzed. --- paper_title: A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems paper_content: [1] We present an approach for dealing with coarse‐resolution Earth observations (EO) in terrestrial ecosystem data assimilation schemes. The use of coarse‐scale observations in ecological data assimilation schemes is complicated by spatial heterogeneity and nonlinear processes in natural ecosystems. If these complications are not appropriately dealt with, then the data assimilation will produce biased results. The “disaggregation” approach that we describe in this paper combines frequent coarse‐resolution observations with temporally sparse fine‐resolution measurements. We demonstrate the approach using a demonstration data set based on measurements of an Arctic ecosystem. In this example, normalized difference vegetation index observations are assimilated into a “zero‐order” model of leaf area index and carbon uptake. The disaggregation approach conserves key ecosystem characteristics regardless of the observation resolution and estimates the carbon uptake to within 1% of the demonstration data set “truth.” Assimilating the same data in the normal manner, but without the disaggregation approach, results in carbon uptake being underestimated by 58% at an observation resolution of 250 m. The disaggregation method allows the combination of multiresolution EO and improves in spatial resolution if observations are located on a grid that shifts from one observation time to the next. Additionally, the approach is not tied to a particular data assimilation scheme, model, or EO product and can cope with complex observation distributions, as it makes no implicit assumptions of normality. --- paper_title: ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition paper_content: A nontraditional approach to the problem of estimating the parameters of a stochastic linear system is presented. The method is based on the expectation-maximization algorithm and can be considered as the continuous analog of the Baum-Welch estimation algorithm for hidden Markov models. The algorithm is used for training the parameters of a dynamical system model that is proposed for better representing the spectral dynamics of speech for recognition. It is assumed that the observed feature vectors of a phone segment are the output of a stochastic linear dynamical system, and it is shown how the evolution of the dynamics as a function of the segment length can be modeled using alternative assumptions. A phoneme classification task using the TIMIT database demonstrates that the approach is the first effective use of an explicit model for statistical dependence between frames of speech. > --- paper_title: Impact of Accuracy, Spatial Availability, and Revisit Time of Satellite-Derived Surface Soil Moisture in a Multiscale Ensemble Data Assimilation System paper_content: This study evaluates the sensitivity of a multiscale ensemble assimilation system to different configurations of satellite soil moisture observations, namely the retrieval accuracy, spatial availability, and revisit time. We perform horizontally coupled assimilation experiments where pixels are updated not only by observations at the same location but also all in the study domain. Carrying out sensitivity studies within a multiscale assimilation system is a significant advancement over previous studies that used a 1-D assimilation framework where all horizontal grids are uncoupled. Twin experiments are performed with synthetic soil moisture retrievals. The hydrologic modeling system is forced with satellite estimated rainfall, and the assimilation performance is evaluated against model simulations using in-situ measured rainfall. The study shows that the assimilation performance is most sensitive to the spatial availability of soil moisture observations, then to revisit time and least sensitive to retrieval accuracy. The horizontally coupled assimilation system performs reasonably well even with large observation errors, and it is less sensitive to retrieval accuracy than the uncoupled system, as reported by previous studies. This suggests that more information may be extracted from satellite soil moisture observations using multiscale assimilation systems resulting in a potentially higher value of such satellite products. --- paper_title: A simple hydrologically based model of land surface water and energy fluxes for general circulation models paper_content: A generalization of the single soil layer variable infiltration capacity (VIC) land surface hydrological model previously implemented in the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model (GCM) is described. The new model is comprised of a two-layer characterization of the soil column, and uses an aerodynamic representation of the latent and sensible heat fluxes at the land surface. The infiltration algorithm for the upper layer is essentially the same as for the single layer VIC model, while the lower layer drainage formulation is of the form previously implemented in the Max-Planck-Institut GCM. The model partitions the area of interest (e.g., grid cell) into multiple land surface cover types; for each land cover type the fraction of roots in the upper and lower zone is specified. Evapotranspiration consists of three components: canopy evaporation, evaporation from bare soils, and transpiration, which is represented using a canopy and architectural resistance formulation. Once the latent heat flux has been computed, the surface energy balance is iterated to solve for the land surface temperature at each time step. The model was tested using long-term hydrologic and climatological data for Kings Creek, Kansas to estimate and validate the hydrological parameters, and surface flux data frommore » three First International Satellite Land Surface Climatology Project Field Experiment (FIFE) intensive field campaigns in the summer-fall of 1987 to validate the surface energy fluxes.« less --- paper_title: A Multiscale Ensemble Filtering System for Hydrologic Data Assimilation. Part I: Implementation and Synthetic Experiment paper_content: Abstract The multiscale autoregressive (MAR) framework was introduced in the last decade to process signals that exhibit multiscale features. It provides the method for identifying the multiscale structure in signals and a filtering procedure, and thus is an efficient way to solve the optimal estimation problem for many high-dimensional dynamic systems. Later, an ensemble version of this multiscale filtering procedure, the ensemble multiscale filter (EnMSF), was developed for estimation systems that rely on Monte Carlo samples, making this technique suitable for a range of applications in geosciences. Following the prototype study that introduced EnMSF, a strategy is devised here to implement the multiscale method in a hydrologic data assimilation system, which runs a land surface model. Assimilation experiments are carried out over the Arkansas–Red River basin, located in the central United States (∼645 000 km2), using the Variable Infiltration Capacity (VIC) model with a computing grid of 1062 pixels. A... --- paper_title: The Soil Moisture Active Passive (SMAP) Mission paper_content: The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame. --- paper_title: Impact of Multiresolution Active and Passive Microwave Measurements on Soil Moisture Estimation Using the Ensemble Kalman Smoother paper_content: An observing system simulation experiment is developed to test tradeoffs in resolution and accuracy for soil moisture estimation using active and passive L-band remote sensing. Concepts for combined radar and radiometer missions include designs that will provide multiresolution measurements. In this paper, the scientific impacts of instrument performance are analyzed to determine the measurement requirements for the mission concept. The ensemble Kalman smoother (EnKS) is used to merge these multiresolution observations with modeled soil moisture from a land surface model to estimate surface and subsurface soil moisture at 6-km resolution. The model used for assimilation is different from that used to generate "truth." Consequently, this experiment simulates how data assimilation performs in real applications when the model is not a perfect representation of reality. The EnKS is an extension of the ensemble Kalman filter (EnKF) in which observations are used to update states at previous times. Previous work demonstrated that it provides a computationally inexpensive means to improve the results from the EnKF, and that the limited memory in soil moisture can be exploited by employing it as a fixed lag smoother. Here, it is shown that the EnKS can be used in large problems with spatially distributed state vectors and spatially distributed multiresolution observations. The EnKS-based data assimilation framework is used to study the synergy between passive and active observations that have different resolutions and measurement error distributions. The extent to which the design parameters of the EnKS vary depending on the combination of observations assimilated is investigated --- paper_title: Optimal multiscale Kalman filter for assimilation of near-surface soil moisture into land surface models paper_content: [1] We undertake an alternative and novel approach to assimilation of near-surface soil moisture into land surface models by means of an extension of multiscale Kalman filtering (MKF). While most data assimilation studies rely on the assumption of spatially independent near-surface soil moisture observations to attain computational tractability in large-scale problems, MKF allows us to explicitly and very efficiently model the spatial dependence and scaling properties of near-surface soil moisture fields. Furthermore, MKF has the appealing ability to cope with model predictions and observations made at different spatial scales. Yet another essential feature of our approach is that we resort to the use of the expectation maximization (EM) algorithm in conjunction with MKF so that the statistical parameters inherent to MKF may be optimally determined directly from the data at hand and allowed to vary over time. This constitutes a significant advantage since these parameters (e.g., observation and model error noise variances) essentially determine the performance of the assimilation approach and have so far been most commonly prescribed heuristically and not allowed to evolve in time. We test our approach by assimilating the near-surface soil moisture fields derived from electronically scanned thinned array radiometer (ESTAR) during the Southern Great Plains Hydrology experiment of 1997 (SGP97) into the three-layer variable infiltration capacity (VIC-3L) land surface model. The results show that assimilation significantly improves the short-term predictions of soil moisture and energy fluxes from VIC-3L, especially with regard to capturing the spatial structure of these state variables. Additionally, we find that allowing the statistical parameters of the assimilation algorithm to evolve in time allows for an adequate representation of the time-varying uncertainties in land surface model predictions. --- paper_title: A soil-vegetation-atmosphere transfer scheme for modeling spatially variable water and energy balance processes paper_content: In support of the eventual goal to integrate remotely sensed observations with coupled land-atmosphere models, a soil-vegetation-atmosphere transfer scheme is presented which can represent spatially variable water and energy balance processes on timescales of minutes to months. This scheme differs from previous schemes developed to address similar objectives in that it: (1) represents horizontal heterogeneity and transport in a TOPMODEL framework, and (2) maintains computational efficiency while representing the processes most important for our applications. The model is based on the original TOPMODEL-based land surface-atmosphere transfer scheme [Famiglietti and Wood, 1994a] with modifications to correct for deficiencies in the representation of ground heat flux, soil column geometry, soil evaporation, transpiration, and the effect of atmospheric stability on energy fluxes. These deficiencies were found to cause errors in the model predictions in quantities such as the sensible heat flux, to which the development of the atmospheric boundary layer is particularly sensitive. Application of the model to the entire First International Satellite Land Surface Climatology Project Field Experiment 1987 experimental period, focusing on Intensive Field Campaigns 3 and 4, shows that it successfully represents the essential processes of interest. --- paper_title: How much improvement can precipitation data fusion achieve with a Multiscale Kalman Smoother-based framework? paper_content: [1] With advancements in measuring techniques and modeling approaches, more and more precipitation data products, with different spatial resolutions and accuracies, become available. Therefore, there is an increasing need to produce a fused precipitation product that can take advantage of the strengths of each individual precipitation data product. This study systematically and quantitatively evaluates the improvements of the fused precipitation data as a result of using the Mulitscale Kalman Smoother-based (i.e., MKS-based) framework. Impacts of two types of errors, i.e., white noise and bias that are associated with individual precipitation products, are investigated through hypothetical experiments. Two measures, correlation and root-mean-square error, are used to evaluate the improvements of the fused precipitation data. Our study shows that the MKS-based framework can significantly recover the loss of precipitation's spatial patterns and magnitudes that are associated with the white noise and bias when the erroneous data at different spatial scales are fused together. Although the erroneous data at a finer resolution are generally more effective in improving the spatial patterns and magnitudes of the erroneous data at a coarser resolution, data at a coarser resolution can also provide valuable information in improving the quality of the data at a finer resolution when they are fused. This study provides insights on the values of the MKS-based framework and a guideline for determining a potentially optimal spatial scale over which improvements in both the spatial patterns and the magnitudes can be maximized based on given data with different spatial resolutions. --- paper_title: Data assimilation for transient flow in geologic formations via ensemble Kalman filter paper_content: Formation properties are one of the key factors in numerical modeling of flow and transport in geologic formations in spite of the fact that they may not be completely characterized. The incomplete knowledge or uncertainty in the description of the formation properties leads to uncertainty in simulation results. In this study, the ensemble Kalman filter (EnKF) approach is used for continuously updating model parameters such as hydraulic conductivity and model variables such as pressure head while simultaneously providing an estimate of the uncertainty through assimilating dynamic and static measurements, without resorting to the explicit computation of the covariance or the Jacobian of the state variables. A two-dimensional example is built to demonstrate the capability of EnKF and to analyze its sensitivity with respect to different factors such as the number of realizations, measurement timings, and initial guesses. An additional example is given to illustrate the applicability of EnKF to three-dimensional problems and to examine the model predictability after dynamic data assimilation. It is found from these examples that EnKF provides an efficient approach for obtaining satisfactory estimation of the hydraulic conductivity field with dynamic measurements. After data assimilation the conductivity field matches the reference field very well, and different kinds of incorrect prior knowledge of the formation properties may also be rectified to a certain extent. --- paper_title: Groundwater parameter estimation using the ensemble Kalman filter with localization paper_content: The ensemble Kalman filter (EnKF), an efficient data assimilation method showing advantages in many numerical experiments, is deficient when used in approximating covariance from an ensemble of small size. Implicit localization is used to add distance-related weight to covariance and filter spurious correlations which weaken the EnKF’s capability to estimate uncertainty correctly. The effect of this kind of localization is studied in two-dimensional (2D) and three-dimensional (3D) synthetic cases. It is found that EnKF with localization can capture reliably both the mean and variance of the hydraulic conductivity field with higher efficiency; it can also greatly stabilize the assimilation process as a small-size ensemble is used. Sensitivity experiments are conducted to explore the effect of localization function format and filter lengths. It is suggested that too long or too short filter lengths will prevent implicit localization from modifying the covariance appropriately. Steep localization functions will greatly disturb local dynamics like the 0-1 function even if the function is continuous; four relatively gentle localization functions succeed in avoiding obvious disturbance to the system and improve estimation. As the degree of localization of the L function increases, the parameter sensitivity becomes weak, making parameter selection easier, but more information may be lost in the assimilation process. --- paper_title: Investigation of flow and transport processes at the MADE site using ensemble Kalman filter paper_content: In this work the ensemble Kalman filter (EnKF) is applied to investigate the flow and transport processes at the macro-dispersion experiment (MADE) site in Columbus, MS. The EnKF is a sequential data assimilation approach that adjusts the unknown model parameter values based on the observed data with time. The classic advection–dispersion (AD) and the dual-domain mass transfer (DDMT) models are employed to analyze the tritium plume during the second MADE tracer experiment. The hydraulic conductivity (K), longitudinal dispersivity in the AD model, and mass transfer rate coefficient and mobile porosity ratio in the DDMT model, are estimated in this investigation. Because of its sequential feature, the EnKF allows for the temporal scaling of transport parameters during the tritium concentration analysis. Inverse simulation results indicate that for the AD model to reproduce the extensive spatial spreading of the tritium observed in the field, the K in the downgradient area needs to be increased significantly. The estimated K in the AD model becomes an order of magnitude higher than the in situ flowmeter measurements over a large portion of media. On the other hand, the DDMT model gives an estimation of K that is much more comparable with the flowmeter values. In addition, the simulated concentrations by the DDMT model show a better agreement with the observed values. The root mean square (RMS) between the observed and simulated tritium plumes is 0.77 for the AD model and 0.45 for the DDMT model at 328 days. Unlike the AD model, which gives inconsistent K estimates at different times, the DDMT model is able to invert the K values that consistently reproduce the observed tritium concentrations through all times. --- paper_title: Jointly Mapping Hydraulic Conductivity and Porosity by Assimilating Concentration Data via Ensemble Kalman Filter paper_content: Summary Real-time data from on-line sensors offer the possibility to update environmental simulation models in real-time. Information from on-line sensors concerning contaminant concentrations in groundwater allow for the real-time characterization and control of a contaminant plume. In this paper it is proposed to use the CPU-efficient Ensemble Kalman Filter (EnKF) method, a data assimilation algorithm, for jointly updating the flow and transport parameters (hydraulic conductivity and porosity) and state variables (piezometric head and concentration) of a groundwater flow and contaminant transport problem. A synthetic experiment is used to demonstrate the capability of the EnKF to estimate hydraulic conductivity and porosity by assimilating dynamic head and multiple concentration data in a transient flow and transport model. In this work the worth of hydraulic conductivity, porosity, piezometric head, and concentration data is analyzed in the context of aquifer characterization and prediction uncertainty reduction. The results indicate that the characterization of the hydraulic conductivity and porosity fields is continuously improved as more data are assimilated. Also, groundwater flow and mass transport predictions are improved as more and different types of data are assimilated. The beneficial impact of accounting for multiple concentration data is patent. --- paper_title: Best unbiased ensemble linearization and the quasi-linear Kalman ensemble generator: QUASI-LINEAR KALMAN ENSEMBLE GENERATOR paper_content: [1] Linearized representations of the stochastic groundwater flow and transport equations have been heavily used in hydrogeology, e.g., for geostatistical inversion or generating conditional realizations. The respective linearizations are commonly defined via Jacobians (numerical sensitivity matrices). This study will show that Jacobian-based linearizations are biased with nonminimal error variance in the ensemble sense. An alternative linearization approach will be derived from the principles of unbiasedness and minimum error variance. The resulting paradigm prefers empirical cross covariances from Monte Carlo analyses over those from linearized error propagation and points toward methods like ensemble Kalman filters (EnKFs). Unlike conditional simulation in geostatistical applications, EnKFs condition transient state variables rather than geostatistical parameter fields. Recently, modifications toward geostatistical applications have been tested and used. This study completes the transformation of EnKFs to geostatistical conditioning tools on the basis of best unbiased ensemble linearization. To distinguish it from the original EnKF, the new method is called the Kalman ensemble generator (KEG). The new context of best unbiased ensemble linearization provides an additional theoretical foundation to EnKF-like methods (such as the KEG). Like EnKFs and derivates, the KEG is optimal for Gaussian variables. Toward increased robustness and accuracy in non-Gaussian and nonlinear cases, sequential updating, acceptance/rejection sampling, successive linearization, and a Levenberg-Marquardt formalism are added. State variables are updated through simulation with updated parameters, always guaranteeing the physicalness of all state variables. The KEG combines the computational efficiency of linearized methods with the robustness of EnKFs and accuracy of expensive realization-based methods while drawing on the advantages of conditional simulation over conditional estimation (such as adequate representation of solute dispersion). As proof of concept, a large-scale numerical test case with 200 synthetic sets of flow and tracer data is conducted and analyzed. --- paper_title: An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering paper_content: Abstract The ensemble Kalman filter (EnKF) is a commonly used real-time data assimilation algorithm in various disciplines. Here, the EnKF is applied, in a hydrogeological context, to condition log-conductivity realizations on log-conductivity and transient piezometric head data. In this case, the state vector is made up of log-conductivities and piezometric heads over a discretized aquifer domain, the forecast model is a groundwater flow numerical model, and the transient piezometric head data are sequentially assimilated to update the state vector. It is well known that all Kalman filters perform optimally for linear forecast models and a multiGaussian-distributed state vector. Of the different Kalman filters, the EnKF provides a robust solution to address non–linearities; however, it does not handle well non-Gaussian state-vector distributions. In the standard EnKF, as time passes and more state observations are assimilated, the distributions become closer to Gaussian, even if the initial ones are clearly non-Gaussian. A new method is proposed that transforms the original state vector into a new vector that is univariate Gaussian at all times. Back transforming the vector after the filtering ensures that the initial non-Gaussian univariate distributions of the state-vector components are preserved throughout. The proposed method is based in normal-score transforming each variable for all locations and all time steps. This new method, termed the normal-score ensemble Kalman filter (NS-EnKF), is demonstrated in a synthetic bimodal aquifer resembling a fluvial deposit, and it is compared to the standard EnKF. The proposed method performs better than the standard EnKF in all aspects analyzed (log-conductivity characterization and flow and transport predictions). --- paper_title: Diffusion wave modeling of distributed catchment dynamics paper_content: A diffusion wave model of distributed catchment dynamics is presented. The effects of catchment topography and river network structure on storm-flow response are incorporated by routing surface runoff in cascade throughout a digital elevation model (DEM) based conceptual transport network, where the Muskingum-Cunge scheme with variable parameters is used to describe surface runoff dynamics. Dynamic scaling of hydraulic geometry is also incorporated in the model by using the 1953 “at-a-station” and “downstream” relationships by \ILeopold and Maddock\N. Numerical experiments indicate that the model is more than 98% mass conservative for possible slope and roughness configurations, which may occur for hillslopes in a natural catchment. Fluctuations in the simulated discharge may occur in response to discontinuities in rainfall excess representation if Courant number \ICu\N during the simulation exceeds a threshold of about 3. Catchment scale simulations with different temporal resolution show that the model response is independent of structural parameters (model consistency). Also, the overall accuracy is preserved for computationally inexpensive space-time discretizations (for which \ICu\N > 3) because fluctuations that may occur at the local scale are dampened when propagating downstream. Comparison of model results with observed outlet hydrographs of the Rio Missiaga experimental catchment (Eastern Italian Alps) show this approach to be capable of describing both overland and channel phases of surface runoff in mountainous catchments. --- paper_title: A detailed model for simulation of catchment scale subsurface hydrologic processes paper_content: A catchment scale numerical model is developed based on the three-dimensional transient Richards equation describing fluid flow in variably saturated porous media. The model is designed to take advantage of digital elevation data bases and of information extracted from these data bases by topographic analysis. The practical application of the model is demonstrated in simulations of a small subcatchment of the Konza Prairie reserve near Manhattan, Kansas. In a preliminary investigation of computational issues related to model resolution, we obtain satisfactory numerical results using large aspect ratios, suggesting that horizontal grid dimensions may not be unreasonably constrained by the typically much smaller vertical length scale of a catchment and by vertical discretization requirements. Additional tests are needed to examine the effects of numerical constraints and parameter heterogeneity in determining acceptable grid aspect ratios. In other simulations we attempt to match the observed streamflow response of the catchment, and we point out the small contribution of the streamflow component to the overall water balance of the catchment. --- paper_title: Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother paper_content: Groundwater flow models are important tools in assessing baseline conditions and investigating management alternatives in groundwater systems. The usefulness of these models, however, is often hindered by insufficient knowledge regarding the magnitude and spatial distribution of the spatially-distributed parameters, such as hydraulic conductivity ( K ), that govern the response of these models. Proposed parameter estimation methods frequently are demonstrated using simplified aquifer representations, when in reality the groundwater regime in a given watershed is influenced by strongly-coupled surface-subsurface processes. Furthermore, parameter estimation methodologies that rely on a geostatistical structure of K often assume the parameter values of the geostatistical model as known or estimate these values from limited data. In this study, we investigate the use of a data assimilation algorithm, the Ensemble Smoother, to provide enhanced estimates of K within a catchment system using the fully-coupled, surface-subsurface flow model CATHY. Both water table elevation and streamflow data are assimilated to condition the spatial distribution of K . An iterative procedure using the ES update routine, in which geostatistical parameter values defining the true spatial structure of K are identified, is also presented. In this procedure, parameter values are inferred from the updated ensemble of K fields and used in the subsequent iteration to generate the K ensemble, with the process proceeding until parameter values are converged upon. The parameter estimation scheme is demonstrated via a synthetic three-dimensional tilted v-shaped catchment system incorporating stream flow and variably-saturated subsurface flow, with spatio-temporal variability in forcing terms. Results indicate that the method is successful in providing improved estimates of the K field, and that the iterative scheme can be used to identify the geostatistical parameter values of the aquifer system. In general, water table data have a much greater ability than streamflow data to condition K . Future research includes applying the methodology to an actual regional study site. --- paper_title: Parameterization of stream channel geometry in the distributed modeling of catchment dynamics paper_content: A simple and efficient procedure for incorporating the effects of stream channel geometry in the distributed modeling of catchment dynamics is developed. At-a- station and downstream fluvial relationships are combined and the obtained laws of variability in space and time for water-surface width and wetted perimeter are incorporated into a diffusion wave routing model based on the Muskingum-Cunge method with variable parameters. The parameterization obtained is applied to the approximately 840-km 2 Sieve catchment (Central Italian Apennines) to test the possibility of estimating channel geometry parameters from cross-section surveys and to assess the impact of dynamic variations in the channel geometry on catchment dynamics. The use of the estimated channel geometry in surface runoff routing produces a significant improvement in the flood hydrograph description at the catchment outlet with respect to less detailed network parameterizations. In addition, the results obtained from a "downstream" analysis of the velocity field indicate that the stream characteristics related to the locally varying cross-section shape may have a strong control on flow velocities, and thus they should be monitored and synthesized for a comprehensive description of the distributed catchment dynamics. --- paper_title: Ensemble smoother assimilation of hydraulic head and return flow data to estimate hydraulic conductivity distribution paper_content: [1] Numerical groundwater models, frequently used to enhance understanding of the hydrologic and chemical processes in local or regional aquifers, are often hindered by an incomplete representation of the parameters which characterize these processes. In this study, we present the use of a data assimilation algorithm that incorporates all past model results and data measurements, an ensemble smoother (ES) to provide enhanced estimates of aquifer hydraulic conductivity (K) through assimilation of hydraulic head (H) and groundwater return flow volume (RFV) measurements into groundwater model simulation results. On the basis of the Kalman filter methodology, residuals between forecasted model results and measurements, together with covariances between model results at measurement locations and nonmeasurement locations, are used to correct model results. Parameter estimation is achieved by incorporating model parameters into the algorithm, thus allowing the correlation between H, RFV, and K to correct the K fields. The applicability of the ES is demonstrated using a synthetic two-dimensional transient groundwater modeling simulation. Sensitivity analyses are carried out to show the performance of the ES in regard to measurement error, number of measurements, number of assimilation times, correlation length of the K fields, and the number of stream gage locations. Results show that the departure of the K fields from a reference K field is greatly reduced through data assimilation and demonstrate that the ES scheme is a promising alternative to other inverse modeling techniques because of low computational burden and the ability to run the algorithm entirely independent of the groundwater model simulation. --- paper_title: A comparison of Picard and Newton iteration in the numerical solution of multidimensional variably saturated flow problems paper_content: Picard iteration is a widely used procedure for solving the nonlinear equation governing flow in variably saturated porous media. The method is simple to code and computationally cheap, but has been known to fail or converge slowly. The Newton method is more complex and expensive (on a per-iteration basis) than Picard, and as such has not received very much attention. Its robustness and higher rate of convergence, however, make it an attractive alternative to the Picard method, particularly for strongly nonlinear problems. In this paper the Picard and Newton schemes are implemented and compared in one-, two-, and three-dimensional finite element simulations involving both steady state and transient flow. The eight test cases presented highlight different aspects of the performance of the two iterative methods and the different factors that can affect their convergence and efficiency, including problem size, spatial and temporal discretization, initial solution estimates, convergence error norm, mass lumping, time weighting, conductivity and moisture content characteristics, boundary conditions, seepage faces, and the extent of fully saturated zones in the soil. Previous strategies for enhancing the performance of the Picard and Newton schemes are revisited, and new ones are suggested. The strategies include chord slope approximations for the derivatives of the characteristic equations, relaxing convergence requirements along seepage faces, dynamic time step control, nonlinear relaxation, and a mixed Picard-Newton approach. The tests show that the Picard or relaxed Picard schemes are often adequate for solving Richards' equation, but that in cases where these fail to converge or converge slowly, the Newton method should be used. The mixed Picard-Newton approach can effectively overcome the Newton scheme's sensitivity to initial solution estimates, while comparatively poor performance is reported for the various chord slope approximations. Finally, given the reliability and efficiency of current conjugate gradient-like methods for solving linear nonsymmetric systems, the only real drawback of using Newton rather than Picard iteration is the algebraic complexity and computational cost of assembling the derivative terms of the Jacobian matrix, and it is suggested that both methods can be effectively implemented and used in numerical models of Richards' equation. --- paper_title: Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow: EnKF FOR A MODEL OF SURFACE AND SUBSURFACE FLOW paper_content: [1] A sequential data assimilation procedure based on the ensemble Kalman filter (EnKF) is introduced and tested for a process-based numerical model of coupled surface and subsurface flow. The model is based on the three-dimensional Richards equation for variably saturated porous media and a diffusion wave approximation for overland and channel flow. A one-dimensional soil column experiment and a three-dimensional tilted v-catchment test case are presented. A preliminary analysis of the assimilation scheme is undertaken for the one-dimensional test case in order to validate the implementation by comparison with published results and to assess the influence of various factors on the filter's performance. The numerical results suggest robustness with respect to the ensemble size and provide useful information for the more complex tilted v-catchment test case. The assimilation frequency and the effects induced by data assimilation on the surface and/or subsurface system states are then evaluated for the v-catchment experiment using synthetic observations of pressure head and streamflow. The results suggest that streamflow prediction can be improved by assimilation of pressure head and streamflow, either individually or in tandem, whereas assimilation of streamflow data alone does not improve the subsurface system state. In terms of the global system state, i.e., surface and subsurface variables, frequent updates are especially beneficial when assimilating both pressure head and streamflow. Furthermore, it is shown that better evaluation of the subsurface volume resulting from assimilation of head data is crucial for improving subsequent surface response. --- paper_title: Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: Effects of uncertainties in the data and initial model soil moisture states paper_content: Abstract We assess the potential of updating soil moisture states of a distributed hydrologic model by assimilating streamflow and in situ soil moisture data for high-resolution analysis and prediction of streamflow and soil moisture. The model used is the gridded Sacramento (SAC) and kinematic-wave routing models of the National Weather Service (NWS) Hydrology Laboratory’s Research Distributed Hydrologic Model (HL-RDHM) operating at an hourly time step. The data assimilation (DA) technique used is variational assimilation (VAR). Assimilating streamflow and soil moisture data into distributed hydrologic models is new and particularly challenging due to the large degrees of freedom associated with the inverse problem. This paper reports findings from the first phase of the research in which we assume, among others, perfectly known hydrometeorological forcing. The motivation for the simplification is to reduce the complexity of the problem in favour of improved understanding and easier interpretation even if it may compromise the goodness of the results. To assess the potential, two types of experiments, synthetic and real-world, were carried out for Eldon (ELDO2), a 795-km2 headwater catchment located near the Oklahoma (OK) and Arkansas (AR) border in the U.S. The synthetic experiment assesses the upper bound of the performance of the assimilation procedure under the idealized conditions of no structural or parametric errors in the models, a full dynamic range and no microscale variability in the in situ observations of soil moisture, and perfectly known univariate statistics of the observational errors. The results show that assimilating in situ soil moisture data in addition to streamflow data significantly improves analysis and prediction of soil moisture and streamflow, and that assimilating streamflow observations at interior locations in addition to those at the outlet improves analysis and prediction of soil moisture within the drainage areas of the interior stream gauges and of streamflow at downstream cells along the channel network. To assess performance under more realistic conditions, but still under the assumption of perfectly known hydrometeorological forcing to allow comparisons with the synthetic experiment, an exploratory real-world experiment was carried out in which all other assumptions were lifted. The results show that, expectedly, assimilating interior flows in addition to outlet flow improves analysis as well as prediction of streamflow at stream gauge locations, but that assimilating in situ soil moisture data in addition to streamflow data provides little improvement in streamflow analysis and prediction though it reduces systematic biases in soil moisture simulation. --- paper_title: Hydrology laboratory research modeling system (HL-RMS) of the US national weather service paper_content: This study investigates an approach that combines physically-based and conceptual model features in two stages of distributed modeling: model structure development and estimation of spatially variable parameters. The approach adds more practicality to the process of model parameterization, and facilitates an easier transition from current lumped model-based operational systems to more powerful distributed systems. This combination of physically-based and conceptual model features is implemented within the Hydrology Laboratory Research Modeling System (HL-RMS). HL-RMS consists of a well-tested conceptual water balance model applied on a regular spatial grid linked to physically-based kinematic hillslope and channel routing models. Parameter estimation procedures that combine spatially distributed and ‘integrated’ basin-outlet properties have been developed for the water balance and routing components. High-resolution radar-based precipitation data over a large region are used in testing HL-RMS. Initial tests show that HL-RMS yields results comparable to well-calibrated lumped model simulations in several headwater basins, and it outperforms a lumped model in basins where spatial rainfall variability effects are significant. It is important to note that simulations for two nested basins (not calibrated directly, but parameters from the calibration of the parent basin were applied instead) outperformed lumped simulations even more consistently, which means that HL-RMS has the potential to improve the accuracy and resolution of river runoff forecasts. Published by Elsevier B.V. --- paper_title: Real-Time Variational Assimilation of Hydrologic and Hydrometeorological Data into Operational Hydrologic Forecasting paper_content: Variational assimilation (VAR) of hydrologic and hydrometeorological data into operational hydrologic forecasting is explored. The data assimilated are the hourly real-time observations of streamflow and precipitation, and climatological estimates of potential evaporation (PE). The hydrologic system considered is a single headwater basin for which soil moisture accounting and routing are carried out in a lumped fashion via the Sacramento model (SAC) and the unit hydrograph (UH), respectively. The control variables in the VAR formulation are the fast-varying SAC soil moisture states at the beginning of the assimilation window and the multiplicative adjustment factors to the estimates of mean areal precipitation (MAP) and mean areal potential evaporation (MAPE) for each hour in the assimilation window. In a separate application of VAR as a parameter estimation tool, the estimation of empirical UH is also explored by treating its ordinates as the control variables. To evaluate the assimilation procedure thus developed, streamflow was forecast with and without the aid of VAR for three basins in the southern plains under the assumption of perfectly forecast future mean areal precipitation (FMAP). The streamflow forecasts were then compared with each other and with those based on persistence and the state space-based state-updating procedure, the state-space Sacramento model (SS-SAC). The results indicate that the VAR procedure significantly improves the accuracy of the basic forecast at short lead times and compares favorably with SS-SAC. --- paper_title: The soil and water assessment tool—Historical development applications, and future research directions paper_content: The Soil and Water Assessment Tool (SWAT) model is a continuation of nearly 30 years of modeling efforts conducted by the USDA Agricultural Research Service (ARS). SWAT has gained international acceptance as a robust interdisciplinary watershed modeling tool as evidenced by international SWAT conferences, hundreds of SWAT-related papers presented at numerous other scientific meetings, and dozens of articles published in peer-reviewed journals. The model has also been adopted as part of the U.S. Environmental Protection Agency (USEPA) Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) software package and is being used by many U.S. federal and state agencies, including the USDA within the Conservation Effects Assessment Project (CEAP). At present, over 250 peer-reviewed published articles have been identified that report SWAT applications, reviews of SWAT components, or other research that includes SWAT. Many of these peer-reviewed articles are summarized here according to relevant application categories such as streamflow calibration and related hydrologic analyses, climate change impacts on hydrology, pollutant load assessments, comparisons with other models, and sensitivity analyses and calibration techniques. Strengths and weaknesses of the model are presented, and recommended research needs for SWAT are also provided. --- paper_title: Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter paper_content: Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system. --- paper_title: Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow: EnKF FOR A MODEL OF SURFACE AND SUBSURFACE FLOW paper_content: [1] A sequential data assimilation procedure based on the ensemble Kalman filter (EnKF) is introduced and tested for a process-based numerical model of coupled surface and subsurface flow. The model is based on the three-dimensional Richards equation for variably saturated porous media and a diffusion wave approximation for overland and channel flow. A one-dimensional soil column experiment and a three-dimensional tilted v-catchment test case are presented. A preliminary analysis of the assimilation scheme is undertaken for the one-dimensional test case in order to validate the implementation by comparison with published results and to assess the influence of various factors on the filter's performance. The numerical results suggest robustness with respect to the ensemble size and provide useful information for the more complex tilted v-catchment test case. The assimilation frequency and the effects induced by data assimilation on the surface and/or subsurface system states are then evaluated for the v-catchment experiment using synthetic observations of pressure head and streamflow. The results suggest that streamflow prediction can be improved by assimilation of pressure head and streamflow, either individually or in tandem, whereas assimilation of streamflow data alone does not improve the subsurface system state. In terms of the global system state, i.e., surface and subsurface variables, frequent updates are especially beneficial when assimilating both pressure head and streamflow. Furthermore, it is shown that better evaluation of the subsurface volume resulting from assimilation of head data is crucial for improving subsequent surface response. --- paper_title: Real-time forecasting of water table depth and soil moisture profiles paper_content: We present a method for real-time forecasting of water table depth and soil moisture profiles. The method combines a simple form of data-assimilation with a moving window calibration of a deterministic model describing flow in the unsaturated zone and local as well as regional drainage. The local drainage level is calibrated on-line using a moving window calibration. Assigning more weight to the last available measurements then yields a form of model adaptation that is in between on-line calibration and data-assimilation (i.e. a simplified form of Newtonian nudging). Five-day hydrological forecasts are performed based on 5-day weather forecasts, while on-line observations of phreatic level and soil moisture content are assimilated on a daily basis. Advantages of the proposed method are that it improves the real-time forecasts compared to off-line calibration and ordinary moving window calibration and that it yields physically consistent soil moisture profiles. --- paper_title: Modelling forest transpiration and CO2 fluxes: response to soil moisture stress paper_content: The effects of soil drought on the leaf gas exchange variables of a stomatal conductance model are assessed in the case of woody plants, based on a meta-analysis of data sets available from the literature. For the first stage, 32 data sets obtained in well-watered conditions are analysed. For the second stage, we analyse four data sets including a soil drying cycle under present and doubled atmospheric CO2 concentration (350 and 700mol mol −1 , respectively) for two tree species, maritime pine and sessile oak. Interesting relationships emerge, which suggest that woody plants may adopt one or two strategies when facing drought. A simple parameterisation of the effects of soil drought is proposed to account for the two types of drought responses which are: drought-avoiding (maritime pine) and drought-tolerant (sessile oak). © 2004 Elsevier B.V. All rights reserved. --- paper_title: Data Assimilation for Estimating the Terrestrial Water Budget Using a Constrained Ensemble Kalman Filter paper_content: Abstract A procedure is developed to incorporate equality constraints in Kalman filters, including the ensemble Kalman filter (EnKF), and is referred to as the constrained ensemble Kalman filter (CEnKF). The constraint is carried out as a two-step filtering approach, with the first step being the standard (ensemble) Kalman filter. The second step is the constraint step carried out by another Kalman filter that optimally redistributes any imbalance from the first step. The CEnKF is implemented over a 75 000 km2 domain in the southern Great Plains region of the United States, using the terrestrial water balance as the constraint. The observations, consisting of gridded fields of the upper two soil moisture layers from the Oklahoma Mesonet system, Atmospheric Radiation Measurement Program Cloud and Radiation Testbed (ARM-CART) energy balance Bowen ratio (EBBR) latent heat estimates, and U.S. Geological Survey (USGS) streamflow from unregulated basins, are assimilated into the Variable Infiltration Capacity (... --- paper_title: Joint assimilation of surface soil moisture and LAI observations into a land surface model paper_content: Land Surface Models (LSM) offer a description of land surface processes and set the lower boundary conditions for meteorological models. In particular, the accurate description of those surface variables which display a slow response in time, like root-zone soil moisture or vegetation biomass, is of great importance. Errors in their estimation yield significant inaccuracies in the estimation of heat and water fluxes in Numerical Weather Prediction (NWP) models. In the present study, the ISBA-A-gs LSM is used decoupled from the atmosphere. In this configuration, the model is able to simulate the vegetation growth, and consequently LAI. A simplified 1D-VAR assimilation method is applied to observed surface soil moisture and LAI observations of the SMOSREX site near Toulouse, in south-western France, from 2001 to 2004. This period includes severe droughts in 2003 and 2004. The data are jointly assimilated into ISBA-A-gs in order to analyse the root-zone soil moisture and the vegetation biomass. It is shown that the 1D-VAR improves the model results. The efficiency score of the model (Nash criterion) is increased from 0.79 to 0.86 for root-zone soil moisture and from 0.17 to 0.23 for vegetation biomass. --- paper_title: Modeling ground heat flux in land surface parameterization schemes paper_content: A new ground heat flux parameterization for land surface schemes, such as those used in climate and numerical weather prediction models, is described. Compared with other approaches that lump the canopy layer and ground surface, or empirically based approaches that consider the effect of radiation attenuation through the canopy layer, the new parameterization has several advantages. First, the reduction of radiation available for conducting soil surface exchange under vegetated areas is represented in a manner that assures that heat is conserved in the long term. Second, problems in representing properly the phase of the ground heat flux are alleviated. Finally, the approach is relatively simple and is computationally efficient, requiring only two soil thermal layers. Comparison of the method with analytical solutions for special cases shows that the new method approximates the analytical solution very well for different conditions, and that the new method is superior to the force-restore and the Crank-Nicholson method. Model-derived ground heat heat fluxes for the French HAPEX-MOBILHY (Hydrology-Atmosphere Pilot Experiment - Modelisation de Bilan Hydrique) site and the Brazilian ABRACOS (Anglo-Brazilian Amazonian Climate Observation Study) cleared ranch land site are shown to be in close agreement with observations. Sensitivity analyses show that if the attenuation of radiation under vegetation and soil heat storage are ignored, the daytime peak and nighttime minima of ground heat flux, latent and sensible heat fluxes, and surface temperature can be significantly in error. In particular, neglecting the radiation attenuation through the canopy layer can result in significant overestimation (underestimation) of daytime (nighttime) ground heat flux, while neglecting soil heat storage can result in significant phase errors. --- paper_title: One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model paper_content: The two-layer variable infiltration capacity (VIC-2L) model is extended to incorporate a representation of subgrid variability in precipitation, using an analytical one-dimensional statistical dynamic representation for partial area coverage of precipitation. The analytical approach allows the effects of subgrid-scale spatial variability of precipitation on surface fluxes, runoff production, and soil moisture to be represented explicitly. With this method, spatially integrated representations of surface fluxes, runoff, and soil moisture due to subgrid-scale spatial variability in precipitation, infiltration, and vegetation cover are obtained. The results are compared with those obtained using an exhaustive pixel-based approach, and the results obtained by applying uniform precipitation over the precipitation-covered area. The precipitation coverage over a grid cell is shown to play a primary role in estimating the surface fluxes, runoff, and soil moisture. In general, the spatial distribution of precipitation within the precipitation-covered area plays a secondary role, in part because VIC-2L represents the subgrid spatial variability of soil properties. While the analytical approach gives good approximations to the pixel-based approach, and is superior to the uniform precipitation approach in general, the differences are not large. --- paper_title: Root zone soil moisture from the assimilation of screen‐level variables and remotely sensed soil moisture paper_content: [1] In most operational NWP models, root zone soil moisture is constrained using observations of screen-level temperature and relative humidity. While this generally improves low-level atmospheric forecasts, it often leads to unrealistic model soil moisture. Consequently, several NWP centers are moving toward also assimilating remotely sensed near-surface soil moisture observations. Within this context, an EKF is used to compare the assimilation of screen-level observations and near-surface soil moisture data from AMSR-E into the ISBA land surface model over July 2006. Several issues regarding the use of each data type are exposed, and the potential to use the AMSR-E data, either in place of or together with the screen-level data, is examined. When the two data types are assimilated separately, there is little agreement between the root zone soil moisture updates generated by each, indicating that for this experiment the AMSR-E data could not have replaced the screen-level data to obtain similar surface turbulent fluxes. For the screen-level variables, there is a persistent diurnal cycle in the model-observations bias, which is not related to soil moisture. The resulting diurnal cycle in the analysis increments demonstrates how assimilating screen-level observations can lead to unrealistic soil moisture updates, reinforcing the need to assimilate alternative data sets. However, when the two data types are assimilated together, the near-surface soil moisture provides a much weaker constraint of the root zone soil moisture than the screen-level observations do, and the inclusion of the AMSR-E data does not substantially change the results compared to the assimilation of screen-level variables alone. --- paper_title: On the Efficacy of Combining Thermal and Microwave Satellite Data as Observational Constraints for Root-Zone Soil Moisture Estimation paper_content: Abstract Data assimilation applications require the development of appropriate mathematical operators to relate model states to satellite observations. Two such “observation” operators were developed and used to examine the conditions under which satellite microwave and thermal observations provide effective constraints on estimated soil moisture. The first operator uses a two-layer surface energy balance (SEB) model to relate root-zone moisture with top-of-canopy temperature. The second couples SEB and microwave radiative transfer models to yield top-of-atmosphere brightness temperature from surface layer moisture content. Tangent linear models for these operators were developed to examine the sensitivity of modeled observations to variations in soil moisture. Assuming a standard deviation in the observed surface temperature of 0.5 K and maximal model sensitivity, the error in the analysis moisture content decreased by 11% for a background error of 0.025 m3 m−3 and by 29% for a background error of 0.05 m... --- paper_title: Multiscale modeling of spatially variable water and energy balance processes paper_content: This paper presents the model development component of a body of research which addresses aggregation and scaling in multiscale hydrological modeling. Water and energy balance models are developed at the local and catchment scales and at the macroscale by aggregating a simple soil-vegetation-atmosphere transfer scheme (SVATS) across scales in a topographic framework. A spatially distributed approach is followed to aggregate the SVATS to the catchment scale. A statistical-dynamical approach is utilized to simplify the large-scale modeling problem and to aggregate the SVATS to the macroscale. The resulting macroscale hydrological model is proposed for use as a land surface parameterization in atmospheric models. It differs greatly from the current generation of land surface parameterizations owing to its simplified representation of vertical process physics and its statistical representation of horizontally heterogeneous runoff and energy balance processes. The spatially distributed model formulation is explored to understand the role of spatial variability in determining areal-average fluxes and the dynamics of hydrological processes. The simpler macroscale formulation is analyzed to determine how it represents these important dynamics, with implications for the parameterization of runoff and energy balance processes in atmospheric models. --- paper_title: Joint Assimilation of Surface Temperature and L-Band Microwave Brightness Temperature in Land Data Assimilation paper_content: Soil moisture and soil temperature are tightly coupled variables in land surface models. The objective of this study was to evaluate the impact of the joint assimilation of soil moisture and land surface temperature data in a land surface model on soil moisture and soil temperature characterization. Three synthetic tests evaluated the joint assimilation of surface temperature (measured by MODIS) and brightness temperature (from L-band) into the Community Land Model using the local ensemble transform Kalman filter (LETKF). The following three tests were performed for dry and wet conditions: (i) assimilating surface temperature observations only --- paper_title: An interactive vegetation SVAT model tested against data from six contrasting sites paper_content: The interactions between soil, biosphere, and atmosphere scheme (ISBA) is modified in order to account for the atmospheric carbon dioxide concentration on the stomatal aperture. The physiological stomatal resistance scheme proposed by Jacobs (1994) is employed to describe photosynthesis and its coupling with stomatal resistance at leaf level. In addition, the plant response to soil water stress is driven by a normalized soil moisture factor applied to the mesophyll conductance. The computed vegetation net assimilation can be used to feed a simple growth submodel, and to predict the density of vegetation cover. Only two parameters are needed to calibrate the growth model: the leaf life expectancy and the effective biomass per unit leaf area. The new soil–vegetation–atmosphere transfer (SVAT) scheme, called ISBA–A–gs, is tested against data from six micrometeorological databases for vegetation ranging from temperate grassland to tropical forest. It is shown that ISBA–A–gs is able to simulate the water budget and the CO2 flux correctly. Also, the leaf area index predicted by the calibrated model agrees well with observations over canopy types ranging from shortcycled crops to evergreen grasslands or forests. Once calibrated, the model is able to adapt the vegetation density in response to changes in the precipitation distribution. --- paper_title: A simple hydrologically based model of land surface water and energy fluxes for general circulation models paper_content: A generalization of the single soil layer variable infiltration capacity (VIC) land surface hydrological model previously implemented in the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model (GCM) is described. The new model is comprised of a two-layer characterization of the soil column, and uses an aerodynamic representation of the latent and sensible heat fluxes at the land surface. The infiltration algorithm for the upper layer is essentially the same as for the single layer VIC model, while the lower layer drainage formulation is of the form previously implemented in the Max-Planck-Institut GCM. The model partitions the area of interest (e.g., grid cell) into multiple land surface cover types; for each land cover type the fraction of roots in the upper and lower zone is specified. Evapotranspiration consists of three components: canopy evaporation, evaporation from bare soils, and transpiration, which is represented using a canopy and architectural resistance formulation. Once the latent heat flux has been computed, the surface energy balance is iterated to solve for the land surface temperature at each time step. The model was tested using long-term hydrologic and climatological data for Kings Creek, Kansas to estimate and validate the hydrological parameters, and surface flux data frommore » three First International Satellite Land Surface Climatology Project Field Experiment (FIFE) intensive field campaigns in the summer-fall of 1987 to validate the surface energy fluxes.« less --- paper_title: Ability of the land surface model ISBA-A-gs to simulate leaf area index at the global scale: Comparison with satellites products paper_content: [1] The land surface model (LSM) ISBA-A-gs (Interactions between Soil, Biosphere and Atmosphere, CO2-reactive) is specifically designed to simulate leaf stomatal conductance and leaf area index (LAI) in response to climate, soil properties, and atmospheric carbon dioxide concentration. The model is run at the global scale, forced by the GSWP-2 meteorological data at a resolution of 1° for the period of 1986–1995. We test the model by comparing the simulated LAI values against three satellite-derived data sets (ISLSCP Initiative II data, MODIS data and ECOCLIMAP data) and find that the model reproduces the major patterns of spatial and temporal variability in global vegetation. As a result, the mean of the maximum annual LAI estimates of the model falls within the range of the various satellite data sets. Despite no explicit representation of phenology, the model captures the seasonal cycle in LAI well and shows realistic variations in start of the growing season as a function of latitude. The interannual variability is also well reported for numerous regions of the world, particularly where precipitation controls photosynthesis. The comparison also reveals that some processes need to be improved or introduced in the model, in particular the snow dynamics and the treatment of vegetation in cultivated areas, respectively. The overall comparisons demonstrate the potential of ISBA-A-gs model to simulate LAI in a realistic fashion at the global scale. --- paper_title: A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model paper_content: A method for assimilating remotely sensed snow covered area (SCA) into the snow subroutine of a grid dis- tributed precipitation-runoff model (PRM) is presented. The PRM is assumed to simulate the snow state in each grid cell by a snow depletion curve (SDC), which relates that cell's SCA to its snow cover mass balance. The assimilation is based on Bayes' theorem, which requires a joint prior dis- tribution of the SDC variables in all the grid cells. In this paper we propose a spatial model for this prior distribution, and include similarities and dependencies among the grid cells. Used to represent the PRM simulated snow cover state, our joint prior model regards two elevation gradients and a degree-day factor as global variables, rather than describing their effect separately for each cell. This transformation re- sults in smooth normalised surfaces for the two related mass balance variables, supporting a strong inter-cell dependency in their joint prior model. The global features and spatial interdependency in the prior model cause each SCA obser- vation to provide information for many grid cells. The spa- tial approach similarly facilitates the utilisation of observed discharge. Assimilation of SCA data using the proposed spatial model is evaluated in a 2400 km 2 mountainous region in cen- tral Norway (61 N, 9 E), based on two Landsat 7 ETM+ images generalized to 1 km 2 resolution. An image acquired on 11 May, a week before the peak flood, removes 78% of the variance in the remaining snow storage. Even an image from 4 May, less than a week after the melt onset, reduces this variance by 53%. These results are largely improved compared to a cell-by-cell independent assimilation routine previously reported. Including observed discharge in the up- dating information improves the 4 May results, but has weak --- paper_title: Feasibility Test of Multifrequency Radiometric Data Assimilation to Estimate Snow Water Equivalent paper_content: Abstract A season-long, point-scale radiometric data assimilation experiment is performed in order to test the feasibility of snow water equivalent (SWE) estimation. Synthetic passive microwave observations at Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) frequencies and synthetic broadband albedo observations are assimilated simultaneously in order to update snowpack states in a land surface model using the ensemble Kalman filter (EnKF). The effects of vegetation and atmosphere are included in the radiative transfer model (RTM). The land surface model (LSM) was given biased precipitation to represent typical errors introduced in modeling, yet was still able to recover the true value of SWE with a seasonally integrated rmse of only 2.95 cm, despite a snow depth of around 3 m and the presence of liquid water in the snowpack. This ensemble approach is ideal for investigating the complex theoretical relationships between the snowpack proper... --- paper_title: Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations paper_content: Abstract The monitoring of snow water equivalent (SWE) and snow depth (SD) in boreal forests is investigated by applying space-borne microwave radiometer data and synoptic snow depth observations. A novel assimilation technique based on (forward) modelling of observed brightness temperatures as a function of snow pack characteristics is introduced. The assimilation technique is a Bayesian approach that weighs the space-borne data and the reference field on SD interpolated from discrete synoptic observations with their estimated statistical accuracy. The results obtained using SSM/I and AMSR-E data for northern Eurasia and Finland indicate that the employment of space-borne data using the assimilation technique improves the SD and SWE retrieval accuracy when compared with the use of values interpolated from synoptic observations. Moreover, the assimilation technique is shown to reduce systematic SWE/SD estimation errors evident in the inversion of space-borne radiometer data. --- paper_title: Impact of Accuracy, Spatial Availability, and Revisit Time of Satellite-Derived Surface Soil Moisture in a Multiscale Ensemble Data Assimilation System paper_content: This study evaluates the sensitivity of a multiscale ensemble assimilation system to different configurations of satellite soil moisture observations, namely the retrieval accuracy, spatial availability, and revisit time. We perform horizontally coupled assimilation experiments where pixels are updated not only by observations at the same location but also all in the study domain. Carrying out sensitivity studies within a multiscale assimilation system is a significant advancement over previous studies that used a 1-D assimilation framework where all horizontal grids are uncoupled. Twin experiments are performed with synthetic soil moisture retrievals. The hydrologic modeling system is forced with satellite estimated rainfall, and the assimilation performance is evaluated against model simulations using in-situ measured rainfall. The study shows that the assimilation performance is most sensitive to the spatial availability of soil moisture observations, then to revisit time and least sensitive to retrieval accuracy. The horizontally coupled assimilation system performs reasonably well even with large observation errors, and it is less sensitive to retrieval accuracy than the uncoupled system, as reported by previous studies. This suggests that more information may be extracted from satellite soil moisture observations using multiscale assimilation systems resulting in a potentially higher value of such satellite products. --- paper_title: Comparison of Data Assimilation Techniques for a Coupled Model of Surface and Subsurface Flow paper_content: Data assimilation in the geophysical sciences refers to methodologies to optimally merge model predictions and observations. The ensemble Kalman filter (EnKF) is a statistical sequential data assimilation technique explicitly developed for nonlinear filtering problems. It is based on a Monte Carlo approach that approximates the conditional probability densities of the variables of interest by a finite number of randomly generated model trajectories. In Newtonian relaxation or nudging (NN), which can be viewed as a special case of the classic Kalman filter, model variables are driven toward observations by adding to the model equations a forcing term, or relaxation component, that is proportional to the difference between simulation and observation. The forcing term contains four-dimensional weighting functions that can, ideally, incorporate prior knowledge about the characteristic scales of spatial and temporal variability of the state variable(s) being assimilated. In this study, we examined the EnKF and NN algorithms as implemented for a complex hydrologic model that simulates catchment dynamics, coupling a three-dimensional finite element Richards9 equation solver for variably saturated porous media and a finite difference diffusion wave approximation for surface water flow. We report on the retrieval performance of the two assimilation schemes for a small catchment in Belgium. The results of the comparison show that nudging, while more straightforward and less expensive computationally, is not as effective as the ensemble Kalman filter in retrieving the true system state. We discuss some of the strengths and weaknesses, both physical and numerical, of the NN and EnKF schemes. ---	Title: Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review Section 1: Introduction Description 1: Introduces the concept of data assimilation (DA) in terrestrial systems, highlights the importance of combining measurements and models, and outlines the objectives of the review. Section 2: Data Assimilation Theory Description 2: Discusses the theoretical background of various data assimilation techniques, including the Ensemble Kalman Filter (EnKF), Particle Filter (PF), and variational methods (VAR). Section 3: Ensemble Kalman Filter (EnKF) Description 3: Provides an in-depth examination of the Ensemble Kalman Filter methodology, its application, and its variants such as the Ensemble Kalman Smoother (EnKS). Section 4: Particle Filter (PF) Description 4: Reviews the Particle Filter method, detailing its procedure, advantages, drawbacks, and specific applications in hydrological modeling. Section 5: Variational Assimilation (VAR) Description 5: Explains the variational assimilation technique, focusing on its cost function minimization approach and applications in numerical weather prediction and terrestrial systems. Section 6: Data Assimilation across States and Scales Description 6: Analyzes different approaches to DA based on the number of states and scales, detailing univariate single-scale, univariate multiscale, multivariate single-scale, and multivariate multiscale data assimilation. Section 7: Univariate Single-Scale Data Assimilation (UVSS) Description 7: Discusses DA methods that assimilate a single type of data without scale mismatch considerations, providing specific examples and applications. Section 8: Multivariate Single-Scale Data Assimilation (MVSS) Description 8: Reviews the techniques and benefits of assimilating multiple types of observation data on the same spatial scale into simulation models. Section 9: Univariate Multiscale Data Assimilation (UVMS) Description 9: Focuses on DA methods that handle data obtained at different resolutions than the model resolution, employing scaling techniques for proper assimilation. Section 10: Multivariate Multiscale Data Assimilation (MVMS) Description 10: Covers the complex integration of various types and scales of data, detailing studies and methods for efficiently combining and assimilating these data sets. Section 11: Multisource DA Description 11: Defines the scenario where a single state variable is updated by multiple data sources at the same scale, highlighting its distinct advantages and applications. Section 12: Methodology Description 12: Compares different strategies for assimilating data obtained at varying resolutions, including observation operators and prior downscaling techniques. Section 13: Applications Description 13: Provides examples of diverse applications of univariate and multivariate data assimilation across different terrestrial systems. Section 14: Advantages and Disadvantages of Multivariate and Multiscale Data Assimilation Description 14: Summarizes the benefits and challenges of using multivariate and multiscale DA methods versus univariate single-scale approaches. Section 15: Other Aspects Description 15: Discusses additional considerations in DA, such as parameter estimation verification and the computational demands of high-resolution models. Section 16: Conclusions Description 16: Summarizes the current state and future potential of DA in terrestrial systems, emphasizing the need for further research and application to fully leverage available environmental data.
Two decades of local binary patterns: A survey	11	--- paper_title: Face Description with Local Binary Patterns: Application to Face Recognition paper_content: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed --- paper_title: Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions paper_content: Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation --- paper_title: Local Binary Patterns: New Variants and Applications paper_content: This book introduces Local Binary Patterns (LBP), arguably one of the most powerful texture descriptors, and LBP variants. This volume provides the latest reviews of the literature and a presentation of some of the best LBP variants by researchers at the forefront of textual analysis research and research on LBP descriptors and variants. The value of LBP variants is illustrated with reported experiments using many databases representing a diversity of computer vision applications in medicine, biometrics, and other areas. There is also a chapter that provides an excellent theoretical foundation for texture analysis and LBP in particular. A special section focuses on LBP and LBP variants in the area of face recognition, including thermal face recognition. This book will be of value to anyone already in the field as well as to those interested in learning more about this powerful family of texture descriptors. --- paper_title: Texture discrimination with multidimensional distributions of signed gray level differences paper_content: The statistics of gray-level di!erences have been successfully used in a number of texture analysis studies. In this paper we propose to use signed gray-level di!erences and their multidimensional distributions for texture description. The present approach has important advantages compared to earlier related approaches based on gray level cooccurrence matrices or histograms of absolute gray-level di!erences. Experiments with di$cult texture classi"cation and supervised texture segmentation problems show that our approach provides a very good and robust performance in comparison with the mainstream paradigms such as cooccurrence matrices, Gaussian Markov random "elds, or Gabor "ltering. ( 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. --- paper_title: Face Description with Local Binary Patterns: Application to Face Recognition paper_content: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed --- paper_title: Monogenic-LBP: A new approach for rotation invariant texture classification paper_content: Analysis of two-dimensional textures has many potential applications in computer vision. In this paper, we investigate the problem of rotation invariant texture classification, and propose a novel texture feature extractor, namely Monogenic-LBP (M-LBP). M-LBP integrates the traditional Local Binary Pattern (LBP) operator with the other two rotation invariant measures: the local phase and the local surface type computed by the 1st-order and 2ndorder Riesz transforms, respectively. The classification is based on the image's histogram of M-LBP responses. Extensive experiments conducted on the CUReT database demonstrate the overall superiority of M-LBP over the other state-of-the-art methods evaluated. --- paper_title: A comparative study of texture measures with classification based on featured distributions paper_content: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented --- paper_title: Retrieval of translated, rotated and scaled color textures paper_content: A new method for color texture retrieval using color and edge features is proposed in this study. The proposed method unifies color and edge features rather than simply analyzing only color characteristics. First, the distributions of color and local edge patterns are used to derive a similarity measure for a pair of textures. Then, a retrieval method based on the similarity measure is proposed to retrieve texture images from a database of color textures. Finally, the similarity measure is extended to retrieve texture regions from a database of natural images. Since the proposed feature distributions can resist variations in translation, rotation and scale, our method has the ability to retrieve texture images or regions that change in translation, rotation and/or scale. The effectiveness and practicability of the proposed method have been demonstrated by various experiments. --- paper_title: Constructing Local Binary Pattern Statistics by Soft Voting paper_content: In this paper we propose a novel method for constructing Local Binary Pattern (LBP) statistics for image appearance description. The method is inspired by the kernel density estimation designed for estimating the underlying probability function of a random variable. An essential part of the proposed method is the use of Hamming distance. Compared to the standard LBP histogram statistics where one labeled pixel always contributes to one bin of the histogram, the proposed method exploits a kernel-like similarity function to determine weighted votes contributing several possible pattern types in the statistic. As a result, the method yields a more reliable estimate of the underlying LBP distribution of the given image. In overall, the method is easy to implement and outperforms the standard LBP histogram description in texture classification and in biometrics-related face verification. We demonstrate that the method is extremely potential in problems where the number of pixels is limited. This makes the method very promising, for example, in low-resolution image description and the description of interest regions. Another interesting property of the proposed method is that it can be easily integrated with many existing LBP variants that use label statistics as descriptors. --- paper_title: FLBP: Fuzzy Local Binary Patterns paper_content: In this chapter, a variant of the Local Binary Patterns method that extends the ability of the method to cope with noisy texture by utilizing fuzzy modelling techniques is presented. The presented generalised Fuzzy Binary Patterns model is applied to the classic Local Binary Patterns method as well as to the Local Binary Patterns with Contrast measure method, resulting to the respective fuzzy logic based methods. Supervised classification experiments were conducted on a wide range of natural and medical texture images, degraded by different types and intensities of additive noise, in order to evaluate the efficiency of the Fuzzy Local Binary Patterns method and its fusion with other proposed methods. Fuzzy Binary Patterns based methods outperform the respective methods based on the classic Binary Patterns model for all types of images and noise, indicating the efficiency of fuzzy modelling in coping with the uncertainty introduced to texture due to noise. --- paper_title: Classification with color and texture: jointly or separately? paper_content: Abstract Current approaches to color texture analysis can be roughly divided into two categories: methods that process color and texture information separately, and those that consider color and texture a joint phenomenon. In this paper, both approaches are empirically evaluated with a large set of natural color textures. The classification performance of color indexing methods is compared to gray-scale and color texture methods, and to combined color and texture methods, in static and varying illumination conditions. Based on the results, we argue that color and texture are separate phenomena that can, or even should, be treated individually. --- paper_title: CENTRIST: A Visual Descriptor for Scene Categorization paper_content: CENsus TRansform hISTogram (CENTRIST), a new visual descriptor for recognizing topological places or scene categories, is introduced in this paper. We show that place and scene recognition, especially for indoor environments, require its visual descriptor to possess properties that are different from other vision domains (e.g., object recognition). CENTRIST satisfies these properties and suits the place and scene recognition task. It is a holistic representation and has strong generalizability for category recognition. CENTRIST mainly encodes the structural properties within an image and suppresses detailed textural information. Our experiments demonstrate that CENTRIST outperforms the current state of the art in several place and scene recognition data sets, compared with other descriptors such as SIFT and Gist. Besides, it is easy to implement and evaluates extremely fast. --- paper_title: Learning binarized pixel-difference pattern for scene recognition paper_content: Local binary pattern (LBP) and its variants have been used in scene recognition. However, most existing approaches rely on a pre-defined LBP structure to extract features. Those pre-defined structures can be generalized as the patterns constructed from the binarized pixel differences in a local neighborhood. Instead of using a handcraft structure, we propose to learn binarized pixel-difference patterns (BPP). We cast the problem as a feature selection problem and solve it by an incremental search via the criterion of minimum-redundancy-maximum-relevance. Then, BPP features are extracted based on the structures derived. On two challenging scene recognition databases, the proposed approach significantly outperforms the state of the arts. --- paper_title: A comprehensive benchmark of local binary pattern algorithms for texture retrieval paper_content: Image retrieval is a well researched area and often based on integrating various kinds of image features. Apart from colour features, texture features are deemed crucial for successful image retrieval. Local binary pattern (LBP) based texture algorithms have gained significant popularity in recent years and have been shown to be useful for a variety of tasks. In this paper, we provide a comprehensive benchmark of LBP based methods for texture retrieval. In particular, a comparison of 16 LBP variants leading to 38 different texture descriptors, are evaluated on a large dataset of more than 6000 texture images. Interestingly, conventional LBP features are shown to work best, while almost all LBP methods are shown to significantly outperform other texture methods including Tamura, co-occurrence and Gabor features. ---	Title: Two Decades of Local Binary Patterns: A Survey Section 1: An overview of basic LBP operators Description 1: Summarize the fundamental concepts, theoretical bases, and initial implementations of the Local Binary Patterns (LBP) operator. Section 2: Different types of variants Description 2: Discuss various extensions and modifications of the original LBP to enhance its performance and robustness across different applications. Section 3: Preprocessing Description 3: Detail preprocessing techniques used prior to LBP computation, including methods like Gabor filtering and edge detection to improve performance under various conditions. Section 4: Neighborhood topology and sampling Description 4: Explain the flexibility of LBP in terms of neighborhood topology and sampling, and how different shapes and configurations can be used based on the application requirements. Section 5: Thresholding and encoding Description 5: Explore different thresholding and encoding techniques that address the limitations of the original LBP, enhancing robustness to noise and gray level changes. Section 6: Considering co-occurrences Description 6: Describe approaches that incorporate co-occurrences of LBP codes to improve detection performance and feature representation. Section 7: Feature selection and learning Description 7: Highlight methods for reducing the dimensionality of LBP features and selecting the most discriminative patterns through feature selection and machine learning techniques. Section 8: Other methods inspired by LBP Description 8: Summarize new local image descriptors developed based on LBP principles, such as Local Phase Quantization (LPQ) and Patterns of Oriented Edge Magnitudes (POEM). Section 9: Variants of spatiotemporal LBP Description 9: Discuss the adaptations of LBP for spatiotemporal analysis, extending its applicability from static images to dynamic video sequences. Section 10: Analysis of depth and 4D images Description 10: Detail the use of LBP variants for analyzing depth (3D) and 4D images, with applications in fields like medical imaging and gesture recognition. Section 11: Conclusions Description 11: Summarize the overall advantages of LBP, highlight key variants and their contributions, and discuss future challenges and directions for LBP research.
A Review on Speckle Noise Reduction Techniques	18	--- paper_title: A hybrid filter for image despeckling with wavelet-based denoising and spatial filtering paper_content: Noise is one of the most common problems found in different imaging applications. Even a high resolution photo is bound to have some noise in it. Image denoising algorithms may be the oldest in image processing. Many methods, regardless of their implementation, share the same basic idea: noise reduction through image blurring. However, a universal “best” approach has yet to be found. Wavelet transform has been studied extensively in recent years and it has been widely used for noise reduction. In this paper, we propose a novel technique for image despeckling that combines wavelet denoising and an enhanced adaptive version of the old Kuan filter, which results in a significant gain with respect to speckle noise filters and the simple wavelet denoising technique. ---	Title: A Review on Speckle Noise Reduction Techniques Section 1: Introduction Description 1: This section introduces the problem of noise in imaging systems, particularly focusing on speckle noise encountered in medical imaging. Section 2: Statistical Model of Speckle in Ultrasound Description 2: In this section, discuss the statistical models used to understand and represent speckle noise in ultrasound imaging. Section 3: Speckle Filtering Methods Description 3: This section outlines various methods for speckle noise reduction, divided into spatial domain techniques and transform domain techniques. Section 3.1: Spatial Domain Filtering Description 3.1: Discuss the spatial domain techniques for filtering speckle noise, including explanations of common filters. Section 3.1.1: Box Filter Description 3.1.1: Describe the box filter method, its advantages, and its disadvantages. Section 3.1.2: Median Filter Description 3.1.2: Explain the median filter technique, focusing on its effectiveness against different types of noise and its computational requirements. Section 3.1.3: Statistic Lee Filter Description 3.1.3: Detail the Lee filter, its working principles, and any limitations. Section 3.1.4: Statistic Kuan Filter Description 3.1.4: Discuss the Kuan filter, its approach compared to the Lee filter, and its limitations. Section 3.1.5: Frost Filter Description 3.1.5: Describe the Frost filter and how it adapts to different variances within an image for effective noise reduction. Section 3.1.6: Srad Filter Description 3.1.6: Explore the SRAD filter technique, which uses non-linear anisotropic diffusion for reducing speckle noise. Section 3.2: Transform Domain Techniques Description 3.2: Explain the methods that work in the transform domain to reduce speckle noise, including their principles and execution. Section 3.2.1: Fourier Transform Description 3.2.1: Describe the Fourier transform approach for speckle noise reduction, including its steps and applications. Section 3.2.2: Wavelet Transform Description 3.2.2: Discuss the wavelet transform method, its benefits in multi-resolution analysis, and its applications in noise reduction. Section 3.2.3: Wavelet Domain Noise Filtering Description 3.2.3: Explain how noise filtering is achieved in the wavelet domain and the specific processes used. Section 4: Thresholding Description 4: Discuss the role of thresholding techniques in speckle noise reduction, including soft and hard thresholding methods. Section 4.1: Soft Thresholding Description 4.1: Explain the soft thresholding technique and how it improves image quality by reducing discontinuities. Section 4.2: Hard Thresholding Description 4.2: Describe the hard thresholding technique and its application in detail coefficient handling. Section 5: Conclusion Description 5: Summarize the findings and discussion on the various filtering methods, highlighting the advantages of wavelet transform techniques over traditional methods.
A Survey of Design Pattern Based Web Applications	8	--- paper_title: Design reuse in hypermedia applications development paper_content: In this paper we discuss the use of design patterns for the process of building hypermedia applications. The idea of design patterns has been recently developed, and rapidly spread outside the object-oriented community to a general audience of software developers. By using patterns it is not only possible to document design experience with a very simple and comprehensible format, but also reuse the same experience several times for different applications. We argue that the hypermedia community will take a vital step towards better designs of hypermedia applications and systems by developing a pattern language for that domain. --- paper_title: A formal description of design patterns using OWL paper_content: Design patterns have been used successfully in the last decade to reuse and communicate object-oriented design. However, the documentation of pattern usage is often very poor. This motivates the use of tools which can detect and document design patterns found in software. A couple of approaches have been proposed in recent years. The approach introduced is based on a formal description of design patterns using the Web ontology language OWL. Software artefacts used to define design patterns in a formal and machine processable fashion are represented by uniform resource identifiers (URIs). This yields a description that is open and extensible, and facilitates the sharing of design among software engineers. We discuss the developed software design ontology, and how this approach relates to the meta-modelling architecture of the OMG. In the second part, an effective pattern scanner for the Java language is presented. This scanner is based on the ontology developed in part one and uses reflection and AST analysis to verify constraints. Various applications of this scanner are discussed. --- paper_title: Ontology Design Patterns for Semantic Web Content paper_content: The paper presents a framework for introducing design patterns that facilitate or improve the techniques used during ontology lifecycle. Some distinctions are drawn between kinds of ontology design patterns. Some content-oriented patterns are presented in order to illustrate their utility at different degrees of abstraction, and how they can be specialized or composed. The proposed framework and the initial set of patterns are designed in order to function as a pipeline connecting domain modelling, user requirements, and ontology driven tasks/queries to be executed. --- paper_title: Transforming legacy Web applications to the MVC architecture paper_content: With the rapid changes that occur in the area of Web technologies, the porting and adaptation of existing Web applications into new platforms that take advantage of modern technologies has become an issue of increasing importance. This paper presents a reengineering framework whose target system is an architecture based on the model-view-controller (MVC) design pattern and enabled for the Java/spl trade/ 2 Platform, Enterprise Edition (J2EE). The proposed framework is mainly concerned with the decomposition of a legacy Web application by identifying software components to be transformed into Java objects such as JavaBeans, JavaServer Pages (JSP), and Java Servlet. --- paper_title: Legacy Migration to Service-Oriented Computing with Mashups paper_content: Although service-oriented computing holds great promises, it is still not clear when and how the existing systems will exploit this new computational model. The problem is particularly severe for the software having several years of use. This work provides a roadmap for the migration of legacy software to service-oriented computing by means of right levels of abstraction. The key idea is having integration even at the presentation layer, not only at backend layers such as application or data. This requires re-inventing the popular MASHUP technology of Web 2.0 at the enterprise level. Domain- specific-kits and choreography engine concepts that were originally introduced by the software factory automation approach have been reshaped as another enabling technology towards migrating to the service harmonization platform. The paper also exemplifies the proposed approach on a simple case problem. --- paper_title: A Method for Integration of Web Applications Based on Information Extraction paper_content: Integration of Web services from different Web sites has brought new creativity and functionality to Web applications. These integration technologies, called mashup or mixup, have made a shift in Web service development and created a new generation of widely popular and successful Web services such as Google Maps API and YouTube Data API. However, the integration is limited to the Web sites that provide the open Web service APIs, and currently, most existing Web sites do not provide Web services. In this paper, we present a method to integrate the general Web applications. For this purpose, we propose a Web information extraction method to generate the virtual Web service functions from Web applications at client side. Our implementation shows that the general Web applications can be also integrated easily. --- paper_title: A Database and Web Application Based on MVC Architecture paper_content: MVC architecture has had wide acceptance for corporation software development. It plans to divide the system in three different layers that are in charge of interface control logic and data access, this facilitates the maintenance and evolution of systems according to the independence of the present classes in each layer. With the purpose of illustrating a successful application built under MVC, in this work we introduce different phases of analysis, design and implementation of a database and web application using UML. As central component of the application, it has a database made up by fifteen relations and a user interface supported by seventeen web pages. --- paper_title: Design reuse in hypermedia applications development paper_content: In this paper we discuss the use of design patterns for the process of building hypermedia applications. The idea of design patterns has been recently developed, and rapidly spread outside the object-oriented community to a general audience of software developers. By using patterns it is not only possible to document design experience with a very simple and comprehensible format, but also reuse the same experience several times for different applications. We argue that the hypermedia community will take a vital step towards better designs of hypermedia applications and systems by developing a pattern language for that domain. --- paper_title: Ontology Design Patterns for Semantic Web Content paper_content: The paper presents a framework for introducing design patterns that facilitate or improve the techniques used during ontology lifecycle. Some distinctions are drawn between kinds of ontology design patterns. Some content-oriented patterns are presented in order to illustrate their utility at different degrees of abstraction, and how they can be specialized or composed. The proposed framework and the initial set of patterns are designed in order to function as a pipeline connecting domain modelling, user requirements, and ontology driven tasks/queries to be executed. --- paper_title: Reengineering Web applications based on cloned pattern analysis paper_content: Web applications are subject to continuous and rapid evolution. Often it happens that programmers indiscriminately duplicate Web pages without considering systematic development and maintenance methods. This practice creates code clones that make Web applications hard to maintain and reuse. This paper presents an approach for reengineering Web applications based on clone analysis that aims at identifying and generalizing static and dynamic pages and navigational patterns of a Web application. Clone analysis is also helpful for identifying literals that can be generated from a database. A case study is described which shows how the proposed approach can be used for restructuring the navigational structure of a Web application by removing redundant code. --- paper_title: Reengineering to the Web: a reference architecture paper_content: Reengineering existing (large-scale) applications to the Web is a complex and highly challenging task. This is due to a variety of demanding requirements for interactive Web applications. High performance is usually required, old interfaces still have to be supported, high availability requirements are usual, information has to be provided to multiple channels and in different formats, pages should contain individual layouts across different channels, styles should be imposed over presentation, etc. To achieve these goals a variety of different technologies and concepts have to be well understood, including HTTP protocol handling, persistent stores/databases, various XML standards, authentication, session management, dynamic content creation, presentational abstractions, and flexible legacy system wrapping. In a concrete project, all these components have to be integrated properly and appropriate technologies have to be chosen. On basis of practical and theoretical experience in the problem domain, in this paper, we try to identify the recurring components in reengineering projects to the Web, lay out critical issues and choices, and conceptually integrate the components into a reference architecture. ---	Title: A Survey of Design Pattern Based Web Applications Section 1: INTRODUCTION Description 1: This section introduces the challenges faced by web application designers, the role of design patterns in overcoming these challenges, the benefits and threats of pattern-based web applications, and provides an overview of the paper's organization. Section 2: PATTERN-BASED WEB APPLICATIONS Description 2: This section discusses the classification of web applications into pattern-based and non-pattern-based, and provides an analysis of cases where patterns are used in web applications, including hypermedia and semantic web applications. Section 3: Hypermedia Description 3: This subsection under Section 2 elaborates on the role of hypermedia in pattern-based web applications, using examples such as the CAIBCO System and various navigation patterns. Section 4: Semantic Web Description 4: This subsection under Section 2 explains the application of design patterns in the construction of the semantic web, including examples of ontology design patterns and their implications. Section 5: Architectural Solutions for Web applications Description 5: This section addresses the use of architectural patterns like PAC and MVC in web applications, discussing their benefits and limitations, as well as strategies for migrating legacy systems to web-based architectures. Section 6: LIABILITIES DUE TO IMPROPER USAGE OF PATTERNS FOR WEB APPLICATIONS Description 6: This section explores the negative consequences of improper pattern usage, including issues like tight coupling, inefficiencies, and partitioning problems in pattern-based web applications. Section 7: RE-ENGINEERING WEB APPLICATIONS Description 7: This section reviews various re-engineering efforts aimed at addressing the problems caused by the misuse of patterns, including methods for pattern analysis and reference architecture. Section 8: CONCLUSION AND FUTURE WORK Description 8: This section summarizes the survey’s findings on pattern-based web applications and outlines potential future work, including the development of architectures for porting web applications to the MVC framework without partition issues.
Low frequency noise and disturbance assessment methods: A brief literature overview and a new proposal	8	--- paper_title: Study and Assessment of Low Frequency Noise in Occupational Settings paper_content: Low frequency noise is one of the most harmful factors occurring in human working and living environment. Low frequency noise components from 20 to 250 Hz are often the cause of employee complaints. Noise from power stations is an actual problem for large cities, including Cairo. The noise from equipments of station could be a serious problem for station and for environmental area. The development of power stations in Cairo leads to appearing a wide range of gas turbines which are strong source of noise. Two measurement techniques using C-weighted along side the A-weighted scale are explored. C-weighting is far more sensitive to detect low frequency sound. Spectrum analysis in the low frequency range is done in order to identify a significant tonal component. Field studies were supported by a questionnaire to determine whether sociological or other factors might influence the results by using annoyance rating mean value. Subjects included in the study were 153 (mean = 36.86, SD = 8.49) male employees at the three electrical power stations. The (C-A) level difference is an appropriate metric for indicating a potential low frequency noise problem. A-weighting characteristics seem to be able to predict quite accurately annoyance experienced from LFN at workplaces. The aim of the present study is to find simple and reliable method for assessing low frequency noise in occupational environment to prevent its effects on work performance for the workers. The proposed method has to be compared with European methods. --- paper_title: Options for Assessment and Regulation of Low Frequency Noise paper_content: Dutch legal limits for Lden noise levels are different for road traffic, railways, industry and airports. A justification for these differences might be found in the difference between dose-response relationships for noise annoyance. Nevertheless, frequently situations occur where people severely complain of low frequency noise, even when Lden levels amply comply with limits. So far, an effective regulation specifically for low frequency noise is not available. This paper discusses some options for improving the protection for LFN annoyance from Lden legislation. A potential solution might be to penalize Lden levels based on low versus high frequency ratio and tonality. This paper describes how such an approach is applied for a number of LFN cases in the Netherlands and discusses the results. --- paper_title: The Central Role of Interpersonal Conflict in Low Frequency Noise Annoyance paper_content: This paper considers the relationship between the Environmental Health Officer and the Low Frequency Noise complainant (sufferer). It is suggested that the characteristic psychoacoustic properties of Low Frequency Noise may interact with inappropriate assessment protocols to produce a series of interpersonal pressures that play an active part in shaping the overall noise problem. This interaction may be considered as a legitimate and common impact factor within Low Frequency Noise complaints. The confounding role of misperception and miscommunication, between the parties, is explored and models of conflict resolution are considered as a means for providing counter measures to the behavioural consequences of failed assessment procedures and ineffective personal coping strategies. --- paper_title: Danish Guidelines on Environmental Low Frequency Noise, Infrasound and Vibration paper_content: In Denmark a set of guidelines for measurement and assessment of environmental low frequency noise, infrasound and vibration was published in 1997 as “Information from the Danish Environmental Protection Agency no. 9/1997” (Miljøstyrelsen2). Recommended measurement methods are specified, and recommended limit values for noise and vibration are given. In this paper a brief description of the measurement methods is given, the recommended limit values are shown, and the background for the measurement and assessment methods is discussed. This paper is an extended summary of “Information from the Danish Environmental Protection Agency No.9/1997” (in Danish) --- paper_title: Annoyance from transportation noise: relationships with exposure metrics DNL and DENL and their confidence intervals. paper_content: We present a model of the distribution of noise annoyance with the mean varying as a function of the noise exposure. Day-night level (DNL) and day-evening-night level (DENL) were used as noise descriptors. Because the entire annoyace distribution has been modeled, any annoyance measure that summarizes this distribution can be calculated from the model. We fitted the model to data from noise annoyance studies for aircraft, road traffic, and railways separately. Polynomial approximations of relationships implied by the model for the combinations of the following exposure and annoyance measures are presented: DNL or DENL, and percentage "highly annoyed" (cutoff at 72 on a scale of 0-100), percentage "annoyed" (cutoff at 50 on a scale of 0-100), or percentage (at least) "a little annoyed" (cutoff at 28 on a scale of 0-100). These approximations are very good, and they are easier to use for practical calculations than the model itself, because the model involves a normal distribution. Our results are based on the same data set that was used earlier to establish relationships between DNL and percentage highly annoyed. In this paper we provide better estimates of the confidence intervals due to the improved model of the relationship between annoyance and noise exposure. Moreover, relationships using descriptors other than DNL and percentage highly annoyed, which are presented here, have not been established earlier on the basis of a large dataset. --- paper_title: Assessment criterion for indoor noise disturbance in the presence of low frequency sources paper_content: Abstract Several studies have presented the effects of environmental noise in and around buildings and communities in which people live and work. In particular, the noise introduced into a building is mostly evaluated using the A weighted sound pressure level ( L Aeq ) as the only parameter to determine the perceived disturbance. Nevertheless, if noise is produced by activities or sources characterised by a low frequency contribution, the measurement of L Aeq underestimates the real disturbance, in particular during sleeping time. The international literature suggests methods to evaluate the low-frequency noise contribution to annoyance separately from the A weighted sound pressure level; almost all of the proposed methods are based on exceeding a threshold limit. This paper tests international criteria, by applying them in real-life indoor noise situations, and then analysing, comparing and contrasting results. Based on the result of the procedure above, a new criterion consisting of a single threshold is proposed, which simplifies the procedures in case of low-frequency components, but could be used for any situation. --- paper_title: Sound power level of speaking people paper_content: In restaurants and cafes many sound sources are present: music, refrigerant and cooling equipment and people speaking. The smoking prohibition law (in force in Italy since 2005 for instance) did move out people, creating a lot of aggregation areas outdoor, both in summer and in winter time. As a matter of facts many cafes open on the outer part a stallage in order to provide beverages to outside costumers. In this way, the "people speaking" source became a common problem for the neighborhood to deal with and solve. In order to characterize this particular sound source in terms of sound power level of a typical stallage situation full of speaking people, sound pressure power measurements according to ISO 3746 standard [1] were carried out. The results confirm the first investigation achievements provided by Sepulcri et all [2] with a non-direct method. --- paper_title: Low Frequency Hearing Thresholds in Pressure Field and in Free Field paper_content: Thresholds of hearing were determined in pressure field at frequencies fromp 4 Hz to 125 Hz. At the frequencies 4-25 Hz hearing thresholds were found that are in the lower middle of the range already reported by other investigators. At frequencies from 25 Hz to 1 kHz thresholds have already been determined in free field by the same method and using the same subjects. The two investigations overlap at frequencies from 25 Hz to 125 Hz, and in this range the results were almost identical. The differences were below 1 dB, except at 63 Hz where the difference was 2.5 dB. None of the differences was significant in a t-test --- paper_title: Assessment criterion for indoor noise disturbance in the presence of low frequency sources paper_content: Abstract Several studies have presented the effects of environmental noise in and around buildings and communities in which people live and work. In particular, the noise introduced into a building is mostly evaluated using the A weighted sound pressure level ( L Aeq ) as the only parameter to determine the perceived disturbance. Nevertheless, if noise is produced by activities or sources characterised by a low frequency contribution, the measurement of L Aeq underestimates the real disturbance, in particular during sleeping time. The international literature suggests methods to evaluate the low-frequency noise contribution to annoyance separately from the A weighted sound pressure level; almost all of the proposed methods are based on exceeding a threshold limit. This paper tests international criteria, by applying them in real-life indoor noise situations, and then analysing, comparing and contrasting results. Based on the result of the procedure above, a new criterion consisting of a single threshold is proposed, which simplifies the procedures in case of low-frequency components, but could be used for any situation. --- paper_title: Cortical Plasticity: Learning from Cortical Reorganisation paper_content: Neocortical circuits can undergo dynamic rearrangements, not only in response to injury, but also when new skills are acquired. But although training can lead to functional rewiring of the cortex, we are far from being able to reprogram an animal by manipulating its cortical circuitry directly. --- paper_title: Cortical Plasticity: Learning from Cortical Reorganisation paper_content: Neocortical circuits can undergo dynamic rearrangements, not only in response to injury, but also when new skills are acquired. But although training can lead to functional rewiring of the cortex, we are far from being able to reprogram an animal by manipulating its cortical circuitry directly. --- paper_title: Experimental Characterisation of the Low Frequency Noise Annoyance Arising from Industrial Plants paper_content: Complaints about low frequency noise annoyance are frequently reported although the sound levels, measured according to usual procedures, do not seem to justify such reactions. In the present paper some experimental data, gathered by the authors, are illustrated. The results are analyzed on the basis of the available methodologies with the aim of giving a useful contribution to the discussion about the necessity of a special methodology for the correct evaluation of the low frequency noise annoyance. --- paper_title: Impact Sound Pressure Level Performances of Basic Beam Floor Structures paper_content: In order to obtain reliable estimations of the impact sound insulation between rooms, it is necessary to know the acoustic performance of each element composing the floors. The contribution of the flanking transmissions, the attenuation of floating floors and the weighted normalized impact sound pressure level of the basic structure need to be determined in order to apply the simplified calculation method according to the EN ISO 12354-2 standard. With the aim of verifying the range of validity of the calculation method proposed by the EN ISO 12354-2 standard for typical basic beam floor structures, a research based on on-site measurements was conducted. This paper provides an analysis in terms of spectrum trend, predicted average weighted normalized impact sound pressure level and reduction of impact sound pressure level obtainable with a generic floating floor typology. The study can represent a starting point for a correct estimation of the impact sound insulation in new buildings and renovation plans. --- paper_title: Impulse response method for defect detection in polymers: Description of the method and preliminary results paper_content: The major problem encountered in the application of polymer industrial products is the difficulty of effectively modelling and predicting material performance and service life according to applied loads and operating environmental conditions. Furthermore, the presence of defects such as voids or inclusions created during manufacturing may affect the final performance. ::: ::: The aim of this study is to present and investigate the development of an innovative acoustic non-destructive technique (patent pending), able to verify defects into composite laminates. ::: ::: The analysis was carried out in two steps: the first aims to verify if distinct phases can be recognized within a material, while the second has the purpose of testing the proposed method on defective materials ad hoc prepared. --- paper_title: Assessment criterion for indoor noise disturbance in the presence of low frequency sources paper_content: Abstract Several studies have presented the effects of environmental noise in and around buildings and communities in which people live and work. In particular, the noise introduced into a building is mostly evaluated using the A weighted sound pressure level ( L Aeq ) as the only parameter to determine the perceived disturbance. Nevertheless, if noise is produced by activities or sources characterised by a low frequency contribution, the measurement of L Aeq underestimates the real disturbance, in particular during sleeping time. The international literature suggests methods to evaluate the low-frequency noise contribution to annoyance separately from the A weighted sound pressure level; almost all of the proposed methods are based on exceeding a threshold limit. This paper tests international criteria, by applying them in real-life indoor noise situations, and then analysing, comparing and contrasting results. Based on the result of the procedure above, a new criterion consisting of a single threshold is proposed, which simplifies the procedures in case of low-frequency components, but could be used for any situation. ---	Title: Low Frequency Noise and Disturbance Assessment Methods: A Brief Literature Overview and a New Proposal Section 1: INTRODUCTION Description 1: Introduce the topic of low frequency noise (LFN), its impacts, and the limitations of existing assessment methods. Section 2: FREQUENCY RANGE AND THRESHOLDS Description 2: Discuss various frequency ranges used in LFN assessment and the importance of thresholds in evaluation criteria. Section 3: LOW FREQUENCY VS. INFRASOUND Description 3: Clarify the distinction between low frequency noise and infrasound, and highlight different viewpoints on their classification. Section 4: HEARING THRESHOLDS Description 4: Explain how hearing sensitivity varies with age and the implications for LFN perception. Section 5: HEARING VS. ANNOYANCE Description 5: Examine the relationship between the perception of LFN and the annoyance it causes, including psychological factors. Section 6: THE USE OF dB(A) Description 6: Critique the use of the A-weighted sound pressure level as a measure of LFN disturbance and consider alternative metrics. Section 7: DISCUSSION AND NEW PROPOSAL Description 7: Present a discussion on the need for improved LFN assessment methods and introduce a new objective proposal based on robust measurement criteria. Section 8: FINAL REMARKS Description 8: Conclude with the significance of addressing LFN in noise assessments and the potential impact of the proposed method on future standards and legislation.
A Survey on the Use of Graphical Passwords in Security	16	--- paper_title: Recognition memory for words, sentences, and pictures paper_content: The S s looked through a series of about 600 stimuli selected at random from an initially larger population. They were then tested for their ability to recognize these “old” stimuli in pairs in which the alternative was always a “new” stimulus selected at random from the stimuli remaining in the original population. Depending upon whether this original population consisted solely of words, sentences, or pictures, median Ss were able correctly to recognize the “old” stimulus in 90, 88, or 98% of the test pairs, respectively. Estimated lower bounds on the informational capacity of human memory considerably exceed previously published estimates. --- paper_title: Graphical passwords: Learning from the first twelve years paper_content: Starting around 1999, a great many graphical password schemes have been proposed as alternatives to text-based password authentication. We provide a comprehensive overview of published research in the area, covering both usability and security aspects as well as system evaluation. The article first catalogues existing approaches, highlighting novel features of selected schemes and identifying key usability or security advantages. We then review usability requirements for knowledge-based authentication as they apply to graphical passwords, identify security threats that such systems must address and review known attacks, discuss methodological issues related to empirical evaluation, and identify areas for further research and improved methodology. --- paper_title: A Novel Cued-recall Graphical Password Scheme paper_content: Graphical passwords have been proposed as an alternative to alphanumeric passwords with their advantages in usability and security. However, most of these alternate schemes have their own disadvantages. For example, cued-recall graphical password schemes are vulnerable to shoulder-surfing and cannot prevent intersection analysis attack. A novel cued-recall graphical password scheme CBFG (Click Buttons according to Figures in Grids) is proposed in this paper. Inheriting the way of setting password in traditional cued-recall scheme, this scheme is also added the ideology of image identification. CBFG helps users tend to set their passwords more complex. Simultaneously, it has the capability against shoulder surfing attack and intersection analysis attack. Experiments illustrate that CBFG has better performance in usability, especially in security. --- paper_title: UNIX Password Security - Ten Years Later paper_content: Passwords in the UNIX operating system are encrypted with the crypt algorithm and kept in the publicly-readable file /etc/passwd. This paper examines the vulnerability of UNIX to attacks on its password system. Over the past 10 years, improvements in hardware and software have increased the crypts/second/dollai ratio by five orders of magnitude. We reexamine the UNIX password system in light of these advances and point out possible solutions to the problem of easily found passwords. The paper discusses how the authors built some high-speed tools for password cracking and what elements were necessary for their success. These elements are examined to determine if any of them can be removed from the hands of a possible system infiltrator, and thus increase the security of the system. We conclude that the single most important step that can be taken to improve password security is to increase password entropy. --- paper_title: Making Passwords Secure and Usable paper_content: To date, system research has focused on designing security mechanisms to protect systems access although their usability has rarely been investigated. This paper reports a study in which users’ perceptions of password mechanisms were investigated through questionnaires and interviews. Analysis of the questionnaires shows that many users report problems, linked to the number of passwords and frequency of password use. In-depth analysis of the interview data revealed that the degree to which users conform to security mechanisms depends on their perception of security levels, information sensitivity and compatibility with work practices. Security mechanisms incompatible with these perceptions may be circumvented by users and thereby undermine system security overall. --- paper_title: A New Graphical Password Scheme Resistant to Shoulder-Surfing paper_content: Shoulder-surfing is a known risk where an attacker can capture a password by direct observation or by recording the authentication session. Due to the visual interface, this problem has become exacerbated in graphical passwords. There have been some graphical schemes resistant or immune to shoulder-surfing, but they have significant usability drawbacks, usually in the time and effort to log in. In this paper, we propose and evaluate a new shoulder-surfing resistant scheme which has a desirable usability for PDAs. Our inspiration comes from the drawing input method in DAS and the association mnemonics in Story for sequence retrieval. The new scheme requires users to draw a curve across their password images orderly rather than click directly on them. The drawing input trick along with the complementary measures, such as erasing the drawing trace, displaying degraded images, and starting and ending with randomly designated images provide a good resistance to shoulder-surfing. A preliminary user study showed that users were able to enter their passwords accurately and to remember them over time. --- paper_title: Password security: a case history paper_content: This paper describes the history of the design of the password security scheme on a remotely accessed time-sharing system. The present design was the result of countering observed attempts to penetrate the system. The result is a compromise between extreme security and ease of use. --- paper_title: Graphical passwords: a survey paper_content: The most common computer authentication method is to use alphanumerical usernames and passwords. This method has been shown to have significant drawbacks. For example, users tend to pick passwords that can be easily guessed. On the other hand, if a password is hard to guess, then it is often hard to remember. To address this problem, some researchers have developed authentication methods that use pictures as passwords. In this paper, we conduct a comprehensive survey of the existing graphical password techniques. We classify these techniques into two categories: recognition-based and recall-based approaches. We discuss the strengths and limitations of each method and point out the future research directions in this area. We also try to answer two important questions: "Are graphical passwords as secure as text-based passwords?"; "What are the major design and implementation issues for graphical passwords?" This survey will be useful for information security researchers and practitioners who are interested in finding an alternative to text-based authentication methods --- paper_title: Purely Automated Attacks on PassPoints-Style Graphical Passwords paper_content: We introduce and evaluate various methods for purely automated attacks against PassPoints-style graphical passwords. For generating these attacks, we introduce a graph-based algorithm to efficiently create dictionaries based on heuristics such as click-order patterns (e.g., five points all along a line). Some of our methods combine click-order heuristics with focus-of-attention scan-paths generated from a computational model of visual attention, yielding significantly better automated attacks than previous work. One resulting automated attack finds 7%-16% of passwords for two representative images using dictionaries of approximately 226 entries (where the full password space is 243). Relaxing click-order patterns substantially increased the attack efficacy albeit with larger dictionaries of approximately 235 entries, allowing attacks that guessed 48%-54% of passwords (compared to previous results of 1% and 9% on the same dataset for two images with 235 guesses). These latter attacks are independent of focus-of-attention models, and are based on image-independent guessing patterns. Our results show that automated attacks, which are easier to arrange than human-seeded attacks and are more scalable to systems that use multiple images, require serious consideration when deploying basic PassPoints-style graphical passwords. --- paper_title: Graphical passwords: Learning from the first twelve years paper_content: Starting around 1999, a great many graphical password schemes have been proposed as alternatives to text-based password authentication. We provide a comprehensive overview of published research in the area, covering both usability and security aspects as well as system evaluation. The article first catalogues existing approaches, highlighting novel features of selected schemes and identifying key usability or security advantages. We then review usability requirements for knowledge-based authentication as they apply to graphical passwords, identify security threats that such systems must address and review known attacks, discuss methodological issues related to empirical evaluation, and identify areas for further research and improved methodology. --- paper_title: On predictive models and user-drawn graphical passwords paper_content: In commonplace text-based password schemes, users typically choose passwords that are easy to recall, exhibit patterns, and are thus vulnerable to brute-force dictionary attacks. This leads us to ask whether other types of passwords (e.g., graphical) are also vulnerable to dictionary attack because of users tending to choose memorable passwords. We suggest a method to predict and model a number of such classes for systems where passwords are created solely from a user's memory. We hypothesize that these classes define weak password subspaces suitable for an attack dictionary. For user-drawn graphical passwords, we apply this method with cognitive studies on visual recall. These cognitive studies motivate us to define a set of password complexity factors (e.g., reflective symmetry and stroke count), which define a set of classes. To better understand the size of these classes and, thus, how weak the password subspaces they define might be, we use the “Draw-A-Secret” (DAS) graphical password scheme of Jermyn et al. [1999] as an example. We analyze the size of these classes for DAS under convenient parameter choices and show that they can be combined to define apparently popular subspaces that have bit sizes ranging from 31 to 41—a surprisingly small proportion of the full password space (58 bits). Our results quantitatively support suggestions that user-drawn graphical password systems employ measures, such as graphical password rules or guidelines and proactive password checking. --- paper_title: A Novel Cued-recall Graphical Password Scheme paper_content: Graphical passwords have been proposed as an alternative to alphanumeric passwords with their advantages in usability and security. However, most of these alternate schemes have their own disadvantages. For example, cued-recall graphical password schemes are vulnerable to shoulder-surfing and cannot prevent intersection analysis attack. A novel cued-recall graphical password scheme CBFG (Click Buttons according to Figures in Grids) is proposed in this paper. Inheriting the way of setting password in traditional cued-recall scheme, this scheme is also added the ideology of image identification. CBFG helps users tend to set their passwords more complex. Simultaneously, it has the capability against shoulder surfing attack and intersection analysis attack. Experiments illustrate that CBFG has better performance in usability, especially in security. --- paper_title: Design and evaluation of a shoulder-surfing resistant graphical password scheme paper_content: When users input their passwords in a public place, they may be at risk of attackers stealing their password. An attacker can capture a password by direct observation or by recording the individual's authentication session. This is referred to as shoulder-surfing and is a known risk, of special concern when authenticating in public places. Until recently, the only defense against shoulder-surfing has been vigilance on the part of the user. This paper reports on the design and evaluation of a game-like graphical method of authentication that is resistant to shoulder-surfing. The Convex Hull Click (CHC) scheme allows a user to prove knowledge of the graphical password safely in an insecure location because users never have to click directly on their password images. Usability testing of the CHC scheme showed that novice users were able to enter their graphical password accurately and to remember it over time. However, the protection against shoulder-surfing comes at the price of longer time to carry out the authentication. --- paper_title: Graphical passwords: a survey paper_content: The most common computer authentication method is to use alphanumerical usernames and passwords. This method has been shown to have significant drawbacks. For example, users tend to pick passwords that can be easily guessed. On the other hand, if a password is hard to guess, then it is often hard to remember. To address this problem, some researchers have developed authentication methods that use pictures as passwords. In this paper, we conduct a comprehensive survey of the existing graphical password techniques. We classify these techniques into two categories: recognition-based and recall-based approaches. We discuss the strengths and limitations of each method and point out the future research directions in this area. We also try to answer two important questions: "Are graphical passwords as secure as text-based passwords?"; "What are the major design and implementation issues for graphical passwords?" This survey will be useful for information security researchers and practitioners who are interested in finding an alternative to text-based authentication methods --- paper_title: Analyzing User Choice in Graphical Passwords paper_content: In ubiquitous textual password schemes, users choose passwords that contain predictable characteristics that are roughly equated with what users flnd easy to recall. This motivates us to examine user choice in graphical passwords to determine whether predictable characteristics exist that may reduce the entropy of the password space. We present an informal user study of the scheme proposed by Jermyn et al. (1999), and the results, both in context of the study’s goals and a separate analysis of the results performed at a later date. Our results support that user drawings contain the predictable characteristics relating to symmetry, number of composite strokes, and centering within the grid. Our results also highlight a usability challenge with the DAS scheme. --- paper_title: PassPoints: Design and longitudinal evaluation of a graphical password system paper_content: Computer security depends largely on passwords to authenticate human users. However, users have difficulty remembering passwords over time if they choose a secure password, i.e. a password that is long and random. Therefore, they tend to choose short and insecure passwords. Graphical passwords, which consist of clicking on images rather than typing alphanumeric strings, may help to overcome the problem of creating secure and memorable passwords. In this paper we describe PassPoints, a new and more secure graphical password system. We report an empirical study comparing the use of PassPoints to alphanumeric passwords. Participants created and practiced either an alphanumeric or graphical password. The participants subsequently carried out three longitudinal trials to input their password over the course of 6 weeks. The results show that the graphical password users created a valid password with fewer difficulties than the alphanumeric users. However, the graphical users took longer and made more invalid password inputs than the alphanumeric users while practicing their passwords. In the longitudinal trials the two groups performed similarly on memory of their password, but the graphical group took more time to input a password. --- paper_title: Passshapes - utilizing stroke based authentication to increase password memorability paper_content: Authentication today mostly relies on passwords or personal identification numbers (PINs). Therefore the average user has to remember an increasing amount of PINs and passwords. Unfortunately, humans have limited capabilities for remembering abstract alphanumeric sequences. Thus, many people either forget them or use very simple ones, which implies several security risks. In this work, a novel authentication method called PassShapes is presented. In this system users authenticate themselves to a computing system by drawing simple geometric shapes constructed of an arbitrary combination of eight different strokes. We argue that using such shapes will allow more complex and thus more secure authentication tokens with a lower cognitive load and higher memorability. To prove these assumptions, two user studies have been conducted. The memorability evaluation showed that the PassShapes concept is able to increase the memorability when users can practice the PassShapes several times. This effect is even increasing over time. Additionally, a prototype was implemented to conduct a usability study. The results of both studies indicate that the PassShapes approach is able to provide a usable and memorable authentication method. --- paper_title: Graphical Dictionaries and the Memorable Space of Graphical Passwords paper_content: In commonplace textual password schemes, users choose passwords that are easy to recall. Since memorable passwords typically exhibit patterns, they are exploitable by brute-force password crackers using attack dictionaries. This leads us to ask what classes of graphical passwords users find memorable. We postulate one such class supported by a collection of cognitive studies on visual recall, which can be characterized as mirror symmetric (reflective) passwords. We assume that an attacker would put this class in an attack dictionary for graphical passwords and propose how an attacker might order such a dictionary. We extend the existing analysis of graphical passwords by analyzing the size of the mirror symmetric password space relative to the full password space of the graphical password scheme of Jermyn et al. (1999), and show it to be exponentially smaller (assuming appropriate axes of reflection). This reduction in size can be compensated for by longer passwords: the size of the space of mirror symmetric passwords of length about L + 5 exceeds that of the full password space for corresponding length L ≤ 14 on a 5 × 5 grid. This work could be used to help in formulating password rules for graphical password users and in creating proactive graphical password checkers. --- paper_title: On predictive models and user-drawn graphical passwords paper_content: In commonplace text-based password schemes, users typically choose passwords that are easy to recall, exhibit patterns, and are thus vulnerable to brute-force dictionary attacks. This leads us to ask whether other types of passwords (e.g., graphical) are also vulnerable to dictionary attack because of users tending to choose memorable passwords. We suggest a method to predict and model a number of such classes for systems where passwords are created solely from a user's memory. We hypothesize that these classes define weak password subspaces suitable for an attack dictionary. For user-drawn graphical passwords, we apply this method with cognitive studies on visual recall. These cognitive studies motivate us to define a set of password complexity factors (e.g., reflective symmetry and stroke count), which define a set of classes. To better understand the size of these classes and, thus, how weak the password subspaces they define might be, we use the “Draw-A-Secret” (DAS) graphical password scheme of Jermyn et al. [1999] as an example. We analyze the size of these classes for DAS under convenient parameter choices and show that they can be combined to define apparently popular subspaces that have bit sizes ranging from 31 to 41—a surprisingly small proportion of the full password space (58 bits). Our results quantitatively support suggestions that user-drawn graphical password systems employ measures, such as graphical password rules or guidelines and proactive password checking. --- paper_title: Towards secure design choices for implementing graphical passwords paper_content: We study the impact of selected parameters on the size of the password space for "Draw-A-Secret" (DAS) graphical passwords. We examine the role of and relationships between the number of composite strokes, grid dimensions, and password length in the DAS password space. We show that a very significant proportion of the DAS password space depends on the assumption that users will choose long passwords with many composite strokes. If users choose passwords having 4 or fewer strokes, with passwords of length 12 or less on a 5 /spl times/ 5 grid, instead of up to the maximum 12 possible strokes, the size of the DAS password space is reduced from 58 to 40 bits. Additionally, we found a similar reduction when users choose no strokes of length 1. To strengthen security, we propose a technique and describe a representative system that may gain up to 16 more bits of security with an expected negligible increase in input time. Our results can be directly applied to determine secure design choices, graphical password parameter guidelines, and in deciding which parameters deserve focus in graphical password user studies. --- paper_title: An enhanced drawing reproduction graphical password strategy paper_content: Passwords are used in the vast majority of computer and communication systems for authentlcation. The greater security and memorability of graphical passwords make them a possible alternative to traditional textual passwords. In this paper we propose a new graphical password scheme called YAGP, which is an extension of the Draw-A-Secret (DAS) scheme. The main difference between YAGP and DAS is soft matching. The concepts of the stroke-box, image-box, trend quadrant, and similarity are used to describe the images characteristics for soft matching. The reduction in strict user input rules in soft matching improves the usability and therefore creates a great advantage. The denser grid granubtrity enables users to design a longer password, enlarging the practical password space and enhancing security. Meanwhile, YAGP adopts a triple-register process to create multi-templates, increasing the accuracy and memorability of characteristics extraction. Experiments illustrate the effectiveness of YAGP. --- paper_title: Is a picture really worth a thousand words? Exploring the feasibility of graphical authentication systems paper_content: The weakness of knowledge-based authentication systems, such as passwords and Personal Identification Numbers (PINs), is well known, and reflects an uneasy compromise between security and human memory constraints. Research has been undertaken for some years now into the feasibility of graphical authentication mechanisms in the hope that these will provide a more secure and memorable alternative. The graphical approach substitutes the exact recall of alphanumeric codes with the recognition of previously learnt pictures, a skill at which humans are remarkably proficient. So far, little attention has been devoted to usability, and initial research has failed to conclusively establish significant memory improvement. This paper reports two user studies comparing several implementations of the graphical approach with PINs. Results demonstrate that pictures can be a solution to some problems relating to traditional knowledge-based authentication but that they are not a simple panacea, since a poor design can eliminate the picture superiority effect in memory. The paper concludes by discussing the potential of the graphical approach and providing guidelines for developers contemplating using these mechanisms. --- paper_title: Smudge Attacks on Smartphone Touch Screens paper_content: Touch screens are an increasingly common feature on personal computing devices, especially smartphones, where size and user interface advantages accrue from consolidating multiple hardware components (keyboard, number pad, etc.) into a single software definable user interface. Oily residues, or smudges, on the touch screen surface, are one side effect of touches from which frequently used patterns such as a graphical password might be inferred. ::: ::: In this paper we examine the feasibility of such smudge attacks on touch screens for smartphones, and focus our analysis on the Android password pattern. We first investigate the conditions (e.g., lighting and camera orientation) under which smudges are easily extracted. In the vast majority of settings, partial or complete patterns are easily retrieved. We also emulate usage situations that interfere with pattern identification, and show that pattern smudges continue to be recognizable. Finally, we provide a preliminary analysis of applying the information learned in a smudge attack to guessing an Android password pattern. --- paper_title: The Design and Analysis of Graphical Passwords paper_content: In this paper we propose and evaluate new graphical password schemes that exploit features of graphical input displays to achieve better security than text-based passwords. Graphical input devices enable the user to decouple the position of inputs from the temporal order in which those inputs occur, and we show that this decoupling can be used to generate password schemes with substantially larger (memorable) password spaces. In order to evaluate the security of one of our schemes, we devise a novel way to capture a subset of the "memorable" passwords that, we believe, is itself a contribution. In this work we are primarily motivated by devices such as personal digital assistants (PDAs) that offer graphical input capabilities via a stylus, and we describe our prototype implementation of one of our password schemes on such a PDA, namely the Palm PilotTM. --- paper_title: Graphical passwords & qualitative spatial relations paper_content: A potential drawback of graphical password schemes is that they are more vulnerable to shoulder surfing than conventional alphanumeric text passwords. We present a variation of the Draw-a-Secret scheme originally proposed by Jermyn et al [1] that is more resistant to shoulder surfing through the use of a qualitative mapping between user strokes and the password, and the use of dynamic grids to both obfuscate attributes of the user secret and encourage them to use different surface realizations of the secret. The use of qualitative spatial relations relaxes the tight constraints on the reconstruction of a secret; allowing a range of deviations from the original. We describe QDAS (Qualitative Draw-A-Secret), an initial implementation of this graphical password scheme, and the results of an empirical study in which we examined the memorability of secrets, and their susceptibility to shoulder-surfing attacks, for both Draw-A-Secret and QDAS. --- paper_title: The design and implementation of background Pass-Go scheme towards security threats paper_content: Currently, access to computer systems is often based on the use of alpha-numeric. The textual passwords or alpha-numeric passwords have been the basis of authentication systems for centuries. Similarly, it had also been the major attraction for crackers and attackers. However, users tend to face difficulty in remembering a password that is considered as secured password because this type of secured password usually has long string of characters and they appear randomly [14]. Hence, most users tend to create simple, short and insecure passwords. As a consequence, most of the time, the usability level of passwords has not achieved an optimum for a secured password [14]. In order to solve this problem, a new password scheme had been invented, known as Graphical Password System (GPS). Graphical password is an alternative mean of authentication for login intended to be used in place of conventional password; it utilizes images instead of text. In this paper, we discuss the design and intention of our proposed scheme, called Background Pass-Go (BPG). BPG is an improved version of Pass-Go, as it keeps most of the advantages of Pass-Go and achieves better usability. We had analyzed the BPG scheme in terms of 1) how BPG is able to improve other schemes of GPS especially in Pass-Go, 2) how BPG acts as a solution to different types of threats to networked computer systems. We had verified that BPG manages to overcome the shortage of other GPS schemes. Moreover, the BPG also manages to address most of the security threats for the network security system. --- paper_title: Pass-Go: A Proposal to Improve the Usability of Graphical Passwords paper_content: Inspired by an old Chinese game, Go, we have designed a new graphical password scheme, Pass-Go, in which a user selects intersections on a grid as a way to input a password. While offering an extremely large full password space (256 bits for the most basic scheme), our scheme provides acceptable usability, as empirically demonstrated by, to the best of our knowledge, the largest user study (167 subjects involved) on graphical passwords, conducted in the fall semester of 2005 in two university classes. Our scheme supports most application environments and input devices, rather than being limited to small mobile devices (PDAs), and can be used to derive cryptographic keys. We study the memorable password space and show the potential power of this scheme by exploring further improvements and variation mechanisms. --- paper_title: Multi-grid background Pass-Go paper_content: Computer security depends largely on passwords to authenticate the human user for access to secure systems. Remembering the password they have chosen is a frequent problem for all users. As a result, they tend to choose short and insecure passwords as compared to secure passwords which usually consist of a long mixture of random alphanumeric and non-alphanumeric characters. Thus, the tendency of choosing insecure passwords has brought up many security problems. Graphical password is an alternative to replace alphanumeric password in which users only have to click on the images in order to authenticate themselves rather than typing alphanumeric strings. The main objectives of this paper are to present a classification of graphical passwords system (GPS) and identify its future research area. In this paper, we attempt to identify a number of threats to the networked computer systems, focus on the research of graphical password system (GPS) and analysis on some aspects of GPS; 1) how each GPS algorithm works, 2) the advantages and disadvantages of each GPS algorithm, 3) how each GPS algorithm is able to address solutions to the threats. Besides, the paper also concentrates on the design and the implication of a proposed prototype, namely Multi-Grid Background Pass-Go (MGBPG) which is targeted to be its strength and the winning edge over other graphical password systems. The preliminary result and analysis of the proposed prototype is then presented by comparing it on its role in addressing the drawbacks of current existing GPS and several security attacks. Finally, we highlight a few aspects, which need to be improved in the future to overcome the deficiencies of previous GPS methods that have been invented. --- paper_title: YAGP: Yet Another Graphical Password Strategy paper_content: Alphanumeric passwords are widely used in computer and network authentication to protect users' privacy. However, it is well known that long, text-based passwords are hard for people to remember, while shorter ones are susceptible to attack. Graphical password is a promising solution to this problem. Draw-A-Secret (DAS) is a typical implementation based on the user drawing on a grid canvas. Currently, too many constraints result in reduction in user experience and prevent its popularity. A novel graphical password strategy Yet Another Graphical Password (YAGP) inspired by DAS is proposed in this paper. The proposal has the advantages of free drawing positions, strong shoulder surfing resistance and large password space. Experiments illustrate the effectiveness of YAGP. --- paper_title: Analyzing User Choice in Graphical Passwords paper_content: In ubiquitous textual password schemes, users choose passwords that contain predictable characteristics that are roughly equated with what users flnd easy to recall. This motivates us to examine user choice in graphical passwords to determine whether predictable characteristics exist that may reduce the entropy of the password space. We present an informal user study of the scheme proposed by Jermyn et al. (1999), and the results, both in context of the study’s goals and a separate analysis of the results performed at a later date. Our results support that user drawings contain the predictable characteristics relating to symmetry, number of composite strokes, and centering within the grid. Our results also highlight a usability challenge with the DAS scheme. --- paper_title: Do background images improve "draw a secret" graphical passwords? paper_content: Draw a secret (DAS) is a representative graphical password scheme. Rigorous theoretical analysis suggests that DAS supports an overall password space larger than that of the ubiquitous textual password scheme. However, recent research suggests that DAS users tend to choose weak passwords, and their choices would render this theoretically sound scheme less secure in real life. In this paper we investigate the novel idea of introducing background images to the DAS scheme, where users were initially supposed to draw passwords on a blank canvas overlaid with a grid. Encouraging results from our two user studies have shown that people aided with background images tended to set significantly more complicated passwords than their counterparts using the original scheme. The background images also reduced other predictable characteristics in DAS passwords such as symmetry and centering within the drawing grid, further improving the strength of the passwords. We estimate that the average strength of successfully recalled passwords in the enhanced scheme was increased over those created using the original scheme by more than 10 bits. Moreover, a positive effect was observed with respect to the memorability of the more complex passwords encouraged by the background images. --- paper_title: Your Memory: How It Works and How to Improve It paper_content: The word mnemonic (pronounced “ne MON ik”) is briefly defined as “aiding the memory.” It is derived from Mnemosyne, the name of the ancient Greek goddess of memory. “Mnemonics” refers in general to methods for improving memory; a mnemonic technique is any technique that aids the memory. Most researchers, however, define mnemonics more narrowly as being what most people consider to be rather unusual, artificial memory aids. --- paper_title: Persuasive Cued Click-Points: Design, Implementation, and Evaluation of a Knowledge-Based Authentication Mechanism paper_content: This paper presents an integrated evaluation of the Persuasive Cued Click-Points graphical password scheme, including usability and security evaluations, and implementation considerations. An important usability goal for knowledge-based authentication systems is to support users in selecting passwords of higher security, in the sense of being from an expanded effective security space. We use persuasion to influence user choice in click-based graphical passwords, encouraging users to select more random, and hence more difficult to guess, click-points. --- paper_title: Graphical passwords: Learning from the first twelve years paper_content: Starting around 1999, a great many graphical password schemes have been proposed as alternatives to text-based password authentication. We provide a comprehensive overview of published research in the area, covering both usability and security aspects as well as system evaluation. The article first catalogues existing approaches, highlighting novel features of selected schemes and identifying key usability or security advantages. We then review usability requirements for knowledge-based authentication as they apply to graphical passwords, identify security threats that such systems must address and review known attacks, discuss methodological issues related to empirical evaluation, and identify areas for further research and improved methodology. --- paper_title: Graphical Password Authentication using Cued Click Points paper_content: With the rapid use of computers, the security of passwords is must where privacy is important. For password protection various techniques are available. The main issues of knowledge based authentication, usually text based passwords, are well known. Users tend to choose passwords they can easily remember and hence can also be guessed by the hacker. To avoid the limitation of textual passwords, this paper focuses on the implementation of a Graphical Password Authentication System using cued click points based on the integrated evaluation of the cued click points. Click cued points (CCP) is a click-based graphical password scheme. Users click on one point per image for a sequence of images. The next image is based on the previous click-point. Users prefer CCP over Passpoints because selecting and remembering only one point per image is less secure, than having each image being triggered by the user’s memory as to where the corresponding point was located. CCP also provides greater security than PassPoints because the number of images increases the workload for attackers. --- paper_title: A Novel Cued-recall Graphical Password Scheme paper_content: Graphical passwords have been proposed as an alternative to alphanumeric passwords with their advantages in usability and security. However, most of these alternate schemes have their own disadvantages. For example, cued-recall graphical password schemes are vulnerable to shoulder-surfing and cannot prevent intersection analysis attack. A novel cued-recall graphical password scheme CBFG (Click Buttons according to Figures in Grids) is proposed in this paper. Inheriting the way of setting password in traditional cued-recall scheme, this scheme is also added the ideology of image identification. CBFG helps users tend to set their passwords more complex. Simultaneously, it has the capability against shoulder surfing attack and intersection analysis attack. Experiments illustrate that CBFG has better performance in usability, especially in security. --- paper_title: Influencing users towards better passwords: persuasive cued click-points paper_content: Usable security has unique usability challenges because the need for security often means that standard human-computer-interaction approaches cannot be directly applied. An important usability goal for authentication systems is to support users in selecting better passwords, thus increasing security by expanding the effective password space. In click-based graphical passwords, poorly chosen passwords lead to the emergence of hotspots -- portions of the image where users are more likely to select click-points, allowing attackers to mount more successful dictionary attacks. We use persuasion to influence user choice in click-based graphical passwords, encouraging users to select more random, and hence more secure, click-points. Our approach is to introduce persuasion to the Cued Click-Points graphical password scheme (Chiasson, van Oorschot, Biddle, 2007). Our resulting scheme significantly reduces hotspots while still maintaining its usability. --- paper_title: Graphical passwords: a survey paper_content: The most common computer authentication method is to use alphanumerical usernames and passwords. This method has been shown to have significant drawbacks. For example, users tend to pick passwords that can be easily guessed. On the other hand, if a password is hard to guess, then it is often hard to remember. To address this problem, some researchers have developed authentication methods that use pictures as passwords. In this paper, we conduct a comprehensive survey of the existing graphical password techniques. We classify these techniques into two categories: recognition-based and recall-based approaches. We discuss the strengths and limitations of each method and point out the future research directions in this area. We also try to answer two important questions: "Are graphical passwords as secure as text-based passwords?"; "What are the major design and implementation issues for graphical passwords?" This survey will be useful for information security researchers and practitioners who are interested in finding an alternative to text-based authentication methods --- paper_title: PassPoints: Design and longitudinal evaluation of a graphical password system paper_content: Computer security depends largely on passwords to authenticate human users. However, users have difficulty remembering passwords over time if they choose a secure password, i.e. a password that is long and random. Therefore, they tend to choose short and insecure passwords. Graphical passwords, which consist of clicking on images rather than typing alphanumeric strings, may help to overcome the problem of creating secure and memorable passwords. In this paper we describe PassPoints, a new and more secure graphical password system. We report an empirical study comparing the use of PassPoints to alphanumeric passwords. Participants created and practiced either an alphanumeric or graphical password. The participants subsequently carried out three longitudinal trials to input their password over the course of 6 weeks. The results show that the graphical password users created a valid password with fewer difficulties than the alphanumeric users. However, the graphical users took longer and made more invalid password inputs than the alphanumeric users while practicing their passwords. In the longitudinal trials the two groups performed similarly on memory of their password, but the graphical group took more time to input a password. --- paper_title: Analysis and Evaluation of the ColorLogin Graphical Password Scheme paper_content: It is believed that graphical passwords are more memorable than traditional textual passwords, but usually seen as complex and time-consuming for users. Furthermore, most of the existing graphical password schemes are vulnerable to spyware and shoulder surfing. ColorLogin uses color, a method not previously considered, to decrease login time. Multiple colors are used to confuse the peepers, while not burdening the legitimate users. Meanwhile, the scheme is resistant to shoulder surfing and intersection attack to a certain extent. This paper analyzes and evaluates the ColorLogin scheme using some experiments. --- paper_title: Towards Usable Solutions to Graphical Password Hotspot Problem paper_content: Click based graphical passwords that use background images suffer from hot-spot problem. Previous graphical password schemes based on recognition of images do not have a sufficiently large password space suited for most Internet applications. In this paper, we propose two novel graphical password methods based on recognition of icons to solve the hotspot problem without decreasing the password space. The experiment we have conducted that compares the security and usability of proposed methods with earlier work (i.e. Passpoints) shows that hotspot problem can be eliminated if a small increase in password entrance and confirmation times is tolerable. --- paper_title: Is a picture really worth a thousand words? Exploring the feasibility of graphical authentication systems paper_content: The weakness of knowledge-based authentication systems, such as passwords and Personal Identification Numbers (PINs), is well known, and reflects an uneasy compromise between security and human memory constraints. Research has been undertaken for some years now into the feasibility of graphical authentication mechanisms in the hope that these will provide a more secure and memorable alternative. The graphical approach substitutes the exact recall of alphanumeric codes with the recognition of previously learnt pictures, a skill at which humans are remarkably proficient. So far, little attention has been devoted to usability, and initial research has failed to conclusively establish significant memory improvement. This paper reports two user studies comparing several implementations of the graphical approach with PINs. Results demonstrate that pictures can be a solution to some problems relating to traditional knowledge-based authentication but that they are not a simple panacea, since a poor design can eliminate the picture superiority effect in memory. The paper concludes by discussing the potential of the graphical approach and providing guidelines for developers contemplating using these mechanisms. --- paper_title: Design and evaluation of a shoulder-surfing resistant graphical password scheme paper_content: When users input their passwords in a public place, they may be at risk of attackers stealing their password. An attacker can capture a password by direct observation or by recording the individual's authentication session. This is referred to as shoulder-surfing and is a known risk, of special concern when authenticating in public places. Until recently, the only defense against shoulder-surfing has been vigilance on the part of the user. This paper reports on the design and evaluation of a game-like graphical method of authentication that is resistant to shoulder-surfing. The Convex Hull Click (CHC) scheme allows a user to prove knowledge of the graphical password safely in an insecure location because users never have to click directly on their password images. Usability testing of the CHC scheme showed that novice users were able to enter their graphical password accurately and to remember it over time. However, the protection against shoulder-surfing comes at the price of longer time to carry out the authentication. --- paper_title: Use Your Illusion: secure authentication usable anywhere paper_content: In this paper, we propose and evaluate Use Your Illusion, a novel mechanism for user authentication that is secure and usable regardless of the size of the device on which it is used. Our system relies on the human ability to recognize a degraded version of a previously seen image. We illustrate how distorted images can be used to maintain the usability of graphical password schemes while making them more resilient to social engineering or observation attacks. Because it is difficult to mentally "revert" a degraded image, without knowledge of the original image, our scheme provides a strong line of defense against impostor access, while preserving the desirable memorability properties of graphical password schemes. Using low-fidelity tests to aid in the design, we implement prototypes of Use Your Illusion as i) an Ajax-based web service and ii) on Nokia N70 cellular phones. We conduct a between-subjects usability study of the cellular phone prototype with a total of 99 participants in two experiments. We demonstrate that, regardless of their age or gender, users are very skilled at recognizing degraded versions of self-chosen images, even on small displays and after time periods of one month. Our results indicate that graphical passwords with distorted images can achieve equivalent error rates to those using traditional images, but only when the original image is known. --- paper_title: Are Passfaces 1 More Usable Than Passwords? A Field Trial Investigation paper_content: The proliferation of technology requiring user authentication has increased the number of passwords which users have to remember, creating a significant usability problem. This paper reports a usability comparison between a new mechanism for user authentication — Passfaces — and passwords, with 34 student participants in a 3-month field trial. Fewer login errors were made with Passfaces, even when periods between logins were long. On the computer facilities regularly chosen by participants to log in, Passfaces took a long time to execute. Participants consequently started their work later when using Passfaces than when using passwords, and logged into the system less often. The results emphasise the importance of evaluating the usability of security mechanisms in field trials. --- paper_title: On User Choice in Graphical Password Schemes paper_content: Graphical password schemes have been proposed as an alternative to text passwords in applications that support graphics and mouse or stylus entry. In this paper we detail what is, to our knowledge, the largest published empirical evaluation of the effects of user choice on the security of graphical password schemes. We show that permitting user selection of passwords in two graphical password schemes, one based directly on an existing commercial product, can yield passwords with entropy far below the theoretical optimum and, in some cases, that are highly correlated with the race or gender of the user. For one scheme, this effect is so dramatic so as to render the scheme insecure. A conclusion of our work is that graphical password schemes of the type we study may generally require a different posture toward password selection than text passwords, where selection by the user remains the norm today. --- paper_title: Cognitive authentication schemes safe against spyware paper_content: Can we secure user authentication against eavesdropping adversaries, relying on human cognitive functions alone, unassisted by any external computational device? To accomplish this goal, we propose challenge response protocols that rely on a shared secret set of pictures. Under the considered brute-force attack the protocols are safe against eavesdropping, in that a modestly powered adversary who fully records a series of successful interactions cannot compute the user's secret. Moreover, the protocols can be tuned to any desired level of security against random guessing, where security can be traded-off with authentication time. The proposed protocols have two drawbacks: First, training is required to familiarize the user with the secret set of pictures. Second, depending on the level of security required, entry time can be significantly longer than with alternative methods. We describe user studies showing that people can use these protocols successfully, and quantify the time it takes for training and for successful authentication. We show evidence that the secret can be maintained for a long time (up to a year) with relatively low loss. --- paper_title: Awase-E: Image-Based Authentication for Mobile Phones Using User’s Favorite Images paper_content: There is a trade-off between security and usability in user authentication for mobile phones. Since such devices have a poor input interfaces, 4-digit number passwords are widely used at present. Therefore, a more secure and user friendly authentication is needed. This paper proposes a novel authentication method called “Awase-E”. The system uses image passwords. It, moreover, integrates image registration and notification interfaces. Image registration enables users to use their favorite image instead of a text password. Notification gives users a trigger to take action against a threat when it happens. Awase-E is implemented so that it has a higher usability even when it is used through a mobile phone. --- paper_title: Action-based graphical password: “Click-a-Secret” paper_content: This paper relates to a novel graphical system named “Click-a-Secret” that allows entering a secret through interactions with an image. Seamless modifications of the image will create a new image and derive corresponding secret. In this article, we will focus on access control related applications. However, other usages are possible in various cryptographic systems (e.g.: secret key to encrypt data, etc.). It is well known that humans use long-term memory for storing pictures therefore leading to better recall of images compared to text. This leads to improved memorization of graphical passwords. --- paper_title: S3PAS: A Scalable Shoulder-Surfing Resistant Textual-Graphical Password Authentication Scheme paper_content: The vulnerabilities of the textual password have been well known. Users tend to pick short passwords or passwords that are easy to remember, which makes the passwords vulnerable for attackers to break. Furthermore, textual password is vulnerable to shoulder-surfing, hidden-camera and spyware attacks. Graphical password schemes have been proposed as a possible alternative to text-based scheme. However, they are mostly vulnerable to shoulder-surfing. In this paper, we propose a Scalable Shoulder-Surfing Resistant Textual-Graphical Password Authentication Scheme (S3PAS). S3PAS seamlessly integrates both graphical and textual password schemes and provides nearly perfect resistant to shoulder-surfing, hidden-camera and spyware attacks. It can replace or coexist with conventional textual password systems without changing existing user password profiles. Moreover, it is immune to brute-force attacks through dynamic and volatile session passwords. S3PAS shows significant potential bridging the gap between conventional textual password and graphical password. Further enhancements of S3PAS scheme are proposed and briefly discussed. Theoretical analysis of the security level using S3PAS is also investigated. --- paper_title: Graphical passwords: Learning from the first twelve years paper_content: Starting around 1999, a great many graphical password schemes have been proposed as alternatives to text-based password authentication. We provide a comprehensive overview of published research in the area, covering both usability and security aspects as well as system evaluation. The article first catalogues existing approaches, highlighting novel features of selected schemes and identifying key usability or security advantages. We then review usability requirements for knowledge-based authentication as they apply to graphical passwords, identify security threats that such systems must address and review known attacks, discuss methodological issues related to empirical evaluation, and identify areas for further research and improved methodology. --- paper_title: A Novel Cued-recall Graphical Password Scheme paper_content: Graphical passwords have been proposed as an alternative to alphanumeric passwords with their advantages in usability and security. However, most of these alternate schemes have their own disadvantages. For example, cued-recall graphical password schemes are vulnerable to shoulder-surfing and cannot prevent intersection analysis attack. A novel cued-recall graphical password scheme CBFG (Click Buttons according to Figures in Grids) is proposed in this paper. Inheriting the way of setting password in traditional cued-recall scheme, this scheme is also added the ideology of image identification. CBFG helps users tend to set their passwords more complex. Simultaneously, it has the capability against shoulder surfing attack and intersection analysis attack. Experiments illustrate that CBFG has better performance in usability, especially in security. --- paper_title: A New Graphical Password Scheme Resistant to Shoulder-Surfing paper_content: Shoulder-surfing is a known risk where an attacker can capture a password by direct observation or by recording the authentication session. Due to the visual interface, this problem has become exacerbated in graphical passwords. There have been some graphical schemes resistant or immune to shoulder-surfing, but they have significant usability drawbacks, usually in the time and effort to log in. In this paper, we propose and evaluate a new shoulder-surfing resistant scheme which has a desirable usability for PDAs. Our inspiration comes from the drawing input method in DAS and the association mnemonics in Story for sequence retrieval. The new scheme requires users to draw a curve across their password images orderly rather than click directly on them. The drawing input trick along with the complementary measures, such as erasing the drawing trace, displaying degraded images, and starting and ending with randomly designated images provide a good resistance to shoulder-surfing. A preliminary user study showed that users were able to enter their passwords accurately and to remember them over time. --- paper_title: Evaluating the usability and security of a graphical one-time PIN system paper_content: Traditional Personal Identification Numbers (PINs) are widely used, but the attacks in which they are captured have been increasing. One-time PINs offer better security, but potentially create greater workload for users. In this paper, we present an independent evaluation of a commercial system that makes PINs more resistant to observation attacks by using graphical passwords on a grid to generate a one-time PIN. 83 participants were asked to register with the system and log in at varying intervals. The successful login rate was approximately 91% after 3--4 days, and 97% after 9--10 days. Twenty five participants were retested after two years, and 27% of those were able to recall their pattern. We recorded 17 instances of failed attempts, and found that even though participants recalled the general shape of the pass-pattern in 13 of these instances, they could not recall its detailed location or sequence of cells. We conclude that GrIDsure is usable if people have one pass-pattern, but the level of security will depend on the context of use (it will work best in scenarios where repeated observations of transactions are unlikely), and the instructions given to users (without guidance, they are likely to chose from a small subset of the possible patterns which are easily guessed). --- paper_title: A Hybrid Password Authentication Scheme Based on Shape and Text paper_content: Text-based password authentication scheme tends to be more vulnerable to attacks such as shoulder surfing or a hidden camera. To overcome the vulnerabilities of traditional methods, visual or graphical password schemes have been developed as possible alternative solutions to text-based password schemes. Since it also has some drawbacks to simply adopt graphical password authentication, schemes using graphic and text have been developed. In this paper, a hybrid password authentication scheme based on shapes and texts is proposed. Shapes of strokes are used in the grid as the original passwords and users can log in with textual passwords via traditional input device. The method provides strong resistance to shoulder surfing or a hidden camera。Moreover, the scheme has high scalability and flexibility to enhance the authentication process security. The analysis of the security level of this approach is also discussed. --- paper_title: Exploration of a hand-based graphical password scheme paper_content: Graphical passwords have been proposed as an alternative to alphanumeric passwords with their advantages in usability and security. However, most of these alternate schemes have their own problems about memorability. Biometrics schemes are not widely adopted though they need no remembrance and provide the highest level of security, owing to their great cost both in device and time. In this paper, we explore the feasibility of introducing hand-based biometrics into the realm of graphical passwords with a simplified scheme named PassHands. PassHands has similar authentication form with Passfaces and has the advantages of unnecessary to remember the password and unpredictable of user choice. Some primary experiments are conducted and the results illustrate the performance of PassHands. --- paper_title: Graphical passwords: a survey paper_content: The most common computer authentication method is to use alphanumerical usernames and passwords. This method has been shown to have significant drawbacks. For example, users tend to pick passwords that can be easily guessed. On the other hand, if a password is hard to guess, then it is often hard to remember. To address this problem, some researchers have developed authentication methods that use pictures as passwords. In this paper, we conduct a comprehensive survey of the existing graphical password techniques. We classify these techniques into two categories: recognition-based and recall-based approaches. We discuss the strengths and limitations of each method and point out the future research directions in this area. We also try to answer two important questions: "Are graphical passwords as secure as text-based passwords?"; "What are the major design and implementation issues for graphical passwords?" This survey will be useful for information security researchers and practitioners who are interested in finding an alternative to text-based authentication methods --- paper_title: Securing passwords against dictionary attacks paper_content: The use of passwords is a major point of vulnerability in computer security, as passwords are often easy to guess by automated programs running dictionary attacks. Passwords remain the most widely used authentication method despite their well-known security weaknesses. User authentication is clearly a practical problem. From the perspective of a service provider this problem needs to be solved within real-world constraints such as the available hardware and software infrastructures. From a user's perspective user-friendliness is a key requirement.In this paper we suggest a novel authentication scheme that preserves the advantages of conventional password authentication, while simultaneously raising the costs of online dictionary attacks by orders of magnitude. The proposed scheme is easy to implement and overcomes some of the difficulties of previously suggested methods of improving the security of user authentication schemes.Our key idea is to efficiently combine traditional password authentication with a challenge that is very easy to answer by human users, but is (almost) infeasible for automated programs attempting to run dictionary attacks. This is done without affecting the usability of the system. The proposed scheme also provides better protection against denial of service attacks against user accounts. --- paper_title: Purely Automated Attacks on PassPoints-Style Graphical Passwords paper_content: We introduce and evaluate various methods for purely automated attacks against PassPoints-style graphical passwords. For generating these attacks, we introduce a graph-based algorithm to efficiently create dictionaries based on heuristics such as click-order patterns (e.g., five points all along a line). Some of our methods combine click-order heuristics with focus-of-attention scan-paths generated from a computational model of visual attention, yielding significantly better automated attacks than previous work. One resulting automated attack finds 7%-16% of passwords for two representative images using dictionaries of approximately 226 entries (where the full password space is 243). Relaxing click-order patterns substantially increased the attack efficacy albeit with larger dictionaries of approximately 235 entries, allowing attacks that guessed 48%-54% of passwords (compared to previous results of 1% and 9% on the same dataset for two images with 235 guesses). These latter attacks are independent of focus-of-attention models, and are based on image-independent guessing patterns. Our results show that automated attacks, which are easier to arrange than human-seeded attacks and are more scalable to systems that use multiple images, require serious consideration when deploying basic PassPoints-style graphical passwords. --- paper_title: Graphical Dictionaries and the Memorable Space of Graphical Passwords paper_content: In commonplace textual password schemes, users choose passwords that are easy to recall. Since memorable passwords typically exhibit patterns, they are exploitable by brute-force password crackers using attack dictionaries. This leads us to ask what classes of graphical passwords users find memorable. We postulate one such class supported by a collection of cognitive studies on visual recall, which can be characterized as mirror symmetric (reflective) passwords. We assume that an attacker would put this class in an attack dictionary for graphical passwords and propose how an attacker might order such a dictionary. We extend the existing analysis of graphical passwords by analyzing the size of the mirror symmetric password space relative to the full password space of the graphical password scheme of Jermyn et al. (1999), and show it to be exponentially smaller (assuming appropriate axes of reflection). This reduction in size can be compensated for by longer passwords: the size of the space of mirror symmetric passwords of length about L + 5 exceeds that of the full password space for corresponding length L ≤ 14 on a 5 × 5 grid. This work could be used to help in formulating password rules for graphical password users and in creating proactive graphical password checkers. --- paper_title: Graphical passwords: Learning from the first twelve years paper_content: Starting around 1999, a great many graphical password schemes have been proposed as alternatives to text-based password authentication. We provide a comprehensive overview of published research in the area, covering both usability and security aspects as well as system evaluation. The article first catalogues existing approaches, highlighting novel features of selected schemes and identifying key usability or security advantages. We then review usability requirements for knowledge-based authentication as they apply to graphical passwords, identify security threats that such systems must address and review known attacks, discuss methodological issues related to empirical evaluation, and identify areas for further research and improved methodology. --- paper_title: Analyzing User Choice in Graphical Passwords paper_content: In ubiquitous textual password schemes, users choose passwords that contain predictable characteristics that are roughly equated with what users flnd easy to recall. This motivates us to examine user choice in graphical passwords to determine whether predictable characteristics exist that may reduce the entropy of the password space. We present an informal user study of the scheme proposed by Jermyn et al. (1999), and the results, both in context of the study’s goals and a separate analysis of the results performed at a later date. Our results support that user drawings contain the predictable characteristics relating to symmetry, number of composite strokes, and centering within the grid. Our results also highlight a usability challenge with the DAS scheme. --- paper_title: Modeling user choice in the PassPoints graphical password scheme paper_content: We develop a model to identify the most likely regions for users to click in order to create graphical passwords in the PassPoints system. A PassPoints password is a sequence of points, chosen by a user in an image that is displayed on the screen. Our model predicts probabilities of likely click points; this enables us to predict the entropy of a click point in a graphical password for a given image. The model allows us to evaluate automatically whether a given image is well suited for the PassPoints system, and to analyze possible dictionary attacks against the system. We compare the predictions provided by our model to results of experiments involving human users. At this stage, our model and the experiments are small and limited; but they show that user choice can be modeled and that expansions of the model and the experiments are a promising direction of research. --- paper_title: On User Choice in Graphical Password Schemes paper_content: Graphical password schemes have been proposed as an alternative to text passwords in applications that support graphics and mouse or stylus entry. In this paper we detail what is, to our knowledge, the largest published empirical evaluation of the effects of user choice on the security of graphical password schemes. We show that permitting user selection of passwords in two graphical password schemes, one based directly on an existing commercial product, can yield passwords with entropy far below the theoretical optimum and, in some cases, that are highly correlated with the race or gender of the user. For one scheme, this effect is so dramatic so as to render the scheme insecure. A conclusion of our work is that graphical password schemes of the type we study may generally require a different posture toward password selection than text passwords, where selection by the user remains the norm today. --- paper_title: A Novel Cued-recall Graphical Password Scheme paper_content: Graphical passwords have been proposed as an alternative to alphanumeric passwords with their advantages in usability and security. However, most of these alternate schemes have their own disadvantages. For example, cued-recall graphical password schemes are vulnerable to shoulder-surfing and cannot prevent intersection analysis attack. A novel cued-recall graphical password scheme CBFG (Click Buttons according to Figures in Grids) is proposed in this paper. Inheriting the way of setting password in traditional cued-recall scheme, this scheme is also added the ideology of image identification. CBFG helps users tend to set their passwords more complex. Simultaneously, it has the capability against shoulder surfing attack and intersection analysis attack. Experiments illustrate that CBFG has better performance in usability, especially in security. --- paper_title: A New Graphical Password Scheme Resistant to Shoulder-Surfing paper_content: Shoulder-surfing is a known risk where an attacker can capture a password by direct observation or by recording the authentication session. Due to the visual interface, this problem has become exacerbated in graphical passwords. There have been some graphical schemes resistant or immune to shoulder-surfing, but they have significant usability drawbacks, usually in the time and effort to log in. In this paper, we propose and evaluate a new shoulder-surfing resistant scheme which has a desirable usability for PDAs. Our inspiration comes from the drawing input method in DAS and the association mnemonics in Story for sequence retrieval. The new scheme requires users to draw a curve across their password images orderly rather than click directly on them. The drawing input trick along with the complementary measures, such as erasing the drawing trace, displaying degraded images, and starting and ending with randomly designated images provide a good resistance to shoulder-surfing. A preliminary user study showed that users were able to enter their passwords accurately and to remember them over time. --- paper_title: Evaluating the usability and security of a graphical one-time PIN system paper_content: Traditional Personal Identification Numbers (PINs) are widely used, but the attacks in which they are captured have been increasing. One-time PINs offer better security, but potentially create greater workload for users. In this paper, we present an independent evaluation of a commercial system that makes PINs more resistant to observation attacks by using graphical passwords on a grid to generate a one-time PIN. 83 participants were asked to register with the system and log in at varying intervals. The successful login rate was approximately 91% after 3--4 days, and 97% after 9--10 days. Twenty five participants were retested after two years, and 27% of those were able to recall their pattern. We recorded 17 instances of failed attempts, and found that even though participants recalled the general shape of the pass-pattern in 13 of these instances, they could not recall its detailed location or sequence of cells. We conclude that GrIDsure is usable if people have one pass-pattern, but the level of security will depend on the context of use (it will work best in scenarios where repeated observations of transactions are unlikely), and the instructions given to users (without guidance, they are likely to chose from a small subset of the possible patterns which are easily guessed). --- paper_title: A comparison of perceived and real shoulder-surfing risks between alphanumeric and graphical passwords paper_content: Previous research has found graphical passwords to be more memorable than non-dictionary or "strong" alphanumeric passwords. Participants in a prior study expressed concerns that this increase in memorability could also lead to an increased susceptibility of graphical passwords to shoulder-surfing. This appears to be yet another example of the classic trade-off between usability and security for authentication systems. This paper explores whether graphical passwords' increased memorability necessarily leads to risks of shoulder-surfing. To date, there are no studies examining the vulnerability of graphical versus alphanumeric passwords to shoulder-surfing.This paper examines the real and perceived vulnerability to shoulder-surfing of two configurations of a graphical password, Passfaces™[30], compared to non-dictionary and dictionary passwords. A laboratory experiment with 20 participants asked them to try to shoulder surf the two configurations of Passfaces™ (mouse versus keyboard data entry) and strong and weak passwords. Data gathered included the vulnerability of the four authentication system configurations to shoulder-surfing and study participants' perceptions concerning the same vulnerability. An analysis of these data compared the relative vulnerability of each of the four configurations to shoulder-surfing and also compared study participants' real and perceived success in shoulder-surfing each of the configurations. Further analysis examined the relationship between study participants' real and perceived success in shoulder-surfing and determined whether there were significant differences in the vulnerability of the four authentication configurations to shoulder-surfing.Findings indicate that configuring data entry for Passfaces™ through a keyboard is the most effective deterrent to shoulder-surfing in a laboratory setting and the participants' perceptions were consistent with that result. While study participants believed that Passfaces™ with mouse data entry would be most vulnerable to shoulder-surfing attacks, the empirical results found that strong passwords were actually more vulnerable. --- paper_title: Design and evaluation of a shoulder-surfing resistant graphical password scheme paper_content: When users input their passwords in a public place, they may be at risk of attackers stealing their password. An attacker can capture a password by direct observation or by recording the individual's authentication session. This is referred to as shoulder-surfing and is a known risk, of special concern when authenticating in public places. Until recently, the only defense against shoulder-surfing has been vigilance on the part of the user. This paper reports on the design and evaluation of a game-like graphical method of authentication that is resistant to shoulder-surfing. The Convex Hull Click (CHC) scheme allows a user to prove knowledge of the graphical password safely in an insecure location because users never have to click directly on their password images. Usability testing of the CHC scheme showed that novice users were able to enter their graphical password accurately and to remember it over time. However, the protection against shoulder-surfing comes at the price of longer time to carry out the authentication. --- paper_title: Graphical passwords & qualitative spatial relations paper_content: A potential drawback of graphical password schemes is that they are more vulnerable to shoulder surfing than conventional alphanumeric text passwords. We present a variation of the Draw-a-Secret scheme originally proposed by Jermyn et al [1] that is more resistant to shoulder surfing through the use of a qualitative mapping between user strokes and the password, and the use of dynamic grids to both obfuscate attributes of the user secret and encourage them to use different surface realizations of the secret. The use of qualitative spatial relations relaxes the tight constraints on the reconstruction of a secret; allowing a range of deviations from the original. We describe QDAS (Qualitative Draw-A-Secret), an initial implementation of this graphical password scheme, and the results of an empirical study in which we examined the memorability of secrets, and their susceptibility to shoulder-surfing attacks, for both Draw-A-Secret and QDAS. --- paper_title: A Hybrid Password Authentication Scheme Based on Shape and Text paper_content: Text-based password authentication scheme tends to be more vulnerable to attacks such as shoulder surfing or a hidden camera. To overcome the vulnerabilities of traditional methods, visual or graphical password schemes have been developed as possible alternative solutions to text-based password schemes. Since it also has some drawbacks to simply adopt graphical password authentication, schemes using graphic and text have been developed. In this paper, a hybrid password authentication scheme based on shapes and texts is proposed. Shapes of strokes are used in the grid as the original passwords and users can log in with textual passwords via traditional input device. The method provides strong resistance to shoulder surfing or a hidden camera。Moreover, the scheme has high scalability and flexibility to enhance the authentication process security. The analysis of the security level of this approach is also discussed. --- paper_title: The urgency for effective user privacy-education to counter social engineering attacks on secure computer systems paper_content: Trusted people can fail to be trustworthy when it comes to protecting their aperture of access to secure computer systems due to inadequate education, negligence, and various social pressures. People are often the weakest link in an otherwise secure computer system and, consequently, are targeted for social engineering attacks. Social Engineering is a technique used by hackers or other attackers to gain access to information technology systems by getting the needed information (for example, a username and password) from a person rather than breaking into the system through electronic or algorithmic hacking methods. Such attacks can occur on both a physical and psychological level. The physical setting for these attacks occurs where a victim feels secure: often the workplace, the phone, the trash, and even on-line. Psychology is often used to create a rushed or officious ambiance that helps the social engineer to cajole information about accessing the system from an employee. Data privacy legislation in the United States and international countries that imposes privacy standards and fines for negligent or willful non-compliance increases the urgency to measure the trustworthiness of people and systems. One metric for determining compliance is to simulate, by audit, a social engineering attack upon an organization required to follow data privacy standards. Such an organization commits to protect the confidentiality of personal data with which it is entrusted. This paper presents the results of an approved social engineering audit made without notice within an organization where data security is a concern. Areas emphasized include experiences between the Social Engineer and the audited users, techniques used by the Social Engineer, and other findings from the audit. Possible steps to mitigate exposure to the dangers of Social Engineering through improved user education are reviewed. --- paper_title: Graphical passwords: Learning from the first twelve years paper_content: Starting around 1999, a great many graphical password schemes have been proposed as alternatives to text-based password authentication. We provide a comprehensive overview of published research in the area, covering both usability and security aspects as well as system evaluation. The article first catalogues existing approaches, highlighting novel features of selected schemes and identifying key usability or security advantages. We then review usability requirements for knowledge-based authentication as they apply to graphical passwords, identify security threats that such systems must address and review known attacks, discuss methodological issues related to empirical evaluation, and identify areas for further research and improved methodology. --- paper_title: A Small Subgroup Attack for Recovering Ephemeral Keys in Chang and Chang Password Key Exchange Protocol paper_content: Three-party authenticated key exchange protocol is an important cryptographic technique in the secure communication areas. Recently Chang and Chang proposed a novel three party simple key exchange protocol and claimed the protocol is secure, efficient and practical. Unless their claim, a key recovery attack is proposed on the above protocol by recovering the ephemeral keys. One way of recovering the ephemeral key is to solve the mathematical hard Discrete Logarithm Problem (DLP). The DLP is solved by using a popular Pohlig-Hellman method in the above key recovery attack. In the present study, a new method based on the small subgroup attack to solve the DLP is discussed to recover the ephemeral keys. Computation of DLP is carried out by two stages, such as the prior-computation and DLP computation. The prior-computation is performed on off-line and the DLP computation is performed on on-line. The method is analyzed on a comprehensive set of experiments and the ephemeral keys are recovered in reduced time. Also, the key recovery attack on Chang and Chang password key exchange protocol is implemented by using the new method to recover the ephemeral key. --- paper_title: An Authentication and Key Agreement Scheme with Key Confirmation and Privacy-preservation for Multi-server Environments paper_content: In the internet environment, it is desirable for a user to login different servers by keying the same password and using the same smart card. This paper proposes an authentication and key agreement scheme with key confirmation for multi-server environments. Compared with the previous authentication and key agreement schemes for multi-server environments, the new scheme holds many merits. It satisfies the following properties: R1. Single registration; R2. User friendly; R3. Prevention of the replay, the password guessing without smart cards, the impersonation and the stolen-verifier attacks; R4. Resistance against server spoofing; R5. Mutual authentication; R6. Two-factor authentication; R7.Resistance against known-key attacks; R8. Perfect forward secrecy; R9. Scalability of login; R10. Anonymity of users. ---	Title: A Survey on the Use of Graphical Passwords in Security Section 1: INTRODUCTION Description 1: This section introduces the importance of authentication in information security, the drawbacks of traditional alphanumeric passwords, and the advantages of graphical passwords which use images to enhance memorability and security. Section 2: LITERATURE REVIEW Description 2: This section reviews existing studies on graphical passwords, focusing on specific schemes, security analyses, and prior surveys that laid the foundation for further research. Section 3: CATEGORIZATION OF GRAPHICAL PASSWORDS Description 3: This section categorizes graphical password schemes into four main categories: Drawmetric schemes, Locimetric schemes, Cognometric schemes, and Hybrid schemes, providing an overview and discussion of each category. Section 4: Drawmetric Schemes Description 4: This subsection provides an in-depth analysis of Drawmetric schemes, including various specific schemes, their development, strengths, and weaknesses. Section 5: Locimetric Schemes Description 5: This subsection explores Locimetric schemes, detailing their methodology, notable examples, and associated security concerns. Section 6: Cognometric Schemes Description 6: This subsection discusses Cognometric schemes, their reliance on image recognition for authentication, and the usability and security implications. Section 7: Hybrid Schemes Description 7: This subsection examines Hybrid schemes that combine elements from different types of schemes to enhance security and usability. Section 8: ATTACK TYPES BASED ON PASSWORD SPACE Description 8: This section introduces common attack methods like brute force and dictionary attacks and provides universal password space formulas for graphical password schemes. Section 9: Brute Force Attack Description 9: This subsection explains brute force attacks, their application to graphical passwords, and methods to resist these attacks by increasing password space. Section 10: Dictionary Attack Description 10: This subsection describes dictionary attacks, their implications for graphical passwords, and how user choice patterns can affect the efficacy of such attacks. Section 11: ATTACK TYPES BASED ON PASSWORD CAPTURE Description 11: This section discusses attacks based on capturing passwords, including shoulder surfing, intersection analysis, social engineering, and spyware attacks. Section 12: Shoulder Surfing Description 12: This subsection addresses the susceptibility of graphical passwords to shoulder surfing attacks and the schemes developed to resist such attacks. Section 13: Intersection Analysis Description 13: This subsection details intersection analysis attacks, particularly problematic for schemes using multiple image selections. Section 14: Social Engineering Description 14: This subsection explores social engineering attacks like tricking, phishing, and pharming targeting graphical passwords. Section 15: Spyware Attack Description 15: This subsection discusses the threat of spyware attacks, including keystroke-loggers, mouse-loggers, and screen-scrapers, and their impact on graphical password schemes. Section 16: CONCLUSION AND RECOMMENDATION Description 16: This section provides a summary of the survey, analyzing the strengths and weaknesses of graphical password schemes and offering recommendations for improving security and usability.
Bridging the Chasm: A Survey of Software Engineering Practice in Scientific Programming	18	--- paper_title: The Mythical Man-Month paper_content: The book, The Mythical Man-Month, Addison-Wesley, 1975 (excerpted in Datamation, December 1974), gathers some of the published data about software engineering and mixes it with the assertion of a lot of personal opinions. In this presentation, the author will list some of the assertions and invite dispute or support from the audience. This is intended as a public discussion of the published book, not a regular paper. --- paper_title: Engineering the Software for Understanding Climate Change paper_content: Climate scientists build large, complex simulations with little or no software engineering training—and don't readily adopt the latest software engineering tools and techniques. This ethnographic study of climate scientists shows that their culture and practices share many features of agile and open source projects, but with highly customized software validation and verification techniques. --- paper_title: The Chimera of Software Quality paper_content: Despite years of computing progress, today's systems experience spectacular and all-too-frequent crashes, while many enormously expensive projects fail to produce anything useful. Of equal importance, and potentially more damaging, are the misleading smaller defects we tend to miss. From time to time, we must remind ourselves that the underlying quality of the software that our results and progress increasingly depend on will likely be flawed and even more dependent on independent corroboration than the science itself. Many scientific results are corrupted, perhaps fatally so, by undiscovered mistakes in the software used to calculate and present those results. --- paper_title: The T-experiments: errors in scientific software paper_content: Extensive tests showed that many software codes widely used in science and engineering are not as accurate as we would like to think. It is argued that better software engineering practices would help solve this problem, but realizing that the problem exists is an important first step. --- paper_title: Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling: Are GCMs the Best Tools? paper_content: In this paper we explore the dominant position of a particular style of scientific modelling in the provision of policy-relevant scientific knowledge on future climate change. We describe how the apical position of General Circulation Models (GCMs) appears to follow ‘logically’ both from conventional understandings of scientific representation and the use of knowledge, so acquired, in decision-making. We argue, however, that both of these particular understandings are contestable. In addition to questioning their current policy-usefulness, we draw upon existing analyses of GCMs which discuss model trade-offs, errors, and the effects of parameterisations, to raise questions about the validity of the conception of complexity in conventional accounts. An alternative approach to modelling, incorporating concepts of uncertainty, is discussed, and an illustrative example given for the case of the global carbon cycle. In then addressing the question of how GCMs have come to occupy their dominant position, we argue that the development of global climate change science and global environmental ‘management’ frameworks occurs concurrently and in a mutually supportive fashion, so uniting GCMs and environmental policy developments in certain industrialised nations and international organisations. The more basic questions about what kinds of commitments to theories of knowledge underpin different models of ‘complexity’ as a normative principle of ‘good science’ are concealed in this mutual reinforcement. Additionally, a rather technocratic policy orientation to climate change may be supported by such science, even though it involves political choices which deserve to be more widely debated. --- paper_title: Mechanizing Proof: Computing, Risk, and Trust paper_content: Most aspects of our private and social lives---our safety, the integrity of the financial system, the functioning of utilities and other services, and national security---now depend on computing. But how can we know that this computing is trustworthy? In Mechanizing Proof, Donald MacKenzie addresses this key issue by investigating the interrelations of computing, risk, and mathematical proof over the last half century from the perspectives of history and sociology. His discussion draws on the technical literature of computer science and artificial intelligence and on extensive interviews with scientists and engineers. --- paper_title: Software release build process and components in ATLAS offline paper_content: ATLAS is one of the largest collaborations in the physical sciences and involves 3000 scientists and engineers from 174 institutions in 38 countries. The geographically dispersed developer community has produced a large amount of software which is organized in 10 projects. In this presentation we discuss how the software is built on a variety of compiler and operating system combinations every night. File level and package level parallelism together with multi-core build servers are used to perform fast builds of the different platforms in several branches. We discuss the different tools involved during the software release build process and also the various mechanisms implemented to provide performance gains and error detection and retry mechanisms in order to counteract network and other instabilities that would otherwise degrade the robustness of the system. The goal is to provide high quality software built as fast as possible ready for final validation and deployment. --- paper_title: Lessons from the JMCB Archive paper_content: We examine the online archive of the Journal of Money, Credit, and Banking, in which an author is required to deposit the data and code that replicate the results of his paper. We find that most authors do not fulfill this requirement. Of more than 150 empirical articles, fewer than 15 could be replicated. Despite all this, there is no doubt that a data/code archive is more conducive to replicable research than the alternatives. We make recommendations to improve the functioning of the archive. --- paper_title: The Role of Data and Program Code Archives in the Future of Economic Research paper_content: This essay examines the role of data and program-code archives in making economic research “replicable.” Replication of published results is recognized as an essential part of the scientific method. Yet, historically, both the “demand for” and “supply of” replicable results in economics has been minimal. “Respect for the scientific method” is not sufficient to motivate either economists or editors of professional journals to ensure the replicability of published results. We enumerate the costs and benefits of mandatory data and code archives, and argue that the benefits far exceed the costs. Progress has been made since the gloomy assessment of Dewald, Thursby and Anderson some twenty years ago in the American Economic Review, but much remains to be done before empirical economics ceases to be a “dismal science” when judged by the replicability of its published results. JEL Classification: B4, C8 (This is Federal Reserve Bank of St. Louis Research Division working paper 2005-14.) Revised version of paper prepared for the American Economic Association meeting, Philadelphia PA, January 9, 2005. Views expressed herein are solely those of the authors and not necessarily those of the Federal Reserve Bank of St. Louis or the Federal Reserve System, New York University, Drexel University, Fordham University, or their staffs. Correspondence: (1) [email protected]; (2) [email protected]; (3) [email protected]; (4) [email protected]. --- paper_title: A longitudinal study of development and maintenance in Norway: Report from the 2003 investigation paper_content: Abstract The amount of work on application systems being taken up by maintenance activities (work done on an IT-system after being put in production) has been one of the arguments of those speaking about a ‘software crisis’. We have earlier investigated the applicability of this notion, and propose to rather look at the percentage of work being done on application portfolio upkeep (work made to keep up the functional coverage of the application system portfolio of the organization. This also includes the development of replacement systems), to assess the efficiency of the application systems support in an organisation. This paper presents the main results of a survey investigation performed in 2003 in 54 Norwegian organisations within this area. The amount of application portfolio upkeep is significantly higher than in a similar investigation conducted in 1993. The level of maintenance is smaller (although not significantly) than in another similar investigation conducted in 1998. There was a significant increase in both maintenance and application portfolio upkeep from 1993 to 1998, which could partly be attributed to be the extra maintenance and replacement-oriented work necessary to deal with the ‘year 2000 problem.’ As for the 2003 investigation, the slow IT-market in general seems to have influenced the results negatively seen from the point of view of application systems support efficiency in organization. --- paper_title: Toward Reproducible Computational Research: An Empirical Analysis of Data and Code Policy Adoption by Journals paper_content: Journal policy on research data and code availability is an important part of the ongoing shift toward publishing reproducible computational science. This article extends the literature by studying journal data sharing policies by year (for both 2011 and 2012) for a referent set of 170 journals. We make a further contribution by evaluating code sharing policies, supplemental materials policies, and open access status for these 170 journals for each of 2011 and 2012. We build a predictive model of open data and code policy adoption as a function of impact factor and publisher and find higher impact journals more likely to have open data and code policies and scientific societies more likely to have open data and code policies than commercial publishers. We also find open data policies tend to lead open code policies, and we find no relationship between open data and code policies and either supplemental material policies or open access journal status. Of the journals in this study, 38% had a data policy, 22% had a code policy, and 66% had a supplemental materials policy as of June 2012. This reflects a striking one year increase of 16% in the number of data policies, a 30% increase in code policies, and a 7% increase in the number of supplemental materials policies. We introduce a new dataset to the community that categorizes data and code sharing, supplemental materials, and open access policies in 2011 and 2012 for these 170 journals. --- paper_title: Measuring Reproducibility in Computer Systems Research paper_content: We describe a study into the willingness of Computer Systems researchers to share their code and data. We nd that . . . . We also propose a novel sharing specication scheme that will require researchers to specify the level of reproducibility that reviewers and readers can assume from a paper either submitted for publication, or published. --- paper_title: Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling: Are GCMs the Best Tools? paper_content: In this paper we explore the dominant position of a particular style of scientific modelling in the provision of policy-relevant scientific knowledge on future climate change. We describe how the apical position of General Circulation Models (GCMs) appears to follow ‘logically’ both from conventional understandings of scientific representation and the use of knowledge, so acquired, in decision-making. We argue, however, that both of these particular understandings are contestable. In addition to questioning their current policy-usefulness, we draw upon existing analyses of GCMs which discuss model trade-offs, errors, and the effects of parameterisations, to raise questions about the validity of the conception of complexity in conventional accounts. An alternative approach to modelling, incorporating concepts of uncertainty, is discussed, and an illustrative example given for the case of the global carbon cycle. In then addressing the question of how GCMs have come to occupy their dominant position, we argue that the development of global climate change science and global environmental ‘management’ frameworks occurs concurrently and in a mutually supportive fashion, so uniting GCMs and environmental policy developments in certain industrialised nations and international organisations. The more basic questions about what kinds of commitments to theories of knowledge underpin different models of ‘complexity’ as a normative principle of ‘good science’ are concealed in this mutual reinforcement. Additionally, a rather technocratic policy orientation to climate change may be supported by such science, even though it involves political choices which deserve to be more widely debated. --- paper_title: Dealing with Risk in Scientific Software Development paper_content: The development of scientific software involves risk in the underlying theory, its implementation, and its use. Through a series of interviews, the authors explored how research scientists at two Canadian universities developed their software. These interviews indicated that the scientists used a set of strategies to address risk. They also suggested where the software engineering community could perform research focused on specific problems faced by scientific software developers. --- paper_title: Growth in a Time of Debt paper_content: We study economic growth and inflation at different levels of government and external debt. Our analysis is based on new data on forty-four countries spanning about two hundred years. The dataset incorporates over 3,700 annual observations covering a wide range of political systems, institutions, exchange rate arrangements, and historic circumstances. Our main findings are: First, the relationship between government debt and real GDP growth is weak for debt/GDP ratios below a threshold of 90 percent of GDP. Above 90 percent, median growth rates fall by one percent, and average growth falls considerably more. We find that the threshold for public debt is similar in advanced and emerging economies. Second, emerging markets face lower thresholds for external debt (public and private)--which is usually denominated in a foreign currency. When external debt reaches 60 percent of GDP, annual growth declines by about two percent; for higher levels, growth rates are roughly cut in half. Third, there is no apparent contemporaneous link between inflation and public debt levels for the advanced countries as a group (some countries, such as the United States, have experienced higher inflation when debt/GDP is high.) The story is entirely different for emerging markets, where inflation rises sharply as debt increases. --- paper_title: Lessons from the JMCB Archive paper_content: We examine the online archive of the Journal of Money, Credit, and Banking, in which an author is required to deposit the data and code that replicate the results of his paper. We find that most authors do not fulfill this requirement. Of more than 150 empirical articles, fewer than 15 could be replicated. Despite all this, there is no doubt that a data/code archive is more conducive to replicable research than the alternatives. We make recommendations to improve the functioning of the archive. --- paper_title: When Software Engineers Met Research Scientists: A Case Study paper_content: This paper describes a case study of software engineers developing a library of software components for a group of research scientists, using a traditional, staged, document-led methodology. The case study reveals two problems with the use of the methodology. The first is that it demands an upfront articulation of requirements, whereas the scientists had experience, and hence expectations, of emergent requirements; the second is that the project documentation does not suffice to construct a shared understanding. Reflecting on our case study, we discuss whether combining agile elements with a traditional methodology might have alleviated these problems. We then argue that the rich picture painted by the case study, and the reflections on methodology that it inspires, has a relevance that reaches beyond the original context of the study. --- paper_title: An overview of the Trilinos project paper_content: The Trilinos Project is an effort to facilitate the design, development, integration, and ongoing support of mathematical software libraries within an object-oriented framework for the solution of large-scale, complex multiphysics engineering and scientific problems. Trilinos addresses two fundamental issues of developing software for these problems: (i) providing a streamlined process and set of tools for development of new algorithmic implementations and (ii) promoting interoperability of independently developed software.Trilinos uses a two-level software structure designed around collections of packages. A Trilinos package is an integral unit usually developed by a small team of experts in a particular algorithms area such as algebraic preconditioners, nonlinear solvers, etc. Packages exist underneath the Trilinos top level, which provides a common look-and-feel, including configuration, documentation, licensing, and bug-tracking.Here we present the overall Trilinos design, describing our use of abstract interfaces and default concrete implementations. We discuss the services that Trilinos provides to a prospective package and how these services are used by various packages. We also illustrate how packages can be combined to rapidly develop new algorithms. Finally, we discuss how Trilinos facilitates high-quality software engineering practices that are increasingly required from simulation software. --- paper_title: Configuration Management for Large-Scale Scientific Computing at the UK Met Office paper_content: The UK Met Office's flexible configuration management (FCM) system uses existing open source tools, adapted for use with high-performance scientific Fortran code, to help manage evolving code in its large-scale climate simulation and weather forecasting models. FCM has simplified the development process, improved team coordination, and reduced release cycles. --- paper_title: A survey of scientific software development paper_content: Software for scientific research purposes has received increased attention in recent years. Case studies have noted development practices, limitations, and problems in the development of scientific software. However, applicability of the results of these studies to improving the wider scientific software development practices is not known. This paper presents a survey of 60 scientific software developers. The survey was conducted online from August--September 2009, and aims to identify where improvements to scientific software practices can be made. While our results generally confirm previous work, we have found some notable differences. The use of IDEs and version control tools among the surveyed scientific software developers has increased, and trace-ability of scientific software is not as important to scientific software developers as it is to scientific software users. Documentation also appears to be more widely produced than previous studies indicate. However, there remains room for improvement in tool use, documentation, testing, and verification activities for scientific software development. --- paper_title: When Software Engineers Met Research Scientists: A Case Study paper_content: This paper describes a case study of software engineers developing a library of software components for a group of research scientists, using a traditional, staged, document-led methodology. The case study reveals two problems with the use of the methodology. The first is that it demands an upfront articulation of requirements, whereas the scientists had experience, and hence expectations, of emergent requirements; the second is that the project documentation does not suffice to construct a shared understanding. Reflecting on our case study, we discuss whether combining agile elements with a traditional methodology might have alleviated these problems. We then argue that the rich picture painted by the case study, and the reflections on methodology that it inspires, has a relevance that reaches beyond the original context of the study. --- paper_title: An overview of the Trilinos project paper_content: The Trilinos Project is an effort to facilitate the design, development, integration, and ongoing support of mathematical software libraries within an object-oriented framework for the solution of large-scale, complex multiphysics engineering and scientific problems. Trilinos addresses two fundamental issues of developing software for these problems: (i) providing a streamlined process and set of tools for development of new algorithmic implementations and (ii) promoting interoperability of independently developed software.Trilinos uses a two-level software structure designed around collections of packages. A Trilinos package is an integral unit usually developed by a small team of experts in a particular algorithms area such as algebraic preconditioners, nonlinear solvers, etc. Packages exist underneath the Trilinos top level, which provides a common look-and-feel, including configuration, documentation, licensing, and bug-tracking.Here we present the overall Trilinos design, describing our use of abstract interfaces and default concrete implementations. We discuss the services that Trilinos provides to a prospective package and how these services are used by various packages. We also illustrate how packages can be combined to rapidly develop new algorithms. Finally, we discuss how Trilinos facilitates high-quality software engineering practices that are increasingly required from simulation software. --- paper_title: Software Development Environments for Scientific and Engineering Software: A Series of Case Studies paper_content: The need for high performance computing applications for computational science and engineering projects is growing rapidly, yet there have been few detailed studies of the software engineering process used for these applications. The DARPA High Productivity Computing Systems Program has sponsored a series of case studies of representative computational science and engineering projects to identify the steps involved in developing such applications (i.e. the life cycle, the workflows, technical challenges, and organizational challenges). Secondary goals were to characterize tool usage and identify enhancements that would increase the programmers' productivity. Finally, these studies were designed to develop a set of lessons learned that can be transferred to the general computational science and engineering community to improve the software engineering process used for their applications. Nine lessons learned from five representative projects are presented, along with their software engineering implications, to provide insight into the software development environments in this domain. --- paper_title: Software Development Cultures and Cooperation Problems: A Field Study of the Early Stages of Development of Software for a Scientific Community paper_content: In earlier work, I identified a particular class of end-user developers, who include scientists and whom I term `professional end-user developers', as being of especial interest. Here, I extend this work by articulating a culture of professional end-user development, and illustrating by means of a field-study how the influence of this culture causes cooperation problems in an inter-disciplinary team developing a software system for a scientific community. My analysis of the field study data is informed by some recent literature on multi-national work cultures. Whilst acknowledging that viewing a scientific development through a lens of software development culture does not give a full picture, I argue that it nonetheless provides deep insights. --- paper_title: Configuration Management for Large-Scale Scientific Computing at the UK Met Office paper_content: The UK Met Office's flexible configuration management (FCM) system uses existing open source tools, adapted for use with high-performance scientific Fortran code, to help manage evolving code in its large-scale climate simulation and weather forecasting models. FCM has simplified the development process, improved team coordination, and reduced release cycles. --- paper_title: The ASC-Alliance Projects: A Case Study of Large-Scale Parallel Scientific Code Development paper_content: Computational scientists face many challenges when developing software that runs on large-scale parallel machines. However, software-engineering researchers haven't studied their software development processes in much detail. To better understand the nature of software development in this context, the authors examined five large-scale computational science software projects operated at the five ASC-Alliance centers. --- paper_title: Supporting scientific SE process improvement paper_content: The increasing complexity of scientific software can result in significant impacts on the research itself. In traditional software development projects, teams adopt historical best practices into their development processes to mitigate the risk of such problems. In contrast, the gap that has formed between the traditional and scientific software communities leaves scientists to rely on only their own experience when facing software process improvement (SPI) decisions. Rather than expect scientists to become software engineering (SE) experts or the SE community to learn all of the intricacies involved in scientific software development projects, we seek a middle ground. The Scientific Software Process Improvement Framework (SciSPIF) will allow scientists to self-drive their own SPI efforts while leveraging the collective experiences of their peers and linking their concerns to established SE best practices. This proposal outlines the known challenges of scientific software development, relevant concepts from traditional SE research, and our planned methodology for collecting the data required to construct SciSPIF while staying grounded in the actual goals and concerns of the scientists. --- paper_title: Spatial Data e-Infrastructure paper_content: This paper examines overlap between e-Science and geospatial communities using work undertaken as part of the JISC funded Secure Access to Geospatial Services (SEE-GEO) project. Working from a position that open standards provide the best means of achieving interoperability, the case studies demonstrate the use of Grid technology and open geospatial standard interfaces to realise classic spatial data infrastructure scenarios. These examples are used to illustrate just how e-infrastructures enable seamless, security driven spatial data access at the national, regional and global scale. --- paper_title: Exploring XP for scientific research paper_content: Can we successfully apply XP (Extreme Programming) in a scientific research context? A pilot project at the NASA Langley Research Center tested XPs applicability in this context. Since the cultural environment at a government research center differs from the customer-centric business view, eight of XPs 12 practices seemed incompatible with the existing research culture. Despite initial awkwardness, the authors determined that XP can function in situations for which it appears to be ill suited. --- paper_title: Engineering the Software for Understanding Climate Change paper_content: Climate scientists build large, complex simulations with little or no software engineering training—and don't readily adopt the latest software engineering tools and techniques. This ethnographic study of climate scientists shows that their culture and practices share many features of agile and open source projects, but with highly customized software validation and verification techniques. --- paper_title: Assuring the Future? A Look at Validating Climate Model Software paper_content: The scientific community studying climate change uses a variety of strategies to assess the correctness of their models. These software systems represent large, sophisticated, fine-grained scientific tools. The validation practices described are thus tailored to a domain in which software and software engineering practices are useful but cannot be allowed to get in the way of the science. In audio interviews, two scientists--Robert Jacob, a computational climate scientist at Argonne National Laboratory, and Gavin Schmidt, a climatologist and climate modeler at the NASA Goddard Institute for Space Studies--discuss what it means to develop and communicate ground-breaking results. --- paper_title: Scientific Software Development at a Research Facility paper_content: Software engineers at Daresbury Laboratory develop experiment control and data acquisition software to support scientific research. Here, they review their experiences and learning over the years. --- paper_title: Agile methods in biomedical software development: a multi-site experience report paper_content: BackgroundAgile is an iterative approach to software development that relies on strong collaboration and automation to keep pace with dynamic environments. We have successfully used agile development approaches to create and maintain biomedical software, including software for bioinformatics. This paper reports on a qualitative study of our experiences using these methods.ResultsWe have found that agile methods are well suited to the exploratory and iterative nature of scientific inquiry. They provide a robust framework for reproducing scientific results and for developing clinical support systems. The agile development approach also provides a model for collaboration between software engineers and researchers. We present our experience using agile methodologies in projects at six different biomedical software development organizations. The organizations include academic, commercial and government development teams, and included both bioinformatics and clinical support applications. We found that agile practices were a match for the needs of our biomedical projects and contributed to the success of our organizations.ConclusionWe found that the agile development approach was a good fit for our organizations, and that these practices should be applicable and valuable to other biomedical software development efforts. Although we found differences in how agile methods were used, we were also able to identify a set of core practices that were common to all of the groups, and that could be a focus for others seeking to adopt these methods. --- paper_title: What Do We Know about Scientific Software Development's Agile Practices? paper_content: The development of scientific software has similarities with processes that follow the software engineering "agile manifesto": responsiveness to change and collaboration are of utmost importance. But how well do current scientific software-development processes match the practices found in agile development methods, and what are the effects of using agile practices in such processes? --- paper_title: Dealing with Risk in Scientific Software Development paper_content: The development of scientific software involves risk in the underlying theory, its implementation, and its use. Through a series of interviews, the authors explored how research scientists at two Canadian universities developed their software. These interviews indicated that the scientists used a set of strategies to address risk. They also suggested where the software engineering community could perform research focused on specific problems faced by scientific software developers. --- paper_title: Chaste: using agile programming techniques to develop computational biology software paper_content: Cardiac modelling is the area of physiome modelling where the available simulation software is perhaps most mature, and it therefore provides an excellent starting point for considering the software requirements for the wider physiome community. In this paper, we will begin by introducing some of the most advanced existing software packages for simulating cardiac electrical activity. We consider the software development methods used in producing codes of this type, and discuss their use of numerical algorithms, relative computational efficiency, usability, robustness and extensibility. We then go on to describe a class of software development methodologies known as test-driven agile methods and argue that such methods are more suitable for scientific software development than the traditional academic approaches. A case study is a project of our own, Cancer, Heart and Soft Tissue Environment, which is a library of computational biology software that began as an experiment in the use of agile programming methods. We present our experiences with a review of our progress thus far, focusing on the advantages and disadvantages of this new approach compared with the development methods used in some existing packages. We conclude by considering whether the likely wider needs of the cardiac modelling community are currently being met and suggest that, in order to respond effectively to changing requirements, it is essential that these codes should be more malleable. Such codes will allow for reliable extensions to include both detailed mathematical models--of the heart and other organs--and more efficient numerical techniques that are currently being developed by many research groups worldwide. --- paper_title: Introducing agile development into bioinformatics: an experience report paper_content: This experience report describes our efforts to introduce agile development techniques incrementally into our customer's organization in the National Cancer Institute and develop a partnering relationship in the process. The report addresses the steps we have taken not only to deploy the practices, but also to gain customer support for them. It addresses variations we have used to adapt to our customer's environment, including our approach to involving customer personnel at remote locations. We also address challenges we still must face, including how best to manage a product-line with agile development techniques. --- paper_title: Domain-specific languages: an annotated bibliography paper_content: We survey the literature available on the topic of domain-specific languages as used for the construction and maintenance of software systems. We list a selection of 75 key publications in the area, and provide a summary for each of the papers. Moreover, we discuss terminology, risks and benefits, example domain-specific languages, design methodologies, and implementation techniques. --- paper_title: Braincurry: A Domain- Specific Language for Integrative Neuroscience paper_content: This paper describes Braincurry, a domain-specific, declarative language for describing and analysing experiments in neuroscience. Braincurry has three goals: to allow experiments and data analysis to be described in a way that is sufficiently abstract to serve as a definition; to facilitate carrying out experiments by executing such descriptions; and to be directly usable by end users: neuroscientists. We adopted an experimental and incremental approach to the design and implementation of Braincurry, focusing on the neurophysiological response to visual stimuli in locusts as a test case. Braincurry is currently implemented as an embedding in Haskell, which is a highly effective tool for this kind of exploratory language design. The declarative nature of Haskell and its flexible syntax fitted with our goals. We discuss the requirements for a realistic language meeting the above goals, describe the current Braincurry design and how it may be generalised, and explain how some particularly challenging hard real-time requirements were met. --- paper_title: Dealing with Risk in Scientific Software Development paper_content: The development of scientific software involves risk in the underlying theory, its implementation, and its use. Through a series of interviews, the authors explored how research scientists at two Canadian universities developed their software. These interviews indicated that the scientists used a set of strategies to address risk. They also suggested where the software engineering community could perform research focused on specific problems faced by scientific software developers. --- paper_title: Chaste: using agile programming techniques to develop computational biology software paper_content: Cardiac modelling is the area of physiome modelling where the available simulation software is perhaps most mature, and it therefore provides an excellent starting point for considering the software requirements for the wider physiome community. In this paper, we will begin by introducing some of the most advanced existing software packages for simulating cardiac electrical activity. We consider the software development methods used in producing codes of this type, and discuss their use of numerical algorithms, relative computational efficiency, usability, robustness and extensibility. We then go on to describe a class of software development methodologies known as test-driven agile methods and argue that such methods are more suitable for scientific software development than the traditional academic approaches. A case study is a project of our own, Cancer, Heart and Soft Tissue Environment, which is a library of computational biology software that began as an experiment in the use of agile programming methods. We present our experiences with a review of our progress thus far, focusing on the advantages and disadvantages of this new approach compared with the development methods used in some existing packages. We conclude by considering whether the likely wider needs of the cardiac modelling community are currently being met and suggest that, in order to respond effectively to changing requirements, it is essential that these codes should be more malleable. Such codes will allow for reliable extensions to include both detailed mathematical models--of the heart and other organs--and more efficient numerical techniques that are currently being developed by many research groups worldwide. --- paper_title: A Software Chasm: Software Engineering and Scientific Computing paper_content: Some time ago, a chasm opened between the scientific-computing community and the software engineering community. Originally, computing meant scientific computing. Today, science and engineering applications are at the heart of software systems such as environmental monitoring systems, rocket guidance systems, safety studies for nuclear stations, and fuel injection systems. Failures of such health-, mission-, or safety-related systems have served as examples to promote the use of software engineering best practices. Yet, the bulk of the software engineering community's research is on anything but scientific-application software. This chasm has many possible causes. In this article, we look at the impact of one particular contributor in industry. --- paper_title: Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research paper_content: Scholarly dissemination and communication standards are changing to reflect the increasingly computational nature of scholarly research, primarily to include the sharing of the data and code associated with published results. This paper presents a formalized set of best practice recommendations for computational scientists wishing to disseminate reproducible research, facilitate innovation by enabling data and code re-use, and enable broader communication of the output of digital scientific research. We distinguish two forms of collaboration to motivate choices of software environment for computational scientific research. We also present these Best Practices as a living, evolving, and changing document on wiki. --- paper_title: A Risk-Based, Practice-Centered Approach to Project Management for HPCMP CREATE paper_content: The Department of Defense's High Performance Computing Modernization Program Computational Research and Engineering Acquisition Tools and Environments Computational Research and Engineering Acquisition Tools and Environments (CREATE) program is developing and deploying multiphysics high-performance computing software applications for engineers to design and make accurate performance predictions for military aircraft, ships, ground vehicles, and radio frequency antennas. When CREATE started (2007), no commonly recognized set of successful software project management practices existed for developing multiphysics, HPC engineering software. Based on lessons learned from the HPC and computational engineering communities, CREATE leadership developed and implemented a risk-based, practice-centered strategy to organize and manage the highly distributed program. This approach led to a good balance between ensuring a sufficiently structured workflow and accountability and providing the flexibility and agility necessary to create new sets of engineering tools with the capabilities needed to design next-generation weapon systems. --- paper_title: A new DoD initiative: the Computational Research and Engineering Acquisition Tools and Environments (CREATE) program paper_content: In FY2008, the U.S. Department of Defense (DoD) initiated the Computational Research and Engineering Acquisition Tools and Environments (CREATE) program, a $360M program with a two-year planning phase and a ten-year execution phase. CREATE will develop and deploy three computational engineering tool sets for DoD acquisition programs to use to design aircraft, ships and radio-frequency antennas. The planning and execution of CREATE are based on the 'lessons learned' from case studies of large-scale computational science and engineering projects. The case studies stress the importance of a stable, close-knit development team; a focus on customer needs and requirements; verification and validation; flexible and agile planning, management, and development processes; risk management; realistic schedules and resource levels; balanced short- and long-term goals and deliverables; and stable, long-term support by the program sponsor. Since it began in FY2008, the CREATE program has built a team and project structure, developed requirements and begun validating them, identified candidate products, established initial connections with the acquisition programs, begun detailed project planning and development, and generated the initial collaboration infrastructure necessary for success by its multi-institutional, multidisciplinary teams. --- paper_title: MAINTAINING CORRECTNESS IN SCIENTIFIC PROGRAMS paper_content: Combine a high rate of change (which makes correctness hard to maintain) with an increased sensitivity to failure to maintain correctness and you have a big problem. Solving this problem must be the focus of our methodology. In this paper, the author describes the layered approach that he found to be the most successful in maintaining correctness in the face of rapid change. --- paper_title: Five Recommended Practices for Computational Scientists Who Write Software paper_content: Few software engineering techniques and approaches are specifically useful for computational scientists, and despite recent efforts, it could be many years before a consolidated handbook is available. Meanwhile, computational scientists can look to the practices of other scientists who write successful software. --- paper_title: Software Carpentry: Getting Scientists to Write Better Code by Making Them More Productive paper_content: For the past years, my colleagues and I have developed a one-semester course that teaches scientists and engineers the "common core" of modern software development. Our experience shows that an investment of 150 hours-25 of lectures and the rest of practical work-can improve productivity by roughly 20 percent. That's one day a week, one less semester in a master's degree, or one less year for a typical PhD. The course is called software carpentry, rather than software engineering, to emphasize the fact that it focuses on small-scale and immediately practical issues. All of the material is freely available under an open-source license at www.swc.scipy.org and can be used both for self-study and in the classroom. This article describes what the course contains, and why --- paper_title: Verification of Computer Simulation Models paper_content: The problem of validating computer simulation models of industrial systems has received only limited attention in the management science literature. The purpose of this paper is to consider the problem of validating computer models in the light of contemporary thought in the fields of philosophy of science, economic theory, and statistics. In order to achieve this goal we have attempted to gather together and present some of the ideas of scientific philosophers, economists, statisticians, and practitioners in the field of simulation which are relevant to the problem of verifying simulation models. We have paid particular attention to the writings of economists who have been concerned with testing the validity of economic models. Among the questions which we shall consider are included: What does it mean to verify a computer model of an industrial system? Are there any differences between the verification of computer models and the verification of other types of models? If so, what are some of these differences? Also considered are a number of measures and techniques for testing the "goodness of fit" of time series generated by computer models to observed historical series. --- paper_title: 'Integronsters', integral and integrated modeling paper_content: In many cases model integration treats models as software components only, ignoring the fluid relationship between models and reality, the evolving nature of models and their constant modification and recalibration. As a result, with integrated models we find increased complexity, where changes that used to impact only relatively contained models of subsystems, now propagate throughout the whole integrated system. This makes it harder to keep the overall complexity under control and, in a way, defeats the purpose of modularity, when efficiency is supposed to be gained from independent development of modules. Treating models only as software in solving the integration challenge may give birth to 'integronsters' - constructs that are perfectly valid as software products but ugly or even useless as models. We argue that one possible remedy is to learn to use data sets as modules and integrate them into the models. Then the data that are available for module calibration can serve as an intermediate linkage tool, sitting between modules and providing a module-independent baseline dynamics, which is then incremented when scenarios are to be run. In this case it is not the model output that is directed into the next model input, but model output is presented as a variation around the baseline trajectory, and it is this variation that is then fed into the next module down the chain. However still with growing overall complexity, calibration can become an important limiting factor, giving more promise to the integral approach, when the system is modeled and simplified as a whole. --- paper_title: Computational Science Demands a New Paradigm paper_content: The field has reached a threshold at which better organization becomes crucial. New methods of verifying and validating complex codes are mandatory if computational science is to fulfill its promise for science and society. --- paper_title: Lessons from Space paper_content: Given the parallels between the complexity of human spaceflight and large software systems, there are many things we developers can learn from successful space programs, such as the Soyuz. First, limiting a project's scope and complexity early on can have a dramatic payoff in its success and longevity. In addition, adding generous margins to early estimates (and any subsequent revisions) will ease the pain of development and deployment. Furthermore, gradual evolution with a working program at each step, rather than massive rewrites, benefits from successful architectures and teams, while also retaining the software's customer base and third-party contributors. Finally, a well-defined modular structure can increase the software's versatility yielding economies of scope and scale over its lifetime. --- paper_title: Engineering the Software for Understanding Climate Change paper_content: Climate scientists build large, complex simulations with little or no software engineering training—and don't readily adopt the latest software engineering tools and techniques. This ethnographic study of climate scientists shows that their culture and practices share many features of agile and open source projects, but with highly customized software validation and verification techniques. --- paper_title: Predictive Capability Maturity Model for computational modeling and simulation. paper_content: The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering applicationmore » of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.« less --- paper_title: Self-Perceptions about Software Engineering: A Survey of Scientists and Engineers paper_content: Scientists and engineers devote considerable effort to developing large, complex codes to solve important problems. However, while they often develop useful code, many scientists and engineers are frequently unaware of how various software engineering practices can help them write better code. This article presents the results of a survey on this topic. --- paper_title: Understanding the High-Performance-Computing Community: A Software Engineer's Perspective paper_content: Computational scientists developing software for HPC systems face unique software engineering issues. Attempts to transfer SE technologies to this domain must take these issues into account. --- paper_title: Dealing with Risk in Scientific Software Development paper_content: The development of scientific software involves risk in the underlying theory, its implementation, and its use. Through a series of interviews, the authors explored how research scientists at two Canadian universities developed their software. These interviews indicated that the scientists used a set of strategies to address risk. They also suggested where the software engineering community could perform research focused on specific problems faced by scientific software developers. --- paper_title: Managing Chaos: Lessons Learned Developing Software in the Life Sciences paper_content: In the life sciences, the need to balance the costs and benefits of introducing software processes into a research environment presents a distinct set of challenges due to the cultural disconnect between life sciences research and software engineering. The Institute for Systems Biology's research informatics team has studied these challenges and developed a software process to address them. --- paper_title: Hints on Test Data Selection: Help for the Practicing Programmer paper_content: In many cases tests of a program that uncover simple errors are also effective in uncovering much more complex errors. This so-called coupling effect can be used to save work during the testing process. --- paper_title: A Scientific Function Test Framework for Modular Environmental Model Development: Application to the Community Land Model paper_content: As environmental models have become more complicated, we need new tools to analyze and validate these models and to facilitate collaboration among field scientists, observation dataset providers, environmental system modelers, and computer scientists. Modular design and function test of environmental models have gained attention recently within the Biological and Environmental Research Program of the U.S. Department of Energy. In this paper, we will present our methods and software tools 1) to analyze environmental software and 2) to generate modules for scientific function testing of environmental models. We have applied these methods to the Community Land Model with three typical scenarios: 1) benchmark case function validation, 2) observation-constraint function validation, and 3) a virtual root module generation for root function investigation and evaluation. We believe that our strategies and experience in scientific function test framework can be beneficial to many other research programs that adapt integrated environmental modeling methodology. --- paper_title: Testing Scientific Software: A Systematic Literature Review paper_content: Context: Scientific software plays an important role in critical decision making, for example making weather predictions based on climate models, and computation of evidence for research publications. Recently, scientists have had to retract publications due to errors caused by software faults. Systematic testing can identify such faults in code. Objective: This study aims to identify specific challenges, proposed solutions, and unsolved problems faced when testing scientific software. Method: We conducted a systematic literature survey to identify and analyze relevant literature. We identified 62 studies that provided relevant information about testing scientific software. Results: We found that challenges faced when testing scientific software fall into two main categories: (1) testing challenges that occur due to characteristics of scientific software such as oracle problems and (2) testing challenges that occur due to cultural differences between scientists and the software engineering community such as viewing the code and the model that it implements as inseparable entities. In addition, we identified methods to potentially overcome these challenges and their limitations. Finally we describe unsolved challenges and how software engineering researchers and practitioners can help to overcome them. Conclusions: Scientific software presents special challenges for testing. Specifically, cultural differences between scientist developers and software engineers, along with the characteristics of the scientific software make testing more difficult. Existing techniques such as code clone detection can help to improve the testing process. Software engineers should consider special challenges posed by scientific software such as oracle problems when developing testing techniques. --- paper_title: Examining random and designed tests to detect code mistakes in scientific software paper_content: Abstract Successfully testing computational software to detect code mistakes is impacted by multiple factors. One factor is the tolerance accepted in test output. Other factors are the nature of the code mistake, the characteristics of the code structure, and the choice of test input. We have found that randomly generated test input is a viable approach to testing for code mistakes and that simple structural metrics have little predictive power in the type of testing required. We provide further evidence that reduction of tolerance in expected test output has a much larger impact than running many more tests to discover code mistakes. --- paper_title: Lessons from Space paper_content: Given the parallels between the complexity of human spaceflight and large software systems, there are many things we developers can learn from successful space programs, such as the Soyuz. First, limiting a project's scope and complexity early on can have a dramatic payoff in its success and longevity. In addition, adding generous margins to early estimates (and any subsequent revisions) will ease the pain of development and deployment. Furthermore, gradual evolution with a working program at each step, rather than massive rewrites, benefits from successful architectures and teams, while also retaining the software's customer base and third-party contributors. Finally, a well-defined modular structure can increase the software's versatility yielding economies of scope and scale over its lifetime. --- paper_title: Building PDE codes to be verifiable and validatable paper_content: For codes that solve nonlinear partial differential equations (PDEs), powerful methodologies already exist for verification of codes, verification of calculations, and validation (V2V). If computational scientists and engineers are serious about these issues, they will take the responsibility and the relatively little extra effort to design (or modify) their codes so that independent users can confirm V2V. --- paper_title: Engineering the Software for Understanding Climate Change paper_content: Climate scientists build large, complex simulations with little or no software engineering training—and don't readily adopt the latest software engineering tools and techniques. This ethnographic study of climate scientists shows that their culture and practices share many features of agile and open source projects, but with highly customized software validation and verification techniques. --- paper_title: Assuring the Future? A Look at Validating Climate Model Software paper_content: The scientific community studying climate change uses a variety of strategies to assess the correctness of their models. These software systems represent large, sophisticated, fine-grained scientific tools. The validation practices described are thus tailored to a domain in which software and software engineering practices are useful but cannot be allowed to get in the way of the science. In audio interviews, two scientists--Robert Jacob, a computational climate scientist at Argonne National Laboratory, and Gavin Schmidt, a climatologist and climate modeler at the NASA Goddard Institute for Space Studies--discuss what it means to develop and communicate ground-breaking results. --- paper_title: Limited discrepancy search revisited paper_content: Harvey and Ginsberg's limited discrepancy search (LDS) is based on the assumption that costly heuristic mistakes are made early in the search process. Consequently, LDS repeatedly probes the state space, going against the heuristic (i.e., taking discrepancies) a specified number of times in all possible ways and attempts to take those discrepancies as early as possible. LDS was improved by Richard Korf, to become improved LDS (ILDS), but in doing so, discrepancies were taken as late as possible, going against the original assumption. Many subsequent algorithms have faithfully inherited Korf's interpretation of LDS, and take discrepancies late. This then raises the question: Should we take our discrepancies late or early? We repeat the original experiments performed by Harvey and Ginsberg and those by Korf in an attempt to answer this question. We also investigate the early stopping condition of the YIELDS algorithm, demonstrating that it is simple, elegant and efficient. --- paper_title: On Testing Non-testable Programs paper_content: It is widely accepted that the fundamental limitation of using program testing techniques to determine the correctness of a program is the inability to extrapolate from the correctness of results for a proper subset of the input domain to the program's correctness for all elements of the domain. In particular, for any proper subset of the domain there are infinitely many programs which produce the correct output on those elements, but produce an incorrect output for some other domain element. None the less we routinely test programs to increase our confidence in their correctness, and a great deal of research is currently being devoted to improving the effectiveness of program testing. These efforts fall into three primary categories: (1) the development of a sound theoretical basis for testing; (2) devising and improving testing methodologies, particularly mechanizable ones; (3) the definition of accurate measures of and criteria for test data adequacy. Almost all of the research on software testing therefore focuses on the development and analysis of input data. In particular there is an underlying assumption that once this phase is complete, the remaining tasks are straightforward. These consist of running the program on the selected data, producing output which is then examined to determine the program's correctness on the test data. The mechanism which checks this correctness is known as an oracle, and the belief that the tester is routinely able to determine whether or not the test output is correct is the oracle assumption.' • 2 --- paper_title: Software Engineering for Scientists paper_content: At two recent workshops, participants discussed the juxtaposition of software engineering with the development of scientific computational software. --- paper_title: Dealing with Risk in Scientific Software Development paper_content: The development of scientific software involves risk in the underlying theory, its implementation, and its use. Through a series of interviews, the authors explored how research scientists at two Canadian universities developed their software. These interviews indicated that the scientists used a set of strategies to address risk. They also suggested where the software engineering community could perform research focused on specific problems faced by scientific software developers. --- paper_title: The ASC-Alliance Projects: A Case Study of Large-Scale Parallel Scientific Code Development paper_content: Computational scientists face many challenges when developing software that runs on large-scale parallel machines. However, software-engineering researchers haven't studied their software development processes in much detail. To better understand the nature of software development in this context, the authors examined five large-scale computational science software projects operated at the five ASC-Alliance centers. --- paper_title: Assessing climate model software quality : a defect density analysis of three models paper_content: Abstract. A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated. Thus, in order to trust a climate model, one must trust that the software it is built from is built correctly. Our study explores the nature of software quality in the context of climate modelling. We performed an analysis of defect reports and defect fixes in several versions of leading global climate models by collecting defect data from bug tracking systems and version control repository comments. We found that the climate models all have very low defect densities compared to well-known, similarly sized open-source projects. We discuss the implications of our findings for the assessment of climate model software trustworthiness. --- paper_title: How Well Do Coupled Models Simulate Today's Climate? paper_content: Coupled climate models are sophisticated tools designed to simulate the Earth climate system and the complex interactions between its components. Currently, more than a dozen centers around the world develop climate models to enhance our understanding of climate and climate change and to support the activities of the Intergovernmental Panel on Climate Change (IPCC). However, climate models are not perfect. Our theoretical understanding of climate is still incomplete, and certain simplifying assumptions are unavoidable when building these models. This introduces biases into their simulations, which sometimes are surprisingly difficult to correct. Model imperfections have attracted criticism, with some arguing that model-based projections of climate --- paper_title: Software Testing and Verification in Climate Model Development paper_content: Over the past 30 years, most climate models have grown from relatively simple representations of a few atmospheric processes to complex multidisciplinary systems. Computer infrastructure over that period has gone from punchcard mainframes to modern parallel clusters. Model implementations have become complex, brittle, and increasingly difficult to extend and maintain. Verification processes for model implementations rely almost exclusively on some combination of detailed analyses of output from full climate simulations and system-level regression tests. Besides being costly in terms of developer time and computing resources, these testing methodologies are limited in the types of defects they can detect, isolate, and diagnose. Mitigating these weaknesses of coarse-grained testing with finer-grained unit tests has been perceived as cumbersome and counterproductive. Recent advances in commercial software tools and methodologies have led to a renaissance of systematic fine-grained testing. This opens new possibilities for testing climate-modeling-software methodologies. --- paper_title: The ASC-Alliance Projects: A Case Study of Large-Scale Parallel Scientific Code Development paper_content: Computational scientists face many challenges when developing software that runs on large-scale parallel machines. However, software-engineering researchers haven't studied their software development processes in much detail. To better understand the nature of software development in this context, the authors examined five large-scale computational science software projects operated at the five ASC-Alliance centers. --- paper_title: Task-directed software inspection paper_content: Software inspection is recognized as an effective verification technique. Despite this fact, the use of inspection is surprisingly low. This paper describes a new inspection technique, called task-directed inspection (TDI), and a light-weight process, that were used to introduce inspection in a particular industrial environment. This environment had no history of inspections, was resistant to the idea of inspection, but had a situation where confidence in a safety-related legacy suite of software had to be increased. The characteristics of TDI are explored. They give rise to a variety of approaches that may encourage more widespread use of inspections. This paper examines the industrial exercise as a case study, with the intent that it be useful in other situations that share characteristics with the situation described. --- paper_title: Scientific Software Testing: Analysis with Four Dimensions paper_content: By analyzing our testing exercise through the four dimensions of context, goals, techniques, and adequacy, we developed a better understanding of how to effectively test a piece of scientific software. Once we considered the scientist-tester as part of the testing system, the exercise evolved in a way that made use of and increased his knowledge of the software. One result was an approach to software assessment that combines inspection with code execution. An other result was the suppression of process-driven testing in favor of goal centric approaches. The combination of software engineer working with scientist was success ful in this case. The software engineer brings a toolkit of ideas, and the scientist chooses and fashions the tools into some thing that works for a specific situation. Unlike many other types of software systems, scientific software includes the scientist as an integral part of the system. The tools that support the scientist must include the scientist's knowledge and goals in their design. This represents a different way of considering the juxtaposition of software engineering with scientific software development. --- paper_title: The Chimera of Software Quality paper_content: Despite years of computing progress, today's systems experience spectacular and all-too-frequent crashes, while many enormously expensive projects fail to produce anything useful. Of equal importance, and potentially more damaging, are the misleading smaller defects we tend to miss. From time to time, we must remind ourselves that the underlying quality of the software that our results and progress increasingly depend on will likely be flawed and even more dependent on independent corroboration than the science itself. Many scientific results are corrupted, perhaps fatally so, by undiscovered mistakes in the software used to calculate and present those results. --- paper_title: The T-experiments: errors in scientific software paper_content: Extensive tests showed that many software codes widely used in science and engineering are not as accurate as we would like to think. It is argued that better software engineering practices would help solve this problem, but realizing that the problem exists is an important first step. --- paper_title: What every computer scientist should know about floating-point arithmetic paper_content: Floating-point arithmetic is considered as esoteric subject by many people. This is rather surprising, because floating-point is ubiquitous in computer systems: Almost every language has a floating-point datatype; computers from PCs to supercomputers have floating-point accelerators; most compilers will be called upon to compile floating-point algorithms from time to time; and virtually every operating system must respond to floating-point exceptions such as overflow. This paper presents a tutorial on the aspects of floating-point that have a direct impact on designers of computer systems. It begins with background on floating-point representation and rounding error, continues with a discussion of the IEEE floating point standard, and concludes with examples of how computer system builders can better support floating point. --- paper_title: Dealing with Risk in Scientific Software Development paper_content: The development of scientific software involves risk in the underlying theory, its implementation, and its use. Through a series of interviews, the authors explored how research scientists at two Canadian universities developed their software. These interviews indicated that the scientists used a set of strategies to address risk. They also suggested where the software engineering community could perform research focused on specific problems faced by scientific software developers. --- paper_title: A Component Architecture for High-Performance Scientific Computing paper_content: The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance coputing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed coputing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal ovehead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including cobustion research, global climate simulation, and computtional chemistry. --- paper_title: A Component Architecture for High-Performance Scientific Computing paper_content: The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance coputing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed coputing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal ovehead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including cobustion research, global climate simulation, and computtional chemistry. --- paper_title: Object construction and destruction design patterns in Fortran 2003 paper_content: Abstract This paper presents object-oriented design patterns in the context of object construction and destruction. The examples leverage the newly supported object-oriented features of Fortran 2003. We describe from the client perspective two patterns articulated by Gamma et al. [1]: abstract factory and factory method. We also describe from the implementation perspective one new pattern: the object pattern. We apply the Gamma et al. patterns to solve a partial differential equation, and we discuss applying the new pattern to a quantum vortex dynamics code. Finally, we address consequences and describe the use of the patterns in two open-source software projects: ForTrilinos and Morfeus. --- paper_title: Patterns in scientific software: an introduction paper_content: Patterns are well understood methodology for object-oriented software architecture, especially for business applications. Scientific programmers have generally avoided object-oriented approaches because of their heavy computational overhead, but the benefits of using patterns for scientific problems can outweigh their costs. This article introduces the concept of object oriented software patterns and discusses how they can be applied to scientific software problems. After a brief explanation of what patterns are and why they can be relevant to scientific software, the author explores the application of patterns to dynamic-systems simulation, such as molecular dynamics, and identifies four design patterns that emerge in modeling such systems. To illustrate how to reuse a general pattern for a specific problem, he applies one of the dynamic simulation patterns to the different problem of hydrodynamic chemistry tracers. --- paper_title: Object-oriented design patterns in Fortran 90/95: mazev1, mazev2 and mazev3 paper_content: This paper discusses the concept, application, and usefulness of software design patterns for scientific programming in Fortran 90/95. An example from the discipline of object-oriented design patterns, that of a game based on navigation through a maze, is used to describe how some important patterns can be implemented in Fortran 90/95 and how the progressive introduction of design patterns can usefully restructure Fortran software as it evolves. This example is complemented by a discussion of how design patterns have been used in a real-life simulation of Particle-in-Cell plasma physics. The following patterns are mentioned in this paper: Factory, Strategy, Template, Abstract Factory and Facade. --- paper_title: Design Patterns for e-Science paper_content: This is a book about a code and about coding. The code is a case study which has been used to teachcourses in e-Science atthe Australian NationalUniv- sity since 2001. Students learn advanced programming skills and techniques TM in the Java language. Above all, they learn to apply useful object-oriented design patterns as they progressively refactor and enhance the software. We think our case study,EScope, is as close to real life as you can get! It is a smaller version of a networked, graphical, waveform browser which is used in the control rooms of fusion energy experiments around the world. It is quintessential e-Science in the sense of e-Science being computer science and information technology in the service of science. It is not, speci?cally, Grid-enabled, but we develop it in a way that will facilitate its deployment onto the Grid. The standard version ofEScope interfaces with a specialised database for waveforms, and related data, known asMDSplus. On the acc- panying CD, we have provided you with software which will enable you to installMDSplus,EScope and sample data ?les onto Windows or Linux c- puters. There is much additional software including many versions of the case study as it gets built up and progressively refactored using design patterns. There will be a home web-site for this book which will contain up-to-date information about the software and other aspects of the case study. --- paper_title: Design patterns and Fortran 90/95 paper_content: In the literature on object oriented programming (OO), design patterns are a very popular subject. Apart from any hype that may be connected to the concept, they are supposed to help you look at a programming problem and come up with a robust design for its solution. The reason design patterns work is not that they are something new, but instead that they are time-honoured, well-developed solutions.I will not repeat the story about architectural design patterns and Christopher Alexander who recognised their potential. Instead I will try to explain how these (software) design patterns can be used in setting up Fortran 90/95 programs, despite the "fact" that Fortran 90/95 lacks certain OO features, such as inheritance and polymorphism. It may not be stressed in all OO literature, but design patterns help you find solutions that do not necessarily involve inheritance or polymorphism (cf. Shalloway and Trott, 2002).Design patterns come by fancy names such as the Adapter pattern or the Decorations pattern and explaining what they are and how to use them is best done via a few examples. --- paper_title: Software Engineering for Scientists paper_content: At two recent workshops, participants discussed the juxtaposition of software engineering with the development of scientific computational software. --- paper_title: Refactoring to Patterns paper_content: In 1994, Design Patterns changed the landscape of object-oriented development by introducing classic solutions to recurring design problems. In 1999, Refactoring revolutionized design by introducing an effective process for improving code. With the highly anticipated Refactoring to Patterns, Joshua Kerievsky has changed our approach to design by forever uniting patterns with the evolutionary process of refactoring.This book introduces the theory and practice of pattern-directed refactorings: sequences of low-level refactorings that allow designers to safely move designs to, towards, or away from pattern implementations. Using code from real-world projects, Kerievsky documents the thinking and steps underlying over two dozen pattern-based design transformations. Along the way he offers insights into pattern differences and how to implement patterns in the simplest possible ways.Coverage includes: A catalog of twenty-seven pattern-directed refactorings, featuring real-world code examples Descriptions of twelve design smells that indicate the need for this book's refactorings General information and new insights about patterns and refactoring Detailed implementation mechanics: how low-level refactorings are combined to implement high-level patterns Multiple ways to implement the same pattern-and when to use each Practical ways to get started even if you have little experience with patterns or refactoringRefactoring to Patterns reflects three years of refinement and the insights of more than sixty software engineering thought leaders in the global patterns, refactoring, and agile development communities. Whether you're focused on legacy or “greenfield” development, this book will make you a better software designer by helping you learn how to make important design changes safely and effectively. --- paper_title: Developing scientific applications using Generative Programming paper_content: Scientific applications usually involve large number of distributed and dynamic resources and huge datasets. A mechanism like checkpointing is essential to make these applications resilient to failures. Using checkpointing as an example, this paper presents an approach for integrating the latest software engineering techniques with the development of scientific software. Generative programming is used in this research to achieve the goals of non-intrusive reengineering of existing applications to insert the checkpointing mechanism and to decouple the checkpointing-specifications from its actual implementation. The end-user specifies the checkpointing details at a higher level of abstraction, using which the necessary code is generated and woven into the application. The lessons learned and the implementation approach presented in this paper can be applied to the development of scientific applications in general. The paper also demonstrates that the generated code does not introduce any inaccuracies and its performance is comparable to the manually inserted code. --- paper_title: Requirements Analysis for Engineering Computation: A Systematic Approach for Improving Reliability paper_content: This paper argues that the reliability of engineering computation can be significantly improved by adopting software engineering methodologies for requirements analysis and specification. The argument centers around the fact that the only way to judge the reliability of a system is by comparison to a specification of the requirements. This paper also points to methods for documenting the requirements. In particular, a requirements template is proposed for specifying engineering computation software. To make the mathematical specification easily understandable by all stakeholders, the requirements documentation employs the technique of using tabular expressions. To clarify the presentation, this paper includes a case study of the documentation for a system for analyzing statically determinant beams. --- paper_title: Electronic Documents Give Reproducible Research a New Meaning paper_content: A revolution in education and technology transfer follows from the marriage of word processing and software command scripts. In this marriage an a.uthor attaches to every figure caption a pushbutton or a name tag usable t,o recalculate the figure from all its data, parameters, and programs. This provides a concrete definition of reproducibility in computationally oriented research. Experience at the Stanford Exploration Project shows that preparing such electronic documents is lit,tle effort beyond our customary report writing; mainly, we need to file everything in a systematic way. --- paper_title: myExperiment: social networking for workflow-using e-scientists paper_content: We present the Taverna workflow workbench and argue that scientific workflow environments need a rich ecosystem of tools that support the scientists. experimental lifecycle. Workflows are scientific objects in their own right, to be exchanged and reused. myExperiment is a new initiative to create a social networking environment for workflow workers. We present the motivation for myExperiment and sketch the proposed capabilities and challenges. We argue that actively engaging with a scientist's needs, fears and reward incentives is crucial for success. --- paper_title: A Universal Identifier for Computational Results paper_content: Abstract We present a discipline for verifiable computational scienti_c research. Our discipline revolves around three simple new concepts — verifiable computational result (VCR), VCR repository and Verifiable Result Identifier (VRI). These are web- and cloud-computing oriented concepts, which exploit today's web infrastructure to achieve standard, simple and automatic reproducibility in computational scientific research. The VCR discipline requires very slight modifications to the way researchers already conduct their computational research and authoring, and to the way publishers manage their content. In return, the discipline marks a significant step towards delivering on the long-anticipated promises of making scientific computation truly reproducible. A researcher practicing this discipline in everyday work produces computational scripts and word processor files that look very much like those they already produce today, but in which a few lines change very subtly and naturally. Those scripts produce a stream of verifiable results, which are the same tables, figures, charts and datasets the researcher traditionally would have produced, but which are watermarked for permanent identification by a VRI, and are automatically and permanently stored in a VCR repository. In a scientific community practicing Verifiable Computational Research, exchange of both ideas and data involves exchanging result identifiers—VRIs—rather than exchanging files. These identifiers are controlled, trusted and automatically generated strings that point to publicly available result as it was originally created by the computational process itself. When a verifiable result is included in a publication, its identifier can be used by any reader with a web browser to locate, browse and, where appropriate, re-execute the computation that produced the result. Journal readers can therefore scrutinize, dispute, understand and eventually trust these computational results, all to an extent impossible through textual explanations that constitute the core of scientific publications to date. In addition, the result identi_er can be used by subsequent computations to locate and retrieve both the published result (in graphical or numerical form) and the original datasets used by its generating computation. Colleagues can thus cite and import data into their own computations, just as traditional publications allow them to cite and import ideas. We describe an existing software implementation of the Verifiable Computational Research discipline, and argue that it solves many of the crucial problems commonly facing computer-based and computer-aided research in various scientific fields. Our system is secure, naturally adapted to large-scale and cloud computations and to modern massive data analysis, yet places effectively no additional workload on either the researcher or the publisher. --- paper_title: A literate experimentation manifesto paper_content: This paper proposes a new approach to experimental computer systems research, which we call Literate Experimentation. Conventionally, experimental procedure and writeup are divided into distinct phases: i.e. setup (the method), data collection (the results) and analysis (the evaluation of the results). Our concept of a literate experiment is to have a single, rich, human-generated, text-based description of a particular experiment, from which can be automatically derived: (1) a summary of the experimental setup to include in the paper; (2) a sequence of executable commands to setup a computer platform ready to perform the actual experiment; (3) the experiment itself, executed on this appropriately configured platform; and, (4) a means of generating results tables and graphs from the experimental output, ready for inclusion in the paper. Our Literate Experimentation style has largely been inspired by Knuth's Literate Programming philosophy. Effectively, a literate experiment is a small step towards the executable paper panacea. In this work, we argue that a literate experimentation approach makes it easier to produce rigorous experimental evaluation papers. We suggest that such papers are more likely to be accepted for publication, due to (a) the imposed uniformity of structure, and (b) the assurance that experimental results are easily reproducible. We present a case study of a prototype literate experiment involving memory management in Jikes RVM. --- paper_title: Taverna , lessons in creating a workflow environment for the life sciences paper_content: Life sciences research is based on individuals, often with diverse skills, assembled into research groups. These groups use their specialist expertise to address scientific problems. The in silico experiments undertaken by these research groups can be represented as workflows involving the co-ordinated use of analysis programs and information repositories that may be globally distributed. With regards to Grid computing, the requirements relate to the sharing of analysis and information resources rather than sharing computational power. The myGrid project has developed the Taverna Workbench for the composition and execution of workflows for the life sciences community. This experience paper describes lessons learnt during the development of Taverna. A common theme is the importance of understanding how workflows fit into the scientists' experimental context. The lessons reflect an evolving understanding of life scientists' requirements on a workflow environment, which is relevant to other areas of data intensive and exploratory science. --- paper_title: The Collage Authoring Environment paper_content: Abstract The Collage Authoring Environment is a software infrastructure which enables domain scientists to collaboratively develop and publish their work in the form of executable papers. It corresponds to the recent developments in both e-Science and computational technologies which call for a novel publishing paradigm. As part of this paradigm, static content (such as traditional scientific publications) should be supplemented with elements of interactivity, enabling reviewers and readers to reexamine the reported results by executing parts of the software on which such results are based as well as access primary scientific data. Taking into account the presented rationale we propose an environment which enables authors to seamlessly embed chunks of executable code (called assets) into scientific publications and allow repeated execution of such assets on underlying computing and data storage resources, as required by scientists who wish to build upon the presented results. The Collage Authoring Environment can be deployed on arbitrary resources, including those belonging to high performance computing centers, scientific e-Infrastructures and resources contributed by the scientists themselves. The environment provides access to static content, primary datasets (where exposed by authors) and executable assets. Execution features are provided by a dedicated engine (called the Collage Server) and embedded into an interactive view delivered to readers, resembling a traditional research publication but interactive and collaborative in its scope. Along with a textual description of the Collage environment the authors also present a prototype implementation, which supports the features described in this paper. The functionality of this prototype is discussed along with theoretical assumptions underpinning the proposed system. --- paper_title: Automated Capture of Experiment Context for Easier Reproducibility in Computational Research paper_content: Published scientific research that relies on numerical computations is too often not reproducible. For computational research to become consistently and reliably reproducible, the process must become easier to achieve, as part of day-to-day research. A combination of best practices and automated tools can make it easier to create reproducible research. --- paper_title: SHARE: a web portal for creating and sharing executable research papers paper_content: Abstract This paper describes how SHARE (Sharing Hosted Autonomous Research Environments) satisfies the criteria of the Elsevier 2011 Executable Paper Grand Challenge. This challenge aims at disseminating the use of systems that provide reviewers and fellow scientists a convenient way to reproduce computational results of research papers. This can involve among others the calculation of a number, the plotting of a diagram, the automatic proof of a theorem or the interactive transformation of various inputs into a complex output document. Besides reproducing the literate results, readers of an executable paper should also be able to explore the result space by entering different input parameters than the ones reported in the original text. SHARE is a web portal that enables academics to create, share, and access remote virtual machines that can be cited from research papers. By deploying in SHARE a copy of the required operating system as well as all the relevant software and data, authors can make a conventional paper fully reproducible and interactive. Shared virtual machines can also contain the original paper text— when desirable even with embedded computations. This paper shows the concrete potential of SHARE-based articles by means of an example virtual machine that is based on a conventional research article published by Elsevier recently. More generally, it demonstrates how SHARE has supported the publication workflow of a journal special issue and various workshop proceedings. Finally, it clarifies how the SHARE architecture supports among others the Elsevier challenge's licensing and scalability requirements without domain specific restrictions. --- paper_title: Developing open source scientific practice paper_content: 3 Routine practice 8 3.1 Version control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 Execution automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.4 Readability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.5 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 --- paper_title: Literate programming in quantum chemistry: A simple example paper_content: Literate programming methods have not been widely used in computational molecular structure theory. We argue that significant advantages would result from the use of literate programming methods in the computational molecular sciences and, indeed, in computational science, in general. Our arguments are illustrated by a simple example of literate programming methods in ab initio electronic structure theory is described. We distinguish text-embedded code, or literate programs, from code-embedded text and suggest that the latter may form a more useful vehicle for publication than literate programs. © 2005 Wiley Periodicals, Inc. Int J Quantum Chem, 2005 --- paper_title: Beyond accuracy: what data quality means to data consumers paper_content: Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework. --- paper_title: The Earth System Grid: Supporting the Next Generation of Climate Modeling Research paper_content: Understanding the Earth's climate system and how it might be changing is a preeminent scientific challenge. Global climate models are used to simulate past, present, and future climates, and experiments are executed continuously on an array of distributed supercomputers. The resulting data archive, spread over several sites, currently contains upwards of 100 TB of simulation data and is growing rapidly. Looking toward mid-decade and beyond, we must anticipate and prepare for distributed climate research data holdings of many petabytes. The Earth System Grid (ESG) is a collaborative interdisciplinary project aimed at addressing the challenge of enabling management, discovery, access, and analysis of these critically important datasets in a distributed and heterogeneous computational environment. The problem is fundamentally a Grid problem. Building upon the Globus toolkit and a variety of other technologies, ESG is developing an environment that addresses authentication, authorization for data access, large-scale data transport and management, services and abstractions for high-performance remote data access, mechanisms for scalable data replication, cataloging with rich semantic and syntactic information, data discovery, distributed monitoring, and Web-based portals for using the system. --- paper_title: Data Curation at Scale: The Data Tamer System paper_content: Data curation is the act of discovering a data source(s) of interest, cleaning and transforming the new data, semantically integrating it with other local data sources, and deduplicating the resulting composite. There has been much research on the various components of curation (especially data integration and deduplication). However, there has been little work on collecting all of the curation components into an integrated end-to-end system. In addition, most of the previous work will not scale to the sizes of problems that we are nding in the eld. For example, one web aggregator requires the curation of 80,000 URLs and a second biotech company has the problem of curating 8000 spreadsheets. At this scale, data curation cannot be a manual (human) eort, but must entail machine learning approaches with a human assist only when necessary. This paper describes Data Tamer, an end-to-end curation system we have built at M.I.T. Brandeis, and Qatar Computing Research Institute (QCRI). It expects as input a sequence of data sources to add to a composite being constructed over time. A new source is subjected to machine learning algorithms to perform attribute identication, grouping of attributes into tables, transformation of incoming data and deduplication. When necessary, a human can be asked for guidance. Also, Data Tamer includes a data visualization component so a human can examine a data source at will and specify manual transformations. We have run Data Tamer on three real world enterprise curation problems, and it has been shown to lower curation cost by about 90%, relative to the currently deployed production software. --- paper_title: A software architecture-based framework for highly distributed and data intensive scientific applications paper_content: Modern scientific research is increasingly conducted by virtual communities of scientists distributed around the world. The data volumes created by these communities are extremely large, and growing rapidly. The management of the resulting highly distributed, virtual data systems is a complex task, characterized by a number of formidable technical challenges, many of which are of a software engineering nature. In this paper we describe our experience over the past seven years in constructing and deploying OODT, a software framework that supports large, distributed, virtual scientific communities. We outline the key software engineering challenges that we faced, and addressed, along the way. We argue that a major contributor to the success of OODT was its explicit focus on software architecture. We describe several large-scale, real-world deployments of OODT, and the manner in which OODT helped us to address the domain-specific challenges induced by each deployment. --- paper_title: Verification of Computer Simulation Models paper_content: The problem of validating computer simulation models of industrial systems has received only limited attention in the management science literature. The purpose of this paper is to consider the problem of validating computer models in the light of contemporary thought in the fields of philosophy of science, economic theory, and statistics. In order to achieve this goal we have attempted to gather together and present some of the ideas of scientific philosophers, economists, statisticians, and practitioners in the field of simulation which are relevant to the problem of verifying simulation models. We have paid particular attention to the writings of economists who have been concerned with testing the validity of economic models. Among the questions which we shall consider are included: What does it mean to verify a computer model of an industrial system? Are there any differences between the verification of computer models and the verification of other types of models? If so, what are some of these differences? Also considered are a number of measures and techniques for testing the "goodness of fit" of time series generated by computer models to observed historical series. --- paper_title: Automated testing of refactoring engines paper_content: Refactorings are behavior-preserving program transformations that improve the design of a program. Refactoring engines are tools that automate the application of refactorings: first the user chooses a refactoring to apply, then the engine checks if the transformation is safe, and if so, transforms the program. Refactoring engines are a key component of modern IDEs, and programmers rely on them to perform refactorings. A bug in the refactoring engine can have severe consequences as it can erroneously change large bodies of source code. We present a technique for automated testing of refactoring engines. Test inputs for refactoring engines are programs. The core of our technique is a framework for iterative generation of structurally complex test inputs. We instantiate the framework to generate abstract syntax trees that represent Java programs. We also create several kinds of oracles to automatically check that the refactoring engine transformed the generated program correctly. We have applied our technique to testing Eclipse and NetBeans, two popular open-source IDEs for Java, and we have exposed 21 new bugs in Eclipse and 24 new bugs in NetBeans. --- paper_title: The Recomputation Manifesto paper_content: Replication of scientific experiments is critical to the advance of science. Unfortunately, the discipline of Computer Science has never treated replication seriously, even though computers are very good at doing the same thing over and over again. Not only are experiments rarely replicated, they are rarely even replicable in a meaningful way. Scientists are being encouraged to make their source code available, but this is only a small step. Even in the happy event that source code can be built and run successfully, running code is a long way away from being able to replicate the experiment that code was used for. I propose that the discipline of Computer Science must embrace replication of experiments as standard practice. I propose that the only credible technique to make experiments truly replicable is to provide copies of virtual machines in which the experiments are validated to run. I propose that tools and repositories should be made available to make this happen. I propose to be one of those who makes it happen. --- paper_title: Managing Chaos: Lessons Learned Developing Software in the Life Sciences paper_content: In the life sciences, the need to balance the costs and benefits of introducing software processes into a research environment presents a distinct set of challenges due to the cultural disconnect between life sciences research and software engineering. The Institute for Systems Biology's research informatics team has studied these challenges and developed a software process to address them. --- paper_title: A Software Chasm: Software Engineering and Scientific Computing paper_content: Some time ago, a chasm opened between the scientific-computing community and the software engineering community. Originally, computing meant scientific computing. Today, science and engineering applications are at the heart of software systems such as environmental monitoring systems, rocket guidance systems, safety studies for nuclear stations, and fuel injection systems. Failures of such health-, mission-, or safety-related systems have served as examples to promote the use of software engineering best practices. Yet, the bulk of the software engineering community's research is on anything but scientific-application software. This chasm has many possible causes. In this article, we look at the impact of one particular contributor in industry. --- paper_title: A vision towards Scientific Communication Infrastructures On bridging the realms of Research Digital Libraries and Scientific Data Centers paper_content: The two pillars of the modern scientific communication are Data Centers and Research Digital Libraries (RDLs), whose technologies and admin staff support researchers at storing, curating, sharing, and discovering the data and the publications they produce. Being realized to maintain and give access to the results of complementary phases of the scientific research process, such systems are poorly integrated with one another and generally do not rely on the strengths of the other. Today, such a gap hampers achieving the objectives of the modern scientific communication, that is, publishing, interlinking, and discovery of all outcomes of the research process, from the experimental and observational datasets to the final paper. In this work, we envision that instrumental to bridge the gap is the construction of "Scientific Communication Infrastructures". The main goal of these infrastructures is to facilitate interoperability between Data Centers and RDLs and to provide services that simplify the implementation of the large variety of modern scientific communication patterns. --- paper_title: SHARE: a web portal for creating and sharing executable research papers paper_content: Abstract This paper describes how SHARE (Sharing Hosted Autonomous Research Environments) satisfies the criteria of the Elsevier 2011 Executable Paper Grand Challenge. This challenge aims at disseminating the use of systems that provide reviewers and fellow scientists a convenient way to reproduce computational results of research papers. This can involve among others the calculation of a number, the plotting of a diagram, the automatic proof of a theorem or the interactive transformation of various inputs into a complex output document. Besides reproducing the literate results, readers of an executable paper should also be able to explore the result space by entering different input parameters than the ones reported in the original text. SHARE is a web portal that enables academics to create, share, and access remote virtual machines that can be cited from research papers. By deploying in SHARE a copy of the required operating system as well as all the relevant software and data, authors can make a conventional paper fully reproducible and interactive. Shared virtual machines can also contain the original paper text— when desirable even with embedded computations. This paper shows the concrete potential of SHARE-based articles by means of an example virtual machine that is based on a conventional research article published by Elsevier recently. More generally, it demonstrates how SHARE has supported the publication workflow of a journal special issue and various workshop proceedings. Finally, it clarifies how the SHARE architecture supports among others the Elsevier challenge's licensing and scalability requirements without domain specific restrictions. --- paper_title: Reproducible research for scientific computing: Tools and strategies for changing the culture paper_content: This article considers the obstacles involved in creating reproducible computational research as well as some efforts and approaches to overcome them. ---	Title: Bridging the Chasm: A Survey of Software Engineering Practice in Scientific Programming Section 1: INTRODUCTION Description 1: This section introduces the indispensable role of software in scientific research and outlines the benefits and challenges of its use. Section 2: CASE STUDIES OF SOFTWARE PROCESSES Description 2: This section reviews various case studies exploring the relationship between scientific research and software development practices. Section 3: Scientific Programming in Practice Description 3: This section provides an overview of published case studies focusing on general practices in scientific programming. Section 4: Agile Methods Description 4: Exploration of the use and adaptation of agile methods and practices in scientific programming teams is discussed in this section. Section 5: Project Team Evolution and Software Documentation Description 5: This section discusses the evolutionary nature of scientific software development teams and the importance of documentation. Section 6: Best Practices Description 6: Proposals for best practices in scientific programming based on experiences and observations in the domain are summarized in this section. Section 7: QUALITY ASSURANCE PRACTICES Description 7: This section addresses the importance of quality assurance practices in scientific programming and explores various techniques such as testing and inspections. Section 8: Testing Description 8: This section delves into the challenges and methodologies of testing scientific software to ensure reliability and accuracy. Section 9: Inspections Description 9: The use of inspections as a quality assurance technique in scientific programming, including methodologies and case studies, is discussed here. Section 10: Continuous Integration Description 10: Recent practices of continuous integration employed in scientific programming efforts are reviewed in this section. Section 11: Formal Methods Description 11: This section covers the application of formal methods in scientific programming and their benefits in ensuring software correctness. Section 12: DESIGN, EVOLUTION, AND MAINTENANCE Description 12: This section discusses the management and maintenance of long-term scientific software projects, highlighting component architectures, design patterns, and refactoring techniques. Section 13: Component Architectures Description 13: The integration and structuring of heterogeneous component architectures in scientific computing are outlined in this section. Section 14: Design Patterns Description 14: Exploration of applying software design patterns to scientific programming to manage complexity is provided in this section. Section 15: Refactoring and Reengineering Techniques Description 15: Techniques for refactoring and reengineering legacy scientific software to enhance maintainability are discussed in this section. Section 16: Workflow Management and Executable Research Papers Description 16: This section reviews tools and approaches for managing scientific workflows and the role of executable research papers in enhancing reproducibility. Section 17: DATA QUALITY Description 17: The challenges and methods for ensuring data quality in scientific research are discussed in this section. Section 18: CONCLUSIONS Description 18: The concluding section summarizes the challenges and unresolved issues in integrating software engineering practices into scientific programming and proposes future directions for research and practice.
Trust Management Survey	10	--- paper_title: Human Experiments in Trust Dynamics paper_content: In the literature, the validity of theories or models for trust is usually based on intuition and common sense. Theories and models are not often verified experimentally. The research reported here contributes results of experiments on the dynamics of trust over time depending on positive or negative experiences. In previous research a number of dynamic properties for such trust dynamics were identified, but not verified empirically. As a continuation of this work, now these properties have been verified in an experimental setting. The outcomes of the experiment (involving a substantial number of 238 subjects) are discussed and related to the previously formulated dynamic properties. --- paper_title: A computational model of trust and reputation paper_content: Despite their many advantages, e-businesses lag behind brick and mortar businesses in several fundamental respects. This paper concerns one of these: relationships based on trust and reputation. Recent studies on simple reputation systems for e-Businesses such as eBay have pointed to the importance of such rating systems for deterring moral hazard and encouraging trusting interactions. However, despite numerous studies on trust and reputation systems, few have taken studies across disciplines to provide an integrated account of these concepts and their relationships. This paper first surveys existing literatures on trust, reputation and a related concept: reciprocity. Based on sociological and biological understandings of these concepts, a computational model is proposed. This model can be implemented in a real system to consistently calculate agents' trust and reputation scores. --- paper_title: Managing Internet-Mediated Community Trust Relations paper_content: This paper advances a framework for analysing and managing community trust relations. The framework is based upon an analysis of the evidence for different forms of trust in community relations and of the experiential dimensions of community relations that promote trust levels. It features a community trust cycle, a trust compact and an experience management matrix which collectively support managers in addressing the relational dynamics of community trust relations. We show that this framework can be used to analyse relations that are mediated by ICT and that the framework supports the identification of opportunities to better promote ICT-mediated trust development and promulgation. --- paper_title: Towards Dynamic Security Perimeters for Virtual Collaborative Networks paper_content: Rapid technological advancements capitalising on the convergence of information (middleware) and communication (network) technologies now enable open application-to-application communication and bring about the prospect of ad hoc integration of systems across organisational boundaries to support collaborations that may last for a single transaction or evolve dynamically over a longer period. Architectures for managing networks of collaborating peers in such environments face new security and trust management challenges. In this paper we will introduce the basic elements of such an architecture emphasising trust establishment, secure collaboration, distributed monitoring and performance assessment issues. --- paper_title: Engineering Trust Based Collaborations in a Global Computing Environment paper_content: Trust management seems a promising approach for dealing with security concerns in collaborative applications in a global computing environment. However, the characteristics of this environment require a move from reliable identification to mechanisms for the recognition of entities. Furthermore, they require explicit reasoning about the risks of interactions, and a notion of uncertainty in the underlying trust model. From our experience of engineering collaborative applications in such an environment, we found that the relationship between trust and risk is a fundamental issue. In this paper, as an initial step towards an engineering approach for the development of trust based collaborative applications, we focus on the relationship between trust and risk, and explore alternative views of this relationship. We also exemplify how particular views can be exploited in two particular application scenarios. This paper builds upon our previous work in developing a general model for trust based collaborations. --- paper_title: A computational model of trust and reputation paper_content: Despite their many advantages, e-businesses lag behind brick and mortar businesses in several fundamental respects. This paper concerns one of these: relationships based on trust and reputation. Recent studies on simple reputation systems for e-Businesses such as eBay have pointed to the importance of such rating systems for deterring moral hazard and encouraging trusting interactions. However, despite numerous studies on trust and reputation systems, few have taken studies across disciplines to provide an integrated account of these concepts and their relationships. This paper first surveys existing literatures on trust, reputation and a related concept: reciprocity. Based on sociological and biological understandings of these concepts, a computational model is proposed. This model can be implemented in a real system to consistently calculate agents' trust and reputation scores. --- paper_title: Hardware security appliances for trust paper_content: This paper looks at the trust relationships that exist within an outsourcing scenario finding that whilst some of the trust relationships are clear other implicit trust relationships need exposing. These implicit trust relationships are often a result of information supplied for the main explicit task for which an entity is being trusted. The use of hardware security appliance based services is proposed allowing trust to be dissipated over multiple parties whilst retaining efficient execution. Such an approach helps mitigate these implicit trust relationships by increasing the control and transparency given to the trustor. --- paper_title: Analysing the Relationship between Risk and Trust paper_content: Among the various human factors impinging upon making a decision in an uncertain environment, risk and trust are surely crucial ones. Several models for trust have been proposed in the literature but few explicitly take risk into account. This paper analyses the relationship between the two concepts by first looking at how a decision is made to enter into a transaction based on the risk information. We then draw a model of the invested fraction of the capital function of a decision surface. We finally define a model of trust composed of a reliability trust as the probability of transaction success and a decision trust derived from the decision surface. --- paper_title: Trust Development and Management in Virtual Communities paper_content: The web is increasingly used as a platform and an enabler for the existence of virtual communities. However, there is evidence that the growth and adoption of these communities is being held back by many barriers- including that of trust development and management. This paper discusses the potential benefits and barriers to the introduction of trust development and management in virtual communities. Based on the analysis of the barriers and benefits of trust development and management, mechanisms for supporting its development and management is proposed and presented. Ideas for further research are presented and discussed. The paper is based on ongoing research and is part of a research bid towards the introduction of a trust development and management framework to support the creation of trusted virtual communities. --- paper_title: Modeling Controls for Dynamic Value Exchanges in Virtual Organizations paper_content: The e 3 -value modeling tool was developed for the design of a value proposition for virtual organizations. However, it is less suitable for designing the control structure of the virtual organization. We show how e 3 -value can be extended using legal concepts such as ownership, possession, usufruct and license. We also introduce value object transfer diagrams that show the transfers of value objects graphically and that can be used for elicitation of the required control mechanisms in order for the virtual organization to function properly and with a level of risk that is acceptable to all parties in the virtual organization. --- paper_title: Designing and Evaluating E-Business Models paper_content: This article presents an e-business modeling approach that combines the rigorous approach of IT systems analysis with an economic value perspective from business sciences. --- paper_title: Trust Propagation in Small Worlds paper_content: The possibility of a massive, networked infrastructure of diverse entities partaking in collaborative applications with each other increases more and more with the proliferation of mobile devices and the development of ad hoc networking technologies. In this context, traditional security measures do not scale well. We aim to develop trust-based security mechanisms using small world concepts to optimise formation and propagation of trust amongst entities in these vast networks. In this regard, we surmise that in a very large mobile ad hoc network, trust, risk, and recommendations can be propagated through relatively short paths connecting entities. Our work describes the design of trust-formation and risk-assessment systems, as well as that of an entity recognition scheme, within the context of the small world network topology. --- paper_title: KAoS: A Policy and Domain Services Framework for Grid Computing and Semantic Web Services paper_content: In this article we introduce KAoS, a policy and domain services framework based on W3C’s OWL ontology language. KAoS was developed in response to the challenges presented by emerging semantic application requirements for infrastructure, especially in the area of security and trust management. The KAoS architecture, ontologies, policy representation, management and disclosure mechanisms are described. KAoS enables the specification and enforcement of both authorization and obligation policies. The use of ontologies as a source of policy vocabulary enables its extensibility. KAoS has been adapted for use in several applications and deployment platforms. We briefly describe its integration with the Globus Grid Computing environment. --- paper_title: Towards Dynamic Security Perimeters for Virtual Collaborative Networks paper_content: Rapid technological advancements capitalising on the convergence of information (middleware) and communication (network) technologies now enable open application-to-application communication and bring about the prospect of ad hoc integration of systems across organisational boundaries to support collaborations that may last for a single transaction or evolve dynamically over a longer period. Architectures for managing networks of collaborating peers in such environments face new security and trust management challenges. In this paper we will introduce the basic elements of such an architecture emphasising trust establishment, secure collaboration, distributed monitoring and performance assessment issues. --- paper_title: Decentralized trust management paper_content: We identify the trust management problem as a distinct and important component of security in network services. Aspects of the trust management problem include formulating security policies and security credentials, determining whether particular sets of credentials satisfy the relevant policies, and deferring trust to third parties. Existing systems that support security in networked applications, including X.509 and PGP, address only narrow subsets of the overall trust management problem and often do so in a manner that is appropriate to only one application. This paper presents a comprehensive approach to trust management, based on a simple language for specifying trusted actions and trust relationships. It also describes a prototype implementation of a new trust management system, called PolicyMaker, that will facilitate the development of security features in a wide range of network services. --- paper_title: Engineering Trust Based Collaborations in a Global Computing Environment paper_content: Trust management seems a promising approach for dealing with security concerns in collaborative applications in a global computing environment. However, the characteristics of this environment require a move from reliable identification to mechanisms for the recognition of entities. Furthermore, they require explicit reasoning about the risks of interactions, and a notion of uncertainty in the underlying trust model. From our experience of engineering collaborative applications in such an environment, we found that the relationship between trust and risk is a fundamental issue. In this paper, as an initial step towards an engineering approach for the development of trust based collaborative applications, we focus on the relationship between trust and risk, and explore alternative views of this relationship. We also exemplify how particular views can be exploited in two particular application scenarios. This paper builds upon our previous work in developing a general model for trust based collaborations. --- paper_title: Simulating the Effect of Reputation Systems on E-markets paper_content: Studies show that reputation systems have the potential to improve market quality. In this paper we report the results of simulating a market of trading agents that uses the beta reputation system for collecting feedback and computing agents' reputations. The simulation confirms the hypothesis that the presence of the reputation system improves the quality of the market. Among other things it also shows that a market with limited duration rather than infinite longevity of transaction feedback provides the best conditions under which agents can adapt to each others change in behaviour. --- paper_title: Automated trust negotiation paper_content: Distributed software subjects face the problem of determining one another's trustworthiness. The problem considered is managing the exchange of credentials between strangers for the purpose of property-based authentication and authorization when credentials are sensitive. An architecture for trust negotiation between client and server is presented. The notion of a trust negotiation strategy is introduced and examined with respect to an abstract model of trust negotiation. Two strategies with very different properties are defined and analyzed. A language of credential expressions is presented, with two example negotiations illustrating the two negotiation strategies. Ongoing work on policies governing credential disclosure and trust negotiation is summarized. --- paper_title: Revocation in the Privilege Calculus paper_content: We have previously presented a framework for updating privileges and creating management structures by means of authority certificates. These are used both to create access-level permissions and to delegate authority to other agents. In this paper we extend the framework to support a richer set of revocation schemes. As in the original, we present an associated calculus of privileges, encoded as a logic program, for reasoning about certificates, revocations, and the privileges they create and destroy. The discussion of revocation schemes follows an existing classification in the literature based on three separate ::: dimensions: resilience, propagation, and dominance. The first does not apply to this framework. The second is specified straightforwardly. The third can be encoded but raises a number offurther questions for future investigation. --- paper_title: Server based application level authorisation for Rotor paper_content: Delegent is an authorisation server developed to provide a single centralised policy repository for multiple applications with support for decentralised administration by means of delegation. The author investigates how to integrate Delegent with the Rotor implementation of the .NET framework and compare the features of Delegent with those of the existing application level authorisation models of .NET. He concludes that Delegent offers help for application developers and a decentralised administration model, which are not available in standard .NET, and that the .NET model is well suited to be extended to use an authorisation server. --- paper_title: Specifying and Analysing Trust for Internet Applications paper_content: The Internet is now being used for commercial, social and educational interactions, which previously relied on direct face-to-face contact in order to establish trust relationships. Thus, there is a need to be able to establish and evaluate trust relationships relying only on electronic interactions over the Internet. A trust framework for Internet applications should incorporate concepts such as experience, reputation and trusting propensity in order to specify and evaluate trust. SULTAN (Simple Universal Logic-oriented Trust Analysis Notation) is an abstract, logic-oriented notation designed to facilitate the specification and analysis of trust relationships. SULTAN seeks to address all the above issues, although this paper focuses on our initial work on trust specification and analysis. --- paper_title: The Ponder Policy Specification Language paper_content: The Ponder language provides a common means of specifying security policies that map onto various access control implementation mechanisms for firewalls, operating systems, databases and Java. It supports obligation policies that are event triggered condition-action rules for policy based management of networks and distributed systems. Ponder can also be used for security management activities such as registration of users or logging and auditing events for dealing with access to critical resources or security violations. Key concepts of the language include roles to group policies relating to a position in an organisation, relationships to define interactions between roles and management structures to define a configuration of roles and relationships pertaining to an organisational unit such as a department. These reusable composite policy specifications cater for the complexity of large enterprise information systems. Ponder is declarative, strongly-typed and object-oriented which makes the language flexible, extensible and adaptable to a wide range of management requirements. --- paper_title: Analysing the Relationship between Risk and Trust paper_content: Among the various human factors impinging upon making a decision in an uncertain environment, risk and trust are surely crucial ones. Several models for trust have been proposed in the literature but few explicitly take risk into account. This paper analyses the relationship between the two concepts by first looking at how a decision is made to enter into a transaction based on the risk information. We then draw a model of the invested fraction of the capital function of a decision surface. We finally define a model of trust composed of a reliability trust as the probability of transaction success and a decision trust derived from the decision surface. --- paper_title: Challenges for trust, fraud and deception research in multi-agent systems paper_content: Discussions at the 5th Workshop on Deception, Fraud and Trust in Agent Societies held at the 1st International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2002) centered around many important research issues1. This paper attempts to challenge researchers in the community toward future work concerning three issues inspired by the workshop's roundtable discussion: (1) distinguishing elements of an agent's behavior that influence its trustworthiness, (2) building reputation-based trust models without relying on interaction, and (3) benchmarking trust modeling algorithms. Arguments justifying the validity of each problem are presented, and benefits from their solutions are enumerated. --- paper_title: A policy language for a pervasive computing environment paper_content: We describe a policy language designed for pervasive computing applications that is based on deontic concepts and grounded in a semantic language. The pervasive computing environments under consideration are those in which people and devices are mobile and use various wireless networking technologies to discover and access services and devices in their vicinity. Such pervasive environments lend themselves to policy-based security due to their extremely dynamic nature. Using policies allows the security functionality to be modified without changing the implementation of the entities involved. However, along with being extremely dynamic, these environments also tend to span several domains and be made up of entities of varied capabilities. A policy language for environments of this sort needs to be very expressive but lightweight and easily extensible. We demonstrate the feasibility of our policy language in pervasive environments through a prototype used as part of a secure pervasive system. --- paper_title: Supporting trust in virtual communities paper_content: At any given time, the stability of a community depends on the right balance of trust and distrust. Furthermore, we face information overload, increased uncertainty and risk taking as a prominent feature of modern living. As members of society, we cope with these complexities and uncertainties by relying trust, which is the basis of all social interactions. Although a small number of trust models have been proposed for the virtual medium, we find that they are largely impractical and artificial. In this paper we provide and discuss a trust model that is grounded in real-world social trust characteristics, and based on a reputation mechanism, or word-of-mouth. Our proposed model allows agents to decide which other agents' opinions they trust more and allows agents to progressively tune their understanding of another agent's subjective recommendations. --- paper_title: Patterns of trust and policy paper_content: This paper proposes a new paradigm of trust and policy that provides a unified treatment of organizational and data system policies. Policy is the programming language of organizations and just like any other language must be formally specified or specifiable. This paper attempts to demonstrate that it is specifiable. Trust is a major component of policy. Trust is presented as a function of specific elements - identity, reputation, capability, stake and benefit. These elements are defined and presented in the form of a trust equation. The points at which trust enters into the formal definition of policy are identified. The trust equation provides a useful way do &scribe trust in general that is not circular (unlike many previous definitions). The resulting constructs can be st&iently nontechnical that both systems people and those without a technical background can understand them The availability of a common language to guide analysis of policy requirements, policy formulation and policy execution may provide a way for organizations to break out of a recurring cycle of policy failures. --- paper_title: Using trust for secure collaboration in uncertain environments paper_content: The SECURE project investigates the design of security mechanisms for pervasive computing based on trust. It addresses how entities in unfamiliar pervasive computing environments can overcome initial suspicion to provide secure collaboration. --- paper_title: Decentralized trust management paper_content: We identify the trust management problem as a distinct and important component of security in network services. Aspects of the trust management problem include formulating security policies and security credentials, determining whether particular sets of credentials satisfy the relevant policies, and deferring trust to third parties. Existing systems that support security in networked applications, including X.509 and PGP, address only narrow subsets of the overall trust management problem and often do so in a manner that is appropriate to only one application. This paper presents a comprehensive approach to trust management, based on a simple language for specifying trusted actions and trust relationships. It also describes a prototype implementation of a new trust management system, called PolicyMaker, that will facilitate the development of security features in a wide range of network services. --- paper_title: A computational model of trust and reputation paper_content: Despite their many advantages, e-businesses lag behind brick and mortar businesses in several fundamental respects. This paper concerns one of these: relationships based on trust and reputation. Recent studies on simple reputation systems for e-Businesses such as eBay have pointed to the importance of such rating systems for deterring moral hazard and encouraging trusting interactions. However, despite numerous studies on trust and reputation systems, few have taken studies across disciplines to provide an integrated account of these concepts and their relationships. This paper first surveys existing literatures on trust, reputation and a related concept: reciprocity. Based on sociological and biological understandings of these concepts, a computational model is proposed. This model can be implemented in a real system to consistently calculate agents' trust and reputation scores. --- paper_title: Automated trust negotiation paper_content: Distributed software subjects face the problem of determining one another's trustworthiness. The problem considered is managing the exchange of credentials between strangers for the purpose of property-based authentication and authorization when credentials are sensitive. An architecture for trust negotiation between client and server is presented. The notion of a trust negotiation strategy is introduced and examined with respect to an abstract model of trust negotiation. Two strategies with very different properties are defined and analyzed. A language of credential expressions is presented, with two example negotiations illustrating the two negotiation strategies. Ongoing work on policies governing credential disclosure and trust negotiation is summarized. --- paper_title: Reasoning About Trust: A Formal Logical Framework paper_content: There is no consensus about the definition of the concept of trust. In this paper formal definitions of different kinds of trust are given in the framework of modal logic. This framework also allows to define a logic for deriving consequences from a set of assumptions about trust.Trust is defined as a mental attitude of an agent with respect to some property held by another agent. These properties are systematically analysed and we propose 6 epistemic properties, 4 deontic properties and 1 dynamic property. --- paper_title: Specifying and Analysing Trust for Internet Applications paper_content: The Internet is now being used for commercial, social and educational interactions, which previously relied on direct face-to-face contact in order to establish trust relationships. Thus, there is a need to be able to establish and evaluate trust relationships relying only on electronic interactions over the Internet. A trust framework for Internet applications should incorporate concepts such as experience, reputation and trusting propensity in order to specify and evaluate trust. SULTAN (Simple Universal Logic-oriented Trust Analysis Notation) is an abstract, logic-oriented notation designed to facilitate the specification and analysis of trust relationships. SULTAN seeks to address all the above issues, although this paper focuses on our initial work on trust specification and analysis. --- paper_title: Analysing the Relationship between Risk and Trust paper_content: Among the various human factors impinging upon making a decision in an uncertain environment, risk and trust are surely crucial ones. Several models for trust have been proposed in the literature but few explicitly take risk into account. This paper analyses the relationship between the two concepts by first looking at how a decision is made to enter into a transaction based on the risk information. We then draw a model of the invested fraction of the capital function of a decision surface. We finally define a model of trust composed of a reliability trust as the probability of transaction success and a decision trust derived from the decision surface. --- paper_title: "Trust me, I'm an online vendor": towards a model of trust for e-commerce system design paper_content: Consumers' lack of trust has often been cited as a major barrier to the adoption of electronic commerce (e-commerce). To address this problem, a model of trust was developed that describes what design factors affect consumers' assessment of online vendors' trustworthiness. Six components were identified and regrouped into three categories: Prepurchase Knowledge, Interface Properties and Informational Content. This model also informs the Human-Computer Interaction (HCI) design of e-commerce systems in that its components can be taken as trust-specific high-level user requirements. --- paper_title: Automated management of inter-organisational applications paper_content: Inter-organisational applications require improved support from middleware services. This paper analyses the management requirements of multidomain applications, covering both technological domains and organisations (administrative business domains). The analysis leads to a suggested middleware solution for these needs, presenting a group of cooperative, pervasive management services that use a common, abstract management language suitable for expressing a platform-independent model of cooperation between sovereign components and contracts about selected technologies. The approach is compatible with OMG MDA (Model Driven Architecture) work. --- paper_title: Statistical trustability (conceptual work) paper_content: Trust management and trust-aware applications have gained importance in re- cent years (e.g., [1,2]). Conventional security mechanisms are unable to cover all application domains. For example, in ad hoc networks, the frequent lack of an online connection to the Internet makes routine tasks very difficult. Trust seems to be a good way to overcome such shortcomings because it does not necessarily rely on fixed centralized infrastructures [5]. --- paper_title: Simulating the Effect of Reputation Systems on E-markets paper_content: Studies show that reputation systems have the potential to improve market quality. In this paper we report the results of simulating a market of trading agents that uses the beta reputation system for collecting feedback and computing agents' reputations. The simulation confirms the hypothesis that the presence of the reputation system improves the quality of the market. Among other things it also shows that a market with limited duration rather than infinite longevity of transaction feedback provides the best conditions under which agents can adapt to each others change in behaviour. --- paper_title: A Case for Evidence-Aware Distributed Reputation Systems: Overcoming the Limitations of Plausibility Considerations paper_content: Reputation systems support trust formation in artificial societies by keeping track of the behavior of autonomous entities. In the absence of any commonly trusted entity, the reputation system has to be distributed to the autonomous entities themselves. They may cooperate by issuing recommendations of other entities’ trustworthiness. At the time being, distributed reputation systems rely on plausibility for assessing the truthfulness and consistency of such recommendations. In this paper, we point out the limitations of such plausibility considerations and present an alternative concept that is based on evidences. The concept combines the strengths of non-repudiability and distributed reputation systems. We analyze the issues that are related to the issuance and gathering of evidences. In this regard, we identify four patterns of how evidence-awareness overcomes the limitations of plausibility considerations. --- paper_title: Implementation of an Agent-Oriented Trust Management Infrastructure Based on a Hybrid PKI Model paper_content: Access control in modern computing environments is different from access control in the traditional setting of operating systems. For distributed computing systems, specification and enforcement of permissions can be based on a public key infrastructure which deals with public keys for asymmetric cryptography. Previous approaches and their implementations for applying a public key infrastructure are classified as based either on trusted authorities with licencing or on owners with delegations. We present the architecture and main features of a trust management infrastructure based on a hybrid model which unifies and extends the previous public key infrastructure approaches. The trust management infrastructure constitutes a flexible framework for experimenting with the applications of different trust models. --- paper_title: A trust matrix model for electronic commerce paper_content: We propose a so-called trust matrix model to build trust for conducting first trade transactions in electronic commerce; i.e. transactions in an electronic commerce environment between two parties that have never conducted trade transactions between them before. The trust matrix model is based on the idea that for business-to-business electronic commerce a balance has to be found between anonymous procedural trust, i.e. procedural solutions for trust building, and personal trust based on positive past experiences within an existing business relation. Procedural trust building solutions are important for first trade situations, because of the lack of experience in these situations. The procedural trust solutions are related to the notion of institution-based trust, because the trust in the procedural solutions depends on the trust one has in the institutions that issued or enforces the procedure. The trust matrix model can be used as a tool to analyze and develop trust-building services to help organizations conduct first-trade electronic commerce transactions. --- paper_title: Challenges for trust, fraud and deception research in multi-agent systems paper_content: Discussions at the 5th Workshop on Deception, Fraud and Trust in Agent Societies held at the 1st International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2002) centered around many important research issues1. This paper attempts to challenge researchers in the community toward future work concerning three issues inspired by the workshop's roundtable discussion: (1) distinguishing elements of an agent's behavior that influence its trustworthiness, (2) building reputation-based trust models without relying on interaction, and (3) benchmarking trust modeling algorithms. Arguments justifying the validity of each problem are presented, and benefits from their solutions are enumerated. --- paper_title: Pinocchio: Incentives for Honest Participation in Distributed Trust Management paper_content: In this paper, we introduce a framework for providing incentives for honest participation in global-scale distributed trust management infrastructures. Our system can improve the quality of information supplied by these systems by reducing free-riding and encouraging honesty. Our approach is twofold: (1) we provide rewards for participants that advertise their experiences to others, and (2) impose the credible threat of halting the rewards, for a substantial amount of time, for participants who consistently provide suspicious feedback. For this purpose we develop an honesty metric which can indicate the accuracy of feedback. --- paper_title: An Intrusion-Detection Model paper_content: A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described. The model is based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns of system usage. The model includes profiles for representing the behavior of subjects with respect to objects in terms of metrics and statistical models, and rules for acquiring knowledge about this behavior from audit records and for detecting anomalous behavior. The model is independent of any particular system, application environment, system vulnerability, or type of intrusion, thereby providing a framework for a general-purpose intrusion-detection expert system. --- paper_title: Specification-based anomaly detection: a new approach for detecting network intrusions paper_content: Unlike signature or misuse based intrusion detection techniques, anomaly detection is capable of detecting novel attacks. However, the use of anomaly detection in practice is hampered by a high rate of false alarms. Specification-based techniques have been shown to produce a low rate of false alarms, but are not as effective as anomaly detection in detecting novel attacks, especially when it comes to network probing and denial-of-service attacks. This paper presents a new approach that combines specification-based and anomaly-based intrusion detection, mitigating the weaknesses of the two approaches while magnifying their strengths. Our approach begins with state-machine specifications of network protocols, and augments these state machines with information about statistics that need to be maintained to detect anomalies. We present a specification language in which all of this information can be captured in a succinct manner. We demonstrate the effectiveness of the approach on the 1999 Lincoln Labs intrusion detection evaluation data, where we are able to detect all of the probing and denial-of-service attacks with a low rate of false alarms (less than 10 per day). Whereas feature selection was a crucial step that required a great deal of expertise and insight in the case of previous anomaly detection approaches, we show that the use of protocol specifications in our approach simplifies this problem. Moreover, the machine learning component of our approach is robust enough to operate without human supervision, and fast enough that no sampling techniques need to be employed. As further evidence of effectiveness, we present results of applying our approach to detect stealthy email viruses in an intranet environment. --- paper_title: Adaptive real-time anomaly detection using inductively generated sequential patterns paper_content: A time-based inductive learning approach to the problem of real-time anomaly detection is described. This approach uses sequential rules that characterize a user's behavior over time. A rulebase is used to store patterns of user activities, and anomalies are reported whenever a user's activity deviates significantly from those specified in the rules. The rules in the rulebase characterize either the sequential relationships between security audit records or the temporal properties of the records. The rules are created in two ways: they are either dynamically generated and modified by a time-based inductive engine in order to adapt to changes in a user's behavior, or they are specified by the security management to implement a site security policy. This approach allows the correlation between adjacent security events to be exploited for the purpose of greater sensitivity in anomaly detection against seemingly intractable (or erratic) activities using statistical approaches. Real-time detection of anomaly activities is possible. > --- paper_title: A sense of self for Unix processes paper_content: A method for anomaly detection is introduced in which ``normal'' is defined by short-range correlations in a process' system calls. Initial experiments suggest that the definition is stable during normal behavior for standard UNIX programs. Further, it is able to detect several common intrusions involving sendmail and lpr. This work is part of a research program aimed at building computer security systems that incorporate the mechanisms and algorithms used by natural immune systems. --- paper_title: Using Internal Sensors For Computer Intrusion Detection paper_content: This dissertation introduces the concept of using internal sensors to perform intrusion detection in computer systems. It shows its practical feasibility and discusses its characteristics and related design and implementation issues. ::: We introduce a classification of data collection mechanisms for intrusion detection systems. At a conceptual level, these mechanisms are classified as direct and indirect monitoring. At a practical level, direct monitoring can be implemented using external or internal sensors. Internal sensors provide advantages with respect to reliability, completeness, timeliness and volume of data, in addition to efficiency and resistance against attacks. ::: We introduce an architecture called ESP as a framework for building intrusion detection systems based on internal sensors. We describe in detail a prototype implementation based on the ESP architecture and introduce the concept of embedded detectors as a mechanism for localized data reduction. ::: We show that it is possible to build both specific (specialized for a certain intrusion) and generic (able to detect different types of intrusions) detectors. Furthermore, we provide information about the types of data and places of implementation that are most effective in detecting different types of attacks. ::: Finally, performance testing of the ESP implementation shows the impact that embedded detectors can have on a computer system. Detection testing shows that embedded detectors have the capability of detecting a significant percentage of new attacks. --- paper_title: Trust-adapted enforcement of security policies in distributed component-structured applications paper_content: Software component technology on the one hand supports the cost-effective development of specialized applications. On the other hand, however it introduces special security problems. Some major problems can be solved by the automated run-time enforcement of security policies. Each component is controlled by a wrapper which monitors the component's behavior and checks its compliance with the security behavior constraints of the component's employment contract. Since control functions and wrappers can cause substantial overhead, we introduce trust-adapted control functions where the intensity of monitoring and behavior checks depends on the level of trust, the component, its hosting environment, and its vendor have currently in the eyes of the application administration. We report on wrappers and a trust information service, outline the embedding security model and architecture, and describe a Java Bean based experimental implementation. --- paper_title: Using trust for secure collaboration in uncertain environments paper_content: The SECURE project investigates the design of security mechanisms for pervasive computing based on trust. It addresses how entities in unfamiliar pervasive computing environments can overcome initial suspicion to provide secure collaboration. --- paper_title: A computational model of trust and reputation paper_content: Despite their many advantages, e-businesses lag behind brick and mortar businesses in several fundamental respects. This paper concerns one of these: relationships based on trust and reputation. Recent studies on simple reputation systems for e-Businesses such as eBay have pointed to the importance of such rating systems for deterring moral hazard and encouraging trusting interactions. However, despite numerous studies on trust and reputation systems, few have taken studies across disciplines to provide an integrated account of these concepts and their relationships. This paper first surveys existing literatures on trust, reputation and a related concept: reciprocity. Based on sociological and biological understandings of these concepts, a computational model is proposed. This model can be implemented in a real system to consistently calculate agents' trust and reputation scores. --- paper_title: Specifying and Analysing Trust for Internet Applications paper_content: The Internet is now being used for commercial, social and educational interactions, which previously relied on direct face-to-face contact in order to establish trust relationships. Thus, there is a need to be able to establish and evaluate trust relationships relying only on electronic interactions over the Internet. A trust framework for Internet applications should incorporate concepts such as experience, reputation and trusting propensity in order to specify and evaluate trust. SULTAN (Simple Universal Logic-oriented Trust Analysis Notation) is an abstract, logic-oriented notation designed to facilitate the specification and analysis of trust relationships. SULTAN seeks to address all the above issues, although this paper focuses on our initial work on trust specification and analysis. --- paper_title: Enhanced Reputation Mechanism for Mobile Ad Hoc Networks paper_content: Interactions between entities unknown to each other are inevitable in the ambient intelligence vision of service access anytime, anywhere. Trust management through a reputation mechanism to facilitate such interactions is recognized as a vital part of mobile ad hoc networks, which features lack of infrastructure, autonomy, mobility and resource scarcity of composing light-weight terminals. However, the design of a reputation mechanism is faced by challenges of how to enforce reputation information sharing and honest recommendation elicitation. In this paper, we present a reputation model, which incorporates two essential dimensions, time and context, along with mechanisms supporting reputation formation, evolution and propagation. By introducing the notion of recommendation reputation, our reputation mechanism shows effectiveness in distinguishing truth-telling and lying agents, obtaining true reputation of an agent, and ensuring reliability against attacks of defame and collusion. --- paper_title: SULTAN-A Language for Trust Specification and Analysis paper_content: A precast unit, preferably of cementitious material, of circular outline having stone-like block units on the outer surface separated by mortar joints and top shoulder annular portions to serve as a guide to enable stacking like units above each other. The inner surface of each unit and outer surface of each projection extend in a downwardly and outwardly extending direction to provide a stepped inner wall. --- paper_title: Engineering Trust Based Collaborations in a Global Computing Environment paper_content: Trust management seems a promising approach for dealing with security concerns in collaborative applications in a global computing environment. However, the characteristics of this environment require a move from reliable identification to mechanisms for the recognition of entities. Furthermore, they require explicit reasoning about the risks of interactions, and a notion of uncertainty in the underlying trust model. From our experience of engineering collaborative applications in such an environment, we found that the relationship between trust and risk is a fundamental issue. In this paper, as an initial step towards an engineering approach for the development of trust based collaborative applications, we focus on the relationship between trust and risk, and explore alternative views of this relationship. We also exemplify how particular views can be exploited in two particular application scenarios. This paper builds upon our previous work in developing a general model for trust based collaborations. --- paper_title: A computational model of trust and reputation paper_content: Despite their many advantages, e-businesses lag behind brick and mortar businesses in several fundamental respects. This paper concerns one of these: relationships based on trust and reputation. Recent studies on simple reputation systems for e-Businesses such as eBay have pointed to the importance of such rating systems for deterring moral hazard and encouraging trusting interactions. However, despite numerous studies on trust and reputation systems, few have taken studies across disciplines to provide an integrated account of these concepts and their relationships. This paper first surveys existing literatures on trust, reputation and a related concept: reciprocity. Based on sociological and biological understandings of these concepts, a computational model is proposed. This model can be implemented in a real system to consistently calculate agents' trust and reputation scores. --- paper_title: Enhanced Reputation Mechanism for Mobile Ad Hoc Networks paper_content: Interactions between entities unknown to each other are inevitable in the ambient intelligence vision of service access anytime, anywhere. Trust management through a reputation mechanism to facilitate such interactions is recognized as a vital part of mobile ad hoc networks, which features lack of infrastructure, autonomy, mobility and resource scarcity of composing light-weight terminals. However, the design of a reputation mechanism is faced by challenges of how to enforce reputation information sharing and honest recommendation elicitation. In this paper, we present a reputation model, which incorporates two essential dimensions, time and context, along with mechanisms supporting reputation formation, evolution and propagation. By introducing the notion of recommendation reputation, our reputation mechanism shows effectiveness in distinguishing truth-telling and lying agents, obtaining true reputation of an agent, and ensuring reliability against attacks of defame and collusion. --- paper_title: SULTAN-A Language for Trust Specification and Analysis paper_content: A precast unit, preferably of cementitious material, of circular outline having stone-like block units on the outer surface separated by mortar joints and top shoulder annular portions to serve as a guide to enable stacking like units above each other. The inner surface of each unit and outer surface of each projection extend in a downwardly and outwardly extending direction to provide a stepped inner wall. ---	Title: Trust Management Survey Section 1: Introduction Description 1: Introduce the importance and challenges of trust management, and outline the structure of the paper. Section 2: On the Nature of Trust Description 2: Provide a brief overview of the trust phenomenon from a sociological perspective. Section 3: Concepts for Trust Management Description 3: Discuss trust as it is directed at independent actors and the factors affecting trust decisions. Section 4: The Trust Management Model Description 4: Explore the roots of trust management in authentication and authorization and its evolution into dynamic trust models. Section 5: The Trust Information Model Description 5: Examine the representation of trust, reputation, and related concepts in computer systems. Section 6: The Tasks of a Trust Management System Description 6: Identify the main challenges for trust management systems, including initialization, observation, and action based on trust decisions. Section 7: Initializing a Trust Relationship Description 7: Discuss methods for determining initial trust and the role of reputation systems. Section 8: Observation Description 8: Explore techniques for monitoring trustee behavior, including the use of intrusion detection systems. Section 9: Evolving Reputation and Trust Description 9: Describe how reputation and trust are updated based on observed behavior and gathered experience. Section 10: Conclusions Description 10: Summarize the findings, highlight the challenges, and suggest areas for future research in trust management.
Multimodal Machine Learning: A Survey and Taxonomy	7	--- paper_title: Coupled hidden Markov models for complex action recognition paper_content: We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm and a clear Bayesian semantics. However the Markovian framework makes strong restrictive assumptions about the system generating the signal-that it is a single process having a small number of states and an extremely limited state memory. The single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. Coupled HMMs provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions. --- paper_title: VQA: Visual Question Answering paper_content: We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing $$\sim $$~0.25 M images, $$\sim $$~0.76 M questions, and $$\sim $$~10 M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines and methods for VQA are provided and compared with human performance. Our VQA demo is available on CloudCV (http://cloudcv.org/vqa). --- paper_title: Generative Adversarial Text to Image Synthesis paper_content: Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions. --- paper_title: Integration of acoustic and visual speech signals using neural networks paper_content: Results from a series of experiments that use neural networks to process the visual speech signals of a male talker are presented. In these preliminary experiments, the results are limited to static images of vowels. It is demonstrated that these networks are able to extract speech information from the visual images and that this information can be used to improve automatic vowel recognition. The structure of speech and its corresponding acoustic and visual signals are reviewed. The specific data that was used in the experiments along with the network architectures and algorithms are described. The results of integrating the visual and auditory signals for vowel recognition in the presence of acoustic noise are presented. > --- paper_title: A mew ASR approach based on independent processing and recombination of partial frequency bands paper_content: In the framework of hidden Markov models (HMM) or hybrid HMM/artificial neural network (ANN) systems, we present a new approach towards automatic speech recognition (ASR). The general idea is to split the whole frequency band (represented in terms of critical bands) into a few sub-bands on which different recognizers are independently applied and then recombined at a certain speech unit level to yield global scores and a global recognition decision. The preliminary results presented in this paper show that such an approach, even using quite simple recombination strategies, can yield at least comparable performance on clean speech while providing better robustness in the case of noisy speech. --- paper_title: The SEMAINE corpus of emotionally coloured character interactions paper_content: We have recorded a new corpus of emotionally coloured conversations. Users were recorded while holding conversations with an operator who adopts in sequence four roles designed to evoke emotional reactions. The operator and the user are seated in separate rooms; they see each other through teleprompter screens, and hear each other through speakers. To allow high quality recording, they are recorded by five high-resolution, high framerate cameras, and by four microphones. All sensor information is recorded synchronously, with an accuracy of 25 μs. In total, we have recorded 20 participants, for a total of 100 character conversational and 50 non-conversational recordings of approximately 5 minutes each. All recorded conversations have been fully transcribed and annotated for five affective dimensions and partially annotated for 27 other dimensions. The corpus has been made available to the scientific community through a web-accessible database. --- paper_title: Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics paper_content: In [Hodosh et al., 2013], we establish a rankingbased framework for sentence-based image description and retrieval. We introduce a new dataset of images paired with multiple descriptive captions that was specifically designed for these tasks. We also present strong KCCA-based baseline systems for description and search, and perform an in-depth study of evaluation metrics for these two tasks. Our results indicate that automatic evaluation metrics for our ranking-based tasks are more accurate and robust than those proposed for generation-based image description. --- paper_title: Multimodal Video Indexing: A Review of the State-of-the-art paper_content: Efficient and effective handling of video documents depends on the availability of indexes. Manual indexing is unfeasible for large video collections. In this paper we survey several methods aiming at automating this time and resource consuming process. Good reviews on single modality based video indexing have appeared in literature. Effective indexing, however, requires a multimodal approach in which either the most appropriate modality is selected or the different modalities are used in collaborative fashion. Therefore, instead of separately treating the different information sources involved, and their specific algorithms, we focus on the similarities and differences between the modalities. To that end we put forward a unifying and multimodal framework, which views a video document from the perspective of its author. This framework forms the guiding principle for identifying index types, for which automatic methods are found in literature. It furthermore forms the basis for categorizing these different methods. --- paper_title: Multimodal Saliency and Fusion for Movie Summarization Based on Aural, Visual, and Textual Attention paper_content: Multimodal streams of sensory information are naturally parsed and integrated by humans using signal-level feature extraction and higher level cognitive processes. Detection of attention-invoking audiovisual segments is formulated in this work on the basis of saliency models for the audio, visual, and textual information conveyed in a video stream. Aural or auditory saliency is assessed by cues that quantify multifrequency waveform modulations, extracted through nonlinear operators and energy tracking. Visual saliency is measured through a spatiotemporal attention model driven by intensity, color, and orientation. Textual or linguistic saliency is extracted from part-of-speech tagging on the subtitles information available with most movie distributions. The individual saliency streams, obtained from modality-depended cues, are integrated in a multimodal saliency curve, modeling the time-varying perceptual importance of the composite video stream and signifying prevailing sensory events. The multimodal saliency representation forms the basis of a generic, bottom-up video summarization algorithm. Different fusion schemes are evaluated on a movie database of multimodal saliency annotations with comparative results provided across modalities. The produced summaries, based on low-level features and content-independent fusion and selection, are of subjectively high aesthetic and informative quality. --- paper_title: AVEC 2011–The first international audio/visual emotion challenge paper_content: The Audio/Visual Emotion Challenge and Workshop (AVEC 2011) is the first competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and audiovisual emotion analysis, with all participants competing under strictly the same conditions. This paper first describes the challenge participation conditions. Next follows the data used - the SEMAINE corpus - and its partitioning into train, development, and test partitions for the challenge with labelling in four dimensions, namely activity, expectation, power, and valence. Further, audio and video baseline features are introduced as well as baseline results that use these features for the three sub-challenges of audio, video, and audiovisual emotion recognition. --- paper_title: Hidden Markov Models for Speech Recognition paper_content: The use of hidden Markov models for speech recognition has become predominant in the last several years, as evidenced by the number of published papers and talks at major speech conferences. The reasons this method has become so popular are the inherent statistical (mathematically precise) framework; the ease and availability of training algorithms for cstimating the parameters of the models from finite training sets of speech data; the flexibility of the resulting recognition system in which one can easily change the size, type, or architecture of the models to suit particular words, sounds, and so forth; and the ease of implementation of the overall recognition system. In this expository article, we address the role of statistical methods in this powerful technology as applied to speech recognition and discuss a range of theoretical and practical issues that are as yet unsolved in terms of their importance and their effect on performance for different system implementations. --- paper_title: VizWiz: nearly real-time answers to visual questions paper_content: Visual information pervades our environment. Vision is used to decide everything from what we want to eat at a restaurant and which bus route to take to whether our clothes match and how long until the milk expires. Individually, the inability to interpret such visual information is a nuisance for blind people who often have effective, if inefficient, work-arounds to overcome them. Collectively, however, they can make blind people less independent. Specialized technology addresses some problems in this space, but automatic approaches cannot yet answer the vast majority of visual questions that blind people may have. VizWiz addresses this shortcoming by using the Internet connections and cameras on existing smartphones to connect blind people and their questions to remote paid workers' answers. VizWiz is designed to have low latency and low cost, making it both competitive with expensive automatic solutions and much more versatile. --- paper_title: Dynamic modality weighting for multi-stream hmms inaudio-visual speech recognition paper_content: Merging decisions from different modalities is a crucial problem in Audio-Visual Speech Recognition. To solve this, state synchronous multi-stream HMMs have been proposed for their important advantage of incorporating stream reliability in their fusion scheme. This paper focuses on stream weight adaptation based on modality confidence estimators. We assume different and time-varying environment noise, as can be encountered in realistic applications, and, for this, adaptive methods are best suited. Stream reliability is assessed directly through classifier outputs since they are not specific to either noise type or level. The influence of constraining the weights to sum to one is also discussed. --- paper_title: Multimodal Deep Learning paper_content: Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. Our models are validated on the CUAVE and AVLetters datasets on audio-visual speech classification, demonstrating best published visual speech classification on AVLetters and effective shared representation learning. --- paper_title: The AMI Meeting Corpus: A Pre-announcement paper_content: The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. It is being created in the context of a project that is developing meeting browsing technology and will eventually be released publicly. Some of the meetings it contains are naturally occurring, and some are elicited, particularly using a scenario in which the participants play different roles in a design team, taking a design project from kick-off to completion over the course of a day. The corpus is being recorded using a wide range of devices including close-talking and far-field microphones, individual and room-view video cameras, projection, a whiteboard, and individual pens, all of which produce output signals that are synchronized with each other. It is also being hand-annotated for many different phenomena, including orthographic transcription, discourse properties such as named entities and dialogue acts, summaries, emotions, and some head and hand gestures. We describe the data set, including the rationale behind using elicited material, and explain how the material is being recorded, transcribed and annotated. --- paper_title: Comparison of automatic shot boundary detection algorithms paper_content: Various methods of automatic shot boundary detection have been proposed and claimed to perform reliably. Although the detection of edits is fundamental to any kind of video analysis since it segments a video into its basic components, the shots, only few comparative investigations on early shot boundary detection algorithms have been published. These investigations mainly concentrate on measuring the edit detection performance, however, do not consider the algorithms? ability to classify the types and to locate the boundaries of the edits correctly. This paper extends these comparative investigations. More recent algorithms designed explicitly to detect specific complex editing operations such as fades and dissolves are taken into account, and their ability to classify the types and locate the boundaries of such edits are examined. The algorithms? performance is measured in terms of hit rate, number of false hits, and miss rate for hard cuts, fades, and dissolves over a large and diverse set of video sequences. The experiments show that while hard cuts and fades can be detected reliably, dissolves are still an open research issue. The false hit rate for dis-solves is usually unacceptably high, ranging from 50% up to over 400%. Moreover, all algorithms seem to fail under roughly the same conditions. --- paper_title: Text to 3D Scene Generation with Rich Lexical Grounding paper_content: The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics. However, prior work on the text to 3D scene generation task has used manually specified object categories and language that identifies them. We introduce a dataset of 3D scenes annotated with natural language descriptions and learn from this data how to ground textual descriptions to physical objects. Our method successfully grounds a variety of lexical terms to concrete referents, and we show quantitatively that our method improves 3D scene generation over previous work using purely rule-based methods. We evaluate the fidelity and plausibility of 3D scenes generated with our grounding approach through human judgments. To ease evaluation on this task, we also introduce an automated metric that strongly correlates with human judgments. --- paper_title: Multimodal fusion for multimedia analysis: a survey paper_content: This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion. --- paper_title: A survey of recent advances in visual feature detection paper_content: Feature detection is a fundamental and important problem in computer vision and image processing. It is a low-level processing step which serves as the essential part for computer vision based applications. The goal of this paper is to present a survey of recent progress and advances in visual feature detection. Firstly we describe the relations among edges, corners and blobs from the psychological view. Secondly we classify the algorithms in detecting edges, corners and blobs into different categories and provide detailed descriptions for representative recent algorithms in each category. Considering that machine learning becomes more involved in visual feature detection, we put more emphasis on machine learning based feature detection methods. Thirdly, evaluation standards and databases are also introduced. Through this survey we would like to present the recent progress in visual feature detection and identify future trends as well as challenges. We survey the recent progress and advances in visual feature detection.The relations among different kinds of features are covered.Representative feature detection algorithms are described.We categorize and discuss the pros/cons for different kinds of visual features.We put some emphasis on future challenges in feature design through this survey. --- paper_title: Imagenet classification with deep convolutional neural networks paper_content: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry. --- paper_title: Distinctive Image Features from Scale-Invariant Keypoints paper_content: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade. --- paper_title: Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network paper_content: The automatic recognition of spontaneous emotions from speech is a challenging task. On the one hand, acoustic features need to be robust enough to capture the emotional content for various styles of speaking, and while on the other, machine learning algorithms need to be insensitive to outliers while being able to model the context. Whereas the latter has been tackled by the use of Long Short-Term Memory (LSTM) networks, the former is still under very active investigations, even though more than a decade of research has provided a large set of acoustic descriptors. In this paper, we propose a solution to the problem of ‘context-aware’ emotional relevant feature extraction, by combining Convolutional Neural Networks (CNNs) with LSTM networks, in order to automatically learn the best representation of the speech signal directly from the raw time representation. In this novel work on the so-called end-to-end speech emotion recognition, we show that the use of the proposed topology significantly outperforms the traditional approaches based on signal processing techniques for the prediction of spontaneous and natural emotions on the RECOLA database. --- paper_title: Multimodal Learning with Deep Boltzmann Machines paper_content: Data often consists of multiple diverse modalities. For example, images are tagged with textual information and videos are accompanied by audio. Each modality is characterized by having distinct statistical properties. We propose a Deep Boltzmann Machine for learning a generative model of such multimodal data. We show that the model can be used to create fused representations by combining features across modalities. These learned representations are useful for classification and information retrieval. By sampling from the conditional distributions over each data modality, it is possible to create these representations even when some data modalities are missing. We conduct experiments on bimodal image-text and audio-video data. The fused representation achieves good classification results on the MIR-Flickr data set matching or outperforming other deep models as well as SVM based models that use Multiple Kernel Learning. We further demonstrate that this multimodal model helps classification and retrieval even when only unimodal data is available at test time. --- paper_title: Representation Learning: A Review and New Perspectives paper_content: The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning. --- paper_title: Distributed Representations of Words and Phrases and their Compositionality paper_content: The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. ::: ::: An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. --- paper_title: Deep Canonical Correlation Analysis paper_content: We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn complex nonlinear transformations of two views of data such that the resulting representations are highly linearly correlated. Parameters of both transformations are jointly learned to maximize the (regularized) total correlation. It can be viewed as a nonlinear extension of the linear method canonical correlation analysis (CCA). It is an alternative to the nonparametric method kernel canonical correlation analysis (KCCA) for learning correlated nonlinear transformations. Unlike KCCA, DCCA does not require an inner product, and has the advantages of a parametric method: training time scales well with data size and the training data need not be referenced when computing the representations of unseen instances. In experiments on two real-world datasets, we find that DCCA learns representations with significantly higher correlation than those learned by CCA and KCCA. We also introduce a novel non-saturating sigmoid function based on the cube root that may be useful more generally in feedforward neural networks. --- paper_title: Order-Embeddings of Images and Language paper_content: Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. We show that the resulting representations improve performance over current approaches for hypernym prediction and image-caption retrieval. --- paper_title: Learning Grounded Meaning Representations with Autoencoders paper_content: In this paper we address the problem of grounding distributional representations of lexical meaning. We introduce a new model which uses stacked autoencoders to learn higher-level embeddings from textual and visual input. The two modalities are encoded as vectors of attributes and are obtained automatically from text and images, respectively. We evaluate our model on its ability to simulate similarity judgments and concept categorization. On both tasks, our approach outperforms baselines and related models. --- paper_title: Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis paper_content: article i nfo For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromalstage,Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM) 2 , a deep network with a restricted Boltzmann machine as a building block, to find a latent hierar- chical feature representation from a 3D patch, and then devise a systematic method for a joint feature representa- tion from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. --- paper_title: VQA: Visual Question Answering paper_content: We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing $$\sim $$~0.25 M images, $$\sim $$~0.76 M questions, and $$\sim $$~10 M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines and methods for VQA are provided and compared with human performance. Our VQA demo is available on CloudCV (http://cloudcv.org/vqa). --- paper_title: Describing Videos by Exploiting Temporal Structure paper_content: Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description model. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions. --- paper_title: Neural Machine Translation by Jointly Learning to Align and Translate paper_content: Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition. --- paper_title: Learning Representations for Multimodal Data with Deep Belief Nets paper_content: We propose a Deep Belief Network architecture for learning a joint representation of multimodal data. The model denes a probability distribution over the space of multimodal inputs and allows sampling from the conditional distributions over each data modality. This makes it possible for the model to create a multimodal representation even when some data modalities are missing. Our experimental results on bi-modal data consisting of images and text show that the Multimodal DBN can learn a good generative model of the joint space of image and text inputs that is useful for lling in missing data so it can be used both for image annotation and image retrieval. We further demonstrate that using the representation discovered by the Multimodal DBN our model can significantly outperform SVMs and LDA on discriminative tasks. --- paper_title: SoundNet: Learning Sound Representations from Unlabeled Video paper_content: We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected in the wild. We leverage the natural synchronization between vision and sound to learn an acoustic representation using two-million unlabeled videos. Unlabeled video has the advantage that it can be economically acquired at massive scales, yet contains useful signals about natural sound. We propose a student-teacher training procedure which transfers discriminative visual knowledge from well established visual recognition models into the sound modality using unlabeled video as a bridge. Our sound representation yields significant performance improvements over the state-of-the-art results on standard benchmarks for acoustic scene/object classification. Visualizations suggest some high-level semantics automatically emerge in the sound network, even though it is trained without ground truth labels. --- paper_title: Multi-modal Dimensional Emotion Recognition using Recurrent Neural Networks paper_content: Emotion recognition has been an active research area with both wide applications and big challenges. This paper presents our effort for the Audio/Visual Emotion Challenge (AVEC2015), whose goal is to explore utilizing audio, visual and physiological signals to continuously predict the value of the emotion dimensions (arousal and valence). Our system applies the Recurrent Neural Networks (RNN) to model temporal information. We explore various aspects to improve the prediction performance including: the dominant modalities for arousal and valence prediction, duration of features, novel loss functions, directions of Long Short Term Memory (LSTM), multi-task learning, different structures for early feature fusion and late fusion. Best settings are chosen according to the performance on the development set. Competitive experimental results compared with the baseline show the effectiveness of the proposed methods. --- paper_title: Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space paper_content: Past research in analysis of human affect has focused on recognition of prototypic expressions of six basic emotions based on posed data acquired in laboratory settings. Recently, there has been a shift toward subtle, continuous, and context-specific interpretations of affective displays recorded in naturalistic and real-world settings, and toward multimodal analysis and recognition of human affect. Converging with this shift, this paper presents, to the best of our knowledge, the first approach in the literature that: 1) fuses facial expression, shoulder gesture, and audio cues for dimensional and continuous prediction of emotions in valence and arousal space, 2) compares the performance of two state-of-the-art machine learning techniques applied to the target problem, the bidirectional Long Short-Term Memory neural networks (BLSTM-NNs), and Support Vector Machines for Regression (SVR), and 3) proposes an output-associative fusion framework that incorporates correlations and covariances between the emotion dimensions. Evaluation of the proposed approach has been done using the spontaneous SAL data from four subjects and subject-dependent leave-one-sequence-out cross validation. The experimental results obtained show that: 1) on average, BLSTM-NNs outperform SVR due to their ability to learn past and future context, 2) the proposed output-associative fusion framework outperforms feature-level and model-level fusion by modeling and learning correlations and patterns between the valence and arousal dimensions, and 3) the proposed system is well able to reproduce the valence and arousal ground truth obtained from human coders. --- paper_title: Adam: A Method for Stochastic Optimization paper_content: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm. --- paper_title: Bimodal recognition experiments with recurrent neural networks paper_content: A bimodal automatic speech recognition system, using simultaneously auditory model and articulatory parameters, is described. Results given for various speaker dependent phonetic recognition experiments, regarding the Italian plosive class, show the usefulness of this approach especially in noisy conditions. > --- paper_title: Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text paper_content: This paper investigates how linguistic knowledge mined from large text corpora can aid the generation of natural language descriptions of videos. Specifically, we integrate both a neural language model and distributional semantics trained on large text corpora into a recent LSTM-based architecture for video description. We evaluate our approach on a collection of Youtube videos as well as two large movie description datasets showing significant improvements in grammaticality while modestly improving descriptive quality. --- paper_title: Dropout: a simple way to prevent neural networks from overfitting paper_content: Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different "thinned" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets. --- paper_title: Deep multimodal hashing with orthogonal regularization paper_content: Hashing is an important method for performing efficient similarity search. With the explosive growth of multimodal data, how to learn hashing-based compact representations for multimodal data becomes highly non-trivial. Compared with shallow-structured models, deep models present superiority in capturing multimodal correlations due to their high nonlinearity. However, in order to make the learned representation more accurate and compact, how to reduce the redundant information lying in the multimodal representations and incorporate different complexities of different modalities in the deep models is still an open problem. In this paper, we propose a novel deep multimodal hashing method, namely Deep Multimodal Hashing with Orthogonal Regularization (DMHOR), which fully exploits intra-modality and inter-modality correlations. In particular, to reduce redundant information, we impose orthogonal regularizer on the weighting matrices of the model, and theoretically prove that the learned representation is guaranteed to be approximately orthogonal. Moreover, we find that a better representation can be attained with different numbers of layers for different modalities, due to their different complexities. Comprehensive experiments on WIKI and NUS-WIDE, demonstrate a substantial gain of DMHOR compared with state-of-the-art methods. --- paper_title: Multimodal Learning with Deep Boltzmann Machines paper_content: Data often consists of multiple diverse modalities. For example, images are tagged with textual information and videos are accompanied by audio. Each modality is characterized by having distinct statistical properties. We propose a Deep Boltzmann Machine for learning a generative model of such multimodal data. We show that the model can be used to create fused representations by combining features across modalities. These learned representations are useful for classification and information retrieval. By sampling from the conditional distributions over each data modality, it is possible to create these representations even when some data modalities are missing. We conduct experiments on bimodal image-text and audio-video data. The fused representation achieves good classification results on the MIR-Flickr data set matching or outperforming other deep models as well as SVM based models that use Multiple Kernel Learning. We further demonstrate that this multimodal model helps classification and retrieval even when only unimodal data is available at test time. --- paper_title: Understanding the difficulty of training deep feedforward neural networks paper_content: Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence. 1 Deep Neural Networks Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. They include Appearing in Proceedings of the 13 International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, Chia Laguna Resort, Sardinia, Italy. Volume 9 of JMLR: WC Weston et al., 2008). Much attention has recently been devoted to them (see (Bengio, 2009) for a review), because of their theoretical appeal, inspiration from biology and human cognition, and because of empirical success in vision (Ranzato et al., 2007; Larochelle et al., 2007; Vincent et al., 2008) and natural language processing (NLP) (Collobert & Weston, 2008; Mnih & Hinton, 2009). Theoretical results reviewed and discussed by Bengio (2009), suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Most of the recent experimental results with deep architecture are obtained with models that can be turned into deep supervised neural networks, but with initialization or training schemes different from the classical feedforward neural networks (Rumelhart et al., 1986). Why are these new algorithms working so much better than the standard random initialization and gradient-based optimization of a supervised training criterion? Part of the answer may be found in recent analyses of the effect of unsupervised pretraining (Erhan et al., 2009), showing that it acts as a regularizer that initializes the parameters in a “better” basin of attraction of the optimization procedure, corresponding to an apparent local minimum associated with better generalization. But earlier work (Bengio et al., 2007) had shown that even a purely supervised but greedy layer-wise procedure would give better results. So here instead of focusing on what unsupervised pre-training or semi-supervised criteria bring to deep architectures, we focus on analyzing what may be going wrong with good old (but deep) multilayer neural networks. Our analysis is driven by investigative experiments to monitor activations (watching for saturation of hidden units) and gradients, across layers and across training iterations. We also evaluate the effects on these of choices of activation function (with the idea that it might affect saturation) and initialization procedure (since unsupervised pretraining is a particular form of initialization and it has a drastic impact). --- paper_title: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift paper_content: Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters. --- paper_title: Audio-visual deep learning for noise robust speech recognition paper_content: Deep belief networks (DBN) have shown impressive improvements over Gaussian mixture models for automatic speech recognition. In this work we use DBNs for audio-visual speech recognition; in particular, we use deep learning from audio and visual features for noise robust speech recognition. We test two methods for using DBNs in a multimodal setting: a conventional decision fusion method that combines scores from single-modality DBNs, and a novel feature fusion method that operates on mid-level features learned by the single-modality DBNs. On a continuously spoken digit recognition task, our experiments show that these methods can reduce word error rate by as much as 21% relative over a baseline multi-stream audio-visual GMM/HMM system. --- paper_title: Long Short-Term Memory paper_content: Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms. --- paper_title: Representation Learning: A Review and New Perspectives paper_content: The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning. --- paper_title: Multi-source Deep Learning for Human Pose Estimation paper_content: Visual appearance score, appearance mixture type and deformation are three important information sources for human pose estimation. This paper proposes to build a multi-source deep model in order to extract non-linear representation from these different aspects of information sources. With the deep model, the global, high-order human body articulation patterns in these information sources are extracted for pose estimation. The task for estimating body locations and the task for human detection are jointly learned using a unified deep model. The proposed approach can be viewed as a post-processing of pose estimation results and can flexibly integrate with existing methods by taking their information sources as input. By extracting the non-linear representation from multiple information sources, the deep model outperforms state-of-the-art by up to 8.6 percent on three public benchmark datasets. --- paper_title: Multimodal Deep Learning paper_content: Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. Our models are validated on the CUAVE and AVLetters datasets on audio-visual speech classification, demonstrating best published visual speech classification on AVLetters and effective shared representation learning. --- paper_title: Deep learning for robust feature generation in audiovisual emotion recognition paper_content: Automatic emotion recognition systems predict high-level affective content from low-level human-centered signal cues. These systems have seen great improvements in classification accuracy, due in part to advances in feature selection methods. However, many of these feature selection methods capture only linear relationships between features or alternatively require the use of labeled data. In this paper we focus on deep learning techniques, which can overcome these limitations by explicitly capturing complex non-linear feature interactions in multimodal data. We propose and evaluate a suite of Deep Belief Network models, and demonstrate that these models show improvement in emotion classification performance over baselines that do not employ deep learning. This suggests that the learned high-order non-linear relationships are effective for emotion recognition. --- paper_title: A Fast Learning Algorithm for Deep Belief Nets paper_content: We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind. --- paper_title: Deep Boltzmann machines paper_content: We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent expectations are estimated using a variational approximation that tends to focus on a single mode, and dataindependent expectations are approximated using persistent Markov chains. The use of two quite different techniques for estimating the two types of expectation that enter into the gradient of the log-likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer “pre-training” phase that allows variational inference to be initialized with a single bottomup pass. We present results on the MNIST and NORB datasets showing that deep Boltzmann machines learn good generative models and perform well on handwritten digit and visual object recognition tasks. --- paper_title: Multimodal Dynamic Networks for Gesture Recognition paper_content: Multimodal input is a real-world situation in gesture recognition applications such as sign language recognition. In this paper, we propose a novel bi-modal (audio and skeleton joints) dynamic network for gesture recognition. First, state-of-the-art dynamic Deep Belief Networks are deployed to extract high level audio and skeletal joints representations. Then, instead of traditional late fusion, we adopt another layer of perceptron for cross modality learning taking the input from each individual net's penultimate layer. Finally, to account for temporal dynamics, the learned shared representations are used for estimating the emission probability to infer action sequences. In particular, we demonstrate that multimodal feature learning will extract semantically meaningful shared representations, outperforming individual modalities, and the early fusion scheme's efficacy against the traditional method of late fusion. --- paper_title: Deep multimodal learning for Audio-Visual Speech Recognition paper_content: In this paper, we present methods in deep multimodal learning for fusing speech and visual modalities for Audio-Visual Automatic Speech Recognition (AV-ASR). First, we study an approach where uni-modal deep networks are trained separately and their final hidden layers fused to obtain a joint feature space in which another deep network is built. While the audio network alone achieves a phone error rate (PER) of 41% under clean condition on the IBM large vocabulary audio-visual studio dataset, this fusion model achieves a PER of 35.83% demonstrating the tremendous value of the visual channel in phone classification even in audio with high signal to noise ratio. Second, we present a new deep network architecture that uses a bilinear softmax layer to account for class specific correlations between modalities. We show that combining the posteriors from the bilinear networks with those from the fused model mentioned above results in a further significant phone error rate reduction, yielding a final PER of 34.03%. --- paper_title: Autoencoders, Minimum Description Length and Helmholtz Free Energy paper_content: An autoencoder network uses a set of recognition weights to convert an input vector into a code vector. It then uses a set of generative weights to convert the code vector into an approximate reconstruction of the input vector. We derive an objective function for training autoencoders based on the Minimum Description Length (MDL) principle. The aim is to minimize the information required to describe both the code vector and the reconstruction error. We show that this information is minimized by choosing code vectors stochastically according to a Boltzmann distribution, where the generative weights define the energy of each possible code vector given the input vector. Unfortunately, if the code vectors use distributed representations, it is exponentially expensive to compute this Boltzmann distribution because it involves all possible code vectors. We show that the recognition weights of an autoencoder can be used to compute an approximation to the Boltzmann distribution and that this approximation gives an upper bound on the description length. Even when this bound is poor, it can be used as a Lyapunov function for learning both the generative and the recognition weights. We demonstrate that this approach can be used to learn factorial codes. --- paper_title: Exploring Inter-feature and Inter-class Relationships with Deep Neural Networks for Video Classification paper_content: Videos contain very rich semantics and are intrinsically multimodal. In this paper, we study the challenging task of classifying videos according to their high-level semantics such as human actions or complex events. Although extensive efforts have been paid to study this problem, most existing works combined multiple features using simple fusion strategies and neglected the exploration of inter-class semantic relationships. In this paper, we propose a novel unified framework that jointly learns feature relationships and exploits the class relationships for improved video classification performance. Specifically, these two types of relationships are learned and utilized by rigorously imposing regularizations in a deep neural network (DNN). Such a regularized DNN can be efficiently launched using a GPU implementation with an affordable training cost. Through arming the DNN with better capability of exploring both the inter-feature and the inter-class relationships, the proposed regularized DNN is more suitable for identifying video semantics. With extensive experimental evaluations, we demonstrate that the proposed framework exhibits superior performance over several state-of-the-art approaches. On the well-known Hollywood2 and Columbia Consumer Video benchmarks, we obtain to-date the best reported results: 65.7% and 70.6% respectively in terms of mean average precision. --- paper_title: Extending Long Short-Term Memory for Multi-View Structured Learning paper_content: Long Short-Term Memory (LSTM) networks have been successfully applied to a number of sequence learning problems but they lack the design flexibility to model multiple view interactions, limiting their ability to exploit multi-view relationships. In this paper, we propose a Multi-View LSTM (MV-LSTM), which explicitly models the view-specific and cross-view interactions over time or structured outputs. We evaluate the MV-LSTM model on four publicly available datasets spanning two very different structured learning problems: multimodal behaviour recognition and image captioning. The experimental results show competitive performance on all four datasets when compared with state-of-the-art models. --- paper_title: FaceSync: A Linear Operator for Measuring Synchronization of Video Facial Images and Audio Tracks paper_content: FaceSync is an optimal linear algorithm that finds the degree of synchronization between the audio and image recordings of a human speaker. Using canonical correlation, it finds the best direction to combine all the audio and image data, projecting them onto a single axis. FaceSync uses Pearson's correlation to measure the degree of synchronization between the audio and image data. We derive the optimal linear transform to combine the audio and visual information and describe an implementation that avoids the numerical problems caused by computing the correlation matrices. --- paper_title: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions paper_content: We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics , which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph , i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions. --- paper_title: Learning Concept Taxonomies from Multi-modal Data paper_content: We study the problem of automatically building hypernym taxonomies from textual and visual data. Previous works in taxonomy induction generally ignore the increasingly prominent visual data, which encode important perceptual semantics. Instead, we propose a probabilistic model for taxonomy induction by jointly leveraging text and images. To avoid hand-crafted feature engineering, we design end-to-end features based on distributed representations of images and words. The model is discriminatively trained given a small set of existing ontologies and is capable of building full taxonomies from scratch for a collection of unseen conceptual label items with associated images. We evaluate our model and features on the WordNet hierarchies, where our system outperforms previous approaches by a large gap. --- paper_title: Cross-modal Retrieval with Correspondence Autoencoder paper_content: The problem of cross-modal retrieval, e.g., using a text query to search for images and vice-versa, is considered in this paper. A novel model involving correspondence autoencoder (Corr-AE) is proposed here for solving this problem. The model is constructed by correlating hidden representations of two uni-modal autoencoders. A novel optimal objective, which minimizes a linear combination of representation learning errors for each modality and correlation learning error between hidden representations of two modalities, is used to train the model as a whole. Minimization of correlation learning error forces the model to learn hidden representations with only common information in different modalities, while minimization of representation learning error makes hidden representations are good enough to reconstruct input of each modality. A parameter $\alpha$ is used to balance the representation learning error and the correlation learning error. Based on two different multi-modal autoencoders, Corr-AE is extended to other two correspondence models, here we called Corr-Cross-AE and Corr-Full-AE. The proposed models are evaluated on three publicly available data sets from real scenes. We demonstrate that the three correspondence autoencoders perform significantly better than three canonical correlation analysis based models and two popular multi-modal deep models on cross-modal retrieval tasks. --- paper_title: Canonical Correlation Analysis: An Overview with Application to Learning Methods paper_content: We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments, we look at two approaches of retrieving images based on only their content from a text query. We compare orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model. --- paper_title: WSABIE: Scaling Up to Large Vocabulary Image Annotation paper_content: Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method that scales to such datasets by simultaneously learning to optimize precision at the top of the ranked list of annotations for a given image and learning a low-dimensional joint embedding space for both images and annotations. Our method, called WSABIE, both outperforms several baseline methods and is faster and consumes less memory. --- paper_title: Relations Between Two Sets of Variates paper_content: Concepts of correlation and regression may be applied not only to ordinary one-dimensional variates but also to variates of two or more dimensions. Marksmen side by side firing simultaneous shots at targets, so that the deviations are in part due to independent individual errors and in part to common causes such as wind, provide a familiar introduction to the theory of correlation; but only the correlation of the horizontal components is ordinarily discussed, whereas the complex consisting of horizontal and vertical deviations may be even more interesting. The wind at two places may be compared, using both components of the velocity in each place. A fluctuating vector is thus matched at each moment with another fluctuating vector. The study of individual differences in mental and physical traits calls for a detailed study of the relations between sets of correlated variates. For example the scores on a number of mental tests may be compared with physical measurements on the same persons. The questions then arise of determining the number and nature of the independent relations of mind and body shown by these data to exist, and of extracting from the multiplicity of correlations in the system suitable characterizations of these independent relations. As another example, the inheritance of intelligence in rats might be studied by applying not one but s different mental tests to N mothers and to a daughter of each --- paper_title: Deep Visual-Semantic Hashing for Cross-Modal Retrieval paper_content: Due to the storage and retrieval efficiency, hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which enables efficient retrieval of images in response to text queries or vice versa, has received increasing attention recently. Most existing work on cross-modal hashing does not capture the spatial dependency of images and temporal dynamics of text sentences for learning powerful feature representations and cross-modal embeddings that mitigate the heterogeneity of different modalities. This paper presents a new Deep Visual-Semantic Hashing (DVSH) model that generates compact hash codes of images and sentences in an end-to-end deep learning architecture, which capture the intrinsic cross-modal correspondences between visual data and natural language. DVSH is a hybrid deep architecture that constitutes a visual-semantic fusion network for learning joint embedding space of images and text sentences, and two modality-specific hashing networks for learning hash functions to generate compact binary codes. Our architecture effectively unifies joint multimodal embedding and cross-modal hashing, which is based on a novel combination of Convolutional Neural Networks over images, Recurrent Neural Networks over sentences, and a structured max-margin objective that integrates all things together to enable learning of similarity-preserving and high-quality hash codes. Extensive empirical evidence shows that our DVSH approach yields state of the art results in cross-modal retrieval experiments on image-sentences datasets, i.e. standard IAPR TC-12 and large-scale Microsoft COCO. --- paper_title: Large-scale supervised multimodal hashing with semantic correlation maximization paper_content: Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing (SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability. --- paper_title: The classification of multi-modal data with hidden conditional random field paper_content: The classification of multi-modal data has been an active research topic in recent years. It has been used in many applications where the processing of multi-modal data is involved. Motivated by the assumption that different modalities in multi-modal data share latent structure (topics), this paper attempts to learn the shared structure by exploiting the symbiosis of multiple-modality and therefore boost the classification of multi-modal data, we call it Multi-modal Hidden Conditional Random Field (M-HCRF). M-HCRF represents the intrinsical structure shared by different modalities as hidden variables in a undirected general graphical model. When learning the latent shared structure of the multi-modal data, M-HCRF can discover the interactions among the hidden structure and the supervised category information. The experimental results show the effectiveness of our proposed M-HCRF when applied to the classification of multi-modal data. --- paper_title: Deep correspondence restricted Boltzmann machine for cross-modal retrieval paper_content: The task of cross-modal retrieval, i.e., using a text query to search for images or vice versa, has received considerable attention with the rapid growth of multi-modal web data. Modeling the correlations between different modalities is the key to tackle this problem. In this paper, we propose a correspondence restricted Boltzmann machine (Corr-RBM) to map the original features of bimodal data, such as image and text in our setting, into a low-dimensional common space, in which the heterogeneous data are comparable. In our Corr-RBM, two RBMs built for image and text, respectively are connected at their individual hidden representation layers by a correlation loss function. A single objective function is constructed to trade off the correlation loss and likelihoods of both modalities. Through the optimization of this objective function, our Corr-RBM is able to capture the correlations between two modalities and learn the representation of each modality simultaneously. Furthermore, we construct two deep neural structures using Corr-RBM as the main building block for the task of cross-modal retrieval. A number of comparison experiments are performed on three public real-world data sets. All of our models show significantly better results than state-of-the-art models in both searching images via text query and vice versa. --- paper_title: Data fusion through cross-modality metric learning using similarity-sensitive hashing paper_content: Visual understanding is often based on measuring similarity between observations. Learning similarities specific to a certain perception task from a set of examples has been shown advantageous in various computer vision and pattern recognition problems. In many important applications, the data that one needs to compare come from different representations or modalities, and the similarity between such data operates on objects that may have different and often incommensurable structure and dimensionality. In this paper, we propose a framework for supervised similarity learning based on embedding the input data from two arbitrary spaces into the Hamming space. The mapping is expressed as a binary classification problem with positive and negative examples, and can be efficiently learned using boosting algorithms. The utility and efficiency of such a generic approach is demonstrated on several challenging applications including cross-representation shape retrieval and alignment of multi-modal medical images. --- paper_title: Grounded Compositional Semantics for Finding and Describing Images with Sentences paper_content: Previous work on Recursive Neural Networks (RNNs) shows that these models can produce compositional feature vectors for accurately representing and classifying sentences or images. However, the sentence vectors of previous models cannot accurately represent visually grounded meaning. We introduce the DT-RNN model which uses dependency trees to embed sentences into a vector space in order to retrieve images that are described by those sentences. Unlike previous RNN-based models which use constituency trees, DT-RNNs naturally focus on the action and agents in a sentence. They are better able to abstract from the details of word order and syntactic expression. DT-RNNs outperform other recursive and recurrent neural networks, kernelized CCA and a bag-of-words baseline on the tasks of finding an image that fits a sentence description and vice versa. They also give more similar representations to sentences that describe the same image. --- paper_title: Deep Canonical Correlation Analysis paper_content: We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn complex nonlinear transformations of two views of data such that the resulting representations are highly linearly correlated. Parameters of both transformations are jointly learned to maximize the (regularized) total correlation. It can be viewed as a nonlinear extension of the linear method canonical correlation analysis (CCA). It is an alternative to the nonparametric method kernel canonical correlation analysis (KCCA) for learning correlated nonlinear transformations. Unlike KCCA, DCCA does not require an inner product, and has the advantages of a parametric method: training time scales well with data size and the training data need not be referenced when computing the representations of unseen instances. In experiments on two real-world datasets, we find that DCCA learns representations with significantly higher correlation than those learned by CCA and KCCA. We also introduce a novel non-saturating sigmoid function based on the cube root that may be useful more generally in feedforward neural networks. --- paper_title: Order-Embeddings of Images and Language paper_content: Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. We show that the resulting representations improve performance over current approaches for hypernym prediction and image-caption retrieval. --- paper_title: Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models paper_content: Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. image of a blue car - "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison. --- paper_title: Audiovisual Synchronization and Fusion Using Canonical Correlation Analysis paper_content: It is well-known that early integration (also called data fusion) is effective when the modalities are correlated, and late integration (also called decision or opinion fusion) is optimal when modalities are uncorrelated. In this paper, we propose a new multimodal fusion strategy for open-set speaker identification using a combination of early and late integration following canonical correlation analysis (CCA) of speech and lip texture features. We also propose a method for high precision synchronization of the speech and lip features using CCA prior to the proposed fusion. Experimental results show that i) the proposed fusion strategy yields the best equal error rates (EER), which are used to quantify the performance of the fusion strategy for open-set speaker identification, and ii) precise synchronization prior to fusion improves the EER; hence, the best EER is obtained when the proposed synchronization scheme is employed together with the proposed fusion strategy. We note that the proposed fusion strategy outperforms others because the features used in the late integration are truly uncorrelated, since they are output of the CCA analysis. --- paper_title: Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation paper_content: In the traditional object recognition pipeline, descriptors are densely sampled over an image, pooled into a high dimensional non-linear representation and then passed to a classifier. In recent years, Fisher Vectors have proven empirically to be the leading representation for a large variety of applications. The Fisher Vector is typically taken as the gradients of the log-likelihood of descriptors, with respect to the parameters of a Gaussian Mixture Model (GMM). Motivated by the assumption that different distributions should be applied for different datasets, we present two other Mixture Models and derive their Expectation-Maximization and Fisher Vector expressions. The first is a Laplacian Mixture Model (LMM), which is based on the Laplacian distribution. The second Mixture Model presented is a Hybrid Gaussian-Laplacian Mixture Model (HGLMM) which is based on a weighted geometric mean of the Gaussian and Laplacian distribution. An interesting property of the Expectation-Maximization algorithm for the latter is that in the maximization step, each dimension in each component is chosen to be either a Gaussian or a Laplacian. Finally, by using the new Fisher Vectors derived from HGLMMs, we achieve state-of-the-art results for both the image annotation and the image search by a sentence tasks. --- paper_title: Hashing for Similarity Search: A Survey paper_content: Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space. --- paper_title: Kernel and Nonlinear Canonical Correlation Analysis paper_content: We review a neural implementation of the statistical technique of Canonical Correlation Analysis (CCA) and extend it to nonlinear CCA. We then derive the method of kernel-based CCA and compare these two methods on real and artificial data sets before using both on the Blind Separation of Sources. --- paper_title: Jointly modeling deep video and compositional text to bridge vision and language in a unified framework paper_content: Recently, joint video-language modeling has been attracting more and more attention. However, most existing approaches focus on exploring the language model upon on a fixed visual model. In this paper, we propose a unified framework that jointly models video and the corresponding text sentences. The framework consists of three parts: a compositional semantics language model, a deep video model and a joint embedding model. In our language model, we propose a dependency-tree structure model that embeds sentence into a continuous vector space, which preserves visually grounded meanings and word order. In the visual model, we leverage deep neural networks to capture essential semantic information from videos. In the joint embedding model, we minimize the distance of the outputs of the deep video model and compositional language model in the joint space, and update these two models jointly. Based on these three parts, our system is able to accomplish three tasks: 1) natural language generation, and 2) video retrieval and 3) language retrieval. In the experiments, the results show our approach outperforms SVM, CRF and CCA baselines in predicting Subject-Verb-Object triplet and natural sentence generation, and is better than CCA in video retrieval and language retrieval tasks. --- paper_title: Jointly Modeling Embedding and Translation to Bridge Video and Language paper_content: Automatically describing video content with natural language is a fundamental challenge of computer vision. Re-current Neural Networks (RNNs), which models sequence dynamics, has attracted increasing attention on visual interpretation. However, most existing approaches generate a word locally with the given previous words and the visual content, while the relationship between sentence semantics and visual content is not holistically exploited. As a result, the generated sentences may be contextually correct but the semantics (e.g., subjects, verbs or objects) are not true. This paper presents a novel unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visual-semantic embedding. The former aims to locally maximize the probability of generating the next word given previous words and visual content, while the latter is to create a visual-semantic embedding space for enforcing the relationship between the semantics of the entire sentence and visual content. The experiments on YouTube2Text dataset show that our proposed LSTM-E achieves to-date the best published performance in generating natural sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. Superior performances are also reported on two movie description datasets (M-VAD and MPII-MD). In addition, we demonstrate that LSTM-E outperforms several state-of-the-art techniques in predicting Subject-Verb-Object (SVO) triplets. --- paper_title: Learning Hash Functions for Cross-View Similarity Search paper_content: Many applications in Multilingual and Multimodal Information Access involve searching large databases of high dimensional data objects with multiple (conditionally independent) views. In this work we consider the problem of learning hash functions for similarity search across the views for such applications. We propose a principled method for learning a hash function for each view given a set of multiview training data objects. The hash functions map similar objects to similar codes across the views thus enabling cross-view similarity search. We present results from an extensive empirical study of the proposed approach which demonstrate its effectiveness on Japanese language People Search and Multilingual People Search problems. --- paper_title: Deep Cross-Modal Hashing paper_content: Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications. However, most existing CMH methods are based on hand-crafted features which might not be optimally compatible with the hash-code learning procedure. As a result, existing CMH methods with hand-crafted features may not achieve satisfactory performance. In this paper, we propose a novel CMH method, called deep cross-modal hashing (DCMH), by integrating feature learning and hash-code learning intothe same framework. DCMH is an end-to-end learning framework with deep neural networks, one for each modality, to perform feature learning from scratch. Experiments on three real datasets with image-text modalities show that DCMH can outperform other baselines to achieve the state-of-the-art performance in cross-modal retrieval applications. --- paper_title: Neural Module Networks paper_content: Visual question answering is fundamentally compositional in nature---a question like "where is the dog?" shares substructure with questions like "what color is the dog?" and "where is the cat?" This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning neural module networks, which compose collections of jointly-trained neural "modules" into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained. We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes. --- paper_title: Pixel Recurrent Neural Networks paper_content: Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast two-dimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent. --- paper_title: Text-to-visual speech synthesis based on parameter generation from HMM paper_content: This paper presents a new technique for synthesizing visual speech from arbitrarily given text. The technique is based on an algorithm for parameter generation from HMM with dynamic features, which has been successfully applied to text-to-speech synthesis. In the training phase, syllable HMMs are trained with visual speech parameter sequences that represent lip movements. In the synthesis phase, a sentence HMM is constructed by concatenating syllable HMMs corresponding to the phonetic transcription for the input text. Then an optimum visual speech parameter sequence is generated from the sentence HMM in an ML sense. The proposed technique can generate synchronized lip movements with speech in a unified framework. Furthermore, coarticulation is implicitly incorporated into the generated mouth shapes. As a result, synthetic lip motion becomes smooth and realistic. --- paper_title: Generative Adversarial Text to Image Synthesis paper_content: Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions. --- paper_title: Neural Machine Translation by Jointly Learning to Align and Translate paper_content: Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition. --- paper_title: Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research paper_content: In this work, we introduce a dataset of video annotated with high quality natural language phrases describing the visual content in a given segment of time. Our dataset is based on the Descriptive Video Service (DVS) that is now encoded on many digital media products such as DVDs. DVS is an audio narration describing the visual elements and actions in a movie for the visually impaired. It is temporally aligned with the movie and mixed with the original movie soundtrack. We describe an automatic DVS segmentation and alignment method for movies, that enables us to scale up the collection of a DVS-derived dataset with minimal human intervention. Using this method, we have collected the largest DVS-derived dataset for video description of which we are aware. Our dataset currently includes over 84.6 hours of paired video/sentences from 92 DVDs and is growing. --- paper_title: Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures paper_content: Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the existing approaches based on how they conceptualize this problem, viz., models that cast description as either generation problem or as a retrieval problem over a visual or multimodal representational space. We provide a detailed review of existing models, highlighting their advantages and disadvantages. Moreover, we give an overview of the benchmark image datasets and the evaluation measures that have been developed to assess the quality of machine-generated image descriptions. Finally we extrapolate future directions in the area of automatic image description generation. --- paper_title: WaveNet: A Generative Model for Raw Audio paper_content: This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition. --- paper_title: Visually Indicated Sounds paper_content: Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. In this paper, we propose the task of predicting what sound an object makes when struck as a way of studying physical interactions within a visual scene. We present an algorithm that synthesizes sound from silent videos of people hitting and scratching objects with a drumstick. This algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We show that the sounds predicted by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that they convey significant information about material properties and physical interactions. --- paper_title: Show and tell: A neural image caption generator paper_content: Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art. --- paper_title: Unit selection in a concatenative speech synthesis system using a large speech database paper_content: One approach to the generation of natural-sounding synthesized speech waveforms is to select and concatenate units from a large speech database. Units (in the current work, phonemes) are selected to produce a natural realisation of a target phoneme sequence predicted from text which is annotated with prosodic and phonetic context information. We propose that the units in a synthesis database can be considered as a state transition network in which the state occupancy cost is the distance between a database unit and a target, and the transition cost is an estimate of the quality of concatenation of two consecutive units. This framework has many similarities to HMM-based speech recognition. A pruned Viterbi search is used to select the best units for synthesis from the database. This approach to waveform synthesis permits training from natural speech: two methods for training from speech are presented which provide weights which produce more natural speech than can be obtained by hand-tuning. --- paper_title: Microsoft COCO Captions: Data Collection and Evaluation Server paper_content: In this paper we describe the Microsoft COCO Caption dataset and evaluation server. When completed, the dataset will contain over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions will be provided. To ensure consistency in evaluation of automatic caption generation algorithms, an evaluation server is used. The evaluation server receives candidate captions and scores them using several popular metrics, including BLEU, METEOR, ROUGE and CIDEr. Instructions for using the evaluation server are provided. --- paper_title: Natural Language Description of Human Activities from Video Images Based on Concept Hierarchy of Actions paper_content: We propose a method for describing human activities from video images based on concept hierarchies of actions. Major difficulty in transforming video images into textual descriptions is how to bridge a semantic gap between them, which is also known as inverse Hollywood problem. In general, the concepts of events or actions of human can be classified by semantic primitives. By associating these concepts with the semantic features extracted from video images, appropriate syntactic components such as verbs, objects, etc. are determined and then translated into natural language sentences. We also demonstrate the performance of the proposed method by several experiments. --- paper_title: Im2Text: Describing Images Using 1 Million Captioned Photographs paper_content: We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. We also develop methods incorporating many state of the art, but fairly noisy, estimates of image content to produce even more pleasing results. Finally we introduce a new objective performance measure for image captioning. --- paper_title: Deep Fragment Embeddings for Bidirectional Image Sentence Mapping paper_content: We introduce a model for bidirectional retrieval of images and sentences through a deep, multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree relations) into a common space. We then introduce a structured max-margin objective that allows our model to explicitly associate these fragments across modalities. Extensive experimental evaluation shows that reasoning on both the global level of images and sentences and the finer level of their respective fragments improves performance on image-sentence retrieval tasks. Additionally, our model provides interpretable predictions for the image-sentence retrieval task since the inferred inter-modal alignment of fragments is explicit. --- paper_title: Phrase-based Image Captioning paper_content: Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred. Our approach, which is considerably simpler than state-of-the-art models, achieves comparable results in two popular datasets for the task: Flickr30k and the recently proposed Microsoft COCO. --- paper_title: Deep Visual-Semantic Hashing for Cross-Modal Retrieval paper_content: Due to the storage and retrieval efficiency, hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which enables efficient retrieval of images in response to text queries or vice versa, has received increasing attention recently. Most existing work on cross-modal hashing does not capture the spatial dependency of images and temporal dynamics of text sentences for learning powerful feature representations and cross-modal embeddings that mitigate the heterogeneity of different modalities. This paper presents a new Deep Visual-Semantic Hashing (DVSH) model that generates compact hash codes of images and sentences in an end-to-end deep learning architecture, which capture the intrinsic cross-modal correspondences between visual data and natural language. DVSH is a hybrid deep architecture that constitutes a visual-semantic fusion network for learning joint embedding space of images and text sentences, and two modality-specific hashing networks for learning hash functions to generate compact binary codes. Our architecture effectively unifies joint multimodal embedding and cross-modal hashing, which is based on a novel combination of Convolutional Neural Networks over images, Recurrent Neural Networks over sentences, and a structured max-margin objective that integrates all things together to enable learning of similarity-preserving and high-quality hash codes. Extensive empirical evidence shows that our DVSH approach yields state of the art results in cross-modal retrieval experiments on image-sentences datasets, i.e. standard IAPR TC-12 and large-scale Microsoft COCO. --- paper_title: Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics paper_content: In [Hodosh et al., 2013], we establish a rankingbased framework for sentence-based image description and retrieval. We introduce a new dataset of images paired with multiple descriptive captions that was specifically designed for these tasks. We also present strong KCCA-based baseline systems for description and search, and perform an in-depth study of evaluation metrics for these two tasks. Our results indicate that automatic evaluation metrics for our ranking-based tasks are more accurate and robust than those proposed for generation-based image description. --- paper_title: The classification of multi-modal data with hidden conditional random field paper_content: The classification of multi-modal data has been an active research topic in recent years. It has been used in many applications where the processing of multi-modal data is involved. Motivated by the assumption that different modalities in multi-modal data share latent structure (topics), this paper attempts to learn the shared structure by exploiting the symbiosis of multiple-modality and therefore boost the classification of multi-modal data, we call it Multi-modal Hidden Conditional Random Field (M-HCRF). M-HCRF represents the intrinsical structure shared by different modalities as hidden variables in a undirected general graphical model. When learning the latent shared structure of the multi-modal data, M-HCRF can discover the interactions among the hidden structure and the supervised category information. The experimental results show the effectiveness of our proposed M-HCRF when applied to the classification of multi-modal data. --- paper_title: Dynamic units of visual speech paper_content: We present a new method for generating a dynamic, concatenative, unit of visual speech that can generate realistic visual speech animation. We redefine visemes as temporal units that describe distinctive speech movements of the visual speech articulators. Traditionally visemes have been surmized as the set of static mouth shapes representing clusters of contrastive phonemes (e.g. /p, b, m/, and /f, v/). In this work, the motion of the visual speech articulators are used to generate discrete, dynamic visual speech gestures. These gestures are clustered, providing a finite set of movements that describe visual speech, the visemes. Dynamic visemes are applied to speech animation by simply concatenating viseme units. We compare to static visemes using subjective evaluation. We find that dynamic visemes are able to produce more accurate and visually pleasing speech animation given phonetically annotated audio, reducing the amount of time that an animator needs to spend manually refining the animation. --- paper_title: A Distributed Representation Based Query Expansion Approach for Image Captioning paper_content: In this paper, we propose a novel query expansion approach for improving transferbased automatic image captioning. The core idea of our method is to translate the given visual query into a distributional semantics based form, which is generated by the average of the sentence vectors extracted from the captions of images visually similar to the input image. Using three image captioning benchmark datasets, we show that our approach provides more accurate results compared to the state-of-theart data-driven methods in terms of both automatic metrics and subjective evaluation. --- paper_title: Language Models for Image Captioning: The Quirks and What Works paper_content: Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutional neural network (CNN) trained on images, and then a maximum entropy (ME) language model is used to arrange these words into a coherent sentence. The second uses the penultimate activation layer of the CNN as input to a recurrent neural network (RNN) that then generates the caption sequence. In this paper, we compare the merits of these different language modeling approaches for the first time by using the same state-ofthe-art CNN as input. We examine issues in the different approaches, including linguistic irregularities, caption repetition, and data set overlap. By combining key aspects of the ME and RNN methods, we achieve a new record performance over previously published results on the benchmark COCO dataset. However, the gains we see in BLEU do not translate to human judgments. --- paper_title: Collective Generation of Natural Image Descriptions paper_content: We present a holistic data-driven approach to image description generation, exploiting the vast amount of (noisy) parallel image data and associated natural language descriptions available on the web. More specifically, given a query image, we retrieve existing human-composed phrases used to describe visually similar images, then selectively combine those phrases to generate a novel description for the query image. We cast the generation process as constraint optimization problems, collectively incorporating multiple interconnected aspects of language composition for content planning, surface realization and discourse structure. Evaluation by human annotators indicates that our final system generates more semantically correct and linguistically appealing descriptions than two nontrivial baselines. --- paper_title: Speech-Driven Facial Animation Using A Shared Gaussian Process Latent Variable Model paper_content: In this work, synthesis of facial animation is done by modelling the mapping between facial motion and speech using the shared Gaussian process latent variable model. Both data are processed separately and subsequently coupled together to yield a shared latent space. This method allows coarticulation to be modelled by having a dynamical model on the latent space. Synthesis of novel animation is done by first obtaining intermediate latent points from the audio data and then using a Gaussian Process mapping to predict the corresponding visual data. Statistical evaluation of generated visual features against ground truth data compares favourably with known methods of speech animation. The generated videos are found to show proper synchronisation with audio and exhibit correct facial dynamics. --- paper_title: Grounded Compositional Semantics for Finding and Describing Images with Sentences paper_content: Previous work on Recursive Neural Networks (RNNs) shows that these models can produce compositional feature vectors for accurately representing and classifying sentences or images. However, the sentence vectors of previous models cannot accurately represent visually grounded meaning. We introduce the DT-RNN model which uses dependency trees to embed sentences into a vector space in order to retrieve images that are described by those sentences. Unlike previous RNN-based models which use constituency trees, DT-RNNs naturally focus on the action and agents in a sentence. They are better able to abstract from the details of word order and syntactic expression. DT-RNNs outperform other recursive and recurrent neural networks, kernelized CCA and a bag-of-words baseline on the tasks of finding an image that fits a sentence description and vice versa. They also give more similar representations to sentences that describe the same image. --- paper_title: Video Rewrite: driving visual speech with audio paper_content: Video Rewrite uses existing footage to create automatically new video of a person mouthing words that she did not speak in the original footage. This technique is useful in movie dubbing, for example, where the movie sequence can be modified to sync the actors’ lip motions to the new soundtrack. Video Rewrite automatically labels the phonemes in the training data and in the new audio track. Video Rewrite reorders the mouth images in the training footage to match the phoneme sequence of the new audio track. When particular phonemes are unavailable in the training footage, Video Rewrite selects the closest approximations. The resulting sequence of mouth images is stitched into the background footage. This stitching process automatically corrects for differences in head position and orientation between the mouth images and the background footage. Video Rewrite uses computer-vision techniques to track points on the speaker’s mouth in the training footage, and morphing techniques to combine these mouth gestures into the final video sequence. The new video combines the dynamics of the original actor’s articulations with the mannerisms and setting dictated by the background footage. Video Rewrite is the first facial-animation system to automate all the labeling and assembly tasks required to resync existing footage to a new soundtrack. --- paper_title: Unit selection in a concatenative speech synthesis system using a large speech database paper_content: One approach to the generation of natural-sounding synthesized speech waveforms is to select and concatenate units from a large speech database. Units (in the current work, phonemes) are selected to produce a natural realisation of a target phoneme sequence predicted from text which is annotated with prosodic and phonetic context information. We propose that the units in a synthesis database can be considered as a state transition network in which the state occupancy cost is the distance between a database unit and a target, and the transition cost is an estimate of the quality of concatenation of two consecutive units. This framework has many similarities to HMM-based speech recognition. A pruned Viterbi search is used to select the best units for synthesis from the database. This approach to waveform synthesis permits training from natural speech: two methods for training from speech are presented which provide weights which produce more natural speech than can be obtained by hand-tuning. --- paper_title: Every picture tells a story: generating sentences from images paper_content: Humans can prepare concise descriptions of pictures, focusing on what they find important. We demonstrate that automatic methods can do so too. We describe a system that can compute a score linking an image to a sentence. This score can be used to attach a descriptive sentence to a given image, or to obtain images that illustrate a given sentence. The score is obtained by comparing an estimate of meaning obtained from the image to one obtained from the sentence. Each estimate of meaning comes from a discriminative procedure that is learned us-ingdata. We evaluate on a novel dataset consisting of human-annotated images. While our underlying estimate of meaning is impoverished, it is sufficient to produce very good quantitative results, evaluated with a novel score that can account for synecdoche. --- paper_title: Jointly modeling deep video and compositional text to bridge vision and language in a unified framework paper_content: Recently, joint video-language modeling has been attracting more and more attention. However, most existing approaches focus on exploring the language model upon on a fixed visual model. In this paper, we propose a unified framework that jointly models video and the corresponding text sentences. The framework consists of three parts: a compositional semantics language model, a deep video model and a joint embedding model. In our language model, we propose a dependency-tree structure model that embeds sentence into a continuous vector space, which preserves visually grounded meanings and word order. In the visual model, we leverage deep neural networks to capture essential semantic information from videos. In the joint embedding model, we minimize the distance of the outputs of the deep video model and compositional language model in the joint space, and update these two models jointly. Based on these three parts, our system is able to accomplish three tasks: 1) natural language generation, and 2) video retrieval and 3) language retrieval. In the experiments, the results show our approach outperforms SVM, CRF and CCA baselines in predicting Subject-Verb-Object triplet and natural sentence generation, and is better than CCA in video retrieval and language retrieval tasks. --- paper_title: Choosing linguistics over vision to describe images paper_content: In this paper, we address the problem of automatically generating human-like descriptions for unseen images, given a collection of images and their corresponding human-generated descriptions. Previous attempts for this task mostly rely on visual clues and corpus statistics, but do not take much advantage of the semantic information inherent in the available image descriptions. Here, we present a generic method which benefits from all these three sources (i. e. visual clues, corpus statistics and available descriptions) simultaneously, and is capable of constructing novel descriptions. Our approach works on syntactically and linguistically motivated phrases extracted from the human descriptions. Experimental evaluations demonstrate that our formulation mostly generates lucid and semantically correct descriptions, and significantly outperforms the previous methods on automatic evaluation metrics. One of the significant advantages of our approach is that we can generate multiple interesting descriptions for an image. Unlike any previous work, we also test the applicability of our method on a large dataset containing complex images with rich descriptions. --- paper_title: TTS Synthesis with Bidirectional LSTM based Recurrent Neural Networks paper_content: Feed-forward, Deep neural networks (DNN)-based text-tospeech (TTS) systems have been recently shown to outperform decision-tree clustered context-dependent HMM TTS systems [1, 4]. However, the long time span contextual effect in a speech utterance is still not easy to accommodate, due to the intrinsic, feed-forward nature in DNN-based modeling. Also, to synthesize a smooth speech trajectory, the dynamic features are commonly used to constrain speech parameter trajectory generation in HMM-based TTS [2]. In this paper, Recurrent Neural Networks (RNNs) with Bidirectional Long Short Term Memory (BLSTM) cells are adopted to capture the correlation or co-occurrence information between any two instants in a speech utterance for parametric TTS synthesis. Experimental results show that a hybrid system of DNN and BLSTM-RNN, i.e., lower hidden layers with a feed-forward structure which is cascaded with upper hidden layers with a bidirectional RNN structure of LSTM, can outperform either the conventional, decision tree-based HMM, or a DNN TTS system, both objectively and subjectively. The speech trajectory generated by the BLSTM-RNN TTS is fairly smooth and no dynamic constraints are needed. --- paper_title: Listen, attend and spell: A neural network for large vocabulary conversational speech recognition paper_content: We present Listen, Attend and Spell (LAS), a neural speech recognizer that transcribes speech utterances directly to characters without pronunciation models, HMMs or other components of traditional speech recognizers. In LAS, the neural network architecture subsumes the acoustic, pronunciation and language models making it not only an end-to-end trained system but an end-to-end model. In contrast to DNN-HMM, CTC and most other models, LAS makes no independence assumptions about the probability distribution of the output character sequences given the acoustic sequence. Our system has two components: a listener and a speller. The listener is a pyramidal recurrent network encoder that accepts filter bank spectra as inputs. The speller is an attention-based recurrent network decoder that emits each character conditioned on all previous characters, and the entire acoustic sequence. On a Google voice search task, LAS achieves a WER of 14.1% without a dictionary or an external language model and 10.3% with language model rescoring over the top 32 beams. In comparison, the state-of-the-art CLDNN-HMM model achieves a WER of 8.0% on the same set. --- paper_title: Video In Sentences Out paper_content: We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it. Action class is rendered as a verb, participant objects as noun phrases, properties of those objects as adjectival modifiers in those noun phrases, spatial relations between those participants as prepositional phrases, and characteristics of the event as prepositional-phrase adjuncts and adverbial modifiers. Extracting the information needed to render these linguistic entities requires an approach to event recognition that recovers object tracks, the trackto-role assignments, and changing body posture. --- paper_title: Sequence to Sequence Learning with Neural Networks paper_content: Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier. --- paper_title: Generative Adversarial Text to Image Synthesis paper_content: Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions. --- paper_title: Expressive Visual Text-to-Speech Using Active Appearance Models paper_content: This paper presents a complete system for expressive visual text-to-speech (VTTS), which is capable of producing expressive output, in the form of a 'talking head', given an input text and a set of continuous expression weights. The face is modeled using an active appearance model (AAM), and several extensions are proposed which make it more applicable to the task of VTTS. The model allows for normalization with respect to both pose and blink state which significantly reduces artifacts in the resulting synthesized sequences. We demonstrate quantitative improvements in terms of reconstruction error over a million frames, as well as in large-scale user studies, comparing the output of different systems. --- paper_title: Statistical Parametric Speech Synthesis paper_content: This paper gives a general overview of techniques in statistical parametric speech synthesis. One of the instances of these techniques, called HMM-based generation synthesis (or simply HMM-based synthesis), has recently been shown to be very effective in generating acceptable speech synthesis. This paper also contrasts these techniques with the more conventional unit selection technology that has dominated speech synthesis over the last ten years. Advantages and disadvantages of statistical parametric synthesis are highlighted as well as identifying where we expect the key developments to appear in the immediate future. --- paper_title: Composing Simple Image Descriptions using Web-scale N-grams paper_content: Studying natural language, and especially how people describe the world around them can help us better understand the visual world. In turn, it can also help us in the quest to generate natural language that describes this world in a human manner. We present a simple yet effective approach to automatically compose image descriptions given computer vision based inputs and using web-scale n-grams. Unlike most previous work that summarizes or retrieves pre-existing text relevant to an image, our method composes sentences entirely from scratch. Experimental results indicate that it is viable to generate simple textual descriptions that are pertinent to the specific content of an image, while permitting creativity in the description -- making for more human-like annotations than previous approaches. --- paper_title: Describing Videos by Exploiting Temporal Structure paper_content: Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description model. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions. --- paper_title: Neural Machine Translation by Jointly Learning to Align and Translate paper_content: Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition. --- paper_title: Imagenet classification with deep convolutional neural networks paper_content: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry. --- paper_title: Guiding the Long-Short Term Memory Model for Image Caption Generation paper_content: In this work we focus on the problem of image caption generation. We propose an extension of the long short term memory (LSTM) model, which we coin gLSTM for short. In particular, we add semantic information extracted from the image as extra input to each unit of the LSTM block, with the aim of guiding the model towards solutions that are more tightly coupled to the image content. Additionally, we explore different length normalization strategies for beam search to avoid bias towards short sentences. On various benchmark datasets such as Flickr8K, Flickr30K and MS COCO, we obtain results that are on par with or better than the current state-of-the-art. --- paper_title: Two-Stream Convolutional Networks for Action Recognition in Videos paper_content: We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video. The challenge is to capture the complementary information on appearance from still frames and motion between frames. We also aim to generalise the best performing hand-crafted features within a data-driven learning framework. ::: ::: Our contribution is three-fold. First, we propose a two-stream ConvNet architecture which incorporates spatial and temporal networks. Second, we demonstrate that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data. Finally, we show that multitask learning, applied to two different action classification datasets, can be used to increase the amount of training data and improve the performance on both. Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art. It also exceeds by a large margin previous attempts to use deep nets for video classification. --- paper_title: Bringing Semantics into Focus Using Visual Abstraction paper_content: Relating visual information to its linguistic semantic meaning remains an open and challenging area of research. The semantic meaning of images depends on the presence of objects, their attributes and their relations to other objects. But precisely characterizing this dependence requires extracting complex visual information from an image, which is in general a difficult and yet unsolved problem. In this paper, we propose studying semantic information in abstract images created from collections of clip art. Abstract images provide several advantages. They allow for the direct study of how to infer high-level semantic information, since they remove the reliance on noisy low-level object, attribute and relation detectors, or the tedious hand-labeling of images. Importantly, abstract images also allow the ability to generate sets of semantically similar scenes. Finding analogous sets of semantically similar real images would be nearly impossible. We create 1,002 sets of 10 semantically similar abstract scenes with corresponding written descriptions. We thoroughly analyze this dataset to discover semantically important features, the relations of words to visual features and methods for measuring semantic similarity. --- paper_title: BabyTalk: Understanding and Generating Simple Image Descriptions paper_content: We posit that visually descriptive language offers computer vision researchers both information about the world, and information about how people describe the world. The potential benefit from this source is made more significant due to the enormous amount of language data easily available today. We present a system to automatically generate natural language descriptions from images that exploits both statistics gleaned from parsing large quantities of text data and recognition algorithms from computer vision. The system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work. --- paper_title: Dynamic units of visual speech paper_content: We present a new method for generating a dynamic, concatenative, unit of visual speech that can generate realistic visual speech animation. We redefine visemes as temporal units that describe distinctive speech movements of the visual speech articulators. Traditionally visemes have been surmized as the set of static mouth shapes representing clusters of contrastive phonemes (e.g. /p, b, m/, and /f, v/). In this work, the motion of the visual speech articulators are used to generate discrete, dynamic visual speech gestures. These gestures are clustered, providing a finite set of movements that describe visual speech, the visemes. Dynamic visemes are applied to speech animation by simply concatenating viseme units. We compare to static visemes using subjective evaluation. We find that dynamic visemes are able to produce more accurate and visually pleasing speech animation given phonetically annotated audio, reducing the amount of time that an animator needs to spend manually refining the animation. --- paper_title: Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild paper_content: This paper integrates techniques in natural language processing and computer vision to improve recognition and description of entities and activities in real-world videos. We propose a strategy for generating textual descriptions of videos by using a factor graph to combine visual detections with language statistics. We use state-of-the-art visual recognition systems to obtain confidences on entities, activities, and scenes present in the video. Our factor graph model combines these detection confidences with probabilistic knowledge mined from text corpora to estimate the most likely subject, verb, object, and place. Results on YouTube videos show that our approach improves both the joint detection of these latent, diverse sentence components and the detection of some individual components when compared to using the vision system alone, as well as over a previous n-gram language-modeling approach. The joint detection allows us to automatically generate more accurate, richer sentential descriptions of videos with a wide array of possible content. --- paper_title: Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures paper_content: Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the existing approaches based on how they conceptualize this problem, viz., models that cast description as either generation problem or as a retrieval problem over a visual or multimodal representational space. We provide a detailed review of existing models, highlighting their advantages and disadvantages. Moreover, we give an overview of the benchmark image datasets and the evaluation measures that have been developed to assess the quality of machine-generated image descriptions. Finally we extrapolate future directions in the area of automatic image description generation. --- paper_title: Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) paper_content: In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, we apply the m-RNN model to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval. The project page of this work is: www.stat.ucla.edu/~junhua.mao/m-RNN.html . --- paper_title: Language Models for Image Captioning: The Quirks and What Works paper_content: Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutional neural network (CNN) trained on images, and then a maximum entropy (ME) language model is used to arrange these words into a coherent sentence. The second uses the penultimate activation layer of the CNN as input to a recurrent neural network (RNN) that then generates the caption sequence. In this paper, we compare the merits of these different language modeling approaches for the first time by using the same state-ofthe-art CNN as input. We examine issues in the different approaches, including linguistic irregularities, caption repetition, and data set overlap. By combining key aspects of the ME and RNN methods, we achieve a new record performance over previously published results on the benchmark COCO dataset. However, the gains we see in BLEU do not translate to human judgments. --- paper_title: WaveNet: A Generative Model for Raw Audio paper_content: This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition. --- paper_title: Speech-Driven Facial Animation Using A Shared Gaussian Process Latent Variable Model paper_content: In this work, synthesis of facial animation is done by modelling the mapping between facial motion and speech using the shared Gaussian process latent variable model. Both data are processed separately and subsequently coupled together to yield a shared latent space. This method allows coarticulation to be modelled by having a dynamical model on the latent space. Synthesis of novel animation is done by first obtaining intermediate latent points from the audio data and then using a Gaussian Process mapping to predict the corresponding visual data. Statistical evaluation of generated visual features against ground truth data compares favourably with known methods of speech animation. The generated videos are found to show proper synchronisation with audio and exhibit correct facial dynamics. --- paper_title: Speech recognition with deep recurrent neural networks paper_content: Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score. --- paper_title: I2t: Image parsing to text description paper_content: In this paper, we present an image parsing to text description (I2T) framework that generates text descriptions of image and video content based on image understanding. The proposed I2T framework follows three steps: 1) input images (or video frames) are decomposed into their constituent visual patterns by an image parsing engine, in a spirit similar to parsing sentences in natural language; 2) the image parsing results are converted into semantic representation in the form of Web ontology language (OWL), which enables seamless integration with general knowledge bases; and 3) a text generation engine converts the results from previous steps into semantically meaningful, human readable, and query-able text reports. The centerpiece of the I2T framework is an and-or graph (AoG) visual knowledge representation, which provides a graphical representation serving as prior knowledge for representing diverse visual patterns and provides top-down hypotheses during the image parsing. The AoG embodies vocabularies of visual elements including primitives, parts, objects, scenes as well as a stochastic image grammar that specifies syntactic relations (i.e., compositional) and semantic relations (e.g., categorical, spatial, temporal, and functional) between these visual elements. Therefore, the AoG is a unified model of both categorical and symbolic representations of visual knowledge. The proposed I2T framework has two objectives. First, we use semiautomatic method to parse images from the Internet in order to build an AoG for visual knowledge representation. Our goal is to make the parsing process more and more automatic using the learned AoG model. Second, we use automatic methods to parse image/video in specific domains and generate text reports that are useful for real-world applications. In the case studies at the end of this paper, we demonstrate two automatic I2T systems: a maritime and urban scene video surveillance system and a real-time automatic driving scene understanding system. --- paper_title: Visually Indicated Sounds paper_content: Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. In this paper, we propose the task of predicting what sound an object makes when struck as a way of studying physical interactions within a visual scene. We present an algorithm that synthesizes sound from silent videos of people hitting and scratching objects with a drumstick. This algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We show that the sounds predicted by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that they convey significant information about material properties and physical interactions. --- paper_title: Statistical Parametric Speech Synthesis Based on Speaker and Language Factorization paper_content: An increasingly common scenario in building speech synthesis and recognition systems is training on inhomogeneous data. This paper proposes a new framework for estimating hidden Markov models on data containing both multiple speakers and multiple languages. The proposed framework, speaker and language factorization, attempts to factorize speaker-/language-specific characteristics in the data and then model them using separate transforms. Language-specific factors in the data are represented by transforms based on cluster mean interpolation with cluster-dependent decision trees. Acoustic variations caused by speaker characteristics are handled by transforms based on constrained maximum-likelihood linear regression. Experimental results on statistical parametric speech synthesis show that the proposed framework enables data from multiple speakers in different languages to be used to: train a synthesis system; synthesize speech in a language using speaker characteristics estimated in a different language; and adapt to a new language. --- paper_title: Show, Attend and Tell: Neural Image Caption Generation with Visual Attention paper_content: Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr9k, Flickr30k and MS COCO. --- paper_title: Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models paper_content: Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. image of a blue car - "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison. --- paper_title: Generating Images from Captions with Attention paper_content: Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions. The proposed model iteratively draws patches on a canvas, while attending to the relevant words in the description. After training on Microsoft COCO, we compare our model with several baseline generative models on image generation and retrieval tasks. We demonstrate that our model produces higher quality samples than other approaches and generates images with novel scene compositions corresponding to previously unseen captions in the dataset. --- paper_title: Midge: Generating Image Descriptions From Computer Vision Detections paper_content: This paper introduces a novel generation system that composes humanlike descriptions of images from computer vision detections. By leveraging syntactically informed word co-occurrence statistics, the generator filters and constrains the noisy detections output from a vision system to generate syntactic trees that detail what the computer vision system sees. Results show that the generation system outperforms state-of-the-art systems, automatically generating some of the most natural image descriptions to date. --- paper_title: Show and tell: A neural image caption generator paper_content: Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art. --- paper_title: Recurrent Continuous Translation Models paper_content: We introduce a class of probabilistic continuous translation models called Recurrent Continuous Translation Models that are purely based on continuous representations for words, phrases and sentences and do not rely on alignments or phrasal translation units. The models have a generation and a conditioning aspect. The generation of the translation is modelled with a target Recurrent Language Model, whereas the conditioning on the source sentence is modelled with a Convolutional Sentence Model. Through various experiments, we show first that our models obtain a perplexity with respect to gold translations that is > 43% lower than that of stateof-the-art alignment-based translation models. Secondly, we show that they are remarkably sensitive to the word order, syntax, and meaning of the source sentence despite lacking alignments. Finally we show that they match a state-of-the-art system when rescoring n-best lists of translations. --- paper_title: Wav2Letter: an End-to-End ConvNet-based Speech Recognition System paper_content: This paper presents a simple end-to-end model for speech recognition, combining a convolutional network based acoustic model and a graph decoding. It is trained to output letters, with transcribed speech, without the need for force alignment of phonemes. We introduce an automatic segmentation criterion for training from sequence annotation without alignment that is on par with CTC (Graves et al., 2006) while being simpler. We show competitive results in word error rate on the Librispeech corpus (Panayotov et al., 2015) with MFCC features, and promising results from raw waveform. --- paper_title: Unit selection in a concatenative speech synthesis system using a large speech database paper_content: One approach to the generation of natural-sounding synthesized speech waveforms is to select and concatenate units from a large speech database. Units (in the current work, phonemes) are selected to produce a natural realisation of a target phoneme sequence predicted from text which is annotated with prosodic and phonetic context information. We propose that the units in a synthesis database can be considered as a state transition network in which the state occupancy cost is the distance between a database unit and a target, and the transition cost is an estimate of the quality of concatenation of two consecutive units. This framework has many similarities to HMM-based speech recognition. A pruned Viterbi search is used to select the best units for synthesis from the database. This approach to waveform synthesis permits training from natural speech: two methods for training from speech are presented which provide weights which produce more natural speech than can be obtained by hand-tuning. --- paper_title: WordsEye: an automatic text-to-scene conversion system paper_content: Natural language is an easy and effective medium for describing visual ideas and mental images. Thus, we foresee the emergence of language-based 3D scene generation systems to let ordinary users quickly create 3D scenes without having to learn special software, acquire artistic skills, or even touch a desktop window-oriented interface. WordsEye is such a system for automatically converting text into representative 3D scenes. WordsEye relies on a large database of 3D models and poses to depict entities and actions. Every 3D model can have associated shape displacements, spatial tags, and functional properties to be used in the depiction process. We describe the linguistic analysis and depiction techniques used by WordsEye along with some general strategies by which more abstract concepts are made depictable. --- paper_title: Distributed Representations of Words and Phrases and their Compositionality paper_content: The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. ::: ::: An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. --- paper_title: Image Description using Visual Dependency Representations paper_content: Describing the main event of an image involves identifying the objects depicted and predicting the relationships between them. Previous approaches have represented images as unstructured bags of regions, which makes it difficult to accurately predict meaningful relationships between regions. In this paper, we introduce visual dependency representations to capture the relationships between the objects in an image, and hypothesize that this representation can improve image description. We test this hypothesis using a new data set of region-annotated images, associated with visual dependency representations and gold-standard descriptions. We describe two template-based description generation models that operate over visual dependency representations. In an image description task, we find that these models outperform approaches that rely on object proximity or corpus information to generate descriptions on both automatic measures and on human judgements. --- paper_title: The Long-Short Story of Movie Description paper_content: Generating descriptions for videos has many applications including assisting blind people and human-robot interaction. The recent advances in image captioning as well as the release of large-scale movie description datasets such as MPII-MD [28] and M-VAD [31] allow to study this task in more depth. Many of the proposed methods for image captioning rely on pre-trained object classifier CNNs and Long Short-Term Memory recurrent networks (LSTMs) for generating descriptions. While image description focuses on objects, we argue that it is important to distinguish verbs, objects, and places in the setting of movie description. In this work we show how to learn robust visual classifiers from the weak annotations of the sentence descriptions. Based on these classifiers we generate a description using an LSTM. We explore different design choices to build and train the LSTM and achieve the best performance to date on the challenging MPII-MD and M-VAD datasets. We compare and analyze our approach and prior work along various dimensions to better understand the key challenges of the movie description task. --- paper_title: Very Deep Convolutional Networks for Large-Scale Image Recognition paper_content: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. --- paper_title: Generative Adversarial Nets paper_content: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples. --- paper_title: Convolutional Two-Stream Network Fusion for Video Action Recognition paper_content: Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information. We study a number of ways of fusing ConvNet towers both spatially and temporally in order to best take advantage of this spatio-temporal information. We make the following findings: (i) that rather than fusing at the softmax layer, a spatial and temporal network can be fused at a convolution layer without loss of performance, but with a substantial saving in parameters, (ii) that it is better to fuse such networks spatially at the last convolutional layer than earlier, and that additionally fusing at the class prediction layer can boost accuracy, finally (iii) that pooling of abstract convolutional features over spatiotemporal neighbourhoods further boosts performance. Based on these studies we propose a new ConvNet architecture for spatiotemporal fusion of video snippets, and evaluate its performance on standard benchmarks where this architecture achieves state-of-the-art results. --- paper_title: Jointly Modeling Embedding and Translation to Bridge Video and Language paper_content: Automatically describing video content with natural language is a fundamental challenge of computer vision. Re-current Neural Networks (RNNs), which models sequence dynamics, has attracted increasing attention on visual interpretation. However, most existing approaches generate a word locally with the given previous words and the visual content, while the relationship between sentence semantics and visual content is not holistically exploited. As a result, the generated sentences may be contextually correct but the semantics (e.g., subjects, verbs or objects) are not true. This paper presents a novel unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visual-semantic embedding. The former aims to locally maximize the probability of generating the next word given previous words and visual content, while the latter is to create a visual-semantic embedding space for enforcing the relationship between the semantics of the entire sentence and visual content. The experiments on YouTube2Text dataset show that our proposed LSTM-E achieves to-date the best published performance in generating natural sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. Superior performances are also reported on two movie description datasets (M-VAD and MPII-MD). In addition, we demonstrate that LSTM-E outperforms several state-of-the-art techniques in predicting Subject-Verb-Object (SVO) triplets. --- paper_title: Trainable Methods For Surface Natural Language Generation paper_content: We present three systems for surface natural language generation that are trainable from annotated corpora. The first two systems, called NLG1 and NLG2, require a corpus marked only with domainspecific semantic attributes, while the last system, called NLG3, requires a corpus marked with both semantic attributes and syntactic dependency information. All systems attempt to produce a grammatical natural language phrase from a domain-specific semantic representation. NLG1 serves a baseline system and uses phrase frequencies to generate a whole phrase in one step, while NLG2 and NLG3 use maximum entropy probability models to individually generate each word in the phrase. The systems NLG2 and NLG3 learn to determine both the word choice and the word order of the phrase. We present experiments in which we generate phrases to describe flights in the air travel domain. --- paper_title: Natural Language Description of Human Activities from Video Images Based on Concept Hierarchy of Actions paper_content: We propose a method for describing human activities from video images based on concept hierarchies of actions. Major difficulty in transforming video images into textual descriptions is how to bridge a semantic gap between them, which is also known as inverse Hollywood problem. In general, the concepts of events or actions of human can be classified by semantic primitives. By associating these concepts with the semantic features extracted from video images, appropriate syntactic components such as verbs, objects, etc. are determined and then translated into natural language sentences. We also demonstrate the performance of the proposed method by several experiments. --- paper_title: Deep Fragment Embeddings for Bidirectional Image Sentence Mapping paper_content: We introduce a model for bidirectional retrieval of images and sentences through a deep, multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree relations) into a common space. We then introduce a structured max-margin objective that allows our model to explicitly associate these fragments across modalities. Extensive experimental evaluation shows that reasoning on both the global level of images and sentences and the finer level of their respective fragments improves performance on image-sentence retrieval tasks. Additionally, our model provides interpretable predictions for the image-sentence retrieval task since the inferred inter-modal alignment of fragments is explicit. --- paper_title: Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images paper_content: We address a question answering task on real-world images that is set up as a Visual Turing Test. By combining latest advances in image representation and natural language processing, we propose Neural-Image-QA, an end-to-end formulation to this problem for which all parts are trained jointly. In contrast to previous efforts, we are facing a multi-modal problem where the language output (answer) is conditioned on visual and natural language input (image and question). Our approach Neural-Image-QA doubles the performance of the previous best approach on this problem. We provide additional insights into the problem by analyzing how much information is contained only in the language part for which we provide a new human baseline. To study human consensus, which is related to the ambiguities inherent in this challenging task, we propose two novel metrics and collect additional answers which extends the original DAQUAR dataset to DAQUAR-Consensus. --- paper_title: Expressive Visual Text-to-Speech Using Active Appearance Models paper_content: This paper presents a complete system for expressive visual text-to-speech (VTTS), which is capable of producing expressive output, in the form of a 'talking head', given an input text and a set of continuous expression weights. The face is modeled using an active appearance model (AAM), and several extensions are proposed which make it more applicable to the task of VTTS. The model allows for normalization with respect to both pose and blink state which significantly reduces artifacts in the resulting synthesized sequences. We demonstrate quantitative improvements in terms of reconstruction error over a million frames, as well as in large-scale user studies, comparing the output of different systems. --- paper_title: Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics paper_content: In [Hodosh et al., 2013], we establish a rankingbased framework for sentence-based image description and retrieval. We introduce a new dataset of images paired with multiple descriptive captions that was specifically designed for these tasks. We also present strong KCCA-based baseline systems for description and search, and perform an in-depth study of evaluation metrics for these two tasks. Our results indicate that automatic evaluation metrics for our ranking-based tasks are more accurate and robust than those proposed for generation-based image description. --- paper_title: BabyTalk: Understanding and Generating Simple Image Descriptions paper_content: We posit that visually descriptive language offers computer vision researchers both information about the world, and information about how people describe the world. The potential benefit from this source is made more significant due to the enormous amount of language data easily available today. We present a system to automatically generate natural language descriptions from images that exploits both statistics gleaned from parsing large quantities of text data and recognition algorithms from computer vision. The system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work. --- paper_title: Dynamic units of visual speech paper_content: We present a new method for generating a dynamic, concatenative, unit of visual speech that can generate realistic visual speech animation. We redefine visemes as temporal units that describe distinctive speech movements of the visual speech articulators. Traditionally visemes have been surmized as the set of static mouth shapes representing clusters of contrastive phonemes (e.g. /p, b, m/, and /f, v/). In this work, the motion of the visual speech articulators are used to generate discrete, dynamic visual speech gestures. These gestures are clustered, providing a finite set of movements that describe visual speech, the visemes. Dynamic visemes are applied to speech animation by simply concatenating viseme units. We compare to static visemes using subjective evaluation. We find that dynamic visemes are able to produce more accurate and visually pleasing speech animation given phonetically annotated audio, reducing the amount of time that an animator needs to spend manually refining the animation. --- paper_title: Automatic Evaluation Of Summaries Using N-Gram Co-Occurrence Statistics paper_content: Following the recent adoption by the machine translation community of automatic evaluation using the BLEU/NIST scoring process, we conduct an in-depth study of a similar idea for evaluating summaries. The results show that automatic evaluation using unigram co-occurrences between summary pairs correlates surprising well with human evaluations, based on various statistical metrics; while direct application of the BLEU evaluation procedure does not always give good results. --- paper_title: WaveNet: A Generative Model for Raw Audio paper_content: This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition. --- paper_title: Comparing Automatic Evaluation Measures for Image Description paper_content: Image description is a new natural language generation task, where the aim is to generate a human-like description of an image. The evaluation of computer-generated text is a notoriously difficult problem, however, the quality of image descriptions has typically been measured using unigram BLEU and human judgements. The focus of this paper is to determine the correlation of automatic measures with human judgements for this task. We estimate the correlation of unigram and Smoothed BLEU, TER, ROUGE-SU4, and Meteor against human judgements on two data sets. The main finding is that unigram BLEU has a weak correlation, and Meteor has the strongest correlation with human judgements. --- paper_title: Movie Description paper_content: Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016. --- paper_title: Statistical Parametric Speech Synthesis Based on Speaker and Language Factorization paper_content: An increasingly common scenario in building speech synthesis and recognition systems is training on inhomogeneous data. This paper proposes a new framework for estimating hidden Markov models on data containing both multiple speakers and multiple languages. The proposed framework, speaker and language factorization, attempts to factorize speaker-/language-specific characteristics in the data and then model them using separate transforms. Language-specific factors in the data are represented by transforms based on cluster mean interpolation with cluster-dependent decision trees. Acoustic variations caused by speaker characteristics are handled by transforms based on constrained maximum-likelihood linear regression. Experimental results on statistical parametric speech synthesis show that the proposed framework enables data from multiple speakers in different languages to be used to: train a synthesis system; synthesize speech in a language using speaker characteristics estimated in a different language; and adapt to a new language. --- paper_title: Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models paper_content: Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. image of a blue car - "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison. --- paper_title: Generation and Comprehension of Unambiguous Object Descriptions paper_content: We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or interpret such an expression to infer which object is being described. We show that our method outperforms previous methods that generate descriptions of objects without taking into account other potentially ambiguous objects in the scene. Our model is inspired by recent successes of deep learning methods for image captioning, but while image captioning is difficult to evaluate, our task allows for easy objective evaluation. We also present a new large-scale dataset for referring expressions, based on MSCOCO. We have released the dataset and a toolbox for visualization and evaluation, see https://github.com/ mjhucla/Google_Refexp_toolbox. --- paper_title: Midge: Generating Image Descriptions From Computer Vision Detections paper_content: This paper introduces a novel generation system that composes humanlike descriptions of images from computer vision detections. By leveraging syntactically informed word co-occurrence statistics, the generator filters and constrains the noisy detections output from a vision system to generate syntactic trees that detail what the computer vision system sees. Results show that the generation system outperforms state-of-the-art systems, automatically generating some of the most natural image descriptions to date. --- paper_title: Meteor Universal: Language Specific Translation Evaluation for Any Target Language paper_content: This paper describes Meteor Universal, released for the 2014 ACL Workshop on Statistical Machine Translation. Meteor Universal brings language specific evaluation to previously unsupported target languages by (1) automatically extracting linguistic resources (paraphrase tables and function word lists) from the bitext used to train MT systems and (2) using a universal parameter set learned from pooling human judgments of translation quality from several language directions. Meteor Universal is shown to significantly outperform baseline BLEU on two new languages, Russian (WMT13) and Hindi (WMT14). --- paper_title: Bleu: A Method For Automatic Evaluation Of Machine Translation paper_content: Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations. --- paper_title: Microsoft COCO Captions: Data Collection and Evaluation Server paper_content: In this paper we describe the Microsoft COCO Caption dataset and evaluation server. When completed, the dataset will contain over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions will be provided. To ensure consistency in evaluation of automatic caption generation algorithms, an evaluation server is used. The evaluation server receives candidate captions and scores them using several popular metrics, including BLEU, METEOR, ROUGE and CIDEr. Instructions for using the evaluation server are provided. --- paper_title: What Are You Talking About? Text-to-Image Coreference paper_content: In this paper we exploit natural sentential descriptions of RGB-D scenes in order to improve 3D semantic parsing. Importantly, in doing so, we reason about which particular object each noun/pronoun is referring to in the image. This allows us to utilize visual information in order to disambiguate the so-called coreference resolution problem that arises in text. Towards this goal, we propose a structure prediction model that exploits potentials computed from text and RGB-D imagery to reason about the class of the 3D objects, the scene type, as well as to align the nouns/pronouns with the referred visual objects. We demonstrate the effectiveness of our approach on the challenging NYU-RGBD v2 dataset, which we enrich with natural lingual descriptions. We show that our approach significantly improves 3D detection and scene classification accuracy, and is able to reliably estimate the text-to-image alignment. Furthermore, by using textual and visual information, we are also able to successfully deal with coreference in text, improving upon the state-of-the-art Stanford coreference system [15]. --- paper_title: Deep Fragment Embeddings for Bidirectional Image Sentence Mapping paper_content: We introduce a model for bidirectional retrieval of images and sentences through a deep, multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree relations) into a common space. We then introduce a structured max-margin objective that allows our model to explicitly associate these fragments across modalities. Extensive experimental evaluation shows that reasoning on both the global level of images and sentences and the finer level of their respective fragments improves performance on image-sentence retrieval tasks. Additionally, our model provides interpretable predictions for the image-sentence retrieval task since the inferred inter-modal alignment of fragments is explicit. --- paper_title: Deep visual-semantic alignments for generating image descriptions paper_content: We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions significantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations. --- paper_title: Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books paper_content: Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie/book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for. --- paper_title: What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision paper_content: We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task. In particular, we focus on the cooking domain, where the instructions correspond to the recipe. Our technique relies on an HMM to align the recipe steps to the (automatically generated) speech transcript. We then refine this alignment using a state-of-the-art visual food detector, based on a deep convolutional neural network. We show that our technique outperforms simpler techniques based on keyword spotting. It also enables interesting applications, such as automatically illustrating recipes with keyframes, and searching within a video for events of interest. --- paper_title: Unsupervised alignment of actions in video with text descriptions paper_content: Advances in video technology and data storage have made large scale video data collections of complex activities readily accessible. An increasingly popular approach for automatically inferring the details of a video is to associate the spatio-temporal segments in a video with its natural language descriptions. Most algorithms for connecting natural language with video rely on pre-aligned supervised training data. Recently, several models have been shown to be effective for unsupervised alignment of objects in video with language. However, it remains difficult to generate good spatio-temporal video segments for actions that align well with language. This paper presents a framework that extracts higher level representations of low-level action features through hyperfeature coding from video and aligns them with language. We propose a two-step process that creates a high-level action feature codebook with temporally consistent motions, and then applies an unsupervised alignment algorithm over the action codewords and verbs in the language to identify individual activities. We show an improvement over previous alignment models of objects and nouns on videos of biological experiments, and also evaluate our system on a larger scale collection of videos involving kitchen activities. --- paper_title: Movie/Script: Alignment and Parsing of Video and Text Transcription paper_content: Movies and TV are a rich source of diverse and complex video of people, objects, actions and locales "in the wild". Harvesting automatically labeled sequences of actions from video would enable creation of large-scale and highly-varied datasets. To enable such collection, we focus on the task of recovering scene structure in movies and TV series for object tracking and action retrieval. We present a weakly supervised algorithm that uses the screenplay and closed captions to parse a movie into a hierarchy of shots and scenes. Scene boundaries in the movie are aligned with screenplay scene labels and shots are reordered into a sequence of long continuous tracks or threads which allow for more accurate tracking of people, actions and objects. Scene segmentation, alignment, and shot threading are formulated as inference in a unified generative model and a novel hierarchical dynamic programming algorithm that can handle alignment and jump-limited reorderings in linear time is presented. We present quantitative and qualitative results on movie alignment and parsing, and use the recovered structure to improve character naming and retrieval of common actions in several episodes of popular TV series. --- paper_title: Alignment of Speech to Highly Imperfect Text Transcriptions paper_content: We introduce a novel and inexpensive approach for the temporal alignment of speech to highly imperfect transcripts from automatic speech recognition (ASR). Transcripts are generated for extended lecture and presentation videos, which in some cases feature more than 30 speakers with different accents, resulting in highly varying transcription qualities. In our approach we detect a subset of phonemes in the speech track, and align them to the sequence of phonemes extracted from the transcript. We report on the results for 4 speech-transcript sets ranging from 22 to 108 minutes. The alignment performance is promising, showing a correct matching of phonemes within 10, 20, 30 second error margins for more than 60 %, 75 %, 90 % of text, respectively, on average. For perfect manually generated transcripts, more than 75 % of text is correctly aligned within 5 seconds. --- paper_title: Deep Canonical Time Warping paper_content: Machine learning algorithms for the analysis of timeseries often depend on the assumption that the utilised data are temporally aligned. Any temporal discrepancies arising in the data is certain to lead to ill-generalisable models, which in turn fail to correctly capture the properties of the task at hand. The temporal alignment of time-series is thus a crucial challenge manifesting in a multitude of applications. Nevertheless, the vast majority of algorithms oriented towards the temporal alignment of time-series are applied directly on the observation space, or utilise simple linear projections. Thus, they fail to capture complex, hierarchical non-linear representations which may prove to be beneficial towards the task of temporal alignment, particularly when dealing with multi-modal data (e.g., aligning visual and acoustic information). To this end, we present the Deep Canonical Time Warping (DCTW), a method which automatically learns complex non-linear representations of multiple time-series, generated such that (i) they are highly correlated, and (ii) temporally in alignment. By means of experiments on four real datasets, we show that the representations learnt via the proposed DCTW significantly outperform state-of-the-art methods in temporal alignment, elegantly handling scenarios with highly heterogeneous features, such as the temporal alignment of acoustic and visual features. --- paper_title: Canonical Time Warping for Alignment of Human Behavior paper_content: Alignment of time series is an important problem to solve in many scientific disciplines. In particular, temporal alignment of two or more subjects performing similar activities is a challenging problem due to the large temporal scale difference between human actions as well as the inter/intra subject variability. In this paper we present canonical time warping (CTW), an extension of canonical correlation analysis (CCA) for spatio-temporal alignment of human motion between two subjects. CTW extends previous work on CCA in two ways: (i) it combines CCA with dynamic time warping (DTW), and (ii) it extends CCA by allowing local spatial deformations. We show CTW's effectiveness in three experiments: alignment of synthetic data, alignment of motion capture data of two subjects performing similar actions, and alignment of similar facial expressions made by two people. Our results demonstrate that CTW provides both visually and qualitatively better alignment than state-of-the-art techniques based on DTW. --- paper_title: Weakly Supervised Action Labeling in Videos Under Ordering Constraints paper_content: We are given a set of video clips, each one annotated with an {\em ordered} list of actions, such as "walk" then "sit" then "answer phone" extracted from, for example, the associated text script. We seek to temporally localize the individual actions in each clip as well as to learn a discriminative classifier for each action. We formulate the problem as a weakly supervised temporal assignment with ordering constraints. Each video clip is divided into small time intervals and each time interval of each video clip is assigned one action label, while respecting the order in which the action labels appear in the given annotations. We show that the action label assignment can be determined together with learning a classifier for each action in a discriminative manner. We evaluate the proposed model on a new and challenging dataset of 937 video clips with a total of 787720 frames containing sequences of 16 different actions from 69 Hollywood movies. --- paper_title: Generalized time warping for multi-modal alignment of human motion paper_content: Temporal alignment of human motion has been a topic of recent interest due to its applications in animation, telerehabilitation and activity recognition among others. This paper presents generalized time warping (GTW), an extension of dynamic time warping (DTW) for temporally aligning multi-modal sequences from multiple subjects performing similar activities. GTW solves three major drawbacks of existing approaches based on DTW: (1) GTW provides a feature weighting layer to adapt different modalities (e.g., video and motion capture data), (2) GTW extends DTW by allowing a more flexible time warping as combination of monotonic functions, (3) unlike DTW that typically incurs in quadratic cost, GTW has linear complexity. Experimental results demonstrate that GTW can efficiently solve the multi-modal temporal alignment problem and outperforms state-of-the-art DTW methods for temporal alignment of time series within the same modality. --- paper_title: Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books paper_content: Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie/book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for. --- paper_title: Natural Language Object Retrieval paper_content: In this paper, we address the task of natural language object retrieval, to localize a target object within a given image based on a natural language query of the object. Natural language object retrieval differs from text-based image retrieval task as it involves spatial information about objects within the scene and global scene context. To address this issue, we propose a novel Spatial Context Recurrent ConvNet (SCRC) model as scoring function on candidate boxes for object retrieval, integrating spatial configurations and global scene-level contextual information into the network. Our model processes query text, local image descriptors, spatial configurations and global context features through a recurrent network, outputs the probability of the query text conditioned on each candidate box as a score for the box, and can transfer visual-linguistic knowledge from image captioning domain to our task. Experimental results demonstrate that our method effectively utilizes both local and global information, outperforming previous baseline methods significantly on different datasets and scenarios, and can exploit large scale vision and language datasets for knowledge transfer. --- paper_title: Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models paper_content: The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. Such annotations are essential for continued progress in automatic image description and grounded language understanding. They enable us to define a new benchmark for localization of textual entity mentions in an image. We present a strong baseline for this task that combines an image-text embedding, detectors for common objects, a color classifier, and a bias towards selecting larger objects. While our baseline rivals in accuracy more complex state-of-the-art models, we show that its gains cannot be easily parlayed into improvements on such tasks as image-sentence retrieval, thus underlining the limitations of current methods and the need for further research. --- paper_title: Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion paper_content: Speaker diarization consists of assigning speech signals to people engaged in a dialogue. An audio-visual spatiotemporal diarization model is proposed. The model is well suited for challenging scenarios that consist of several participants engaged in multi-party interaction while they move around and turn their heads towards the other participants rather than facing the cameras and the microphones. Multiple-person visual tracking is combined with multiple speech-source localization in order to tackle the speech-to-person association problem. The latter is solved within a novel audio-visual fusion method on the following grounds: binaural spectral features are first extracted from a microphone pair, then a supervised audio-visual alignment technique maps these features onto an image, and finally a semi-supervised clustering method assigns binaural spectral features to visible persons. The main advantage of this method over previous work is that it processes in a principled way speech signals uttered simultaneously by multiple persons. The diarization itself is cast into a latent-variable temporal graphical model that infers speaker identities and speech turns, based on the output of an audio-visual association process, executed at each time slice, and on the dynamics of the diarization variable itself. The proposed formulation yields an efficient exact inference procedure. A novel dataset, that contains audio-visual training data as well as a number of scenarios involving several participants engaged in formal and informal dialogue, is introduced. The proposed method is thoroughly tested and benchmarked with respect to several state-of-the art diarization algorithms. --- paper_title: Generation and Comprehension of Unambiguous Object Descriptions paper_content: We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or interpret such an expression to infer which object is being described. We show that our method outperforms previous methods that generate descriptions of objects without taking into account other potentially ambiguous objects in the scene. Our model is inspired by recent successes of deep learning methods for image captioning, but while image captioning is difficult to evaluate, our task allows for easy objective evaluation. We also present a new large-scale dataset for referring expressions, based on MSCOCO. We have released the dataset and a toolbox for visualization and evaluation, see https://github.com/ mjhucla/Google_Refexp_toolbox. --- paper_title: Audio-to-text alignment for speech recognition with very limited resources. paper_content: In this paper we present our efforts in building a speech recognizer constrained by the availability of very limited resources. We consider that neither proper training databases nor initial acoustic models are available for the target language. Moreover, for the experiments shown here, we use grapheme-based speech recognizers. Most prior work in the area use initial acoustic models, trained on the target or a similar language, to force-align new data and then retrain the models with it. In the proposed approach a speech recognizer is trained from scratch by using audio recordings aligned with (sometimes approximate) text transcripts. All training data has been harvested online (e.g. audiobooks, parliamentary speeches). First, the audio is decoded into a phoneme sequence by an off-theshelf phonetic recognizer in Hungarian. Phoneme sequences are then aligned to the normalized text transcripts through dynamic programming. Correspondence between phonemes and graphemes is done through a matrix of approximate sound-tographeme matching. Finally, the aligned data is split into short audio/text segments and the speech recognizer is trained using Kaldi toolkit. Alignment experiments performed for Catalan and Spanish show the feasibility to obtain accurate alignments that can be used to successfully train a speech recognizer. --- paper_title: Book2Movie: Aligning video scenes with book chapters paper_content: Film adaptations of novels often visually display in a few shots what is described in many pages of the source novel. In this paper we present a new problem: to align book chapters with video scenes. Such an alignment facilitates finding differences between the adaptation and the original source, and also acts as a basis for deriving rich descriptions from the novel for the video clips. We propose an efficient method to compute an alignment between book chapters and video scenes using matching dialogs and character identities as cues. A major consideration is to allow the alignment to be non-sequential. Our suggested shortest path based approach deals with the non-sequential alignments and can be used to determine whether a video scene was part of the original book. We create a new data set involving two popular novel-to-film adaptations with widely varying properties and compare our method against other text-to-video alignment baselines. Using the alignment, we present a qualitative analysis of describing the video through rich narratives obtained from the novel. --- paper_title: Multimodal Speaker Diarization paper_content: We present a novel probabilistic framework that fuses information coming from the audio and video modality to perform speaker diarization. The proposed framework is a Dynamic Bayesian Network (DBN) that is an extension of a factorial Hidden Markov Model (fHMM) and models the people appearing in an audiovisual recording as multimodal entities that generate observations in the audio stream, the video stream, and the joint audiovisual space. The framework is very robust to different contexts, makes no assumptions about the location of the recording equipment, and does not require labeled training data as it acquires the model parameters using the Expectation Maximization (EM) algorithm. We apply the proposed model to two meeting videos and a news broadcast video, all of which come from publicly available data sets. The results acquired in speaker diarization are in favor of the proposed multimodal framework, which outperforms the single modality analysis results and improves over the state-of-the-art audio-based speaker diarization. --- paper_title: What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision paper_content: We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task. In particular, we focus on the cooking domain, where the instructions correspond to the recipe. Our technique relies on an HMM to align the recipe steps to the (automatically generated) speech transcript. We then refine this alignment using a state-of-the-art visual food detector, based on a deep convolutional neural network. We show that our technique outperforms simpler techniques based on keyword spotting. It also enables interesting applications, such as automatically illustrating recipes with keyframes, and searching within a video for events of interest. --- paper_title: Unsupervised alignment of natural language instructions with video segments paper_content: We propose an unsupervised learning algorithm for automatically inferring the mappings between English nouns and corresponding video objects. Given a sequence of natural language instructions and an unaligned video recording, we simultaneously align each instruction to its corresponding video segment, and also align nouns in each instruction to their corresponding objects in video. While existing grounded language acquisition algorithms rely on pre-aligned supervised data (each sentence paired with corresponding image frame or video segment), our algorithm aims to automatically infer the alignment from the temporal structure of the video and parallel text instructions. We propose two generative models that are closely related to the HMM and IBM 1 word alignment models used in statistical machine translation. We evaluate our algorithm on videos of biological experiments performed in wetlabs, and demonstrate its capability of aligning video segments to text instructions and matching video objects to nouns in the absence of any direct supervision. --- paper_title: Modeling Context in Referring Expressions paper_content: Humans refer to objects in their environments all the time, especially in dialogue with other people. We explore generating and comprehending natural language referring expressions for objects in images. In particular, we focus on incorporating better measures of visual context into referring expression models and find that visual comparison to other objects within an image helps improve performance significantly. We also develop methods to tie the language generation process together, so that we generate expressions for all objects of a particular category jointly. Evaluation on three recent datasets - RefCOCO, RefCOCO+, and RefCOCOg (Datasets and toolbox can be downloaded from https://github.com/lichengunc/refer), shows the advantages of our methods for both referring expression generation and comprehension. --- paper_title: Aligning plot synopses to videos for story-based retrieval paper_content: We propose a method to facilitate search through the storyline of TV series episodes. To this end, we use human written, crowdsourced descriptions—plot synopses—of the story conveyed in the video. We obtain such synopses from websites such as Wikipedia and propose various methods to align each sentence of the plot to shots in the video. Thus, the semantic story-based video retrieval problem is transformed into a much simpler text-based search. Finally, we return the set of shots aligned to the sentences as the video snippet corresponding to the query. The alignment is performed by first computing a similarity score between every shot and sentence through cues such as character identities and keyword matches between plot synopses and subtitles. We then formulate the alignment as an optimization problem and solve it efficiently using dynamic programming. We evaluate our methods on the fifth season of a TV series Buffy the Vampire Slayer and show encouraging results for both the alignment and the retrieval of story events. --- paper_title: Discriminative Unsupervised Alignment of Natural Language Instructions with Corresponding Video Segments paper_content: We address the problem of automatically aligning natural language sentences with corresponding video segments without any direct supervision. Most existing algorithms for integrating language with videos rely on handaligned parallel data, where each natural language sentence is manually aligned with its corresponding image or video segment. Recently, fully unsupervised alignment of text with video has been shown to be feasible using hierarchical generative models. In contrast to the previous generative models, we propose three latent-variable discriminative models for the unsupervised alignment task. The proposed discriminative models are capable of incorporating domain knowledge, by adding diverse and overlapping features. The results show that discriminative models outperform the generative models in terms of alignment accuracy. --- paper_title: Weakly-Supervised Alignment of Video with Text paper_content: Suppose that we are given a set of videos, along with natural language descriptions in the form of multiple sentences (e.g., manual annotations, movie scripts, sport summaries etc.), and that these sentences appear in the same temporal order as their visual counterparts. We propose in this paper a method for aligning the two modalities, i.e., automatically providing a time stamp for every sentence. Given vectorial features for both video and text, we propose to cast this task as a temporal assignment problem, with an implicit linear mapping between the two feature modalities. We formulate this problem as an integer quadratic program, and solve its continuous convex relaxation using an efficient conditional gradient algorithm. Several rounding procedures are proposed to construct the final integer solution. After demonstrating significant improvements over the state of the art on the related task of aligning video with symbolic labels [7], we evaluate our method on a challenging dataset of videos with associated textual descriptions [36], using both bag-of-words and continuous representations for text. --- paper_title: What Are You Talking About? Text-to-Image Coreference paper_content: In this paper we exploit natural sentential descriptions of RGB-D scenes in order to improve 3D semantic parsing. Importantly, in doing so, we reason about which particular object each noun/pronoun is referring to in the image. This allows us to utilize visual information in order to disambiguate the so-called coreference resolution problem that arises in text. Towards this goal, we propose a structure prediction model that exploits potentials computed from text and RGB-D imagery to reason about the class of the 3D objects, the scene type, as well as to align the nouns/pronouns with the referred visual objects. We demonstrate the effectiveness of our approach on the challenging NYU-RGBD v2 dataset, which we enrich with natural lingual descriptions. We show that our approach significantly improves 3D detection and scene classification accuracy, and is able to reliably estimate the text-to-image alignment. Furthermore, by using textual and visual information, we are also able to successfully deal with coreference in text, improving upon the state-of-the-art Stanford coreference system [15]. --- paper_title: On the Integration of Grounding Language and Learning Objects paper_content: This paper presents a multimodal learning system that can ground spoken names of objects in their physical referents and learn to recognize those objects simultaneously from naturally co-occurring multisensory input. There are two technical problems involved: (1) the correspondence problem in symbol grounding - how to associate words (symbols) with their perceptually grounded meanings from multiple cooccurrences between words and objects in the physical environment. (2) object learning - how to recognize and categorize visual objects. We argue that those two problems can be fundamentally simplified by considering them in a general system and incorporating the spatio-temporal and cross-modal constraints of multimodal data. The system collects egocentric data including image sequences as well as speech while users perform natural tasks. It is able to automatically infer the meanings of object names from vision, and categorize objects based on teaching signals potentially encoded in speech. The experimental results reported in this paper reveal the effectiveness of using multimodal data and integrating heterogeneous techniques in machine learning, natural language processing and computer vision. --- paper_title: Isotonic CCA for sequence alignment and activity recognition paper_content: This paper presents an approach for sequence alignment based on canonical correlation analysis(CCA). We show that a novel set of constraints imposed on traditional CCA leads to canonical solutions with the time warping property, i.e., non-decreasing monotonicity in time. This formulation generalizes the more traditional dynamic time warping (DTW) solutions to cases where the alignment is accomplished on arbitrary subsequence segments, optimally determined from data, instead on individual sequence samples. We then introduce a robust and efficient algorithm to find such alignments using non-negative least squares reductions. Experimental results show that this new method, when applied to MOCAP activity recognition problems, can yield improved recognition accuracy. --- paper_title: Listen, attend and spell: A neural network for large vocabulary conversational speech recognition paper_content: We present Listen, Attend and Spell (LAS), a neural speech recognizer that transcribes speech utterances directly to characters without pronunciation models, HMMs or other components of traditional speech recognizers. In LAS, the neural network architecture subsumes the acoustic, pronunciation and language models making it not only an end-to-end trained system but an end-to-end model. In contrast to DNN-HMM, CTC and most other models, LAS makes no independence assumptions about the probability distribution of the output character sequences given the acoustic sequence. Our system has two components: a listener and a speller. The listener is a pyramidal recurrent network encoder that accepts filter bank spectra as inputs. The speller is an attention-based recurrent network decoder that emits each character conditioned on all previous characters, and the entire acoustic sequence. On a Google voice search task, LAS achieves a WER of 14.1% without a dictionary or an external language model and 10.3% with language model rescoring over the top 32 beams. In comparison, the state-of-the-art CLDNN-HMM model achieves a WER of 8.0% on the same set. --- paper_title: Deep Fragment Embeddings for Bidirectional Image Sentence Mapping paper_content: We introduce a model for bidirectional retrieval of images and sentences through a deep, multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree relations) into a common space. We then introduce a structured max-margin objective that allows our model to explicitly associate these fragments across modalities. Extensive experimental evaluation shows that reasoning on both the global level of images and sentences and the finer level of their respective fragments improves performance on image-sentence retrieval tasks. Additionally, our model provides interpretable predictions for the image-sentence retrieval task since the inferred inter-modal alignment of fragments is explicit. --- paper_title: Describing Videos by Exploiting Temporal Structure paper_content: Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description model. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions. --- paper_title: Neural Machine Translation by Jointly Learning to Align and Translate paper_content: Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition. --- paper_title: Deep visual-semantic alignments for generating image descriptions paper_content: We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions significantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations. --- paper_title: Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences paper_content: We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents. Our alignment-based encoder-decoder model with long short-term memory recurrent neural networks (LSTM-RNN) translates natural language instructions to action sequences based upon a representation of the observable world state. We introduce a multi-level aligner that empowers our model to focus on sentence "regions" salient to the current world state by using multiple abstractions of the input sentence. In contrast to existing methods, our model uses no specialized linguistic resources (e.g., parsers) or task-specific annotations (e.g., seed lexicons). It is therefore generalizable, yet still achieves the best results reported to-date on a benchmark single-sentence dataset and competitive results for the limited-training multi-sentence setting. We analyze our model through a series of ablations that elucidate the contributions of the primary components of our model. --- paper_title: Attention-Based Models for Speech Recognition paper_content: Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks including machine translation, handwriting synthesis [1,2] and image caption generation [3]. We extend the attention-mechanism with features needed for speech recognition. We show that while an adaptation of the model used for machine translation in [2] reaches a competitive 18.7% phoneme error rate (PER) on the TIMET phoneme recognition task, it can only be applied to utterances which are roughly as long as the ones it was trained on. We offer a qualitative explanation of this failure and propose a novel and generic method of adding location-awareness to the attention mechanism to alleviate this issue. The new method yields a model that is robust to long inputs and achieves 18% PER in single utterances and 20% in 10-times longer (repeated) utterances. Finally, we propose a change to the attention mechanism that prevents it from concentrating too much on single frames, which further reduces PER to 17.6% level. --- paper_title: Show, Attend and Tell: Neural Image Caption Generation with Visual Attention paper_content: Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr9k, Flickr30k and MS COCO. --- paper_title: Stacked Attention Networks for Image Question Answering paper_content: This paper presents stacked attention networks (SANs) that learn to answer natural language questions from images. SANs use semantic representation of a question as query to search for the regions in an image that are related to the answer. We argue that image question answering (QA) often requires multiple steps of reasoning. Thus, we develop a multiple-layer SAN in which we query an image multiple times to infer the answer progressively. Experiments conducted on four image QA data sets demonstrate that the proposed SANs significantly outperform previous state-of-the-art approaches. The visualization of the attention layers illustrates the progress that the SAN locates the relevant visual clues that lead to the answer of the question layer-by-layer. --- paper_title: Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks paper_content: We present an approach that exploits hierarchical Recurrent Neural Networks (RNNs) to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a realistic video. Our hierarchical framework contains a sentence generator and a paragraph generator. The sentence generator produces one simple short sentence that describes a specific short video interval. It exploits both temporal- and spatial-attention mechanisms to selectively focus on visual elements during generation. The paragraph generator captures the inter-sentence dependency by taking as input the sentential embedding produced by the sentence generator, combining it with the paragraph history, and outputting the new initial state for the sentence generator. We evaluate our approach on two large-scale benchmark datasets: YouTubeClips and TACoS-MultiLevel. The experiments demonstrate that our approach significantly outperforms the current state-of-the-art methods with BLEU@4 scores 0.499 and 0.305 respectively. --- paper_title: Hierarchical Co-Attention for Visual Question Answering paper_content: A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling "where to look" or visual attention, it is equally important to model "what words to listen to" or question attention. We present a novel co-attention model for VQA that jointly reasons about image and question attention. In addition, our model reasons about the question and consequently the image via the co-attention mechanism in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN) model. Our final model outperforms all reported methods, improving the state-of-the-art on the VQA dataset from 60.4% to 62.1%, and from 61.6% to 65.4% on the COCO-QA dataset. --- paper_title: Dynamic Memory Networks for Visual and Textual Question Answering paper_content: Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training or whether it could be applied to other modalities such as images. Based on an analysis of the DMN, we propose several improvements to its memory and input modules. Together with these changes we introduce a novel input module for images in order to be able to answer visual questions. Our new DMN+ model improves the state of the art on both the Visual Question Answering dataset and the \babi-10k text question-answering dataset without supporting fact supervision. --- paper_title: Leveraging Video Descriptions to Learn Video Question Answering paper_content: We propose a scalable approach to learn video-based question answering (QA): answer a "free-form natural language question" about a video content. Our approach automatically harvests a large number of videos and descriptions freely available online. Then, a large number of candidate QA pairs are automatically generated from descriptions rather than manually annotated. Next, we use these candidate QA pairs to train a number of video-based QA methods extended fromMN (Sukhbaatar et al. 2015), VQA (Antol et al. 2015), SA (Yao et al. 2015), SS (Venugopalan et al. 2015). In order to handle non-perfect candidate QA pairs, we propose a self-paced learning procedure to iteratively identify them and mitigate their effects in training. Finally, we evaluate performance on manually generated video-based QA pairs. The results show that our self-paced learning procedure is effective, and the extended SS model outperforms various baselines. --- paper_title: An HMM-based system for automatic segmentation and alignment of speech paper_content: A system for automatic time-aligned phone transcription of spoken Swedish has been developed. Using a speech recording and an orthographic transcription of the words spoken in the recording the system is able to generate a phone-level segmentation without manual intervention. The system uses a technique based on Hidden Markov Models to position 85.5% of all boundary positions within 20 ms of manually segmented reference boundaries on a set of test recordings. --- paper_title: Analyzing the Behavior of Visual Question Answering Models paper_content: Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA). The performance of most models is clustered around 60-70%. In this paper we propose systematic methods to analyze the behavior of these models as a first step towards recognizing their strengths and weaknesses, and identifying the most fruitful directions for progress. We analyze two models, one each from two major classes of VQA models -- with-attention and without-attention and show the similarities and differences in the behavior of these models. We also analyze the winning entry of the VQA Challenge 2016. ::: Our behavior analysis reveals that despite recent progress, today's VQA models are "myopic" (tend to fail on sufficiently novel instances), often "jump to conclusions" (converge on a predicted answer after 'listening' to just half the question), and are "stubborn" (do not change their answers across images). --- paper_title: Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding paper_content: Modeling textual or visual information with vector representations trained from large language or visual datasets has been successfully explored in recent years. However, tasks such as visual question answering require combining these vector representations with each other. Approaches to multimodal pooling include element-wise product or sum, as well as concatenation of the visual and textual representations. We hypothesize that these methods are not as expressive as an outer product of the visual and textual vectors. As the outer product is typically infeasible due to its high dimensionality, we instead propose utilizing Multimodal Compact Bilinear pooling (MCB) to efficiently and expressively combine multimodal features. We extensively evaluate MCB on the visual question answering and grounding tasks. We consistently show the benefit of MCB over ablations without MCB. For visual question answering, we present an architecture which uses MCB twice, once for predicting attention over spatial features and again to combine the attended representation with the question representation. This model outperforms the state-of-the-art on the Visual7W dataset and the VQA challenge. --- paper_title: Integration of acoustic and visual speech signals using neural networks paper_content: Results from a series of experiments that use neural networks to process the visual speech signals of a male talker are presented. In these preliminary experiments, the results are limited to static images of vowels. It is demonstrated that these networks are able to extract speech information from the visual images and that this information can be used to improve automatic vowel recognition. The structure of speech and its corresponding acoustic and visual signals are reviewed. The specific data that was used in the experiments along with the network architectures and algorithms are described. The results of integrating the visual and auditory signals for vowel recognition in the presence of acoustic noise are presented. > --- paper_title: Multimodal Video Indexing: A Review of the State-of-the-art paper_content: Efficient and effective handling of video documents depends on the availability of indexes. Manual indexing is unfeasible for large video collections. In this paper we survey several methods aiming at automating this time and resource consuming process. Good reviews on single modality based video indexing have appeared in literature. Effective indexing, however, requires a multimodal approach in which either the most appropriate modality is selected or the different modalities are used in collaborative fashion. Therefore, instead of separately treating the different information sources involved, and their specific algorithms, we focus on the similarities and differences between the modalities. To that end we put forward a unifying and multimodal framework, which views a video document from the perspective of its author. This framework forms the guiding principle for identifying index types, for which automatic methods are found in literature. It furthermore forms the basis for categorizing these different methods. --- paper_title: A survey of affect recognition methods: audio, visual and spontaneous expressions paper_content: Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. Promising approaches have been reported, including automatic methods for facial and vocal affect recognition. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions-despite the fact that deliberate behavior differs in visual and audio expressions from spontaneously occurring behavior. Recently efforts to develop algorithms that can process naturally occurring human affective behavior have emerged. This paper surveys these efforts. We first discuss human emotion perception from a psychological perspective. Next, we examine the available approaches to solving the problem of machine understanding of human affective behavior occurring in real-world settings. We finally outline some scientific and engineering challenges for advancing human affect sensing technology. --- paper_title: Medical Image Fusion: A survey of the state of the art paper_content: Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years. --- paper_title: Multimodal Emotion Recognition in Response to Videos paper_content: This paper presents a user-independent emotion recognition method with the goal of recovering affective tags for videos using electroencephalogram (EEG), pupillary response and gaze distance. We first selected 20 video clips with extrinsic emotional content from movies and online resources. Then, EEG responses and eye gaze data were recorded from 24 participants while watching emotional video clips. Ground truth was defined based on the median arousal and valence scores given to clips in a preliminary study using an online questionnaire. Based on the participants' responses, three classes for each dimension were defined. The arousal classes were calm, medium aroused, and activated and the valence classes were unpleasant, neutral, and pleasant. One of the three affective labels of either valence or arousal was determined by classification of bodily responses. A one-participant-out cross validation was employed to investigate the classification performance in a user-independent approach. The best classification accuracies of 68.5 percent for three labels of valence and 76.4 percent for three labels of arousal were obtained using a modality fusion strategy and a support vector machine. The results over a population of 24 participants demonstrate that user-independent emotion recognition can outperform individual self-reports for arousal assessments and do not underperform for valence assessments. --- paper_title: Multimodal fusion for multimedia analysis: a survey paper_content: This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion. --- paper_title: Multimedia classification and event detection using double fusion paper_content: Multimedia Event Detection(MED) is a multimedia retrieval task with the goal of finding videos of a particular event in video archives, given example videos and event descriptions; different from MED, multimedia classification is a task that classifies given videos into specified classes. Both tasks require mining features of example videos to learn the most discriminative features, with best performance resulting from a combination of multiple complementary features. How to combine different features is the focus of this paper. Generally, early fusion and late fusion are two popular combination strategies. The former one fuses features before performing classification and the latter one combines output of classifiers from different features. Early fusion can better capture the relationship among features yet is prone to over-fit the training data. Late fusion deals with the over-fitting problem better but does not allow classifiers to train on all the data at the same time. In this paper, we introduce a fusion scheme named double fusion, which simply combines early fusion and late fusion together to incorporate their advantages. Results are reported on the TRECVID MED 2010, MED 2011, UCF50 and HMDB51 datasets. For the MED 2010 dataset, we get a mean minimal normalized detection cost (MMNDC) of 0.49, which exceeds the state-of-the-art performance by more than 12 percent. On the TRECVID MED 2011 test dataset, we achieve a MMNDC of 0.51, which is the second best among all 19 participants. On UCF50 and HMDB51, we obtain classification accuracy of 88.1 % and 48.7 % respectively, which are the best reported results to date. --- paper_title: Recent advances in the automatic recognition of audio-visual speech paper_content: Visual speech information from the speaker's mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability in the human computer interface. In this paper, we review the main components of audiovisual automatic speech recognition (ASR) and present novel contributions in two main areas: first, the visual front-end design, based on a cascade of linear image transforms of an appropriate video region of interest, and subsequently, audiovisual speech integration. On the latter topic, we discuss new work on feature and decision fusion combination, the modeling of audiovisual speech asynchrony, and incorporating modality reliability estimates to the bimodal recognition process. We also briefly touch upon the issue of audiovisual adaptation. We apply our algorithms to three multisubject bimodal databases, ranging from small- to large-vocabulary recognition tasks, recorded in both visually controlled and challenging environments. Our experiments demonstrate that the visual modality improves ASR over all conditions and data considered, though less so for visually challenging environments and large vocabulary tasks. --- paper_title: Multimodal Saliency and Fusion for Movie Summarization Based on Aural, Visual, and Textual Attention paper_content: Multimodal streams of sensory information are naturally parsed and integrated by humans using signal-level feature extraction and higher level cognitive processes. Detection of attention-invoking audiovisual segments is formulated in this work on the basis of saliency models for the audio, visual, and textual information conveyed in a video stream. Aural or auditory saliency is assessed by cues that quantify multifrequency waveform modulations, extracted through nonlinear operators and energy tracking. Visual saliency is measured through a spatiotemporal attention model driven by intensity, color, and orientation. Textual or linguistic saliency is extracted from part-of-speech tagging on the subtitles information available with most movie distributions. The individual saliency streams, obtained from modality-depended cues, are integrated in a multimodal saliency curve, modeling the time-varying perceptual importance of the composite video stream and signifying prevailing sensory events. The multimodal saliency representation forms the basis of a generic, bottom-up video summarization algorithm. Different fusion schemes are evaluated on a movie database of multimodal saliency annotations with comparative results provided across modalities. The produced summaries, based on low-level features and content-independent fusion and selection, are of subjectively high aesthetic and informative quality. --- paper_title: Multi-level Fusion of Audio and Visual Features for Speaker Identification paper_content: This paper explores the fusion of audio and visual evidences through a multi-level hybrid fusion architecture based on dynamic Bayesian network (DBN), which combines model level and decision level fusion to achieve higher performance. In model level fusion, a new audio-visual correlative model (AVCM) based on DBN is proposed, which describes both the inter-correlations and loose timing synchronicity between the audio and video streams. The experiments on the CMU database and our own homegrown database both demonstrate that the methods can improve the accuracies of audio-visual bimodal speaker identification at all levels of acoustic signal-to-noise-ratios (SNR) from 0dB to 30dB with varying acoustic conditions. --- paper_title: Multimodal fusion for multimedia analysis: a survey paper_content: This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion. --- paper_title: Multiple classifier systems for the classificatio of audio-visual emotional states paper_content: Research activities in the field of human-computer interaction increasingly addressed the aspect of integrating some type of emotional intelligence. Human emotions are expressed through different modalities such as speech, facial expressions, hand or body gestures, and therefore the classification of human emotions should be considered as a multimodal pattern recognition problem. The aim of our paper is to investigate multiple classifier systems utilizing audio and visual features to classify human emotional states. For that a variety of features have been derived. From the audio signal the fundamental frequency, LPCand MFCC coefficients, and RASTA-PLP have been used. In addition to that two types of visual features have been computed, namely form and motion features of intermediate complexity. The numerical evaluation has been performed on the four emotional labels Arousal, Expectancy, Power, Valence as defined in the AVEC data set. As classifier architectures multiple classifier systems are applied, these have been proven to be accurate and robust against missing and noisy data. --- paper_title: Multimedia classification and event detection using double fusion paper_content: Multimedia Event Detection(MED) is a multimedia retrieval task with the goal of finding videos of a particular event in video archives, given example videos and event descriptions; different from MED, multimedia classification is a task that classifies given videos into specified classes. Both tasks require mining features of example videos to learn the most discriminative features, with best performance resulting from a combination of multiple complementary features. How to combine different features is the focus of this paper. Generally, early fusion and late fusion are two popular combination strategies. The former one fuses features before performing classification and the latter one combines output of classifiers from different features. Early fusion can better capture the relationship among features yet is prone to over-fit the training data. Late fusion deals with the over-fitting problem better but does not allow classifiers to train on all the data at the same time. In this paper, we introduce a fusion scheme named double fusion, which simply combines early fusion and late fusion together to incorporate their advantages. Results are reported on the TRECVID MED 2010, MED 2011, UCF50 and HMDB51 datasets. For the MED 2010 dataset, we get a mean minimal normalized detection cost (MMNDC) of 0.49, which exceeds the state-of-the-art performance by more than 12 percent. On the TRECVID MED 2011 test dataset, we achieve a MMNDC of 0.51, which is the second best among all 19 participants. On UCF50 and HMDB51, we obtain classification accuracy of 88.1 % and 48.7 % respectively, which are the best reported results to date. --- paper_title: Modeling latent discriminative dynamic of multi-dimensional affective signals paper_content: During face-to-face communication, people continuously exchange para-linguistic information such as their emotional state through facial expressions, posture shifts, gaze patterns and prosody. These affective signals are subtle and complex. In this paper, we propose to explicitly model the interaction between the high level perceptual features using Latent-Dynamic Conditional Random Fields. This approach has the advantage of explicitly learning the sub-structure of the affective signals as well as the extrinsic dynamic between emotional labels. We evaluate our approach on the Audio-Visual Emotion Challenge (AVEC 2011) dataset. By using visual features easily computable using off-the-helf sensing software (vertical and horizontal eye gaze, head tilt and smile intensity), we show that our approach based on LDCRF model outperforms previously published baselines for all four affective dimensions. By integrating audio features, our approach also outperforms the audio-visual baseline. --- paper_title: Recent advances in the automatic recognition of audio-visual speech paper_content: Visual speech information from the speaker's mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability in the human computer interface. In this paper, we review the main components of audiovisual automatic speech recognition (ASR) and present novel contributions in two main areas: first, the visual front-end design, based on a cascade of linear image transforms of an appropriate video region of interest, and subsequently, audiovisual speech integration. On the latter topic, we discuss new work on feature and decision fusion combination, the modeling of audiovisual speech asynchrony, and incorporating modality reliability estimates to the bimodal recognition process. We also briefly touch upon the issue of audiovisual adaptation. We apply our algorithms to three multisubject bimodal databases, ranging from small- to large-vocabulary recognition tasks, recorded in both visually controlled and challenging environments. Our experiments demonstrate that the visual modality improves ASR over all conditions and data considered, though less so for visually challenging environments and large vocabulary tasks. --- paper_title: Multi-view latent variable discriminative models for action recognition paper_content: Many human action recognition tasks involve data that can be factorized into multiple views such as body postures and hand shapes. These views often interact with each other over time, providing important cues to understanding the action. We present multi-view latent variable discriminative models that jointly learn both view-shared and view-specific sub-structures to capture the interaction between views. Knowledge about the underlying structure of the data is formulated as a multi-chain structured latent conditional model, explicitly learning the interaction between multiple views using disjoint sets of hidden variables in a discriminative manner. The chains are tied using a predetermined topology that repeats over time. We present three topologies — linked, coupled, and linked-coupled — that differ in the type of interaction between views that they model. We evaluate our approach on both segmented and unsegmented human action recognition tasks, using the ArmGesture, the NATOPS, and the ArmGesture-Continuous data. Experimental results show that our approach outperforms previous state-of-the-art action recognition models. --- paper_title: Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images paper_content: We address a question answering task on real-world images that is set up as a Visual Turing Test. By combining latest advances in image representation and natural language processing, we propose Neural-Image-QA, an end-to-end formulation to this problem for which all parts are trained jointly. In contrast to previous efforts, we are facing a multi-modal problem where the language output (answer) is conditioned on visual and natural language input (image and question). Our approach Neural-Image-QA doubles the performance of the previous best approach on this problem. We provide additional insights into the problem by analyzing how much information is contained only in the language part for which we provide a new human baseline. To study human consensus, which is related to the ambiguities inherent in this challenging task, we propose two novel metrics and collect additional answers which extends the original DAQUAR dataset to DAQUAR-Consensus. --- paper_title: Video Description Generation using Audio and Visual Cues paper_content: The recent advances in image captioning stimulate the research in generating natural language description for visual content, which can be widely applied in many applications such as assisting blind people. Video description generation is a more complex task than image caption. Most works of video description generation focus on visual information in the video. However, audio provides rich information for describing video contents as well. In this paper, we propose to generate video descriptions in natural sentences using both audio and visual cues. We use unified deep neural networks with both convolutional and recurrent structure. Experimental results on the Microsoft Research Video Description (MSVD) corpus prove that fusing audio information greatly improves the video description performance. --- paper_title: Imagenet classification with deep convolutional neural networks paper_content: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry. --- paper_title: Guiding the Long-Short Term Memory Model for Image Caption Generation paper_content: In this work we focus on the problem of image caption generation. We propose an extension of the long short term memory (LSTM) model, which we coin gLSTM for short. In particular, we add semantic information extracted from the image as extra input to each unit of the LSTM block, with the aim of guiding the model towards solutions that are more tightly coupled to the image content. Additionally, we explore different length normalization strategies for beam search to avoid bias towards short sentences. On various benchmark datasets such as Flickr8K, Flickr30K and MS COCO, we obtain results that are on par with or better than the current state-of-the-art. --- paper_title: Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space paper_content: Past research in analysis of human affect has focused on recognition of prototypic expressions of six basic emotions based on posed data acquired in laboratory settings. Recently, there has been a shift toward subtle, continuous, and context-specific interpretations of affective displays recorded in naturalistic and real-world settings, and toward multimodal analysis and recognition of human affect. Converging with this shift, this paper presents, to the best of our knowledge, the first approach in the literature that: 1) fuses facial expression, shoulder gesture, and audio cues for dimensional and continuous prediction of emotions in valence and arousal space, 2) compares the performance of two state-of-the-art machine learning techniques applied to the target problem, the bidirectional Long Short-Term Memory neural networks (BLSTM-NNs), and Support Vector Machines for Regression (SVR), and 3) proposes an output-associative fusion framework that incorporates correlations and covariances between the emotion dimensions. Evaluation of the proposed approach has been done using the spontaneous SAL data from four subjects and subject-dependent leave-one-sequence-out cross validation. The experimental results obtained show that: 1) on average, BLSTM-NNs outperform SVR due to their ability to learn past and future context, 2) the proposed output-associative fusion framework outperforms feature-level and model-level fusion by modeling and learning correlations and patterns between the valence and arousal dimensions, and 3) the proposed system is well able to reproduce the valence and arousal ground truth obtained from human coders. --- paper_title: The classification of multi-modal data with hidden conditional random field paper_content: The classification of multi-modal data has been an active research topic in recent years. It has been used in many applications where the processing of multi-modal data is involved. Motivated by the assumption that different modalities in multi-modal data share latent structure (topics), this paper attempts to learn the shared structure by exploiting the symbiosis of multiple-modality and therefore boost the classification of multi-modal data, we call it Multi-modal Hidden Conditional Random Field (M-HCRF). M-HCRF represents the intrinsical structure shared by different modalities as hidden variables in a undirected general graphical model. When learning the latent shared structure of the multi-modal data, M-HCRF can discover the interactions among the hidden structure and the supervised category information. The experimental results show the effectiveness of our proposed M-HCRF when applied to the classification of multi-modal data. --- paper_title: Dimensional affect recognition using Continuous Conditional Random Fields paper_content: During everyday interaction people display various non-verbal signals that convey emotions. These signals are multi-modal and range from facial expressions, shifts in posture, head pose, and non-verbal speech. They are subtle, continuous and complex. Our work concentrates on the problem of automatic recognition of emotions from such multimodal signals. Most of the previous work has concentrated on classifying emotions as belonging to a set of categories, or by discretising the continuous dimensional space. We propose the use of Continuous Conditional Random Fields (CCRF) in combination with Support Vector Machines for Regression (SVR) for modeling continuous emotion in dimensional space. Our Correlation Aware Continuous Conditional Random Field (CA-CCRF) exploits the non-orthogonality of emotion dimensions. By using visual features based on geometric shape and appearance, and a carefully selected subset of audio features we show that our CCRF and CA-CCRF approaches outperform previously published baselines for all four affective dimensions of valence, arousal, power and expectancy. --- paper_title: Deep multimodal fusion for persuasiveness prediction paper_content: Persuasiveness is a high-level personality trait that quantifies the influence a speaker has on the beliefs, attitudes, intentions, motivations, and behavior of the audience. With social multimedia becoming an important channel in propagating ideas and opinions, analyzing persuasiveness is very important. In this work, we use the publicly available Persuasive Opinion Multimedia (POM) dataset to study persuasion. One of the challenges associated with this problem is the limited amount of annotated data. To tackle this challenge, we present a deep multimodal fusion architecture which is able to leverage complementary information from individual modalities for predicting persuasiveness. Our methods show significant improvement in performance over previous approaches. --- paper_title: Hidden Conditional Random Fields paper_content: We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state conditional random field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time. --- paper_title: Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis paper_content: We present a novel way of extracting features from short texts, based on the activation values of an inner layer of a deep convolutional neural network. We use the extracted features in multimodal sentiment analysis of short video clips representing one sentence each. We use the combined feature vectors of textual, visual, and audio modalities to train a classifier based on multiple kernel learning, which is known to be good at heterogeneous data. We obtain 14% performance improvement over the state of the art and present a parallelizable decision-level data fusion method, which is much faster, though slightly less accurate. --- paper_title: Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text paper_content: This paper investigates how linguistic knowledge mined from large text corpora can aid the generation of natural language descriptions of videos. Specifically, we integrate both a neural language model and distributional semantics trained on large text corpora into a recent LSTM-based architecture for video description. We evaluate our approach on a collection of Youtube videos as well as two large movie description datasets showing significant improvements in grammaticality while modestly improving descriptive quality. --- paper_title: Multiple Kernel Learning in the Primal for Multimodal Alzheimer’s Disease Classification paper_content: To achieve effective and efficient detection of Alzheimer's disease (AD), many machine learning methods have been introduced into this realm. However, the general case of limited training samples, as well as different feature representations typically makes this problem challenging. In this paper, we propose a novel multiple kernel-learning framework to combine multimodal features for AD classification, which is scalable and easy to implement. Contrary to the usual way of solving the problem in the dual, we look at the optimization from a new perspective. By conducting Fourier transform on the Gaussian kernel, we explicitly compute the mapping function, which leads to a more straightforward solution of the problem in the primal. Furthermore, we impose the mixed L21 norm constraint on the kernel weights, known as the group lasso regularization, to enforce group sparsity among different feature modalities. This actually acts as a role of feature modality selection, while at the same time exploiting complementary information among different kernels. Therefore, it is able to extract the most discriminative features for classification. Experiments on the ADNI dataset demonstrate the effectiveness of the proposed method. --- paper_title: Factorial Hidden Markov Models paper_content: Hidden Markov models (HMMs) have proven to be one of the most widely used tools for learning probabilistic models of time series data. In an HMM, information about the past is conveyed through a single discrete variable—the hidden state. We discuss a generalization of HMMs in which this state is factored into multiple state variables and is therefore represented in a distributed manner. We describe an exact algorithm for inferring the posterior probabilities of the hidden state variables given the observations, and relate it to the forward–backward algorithm for HMMs and to algorithms for more general graphical models. Due to the combinatorial nature of the hidden state representation, this exact algorithm is intractable. As in other intractable systems, approximate inference can be carried out using Gibbs sampling or variational methods. Within the variational framework, we present a structured approximation in which the the state variables are decoupled, yielding a tractable algorithm for learning the parameters of the model. Empirical comparisons suggest that these approximations are efficient and provide accurate alternatives to the exact methods. Finally, we use the structured approximation to model Bach‘s chorales and show that factorial HMMs can capture statistical structure in this data set which an unconstrained HMM cannot. --- paper_title: A coupled HMM for audio-visual speech recognition paper_content: In recent years several speech recognition systems that use visual together with audio information showed significant increase in performance over the standard speech recognition systems. The use of visual features is justified by both the bimodality of the speech generation and by the need of features that are invariant to acoustic noise perturbation. The audio-visual speech recognition system presented in this paper introduces a novel audio-visual fusion technique that uses a coupled hidden Markov model (HMM). The statistical properties of the coupled-HMM allow us to model the state asynchrony of the audio and visual observations sequences while still preserving their natural correlation over time. The experimental results show that the coupled HMM outperforms the multistream HMM in audio visual speech recognition. --- paper_title: A Novel Multiple Kernel Learning Framework for Heterogeneous Feature Fusion and Variable Selection paper_content: We propose a novel multiple kernel learning (MKL) algorithm with a group lasso regularizer, called group lasso regularized MKL (GL-MKL), for heterogeneous feature fusion and variable selection. For problems of feature fusion, assigning a group of base kernels for each feature type in an MKL framework provides a robust way in fitting data extracted from different feature domains. Adding a mixed norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at the group/feature level and automatically learn a compact feature set for recognition purposes. More precisely, our GL-MKL determines the optimal base kernels, including the associated weights and kernel parameters, and results in improved recognition performance. Besides, our GL-MKL can also be extended to address heterogeneous variable selection problems. For such problems, we aim to select a compact set of variables (i.e., feature attributes) for comparable or improved performance. Our proposed method does not need to exhaustively search for the entire variable space like prior sequential-based variable selection methods did, and we do not require any prior knowledge on the optimal size of the variable subset either. To verify the effectiveness and robustness of our GL-MKL, we conduct experiments on video and image datasets for heterogeneous feature fusion, and perform variable selection on various UCI datasets. --- paper_title: Context-sensitive multimodal emotion recognition from speech and facial expression using bidirectional LSTM modeling paper_content: In this paper, we apply a context-sensitive technique for multimodal emotion recognition based on feature-level fusion of acoustic and visual cues. We use bidirectional Long ShortTerm Memory (BLSTM) networks which, unlike most other emotion recognition approaches, exploit long-range contextual information for modeling the evolution of emotion within a conversation. We focus on recognizing dimensional emotional labels, which enables us to classify both prototypical and nonprototypical emotional expressions contained in a large audiovisual database. Subject-independent experiments on various classification tasks reveal that the BLSTM network approach generally prevails over standard classification techniques such as Hidden Markov Models or Support Vector Machines, and achieves F1-measures of the order of 72 %, 65 %, and 55 % for the discrimination of three clusters in emotional space and the distinction between three levels of valence and activation, respectively. --- paper_title: Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering paper_content: We address the problem of Visual Question Answering (VQA), which requires joint image and language understanding to answer a question about a given photograph. Recent approaches have applied deep image captioning methods based on convolutional-recurrent networks to this problem, but have failed to model spatial inference. To remedy this, we propose a model we call the Spatial Memory Network and apply it to the VQA task. Memory networks are recurrent neural networks with an explicit attention mechanism that selects certain parts of the information stored in memory. Our Spatial Memory Network stores neuron activations from different spatial regions of the image in its memory, and uses the question to choose relevant regions for computing the answer, a process of which constitutes a single "hop" in the network. We propose a novel spatial attention architecture that aligns words with image patches in the first hop, and obtain improved results by adding a second attention hop which considers the whole question to choose visual evidence based on the results of the first hop. To better understand the inference process learned by the network, we design synthetic questions that specifically require spatial inference and visualize the attention weights. We evaluate our model on two published visual question answering datasets, DAQUAR [1] and VQA [2], and obtain improved results compared to a strong deep baseline model (iBOWIMG) which concatenates image and question features to predict the answer [3]. --- paper_title: Boosted Learning in Dynamic Bayesian Networks for Multimodal Speaker Detection paper_content: Bayesian network models provide an attractive framework for multimodal sensor fusion. They combine an intuitive graphical representation with efficient algorithms for inference and learning. However, the unsupervised nature of standard parameter learning algorithms for Bayesian networks can lead to poor performance in classification tasks. We have developed a supervised learning framework for Bayesian networks, which is based on the Adaboost algorithm of Schapire and Freund. Our framework covers static and dynamic Bayesian networks with both discrete and continuous states. We have tested our framework in the context of a novel multimodal HCI application: a speech-based command and control interface for a Smart Kiosk. We provide experimental evidence for the utility of our boosted learning approach. --- paper_title: Multimodal human behavior analysis: learning correlation and interaction across modalities paper_content: Multimodal human behavior analysis is a challenging task due to the presence of complex nonlinear correlations and interactions across modalities. We present a novel approach to this problem based on Kernel Canonical Correlation Analysis (KCCA) and Multi-view Hidden Conditional Random Fields (MV-HCRF). Our approach uses a nonlinear kernel to map multimodal data to a high-dimensional feature space and finds a new projection of the data that maximizes the correlation across modalities. We use a multi-chain structured graphical model with disjoint sets of latent variables, one set per modality, to jointly learn both view-shared and view-specific sub-structures of the projected data, capturing interaction across modalities explicitly. We evaluate our approach on a task of agreement and disagreement recognition from nonverbal audio-visual cues using the Canal 9 dataset. Experimental results show that KCCA makes capturing nonlinear hidden dynamics easier and MV-HCRF helps learning interaction across modalities. --- paper_title: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data paper_content: We present conditional random fields , a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states. We present iterative parameter estimation algorithms for conditional random fields and compare the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data. --- paper_title: Dynamic modality weighting for multi-stream hmms inaudio-visual speech recognition paper_content: Merging decisions from different modalities is a crucial problem in Audio-Visual Speech Recognition. To solve this, state synchronous multi-stream HMMs have been proposed for their important advantage of incorporating stream reliability in their fusion scheme. This paper focuses on stream weight adaptation based on modality confidence estimators. We assume different and time-varying environment noise, as can be encountered in realistic applications, and, for this, adaptive methods are best suited. Stream reliability is assessed directly through classifier outputs since they are not specific to either noise type or level. The influence of constraining the weights to sum to one is also discussed. --- paper_title: Show and tell: A neural image caption generator paper_content: Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art. --- paper_title: Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering paper_content: In this paper, we present the mQA model, which is able to answer questions about the content of an image. The answer can be a sentence, a phrase or a single word. Our model contains four components: a Long Short-Term Memory (LSTM) to extract the question representation, a Convolutional Neural Network (CNN) to extract the visual representation, an LSTM for storing the linguistic context in an answer, and a fusing component to combine the information from the first three components and generate the answer. We construct a Freestyle Multilingual Image Question Answering (FM-IQA) dataset to train and evaluate our mQA model. It contains over 150,000 images and 310,000 freestyle Chinese question-answer pairs and their English translations. The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test. Specifically, we mix the answers provided by humans and our model. The human judges need to distinguish our model from the human. They will also provide a score (i.e. 0, 1, 2, the larger the better) indicating the quality of the answer. We propose strategies to monitor the quality of this evaluation process. The experiments show that in 64.7% of cases, the human judges cannot distinguish our model from humans. The average score is 1.454 (1.918 for human). The details of this work, including the FM-IQA dataset, can be found on the project page: \url{http://idl.baidu.com/FM-IQA.html}. --- paper_title: Multimodal Deep Learning paper_content: Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. Our models are validated on the CUAVE and AVLetters datasets on audio-visual speech classification, demonstrating best published visual speech classification on AVLetters and effective shared representation learning. --- paper_title: On feature combination for multiclass object classification paper_content: A key ingredient in the design of visual object classification systems is the identification of relevant class specific aspects while being robust to intra-class variations. While this is a necessity in order to generalize beyond a given set of training images, it is also a very difficult problem due to the high variability of visual appearance within each class. In the last years substantial performance gains on challenging benchmark datasets have been reported in the literature. This progress can be attributed to two developments: the design of highly discriminative and robust image features and the combination of multiple complementary features based on different aspects such as shape, color or texture. In this paper we study several models that aim at learning the correct weighting of different features from training data. These include multiple kernel learning as well as simple baseline methods. Furthermore we derive ensemble methods inspired by Boosting which are easily extendable to several multiclass setting. All methods are thoroughly evaluated on object classification datasets using a multitude of feature descriptors. The key results are that even very simple baseline methods, that are orders of magnitude faster than learning techniques are highly competitive with multiple kernel learning. Furthermore the Boosting type methods are found to produce consistently better results in all experiments. We provide insight of when combination methods can be expected to work and how the benefit of complementary features can be exploited most efficiently. --- paper_title: Emotion Recognition in the Wild with Feature Fusion and Multiple Kernel Learning paper_content: This paper presents our proposed approach for the second Emotion Recognition in The Wild Challenge. We propose a new feature descriptor called Histogram of Oriented Gradients from Three Orthogonal Planes (HOG_TOP) to represent facial expressions. We also explore the properties of visual features and audio features, and adopt Multiple Kernel Learning (MKL) to find an optimal feature fusion. An SVM with multiple kernels is trained for the facial expression classification. Experimental results demonstrate that our method achieves a promising performance. The overall classification accuracy on the validation set and test set are 40.21% and 45.21%, respectively. --- paper_title: Multiple kernel learning for emotion recognition in the wild paper_content: We propose a method to automatically detect emotions in unconstrained settings as part of the 2013 Emotion Recognition in the Wild Challenge [16], organized in conjunction with the ACM International Conference on Multimodal Interaction (ICMI 2013). Our method combines multiple visual descriptors with paralinguistic audio features for multimodal classification of video clips. Extracted features are combined using Multiple Kernel Learning and the clips are classified using an SVM into one of the seven emotion categories: Anger, Disgust, Fear, Happiness, Neutral, Sadness and Surprise. The proposed method achieves competitive results, with an accuracy gain of approximately 10% above the challenge baseline. --- paper_title: Hidden Conditional Random Fields for Meeting Segmentation paper_content: Automatic segmentation and classification of recorded meetings provides a basis towards understanding the content of a meeting. It enables effective browsing and querying in a meeting archive. Though robustness of existing approaches is often not reliable enough. We therefore strive to improve on this task by applying conditional random fields augmented by hidden states. These hidden conditional random fields have been proven to be efficient in low level pattern recognition tasks. Now we propose to use these novel models to segment a pre-recorded meeting into meeting events. Since they can also be seen as an extension to hidden Markov models an elaborate comparison of the two approaches is provided. Extensive test runs on the public M4 Scripted Meeting Corpus prove the great performance of applying our suggested novel approach compared to other similar methods. --- paper_title: EmoNets: Multimodal deep learning approaches for emotion recognition in video paper_content: The task of the Emotion Recognition in the Wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies. The videos depict acted-out emotions under realistic conditions with a large degree of variation in attributes such as pose and illumination, making it worthwhile to explore approaches which consider combinations of features from multiple modalities for label assignment. In this paper we present our approach to learning several specialist models using deep learning techniques, each focusing on one modality. Among these are a convolutional neural network, focusing on capturing visual information in detected faces, a deep belief net focusing on the representation of the audio stream, a K-Means based “bag-of-mouths” model, which extracts visual features around the mouth region and a relational autoencoder, which addresses spatio-temporal aspects of videos. We explore multiple methods for the combination of cues from these modalities into one common classifier. This achieves a considerably greater accuracy than predictions from our strongest single-modality classifier. Our method was the winning submission in the 2013 EmotiW challenge and achieved a test set accuracy of 47.67 % on the 2014 dataset. --- paper_title: Learning Multi-modal Similarity paper_content: In many applications involving multi-media data, the definition of similarity between items is integral to several key tasks, e.g., nearest-neighbor retrieval, classification, and recommendation. Data in such regimes typically exhibits multiple modalities, such as acoustic and visual content of video. Integrating such heterogeneous data to form a holistic similarity space is therefore a key challenge to be overcome in many real-world applications. We present a novel multiple kernel learning technique for integrating heterogeneous data into a single, unified similarity space. Our algorithm learns an optimal ensemble of kernel transfor- mations which conform to measurements of human perceptual similarity, as expressed by relative comparisons. To cope with the ubiquitous problems of subjectivity and inconsistency in multi- media similarity, we develop graph-based techniques to filter similarity measurements, resulting in a simplified and robust training procedure. --- paper_title: Extending Long Short-Term Memory for Multi-View Structured Learning paper_content: Long Short-Term Memory (LSTM) networks have been successfully applied to a number of sequence learning problems but they lack the design flexibility to model multiple view interactions, limiting their ability to exploit multi-view relationships. In this paper, we propose a Multi-View LSTM (MV-LSTM), which explicitly models the view-specific and cross-view interactions over time or structured outputs. We evaluate the MV-LSTM model on four publicly available datasets spanning two very different structured learning problems: multimodal behaviour recognition and image captioning. The experimental results show competitive performance on all four datasets when compared with state-of-the-art models. --- paper_title: Multiple Kernel Learning for Visual Object Recognition: A Review paper_content: Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels for a given recognition task. A number of studies have shown that MKL is a useful tool for object recognition, where each image is represented by multiple sets of features and MKL is applied to combine different feature sets. We review the state-of-the-art for MKL, including different formulations and algorithms for solving the related optimization problems, with the focus on their applications to object recognition. One dilemma faced by practitioners interested in using MKL for object recognition is that different studies often provide conflicting results about the effectiveness and efficiency of MKL. To resolve this, we conduct extensive experiments on standard datasets to evaluate various approaches to MKL for object recognition. We argue that the seemingly contradictory conclusions offered by studies are due to different experimental setups. The conclusions of our study are: (i) given a sufficient number of training examples and feature/kernel types, MKL is more effective for object recognition than simple kernel combination (e.g., choosing the best performing kernel or average of kernels); and (ii) among the various approaches proposed for MKL, the sequential minimal optimization, semi-infinite programming, and level method based ones are computationally most efficient. --- paper_title: Recent advances in the automatic recognition of audio-visual speech paper_content: Visual speech information from the speaker's mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability in the human computer interface. In this paper, we review the main components of audiovisual automatic speech recognition (ASR) and present novel contributions in two main areas: first, the visual front-end design, based on a cascade of linear image transforms of an appropriate video region of interest, and subsequently, audiovisual speech integration. On the latter topic, we discuss new work on feature and decision fusion combination, the modeling of audiovisual speech asynchrony, and incorporating modality reliability estimates to the bimodal recognition process. We also briefly touch upon the issue of audiovisual adaptation. We apply our algorithms to three multisubject bimodal databases, ranging from small- to large-vocabulary recognition tasks, recorded in both visually controlled and challenging environments. Our experiments demonstrate that the visual modality improves ASR over all conditions and data considered, though less so for visually challenging environments and large vocabulary tasks. --- paper_title: Multi-View Learning in the Presence of View Disagreement paper_content: Traditional multi-view learning approaches suffer in the presence of view disagreement, i.e., when samples in each view do not belong to the same class due to view corruption, occlusion or other noise processes. In this paper we present a multi-view learning approach that uses a conditional entropy criterion to detect view disagreement. Once detected, samples with view disagreement are filtered and standard multi-view learning methods can be successfully applied to the remaining samples. Experimental evaluation on synthetic and audio-visual databases demonstrates that the detection and filtering of view disagreement considerably increases the performance of traditional multi-view learning approaches. --- paper_title: Co-Adaptation of audio-visual speech and gesture classifiers paper_content: The construction of robust multimodal interfaces often requires large amounts of labeled training data to account for cross-user differences and variation in the environment. In this work, we investigate whether unlabeled training data can be leveraged to build more reliable audio-visual classifiers through co-training, a multi-view learning algorithm. Multimodal tasks are good candidates for multi-view learning, since each modality provides a potentially redundant view to the learning algorithm. We apply co-training to two problems: audio-visual speech unit classification, and user agreement recognition using spoken utterances and head gestures. We demonstrate that multimodal co-training can be used to learn from only a few labeled examples in one or both of the audio-visual modalities. We also propose a co-adaptation algorithm, which adapts existing audio-visual classifiers to a particular user or noise condition by leveraging the redundancy in the unlabeled data. --- paper_title: Applying Co-Training Methods To Statistical Parsing paper_content: We propose a novel Co-Training method for statistical parsing. The algorithm takes as input a small corpus (9695 sentences) annotated with parse trees, a dictionary of possible lexicalized structures for each word in the training set and a large pool of unlabeled text. The algorithm iteratively labels the entire data set with parse trees. Using empirical results based on parsing the Wall Street Journal corpus we show that training a statistical parser on the combined labeled and unlabeled data strongly out-performs training only on the labeled data. --- paper_title: Multi-view CCA-based acoustic features for phonetic recognition across speakers and domains paper_content: Canonical correlation analysis (CCA) and kernel CCA can be used for unsupervised learning of acoustic features when a second view (e.g., articulatory measurements) is available for some training data, and such projections have been used to improve phonetic frame classification. Here we study the behavior of CCA-based acoustic features on the task of phonetic recognition, and investigate to what extent they are speaker-independent or domain-independent. The acoustic features are learned using data drawn from the University of Wisconsin X-ray Microbeam Database (XRMB). The features are evaluated within and across speakers on XRMB data, as well as on out-of-domain TIMIT and MOCHA-TIMIT data. Experimental results show consistent improvement with the learned acoustic features over baseline MFCCs and PCA projections. In both speaker-dependent and cross-speaker experiments, phonetic error rates are improved by 4-9% absolute (10-23% relative) using CCA-based features over baseline MFCCs. In cross-domain phonetic recognition (training on XRMB and testing on MOCHA or TIMIT), the learned projections provide smaller improvements. --- paper_title: Multimodal Learning with Deep Boltzmann Machines paper_content: Data often consists of multiple diverse modalities. For example, images are tagged with textual information and videos are accompanied by audio. Each modality is characterized by having distinct statistical properties. We propose a Deep Boltzmann Machine for learning a generative model of such multimodal data. We show that the model can be used to create fused representations by combining features across modalities. These learned representations are useful for classification and information retrieval. By sampling from the conditional distributions over each data modality, it is possible to create these representations even when some data modalities are missing. We conduct experiments on bimodal image-text and audio-video data. The fused representation achieves good classification results on the MIR-Flickr data set matching or outperforming other deep models as well as SVM based models that use Multiple Kernel Learning. We further demonstrate that this multimodal model helps classification and retrieval even when only unimodal data is available at test time. --- paper_title: Unsupervised improvement of visual detectors using cotraining paper_content: One significant challenge in the construction of visual detection systems is the acquisition of sufficient labeled data. We describe a new technique for training visual detectors which requires only a small quantity of labeled data, and then uses unlabeled data to improve performance over time. Unsupervised improvement is based on the cotraining framework of Blum and Mitchell, in which two disparate classifiers are trained simultaneously. Unlabeled examples which are confidently labeled by one classifier are added, with labels, to the training set of the other classifier. Experiments are presented on the realistic task of automobile detection in roadway surveillance video. In this application, cotraining reduces the false positive rate by a factor of 2 to 11 from the classifier trained with labeled data alone. --- paper_title: Multimodal Deep Learning paper_content: Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. Our models are validated on the CUAVE and AVLetters datasets on audio-visual speech classification, demonstrating best published visual speech classification on AVLetters and effective shared representation learning. --- paper_title: Combining labeled and unlabeled data with co-training paper_content: We consider the problem of using a large unlabeled sample to boost performance of a learning algorit,hrn when only a small set of labeled examples is available. In particular, we consider a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views. For example, the description of a web page can be partitioned into the words occurring on that page, and the words occurring in hyperlinks t,hat point to that page. We assume that either view of the example would be sufficient for learning if we had enough labeled data, but our goal is to use both views together to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples. Specifically, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each algorithm’s predictions on new unlabeled examples are used to enlarge the training set of the other. Our goal in this paper is to provide a PAC-style analysis for this setting, and, more broadly, a PAC-style framework for the general problem of learning from both labeled and unlabeled data. We also provide empirical results on real web-page data indicating that this use of unlabeled examples can lead to significant improvement of hypotheses in practice. *This research was supported in part by the DARPA HPKB program under contract F30602-97-1-0215 and by NSF National Young investigator grant CCR-9357793. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. TO copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. COLT 98 Madison WI USA Copyright ACM 1998 l-58113-057--0/98/ 7...%5.00 92 Tom Mitchell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 [email protected] --- paper_title: Multimodal Transfer Deep Learning for Audio Visual Recognition paper_content: We propose a multimodal deep learning framework that can transfer the knowledge obtained from a single-modal neural network to a network with a different modality. For instance, we show that we can leverage the speech data to fine-tune the network trained for video recognition, given an initial set of audio-video parallel dataset within the same semantics. Our approach learns the analogy-preserving embeddings between the abstract representations learned from each network, allowing for semantics-level transfer or reconstruction of the data among different modalities. Our method is thus specifically useful when one of the modalities is more scarce in labeled data than other modalities. While we mainly focus on applying transfer learning on the audio-visual recognition task as an application of our approach, our framework is flexible and thus can work with any multimodal datasets. In this work-in-progress report, we show our preliminary results on the AV-Letters dataset. --- paper_title: Grounding Action Descriptions in Videos paper_content: Recent work has shown that the integration of visual information into text-based models can substantially improve model predictions, but so far only visual information extracted from static images has been used. In this paper, we consider the problem of grounding sentences describing actions in visual information extracted from videos . We present a general purpose corpus that aligns high quality videos with multiple natural language descriptions of the actions portrayed in the videos, together with an annotation of how similar the action descriptions are to each other. Experimental results demonstrate that a text-based model of similarity between actions improves substantially when combined with visual information from videos depicting the described actions. --- paper_title: Multimodal distributional semantics paper_content: Distributional semantic models derive computational representations of word meaning from the patterns of co-occurrence of words in text. Such models have been a success story of computational linguistics, being able to provide reliable estimates of semantic relatedness for the many semantic tasks requiring them. However, distributional models extract meaning information exclusively from text, which is an extremely impoverished basis compared to the rich perceptual sources that ground human semantic knowledge. We address the lack of perceptual grounding of distributional models by exploiting computer vision techniques that automatically identify discrete "visual words" in images, so that the distributional representation of a word can be extended to also encompass its co-occurrence with the visual words of images it is associated with. We propose a flexible architecture to integrate text- and image-based distributional information, and we show in a set of empirical tests that our integrated model is superior to the purely text-based approach, and it provides somewhat complementary semantic information with respect to the latter. --- paper_title: Visual Information in Semantic Representation paper_content: The question of how meaning might be acquired by young children and represented by adult speakers of a language is one of the most debated topics in cognitive science. Existing semantic representation models are primarily amodal based on information provided by the linguistic input despite ample evidence indicating that the cognitive system is also sensitive to perceptual information. In this work we exploit the vast resource of images and associated documents available on the web and develop a model of multimodal meaning representation which is based on the linguistic and visual context. Experimental results show that a closer correspondence to human data can be obtained by taking the visual modality into account. --- paper_title: Zero-Shot Learning with Semantic Output Codes paper_content: We consider the problem of zero-shot learning, where the goal is to learn a classifier f : X → Y that must predict novel values of Y that were omitted from the training set. To achieve this, we define the notion of a semantic output code classifier (SOC) which utilizes a knowledge base of semantic properties of Y to extrapolate to novel classes. We provide a formalism for this type of classifier and study its theoretical properties in a PAC framework, showing conditions under which the classifier can accurately predict novel classes. As a case study, we build a SOC classifier for a neural decoding task and show that it can often predict words that people are thinking about from functional magnetic resonance images (fMRI) of their neural activity, even without training examples for those words. --- paper_title: Grounded Language Learning from Video Described with Sentences paper_content: We present a method that learns representations for word meanings from short video clips paired with sentences. Unlike prior work on learning language from symbolic input, our input consists of video of people interacting with multiple complex objects in outdoor environments. Unlike prior computer-vision approaches that learn from videos with verb labels or images with noun labels, our labels are sentences containing nouns, verbs, prepositions, adjectives, and adverbs. The correspondence between words and concepts in the video is learned in an unsupervised fashion, even when the video depicts simultaneous events described by multiple sentences or when different aspects of a single event are described with multiple sentences. The learned word meanings can be subsequently used to automatically generate description of new video. --- paper_title: Grounding Semantics in Olfactory Perception paper_content: Multi-modal semantics has relied on feature norms or raw image data for perceptual input. In this paper we examine grounding semantic representations in olfactory (smell) data, through the construction of a novel bag of chemical compounds model. We use standard evaluations for multi-modal semantics, including measuring conceptual similarity and cross-modal zero-shot learning. To our knowledge, this is the first work to evaluate semantic similarity on representations grounded in olfactory data. --- paper_title: Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models paper_content: The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. Such annotations are essential for continued progress in automatic image description and grounded language understanding. They enable us to define a new benchmark for localization of textual entity mentions in an image. We present a strong baseline for this task that combines an image-text embedding, detectors for common objects, a color classifier, and a bias towards selecting larger objects. While our baseline rivals in accuracy more complex state-of-the-art models, we show that its gains cannot be easily parlayed into improvements on such tasks as image-sentence retrieval, thus underlining the limitations of current methods and the need for further research. --- paper_title: Regularizing Long Short Term Memory with 3D Human-Skeleton Sequences for Action Recognition paper_content: This paper argues that large-scale action recognition in video can be greatly improved by providing an additional modality in training data – namely, 3D human-skeleton sequences – aimed at complementing poorly represented or missing features of human actions in the training videos. For recognition, we use Long Short Term Memory (LSTM) grounded via a deep Convolutional Neural Network (CNN) onto the video. Training of LSTM is regularized using the output of another encoder LSTM (eLSTM) grounded on 3D human-skeleton training data. For such regularized training of LSTM, we modify the standard backpropagation through time (BPTT) in order to address the wellknown issues with gradient descent in constraint optimization. Our evaluation on three benchmark datasets – Sports-1M, HMDB-51, and UCF101 – shows accuracy improvements from 1.7% up to 14.8% relative to the state of the art. --- paper_title: Multi- and Cross-Modal Semantics Beyond Vision: Grounding in Auditory Perception paper_content: Multi-modal semantics has relied on feature norms or raw image data for perceptual input. In this paper we examine grounding semantic representations in raw auditory data, using standard evaluations for multi-modal semantics, including measuring conceptual similarity and relatedness. We also evaluate cross-modal mappings, through a zero-shot learning task mapping between linguistic and auditory modalities. In addition, we evaluate multimodal representations on an unsupervised musical instrument clustering task. To our knowledge, this is the first work to combine linguistic and auditory information into multi-modal representations. --- paper_title: Zero-Shot Learning Through Cross-Modal Transfer paper_content: This work introduces a model that can recognize objects in images even if no training data is available for the object class. The only necessary knowledge about unseen visual categories comes from unsupervised text corpora. Unlike previous zero-shot learning models, which can only differentiate between unseen classes, our model can operate on a mixture of seen and unseen classes, simultaneously obtaining state of the art performance on classes with thousands of training images and reasonable performance on unseen classes. This is achieved by seeing the distributions of words in texts as a semantic space for understanding what objects look like. Our deep learning model does not require any manually defined semantic or visual features for either words or images. Images are mapped to be close to semantic word vectors corresponding to their classes, and the resulting image embeddings can be used to distinguish whether an image is of a seen or unseen class. We then use novelty detection methods to differentiate unseen classes from seen classes. We demonstrate two novelty detection strategies; the first gives high accuracy on unseen classes, while the second is conservative in its prediction of novelty and keeps the seen classes' accuracy high. --- paper_title: Describing objects by their attributes paper_content: We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object (“spotty dog”, not just “dog”); to say something about unfamiliar objects (“hairy and four-legged”, not just “unknown”); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic (“spotty”) or discriminative (“dogs have it but sheep do not”). Learning attributes presents a major new challenge: generalization across object categories, not just across instances within a category. In this paper, we also introduce a novel feature selection method for learning attributes that generalize well across categories. We support our claims by thorough evaluation that provides insights into the limitations of the standard recognition paradigm of naming and demonstrates the new abilities provided by our attribute-based framework. --- paper_title: Distributional Semantics in Technicolor paper_content: Our research aims at building computational models of word meaning that are perceptually grounded. Using computer vision techniques, we build visual and multimodal distributional models and compare them to standard textual models. Our results show that, while visual models with state-of-the-art computer vision techniques perform worse than textual models in general tasks (accounting for semantic relatedness), they are as good or better models of the meaning of words with visual correlates such as color terms, even in a nontrivial task that involves nonliteral uses of such words. Moreover, we show that visual and textual information are tapping on different aspects of meaning, and indeed combining them in multimodal models often improves performance. --- paper_title: Symbol interdependency in symbolic and embodied cognition paper_content: Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information. --- paper_title: Distributed Representations of Words and Phrases and their Compositionality paper_content: The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. ::: ::: An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. --- paper_title: Grounded Cognition paper_content: Grounded cognition rejects traditional views that cognition is computation on amodal symbols in a modular system, independent of the brain's modal systems for perception, action, and introspection. Instead, grounded cognition proposes that modal simulations, bodily states, and situated action underlie cognition. Accumulating behavioral and neural evidence supporting this view is reviewed from research on perception, memory, knowledge, language, thought, social cognition, and development. Theories of grounded cognition are also reviewed, as are origins of the area and common misperceptions of it. Theoretical, empirical, and methodological issues are raised whose future treatment is likely to affect the growth and impact of grounded cognition. --- paper_title: Grounded Models of Semantic Representation paper_content: A popular tradition of studying semantic representation has been driven by the assumption that word meaning can be learned from the linguistic environment, despite ample evidence suggesting that language is grounded in perception and action. In this paper we present a comparative study of models that represent word meaning based on linguistic and perceptual data. Linguistic information is approximated by naturally occurring corpora and sensorimotor experience by feature norms (i.e., attributes native speakers consider important in describing the meaning of a word). The models differ in terms of the mechanisms by which they integrate the two modalities. Experimental results show that a closer correspondence to human data can be obtained by uncovering latent information shared among the textual and perceptual modalities rather than arriving at semantic knowledge by concatenating the two. --- paper_title: Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world paper_content: Following up on recent work on establishing a mapping between vector-based semantic embeddings of words and the visual representations of the corresponding objects from natural images, we first present a simple approach to cross-modal vector-based semantics for the task of zero-shot learning, in which an image of a previously unseen object is mapped to a linguistic representation denoting its word. We then introduce fast mapping, a challenging and more cognitively plausible variant of the zero-shot task, in which the learner is exposed to new objects and the corresponding words in very limited linguistic contexts. By combining prior linguistic and visual knowledge acquired about words and their objects, as well as exploiting the limited new evidence available, the learner must learn to associate new objects with words. Our results on this task pave the way to realistic simulations of how children or robots could use existing knowledge to bootstrap grounded semantic knowledge about new concepts. --- paper_title: Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics paper_content: We construct multi-modal concept representations by concatenating a skip-gram linguistic representation vector with a visual concept representation vector computed using the feature extraction layers of a deep convolutional neural network (CNN) trained on a large labeled object recognition dataset. This transfer learning approach brings a clear performance gain over features based on the traditional bag-of-visual-word approach. Experimental results are reported on the WordSim353 and MEN semantic relatedness evaluation tasks. We use visual features computed using either ImageNet or ESP Game images. --- paper_title: What Are You Talking About? Text-to-Image Coreference paper_content: In this paper we exploit natural sentential descriptions of RGB-D scenes in order to improve 3D semantic parsing. Importantly, in doing so, we reason about which particular object each noun/pronoun is referring to in the image. This allows us to utilize visual information in order to disambiguate the so-called coreference resolution problem that arises in text. Towards this goal, we propose a structure prediction model that exploits potentials computed from text and RGB-D imagery to reason about the class of the 3D objects, the scene type, as well as to align the nouns/pronouns with the referred visual objects. We demonstrate the effectiveness of our approach on the challenging NYU-RGBD v2 dataset, which we enrich with natural lingual descriptions. We show that our approach significantly improves 3D detection and scene classification accuracy, and is able to reliably estimate the text-to-image alignment. Furthermore, by using textual and visual information, we are also able to successfully deal with coreference in text, improving upon the state-of-the-art Stanford coreference system [15]. --- paper_title: Grounding Distributional Semantics in the Visual World paper_content: Distributional semantic models build vector-based word meaning representations on top of contextual information extracted from large collections of text. Object recognition methods from computer vision derive vector-based representations of visual content from natural images. This article reviews how methods from computer vision are exploited to tackle the fundamental problem of grounding distributional semantic models, bringing them closer to providing a full-fledged computational account of meaning. --- paper_title: Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning paper_content: Recently there has been a lot of interest in learning common representations for multiple views of data. Typically, such common representations are learned using a parallel corpus between the two views (say, 1M images and their English captions). In this work, we address a real-world scenario where no direct parallel data is available between two views of interest (say, $V_1$ and $V_2$) but parallel data is available between each of these views and a pivot view ($V_3$). We propose a model for learning a common representation for $V_1$, $V_2$ and $V_3$ using only the parallel data available between $V_1V_3$ and $V_2V_3$. The proposed model is generic and even works when there are $n$ views of interest and only one pivot view which acts as a bridge between them. There are two specific downstream applications that we focus on (i) transfer learning between languages $L_1$,$L_2$,...,$L_n$ using a pivot language $L$ and (ii) cross modal access between images and a language $L_1$ using a pivot language $L_2$. Our model achieves state-of-the-art performance in multilingual document classification on the publicly available multilingual TED corpus and promising results in multilingual multimodal retrieval on a new dataset created and released as a part of this work. --- paper_title: Everybody loves a rich cousin: An empirical study of transliteration through bridge languages paper_content: Most state of the art approaches for machine transliteration are data driven and require significant parallel names corpora between languages. As a result, developing transliteration functionality among n languages could be a resource intensive task requiring parallel names corpora in the order of nC2. In this paper, we explore ways of reducing this high resource requirement by leveraging the available parallel data between subsets of the n languages, transitively. We propose, and show empirically, that reasonable quality transliteration engines may be developed between two languages, X and Y, even when no direct parallel names data exists between them, but only transitively through language Z. Such systems alleviate the need for O(nC2) corpora, significantly. In addition we show that the performance of such transitive transliteration systems is in par with direct transliteration systems, in practical applications, such as CLIR systems. --- paper_title: Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora paper_content: We propose a semi-supervised model which segments and annotates images using very few labeled images and a large unaligned text corpus to relate image regions to text labels. Given photos of a sports event, all that is necessary to provide a pixel-level labeling of objects and background is a set of newspaper articles about this sport and one to five labeled images. Our model is motivated by the observation that words in text corpora share certain context and feature similarities with visual objects. We describe images using visual words, a new region-based representation. The proposed model is based on kernelized canonical correlation analysis which finds a mapping between visual and textual words by projecting them into a latent meaning space. Kernels are derived from context and adjective features inside the respective visual and textual domains. We apply our method to a challenging dataset and rely on articles of the New York Times for textual features. Our model outperforms the state-of-the-art in annotation. In segmentation it compares favorably with other methods that use significantly more labeled training data. --- paper_title: Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data paper_content: While recent deep neural network models have achieved promising results on the image captioning task, they rely largely on the availability of corpora with paired image and sentence captions to describe objects in context. In this work, we propose the Deep Compositional Captioner (DCC) to address the task of generating descriptions of novel objects which are not present in paired imagesentence datasets. Our method achieves this by leveraging large object recognition datasets and external text corpora and by transferring knowledge between semantically similar concepts. Current deep caption models can only describe objects contained in paired image-sentence corpora, despite the fact that they are pre-trained with large object recognition datasets, namely ImageNet. In contrast, our model can compose sentences that describe novel objects and their interactions with other objects. We demonstrate our model's ability to describe novel concepts by empirically evaluating its performance on MSCOCO and show qualitative results on ImageNet images of objects for which no paired image-sentence data exist. Further, we extend our approach to generate descriptions of objects in video clips. Our results show that DCC has distinct advantages over existing image and video captioning approaches for generating descriptions of new objects in context. --- paper_title: Improving Statistical Machine Translation for a Resource-Poor Language Using Related Resource-Rich Languages paper_content: We propose a novel language-independent approach for improving machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X1 into a resourcerich language Y given a bi-text containing a limited number of parallel sentences for X1-Y and a larger bi-text for X2-Y for some resource-rich language X2 that is closely related to X1. This is achieved by taking advantage of the opportunities that vocabulary overlap and similarities between the languages X1 and X2 in spelling, word order, and syntax offer: (1) we improve the word alignments for the resource-poor language, (2) we further augment it with additional translation options, and (3) we take care of potential spelling differences through appropriate transliteration. The evaluation for Indonesian → English using Malay and for Spanish → English using Portuguese and pretending Spanish is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points, respectively, which is an improvement over the best rivaling approaches, while using much less additional data. Overall, our method cuts the amount of necessary "real" training data by a factor of 2-5. ---	Title: Multimodal Machine Learning: A Survey and Taxonomy Section 1: INTRODUCTION Description 1: Provide an introduction to the concept of multimodal machine learning, its importance, and the core challenges faced by researchers in this field. Section 2: APPLICATIONS: A HISTORICAL PERSPECTIVE Description 2: Discuss the historical advancements and applications of multimodal machine learning, from early research to current trends in language and vision applications. Section 3: MULTIMODAL REPRESENTATIONS Description 3: Explain the concept of multimodal representations, distinguishing between joint and coordinated representations, and discuss various techniques for creating these representations. Section 4: TRANSLATION Description 4: Describe the process of translating information from one modality to another, including both example-based and generative approaches. Discuss the challenges and methods associated with these tasks. Section 5: ALIGNMENT Description 5: Define multimodal alignment and its significance. Categorize approaches into explicit and implicit alignment, and discuss the methods used for each type. Section 6: FUSION Description 6: Examine the methods used to combine information from multiple modalities for making predictions. Discuss model-agnostic and model-based approaches to multimodal fusion. Section 7: CO-LEARNING Description 7: Explore the concept of co-learning, where knowledge from one modality aids the modeling of another modality. Discuss approaches based on parallel, non-parallel, and hybrid data. Section 8: CONCLUSION Description 8: Summarize the key points of the survey, discuss the importance of understanding past achievements for future advancements, and highlight potential future research directions, especially in the area of co-learning.
An overview on analysis tools for software product lines	7	--- paper_title: Clafer tools for product line engineering paper_content: Clafer is a lightweight yet expressive language for structural modeling: feature modeling and configuration, class and object modeling, and metamodeling. Clafer Tools is an integrated set of tools based on Clafer. In this paper, we describe some product-line variability modeling scenarios of Clafer Tools from the viewpoints of product-line owner, product-line engineer, and product engineer. --- paper_title: Feature-Oriented Domain Analysis (FODA) Feasibility Study paper_content: Abstract : Successful Software reuse requires the systematic discovery and exploitation of commonality across related software systems. By examining related software systems and the underlying theory of the class of systems they represent, domain analysis can provide a generic description of the requirements of that class of systems and a set of approaches for their implementation. This report will establish methods for performing a domain analysis and describe the products of the domain analysis process. To illustrate the application of domain analysis to a representative class of software systems, this report will provide a domain analysis of window management system software. --- paper_title: A Classification and Survey of Analysis Strategies for Software Product Lines paper_content: Software-product-line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, model checking, and theorem proving, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analyses is both broad and diverse, so it is difficult for researchers and practitioners to understand their similarities and differences. We propose a classification of product-line analyses to enable systematic research and application. Based on our insights with classifying and comparing a corpus of 123 research articles, we develop a research agenda to guide future research on product-line analyses. --- paper_title: Feature-Oriented Software Product Lines: Concepts and Implementation paper_content: While standardization has empowered the software industry to substantially scale software development and to provide affordable software to a broad market, it often does not address smaller market segments, nor the needs and wishes of individual customers. Software product lines reconcile mass production and standardization with mass customization in software engineering. Ideally, based on a set of reusable parts, a software manufacturer can generate a software product based on the requirements of its customer. The concept of features is central to achieving this level of automation, because features bridge the gap between the requirements the customer has and the functionality a product provides. Thus features are a central concept in all phases of product-line development. The authors take a developers viewpoint, focus on the development, maintenance, and implementation of product-line variability, and especially concentrate on automated product derivation based on a users feature selection. The book consists of three parts. Part I provides a general introduction to feature-oriented software product lines, describing the product-line approach and introducing the product-line development process with its two elements of domain and application engineering. The pivotal part II covers a wide variety of implementation techniques including design patterns, frameworks, components, feature-oriented programming, and aspect-oriented programming, as well as tool-based approaches including preprocessors, build systems, version-control systems, and virtual separation of concerns. Finally, part III is devoted to advanced topics related to feature-oriented product lines like refactoring, feature interaction, and analysis tools specific to product lines. In addition, an appendix lists various helpful tools for software product-line development, along with a description of how they relate to the topics covered in this book. To tie the book together, the authors use two running examples that are well documented in the product-line literature: data management for embedded systems, and variations of graph data structures. They start every chapter by explicitly stating the respective learning goals and finish it with a set of exercises; additional teaching material is also available online. All these features make the book ideally suited for teaching both for academic classes and for professionals interested in self-study. --- paper_title: Software Product Lines: Practices and Patterns paper_content: Foreword. Preface. Acknowledgements. Dedication. Reader's Guide. I. SOFTWARE PRODUCT LINE FUNDAMENTALS. 1. Basic Ideas and Terms. What Is a Software Product Line? What Software Product Lines Are Not. Fortuitous Small-Grained Reuse. Single-System Development with Reuse. Just Component-Based Development. Just a Reconfigurable Architecture. Releases and Versions of Single Products. Just a Set of Technical Standards. A Note on Terminology. For Further Reading. Discussion Questions. 2. Benefits. Organizational Benefits. Individual Benefits. Benefits versus Costs. For Further Reading. Discussion Questions. 3. The Three Essential Activities. What Are the Essential Activities? Core Asset Development. Product Development. Management. All Three Together. For Further Reading. Discussion Questions. II. SOFTWARE PRODUCT LINE PRACTICE AREAS. Describing the Practice Areas. Starting versus Running a Product Line. Organizing the Practice Areas. 4. Software Engineering Practice Areas. Architecture Definition. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Architecture Evaluation. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Component Development. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. COTS Utilization. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Mining Existing Assets. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. Discussion Questions. Requirements Engineering. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Software System Integration. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Testing. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Understanding Relevant Domains. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. 5. Technical Management Practice Areas. Configuration Management. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Data Collection, Metrics, and Tracking. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Make/Buy/Mine/Commission Analysis. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Process Definition. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Scoping. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Technical Planning. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. Discussion Questions. Technical Risk Management. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Tool Support. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. 6. Organizational Management Practice Areas. Building a Business Case. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Customer Interface Management. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. Discussion Questions. Developing an Acquisition Strategy. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Funding. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. Discussion Questions. Launching and Institutionalizing. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. Discussion Questions. Market Analysis. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Operations. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Organizational Planning. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. Discussion Questions. Organizational Risk Management. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Structuring the Organization. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. Discussion Questions. Technology Forecasting. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. Training. Aspects Peculiar to Product Lines. Application to Core Asset Development. Application to Product Development. Specific Practices. Practice Risks. For Further Reading. Discussion Questions. III. PUTTING THE PRACTICE AREAS INTO ACTION. 7. Software Product Line Practice Patterns. The Value of Patterns. Software Product Line Practice Pattern Descriptions. The Curriculum Pattern. The Essentials Coverage Pattern. Each Asset Pattern. What to Build Pattern. Product Parts Pattern. Assembly Line Pattern. Monitor Pattern. Product Builder Pattern. Cold Start Pattern. In Motion Pattern. Process Pattern. Factory Pattern. Other Patterns. Practice Area Coverage. Discussion Questions. 8. Product Line Technical Probe. What Is the Product Line Technical Probe? Probe Interview Questions. Probe Participants. Probe Process. Using the Probe Results. Conducting a Mini Self-Probe. Discussion Questions. 9. Cummins Engine Company: Embracing the Future. Prologue. Company History. A Product Line of Engine Software. Getting off the Ground. An Organization Structured for Cooperation. Running the Product Line. Results. Lessons Learned. Epilogue. Practice Area Compendium. For Further Reading. Discussion Questions. 10. Control Channel Toolkit: A Software Product Line that Controls Satellites. Contextual Background. Organizational Profiles. Project History. Control Channels. Launching CCT. Developing a Business Case for CCT. Developing the Acquisition Strategy and Funding CCT. Structuring the CCT Organization. Organizational and Technical Planning. Operations. Engineering the CCT Core Assets. Domain Analysis. Architecture. Component Engineering. Testing: Application and Test Engineering. Sustainment Engineering: Product Line Evolution. Documentation. Managing the CCT Effort. Early Benefits from CCT. First CCT Product. Benefits beyond CCT Products. Lessons and Issues. Tool Support Is Inadequate. Domain Analysis Documentation Is Important. An Early Architecture Focus Is Best. Product Builders Need More Support. CCT Users Need Reuse Metrics. It Pays to Be Flexible, and Cross-Unit Teams Work. A Real Product Is a Benefit. Summary. For Further Reading. Discussion Questions. 11. Successful Software product Line Development in Small Organization. Introduction. The Early Years. The MERGER Software Product Line. Market Maker Software Product Line Practices. Architecture Definition. Component Development. Structuring (and Staffing) the Organization. Testing. Data Collection and Metrics. Launching and Institutionalizing the Product Line. Understanding the Market. Technology Forecasting. A Few Observations. Effects of Company Culture. Cost Issues. The Customer Paradox. Tool Support. Lessons Learned. Drawbacks. Conclusions: Software Product Lines in Small Organizations. For Further Reading. Discussion Questions. 12. Conclusions: Practices, Patterns and Payoffs. The Practices. The Patterns. The Success Factors. The Payoff. Finale. Glossary. Bibliography. Index. --- paper_title: Automated analysis of feature models 20 years later: A literature review paper_content: Software product line engineering is about producing a set of related products that share more commonalities than variabilities. Feature models are widely used for variability and commonality management in software product lines. Feature models are information models where a set of products are represented as a set of features in a single model. The automated analysis of feature models deals with the computer-aided extraction of information from feature models. The literature on this topic has contributed with a set of operations, techniques, tools and empirical results which have not been surveyed until now. This paper provides a comprehensive literature review on the automated analysis of feature models 20 years after of their invention. This paper contributes by bringing together previously disparate streams of work to help shed light on this thriving area. We also present a conceptual framework to understand the different proposals as well as categorise future contributions. We finally discuss the different studies and propose some challenges to be faced in the future. --- paper_title: Delta-oriented programming of software product lines paper_content: Feature-oriented programming (FOP) implements software product lines by composition of feature modules. It relies on the principles of stepwise development. Feature modules are intended to refer to exactly one product feature and can only extend existing implementations. To provide more flexibility for implementing software product lines, we propose delta-oriented programming (DOP) as a novel programming language approach. A product line is represented by a core module and a set of delta modules. The core module provides an implementation of a valid product that can be developed with well-established single application engineering techniques. Delta modules specify changes to be applied to the core module to implement further products by adding, modifying and removing code. Application conditions attached to delta modules allow handling combinations of features explicitly. A product implementation for a particular feature configuration is generated by applying incrementally all delta modules with valid application condition to the core module. In order to evaluate the potential of DOP, we compare it to FOP, both conceptually and empirically. --- paper_title: Feature-Oriented Domain Analysis (FODA) Feasibility Study paper_content: Abstract : Successful Software reuse requires the systematic discovery and exploitation of commonality across related software systems. By examining related software systems and the underlying theory of the class of systems they represent, domain analysis can provide a generic description of the requirements of that class of systems and a set of approaches for their implementation. This report will establish methods for performing a domain analysis and describe the products of the domain analysis process. To illustrate the application of domain analysis to a representative class of software systems, this report will provide a domain analysis of window management system software. --- paper_title: Feature-Oriented Programming: A Fresh Look at Objects paper_content: We propose a new model for flexible composition of objects from a set of features. Features are similar to (abstract) subclasses, but only provide the core functionality of a (sub)class. Overwriting other methods is viewed as resolving feature interactions and is specified separately for two features at a time. This programming model allows to compose features (almost) freely in a way which generalizes inheritance and aggregation. For a set of n features, an exponential number of different feature combinations is possible, assuming a quadratic number of interaction resolutions. We present the feature model as an extension of Java and give two translations to Java, one via inheritance and the other via aggregation. We further discuss parameterized features, which work nicely with our feature model and can be translated into Pizza, an extension of Java. --- paper_title: A Classification and Survey of Analysis Strategies for Software Product Lines paper_content: Software-product-line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, model checking, and theorem proving, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analyses is both broad and diverse, so it is difficult for researchers and practitioners to understand their similarities and differences. We propose a classification of product-line analyses to enable systematic research and application. Based on our insights with classifying and comparing a corpus of 123 research articles, we develop a research agenda to guide future research on product-line analyses. --- paper_title: Feature-Oriented Software Product Lines: Concepts and Implementation paper_content: While standardization has empowered the software industry to substantially scale software development and to provide affordable software to a broad market, it often does not address smaller market segments, nor the needs and wishes of individual customers. Software product lines reconcile mass production and standardization with mass customization in software engineering. Ideally, based on a set of reusable parts, a software manufacturer can generate a software product based on the requirements of its customer. The concept of features is central to achieving this level of automation, because features bridge the gap between the requirements the customer has and the functionality a product provides. Thus features are a central concept in all phases of product-line development. The authors take a developers viewpoint, focus on the development, maintenance, and implementation of product-line variability, and especially concentrate on automated product derivation based on a users feature selection. The book consists of three parts. Part I provides a general introduction to feature-oriented software product lines, describing the product-line approach and introducing the product-line development process with its two elements of domain and application engineering. The pivotal part II covers a wide variety of implementation techniques including design patterns, frameworks, components, feature-oriented programming, and aspect-oriented programming, as well as tool-based approaches including preprocessors, build systems, version-control systems, and virtual separation of concerns. Finally, part III is devoted to advanced topics related to feature-oriented product lines like refactoring, feature interaction, and analysis tools specific to product lines. In addition, an appendix lists various helpful tools for software product-line development, along with a description of how they relate to the topics covered in this book. To tie the book together, the authors use two running examples that are well documented in the product-line literature: data management for embedded systems, and variations of graph data structures. They start every chapter by explicitly stating the respective learning goals and finish it with a set of exercises; additional teaching material is also available online. All these features make the book ideally suited for teaching both for academic classes and for professionals interested in self-study. --- paper_title: Aspect-oriented programming paper_content: We have found many programming problems for which neither procedural nor object-oriented programming techniques are sufficient to clearly capture some of the important design decisions the program must implement. This forces the implementation of those design decisions to be scattered throughout the code, resulting in “tangled” code that is excessively difficult to develop and maintain. We present an analysis of why certain design decisions have been so difficult to clearly capture in actual code. We call the properties these decisions address aspects, and show that the reason they have been hard to capture is that they cross-cut the system's basic functionality. We present the basis for a new programming technique, called aspect-oriented programming, that makes it possible to clearly express programs involving such aspects, including appropriate isolation, composition and reuse of the aspect code. The discussion is rooted in systems we have built using aspect-oriented programming. --- paper_title: Virtual Separation of Concerns -- A Second Chance for Preprocessors paper_content: Conditional compilation with preprocessors like cpp is a simple but eective means to implement variability. By annotating code fragments with #ifdef and #endif directives, dierent program variants with or without these fragments can be created, which can be used (among others) to implement software product lines. Although, preprocessors are frequently used in practice, they are often criticized for their negative eect on code quality and maintainability. In contrast to modularized implementations, for example using components or aspects, preprocessors neglect separation of concerns, are prone to introduce subtle errors, can entirely obfuscate the source code, and limit reuse. Our aim is to rehabilitate the preprocessor by showing how simple tool support can address these problems and emulate some benets of modularized implementations. At the same time we emphasize unique benets of preprocessors, like simplicity and language independence. Although we do not have a denitive answer on how to implement variability, we want highlight opportunities to improve preprocessors and encourage research toward novel preprocessor-based approaches. --- paper_title: Reducing Configurations to Monitor in a Software Product Line paper_content: A software product line is a family of programs where each program is defined by a unique combination of features. Product lines, like conventional programs, can be checked for safety properties through execution monitoring. However, because a product line induces a number of programs that is potentially exponential in the number of features, it would be very expensive to use existing monitoring techniques: one would have to apply those techniques to every single program. Doing so would also be wasteful because many programs can provably never violate the stated property. We introduce a monitoring technique dedicated to product lines that, given a safety property, statically determines the feature combinations that cannot possibly violate the property, thus reducing the number of programs to monitor. Experiments show that our technique is effective, particularly for safety properties that crosscut many optional features. --- paper_title: Model composition in product lines and feature interaction detection using critical pair analysis paper_content: Software product lines (SPL) are an established technology for developing families of systems. In particular, they focus on modeling commonality and variability, that is, they are based on identifying features common to all members of the family and variable features that appear only in some members. Model-based development methods for product lines advocate the construction of SPL requirements, analysis and design models for features. This paper describes an approach for maintaining feature separation during modeling using a UML composition language based on graph transformations. This allows models of features to be reused more easily. The language can be used to compose the SPL models for a given set of features. Furthermore, critical pair analysis is used to detect dependencies and conflicts between features during analysis and design modeling. The approach is supported by a tool that allows automated composition of UML models of features and detection of some kinds of feature interactions. --- paper_title: ASADAL: a tool system for co-development of software and test environment based on product line engineering paper_content: Recently, product line software engineering (PLSE) is gaining popularity. To employ PLSE methods, many organizations are looking for a tool system that supports PLSE methods so that core assets and target software can be developed and tested in an effective and systematic way.ASADAL (A System Analysis and Design Aid tooL) supports the entire lifecycle of software development process based on a PLSE method called FORM (Feature-Oriented Reuse Method) [6]. It supports domain analysis, architecture and component design, code generation, and simulation-based verification and validation (V&V). Using the tool, users may co-develop target software and its test environment and verify software in a continuous and incremental way. --- paper_title: Integration Testing of Software Product Lines Using Compositional Symbolic Execution paper_content: Software product lines are families of products defined by feature commonality and variability, with a well-managed asset base. Recent work in testing of software product lines has exploited similarities across development phases to reuse shared assets and reduce test effort. The use of feature dependence graphs has also been employed to reduce testing effort, but little work has focused on code level analysis of dataflow between features. In this paper we present a compositional symbolic execution technique that works in concert with a feature dependence graph to extract the set of possible interaction trees in a product family. It composes these to incrementally and symbolically analyze feature interactions. We experiment with two product lines and determine that our technique can reduce the overall number of interactions that must be considered during testing, and requires less time to run than a traditional symbolic execution technique. --- paper_title: PLEDGE: a product line editor and test generation tool paper_content: Specific requirements of clients lead to the development of variants of the same software. These variants form a Software Product Line (SPL). Ideally, testing a SPL involves testing all the software products that can be configured through the combination of features. This, however, is intractable in practice since a) large SPLs can lead to millions of possible software variants and b) the testing process is usually limited by budget and time constraints. To overcome this problem, this paper introduces PLEDGE, an open source tool that selects and prioritizes the product configurations maximizing the feature interactions covered. The uniqueness of PLEDGE is that it bypasses the computation of the feature interactions, allowing to scale to large SPLs. --- paper_title: A systematic mapping study of software product lines testing paper_content: ContextIn software development, Testing is an important mechanism both to identify defects and assure that completed products work as specified. This is a common practice in single-system development, and continues to hold in Software Product Lines (SPL). Even though extensive research has been done in the SPL Testing field, it is necessary to assess the current state of research and practice, in order to provide practitioners with evidence that enable fostering its further development. ObjectiveThis paper focuses on Testing in SPL and has the following goals: investigate state-of-the-art testing practices, synthesize available evidence, and identify gaps between required techniques and existing approaches, available in the literature. MethodA systematic mapping study was conducted with a set of nine research questions, in which 120 studies, dated from 1993 to 2009, were evaluated. ResultsAlthough several aspects regarding testing have been covered by single-system development approaches, many cannot be directly applied in the SPL context due to specific issues. In addition, particular aspects regarding SPL are not covered by the existing SPL approaches, and when the aspects are covered, the literature just gives brief overviews. This scenario indicates that additional investigation, empirical and practical, should be performed. ConclusionThe results can help to understand the needs in SPL Testing, by identifying points that still require additional investigation, since important aspects regarding particular points of software product lines have not been addressed yet. --- paper_title: Exploring variability-aware execution for testing plugin-based web applications paper_content: In plugin-based systems, plugin conflicts may occur when two or more plugins interfere with one another, changing their expected behaviors. It is highly challenging to detect plugin conflicts due to the exponential explosion of the combinations of plugins (i.e., configurations). In this paper, we address the challenge of executing a test case over many configurations. Leveraging the fact that many executions of a test are similar, our variability-aware execution runs common code once. Only when encountering values that are different depending on specific configurations will the execution split to run for each of them. To evaluate the scalability of variability-aware execution on a large real-world setting, we built a prototype PHP interpreter called Varex and ran it on the popular WordPress blogging Web application. The results show that while plugin interactions exist, there is a significant amount of sharing that allows variability-aware execution to scale to 2^50 configurations within seven minutes of running time. During our study, with Varex, we were able to detect two plugin conflicts: one was recently reported on WordPress forum and another one was not previously discovered. --- paper_title: Feature integration using a feature construct paper_content: Abstract A feature is a unit of functionality that may be added to (or omitted from) a system. Examples of features are plug-ins for software packages or additional services offered by telecommunications providers. Many features override the default behaviour of the system, which may lead to unforeseen behaviour of the system; this is known as feature interaction . We propose a feature construct for defining features, and use it to provide a plug-and-play framework for exploring feature interactions. Our approach to the feature interaction problem has the following characteristics: • Features are treated as first-class objects during the development phase. • A method is given for integrating a feature into a system description. It allows features to override existing behaviour of the system being developed. • A prototype tool has been developed for performing the integration. • Interactions between features may be witnessed. In principle, our approach is quite general and need not be tied to any particular system description language. In this paper, however, we develop the approach in the context of the SMV model checking system. We describe two case studies in detail: a lift system and a telephone system. --- paper_title: Incremental Test Generation for Software Product Lines paper_content: Recent advances in mechanical techniques for systematic testing have increased our ability to automatically find subtle bugs, and hence, to deploy more dependable software. This paper builds on one such systematic technique, scope-bounded testing, to develop a novel specification-based approach for efficiently generating tests for products in a software product line. Given properties of features as first-order logic formulas in Alloy, our approach uses SAT-based analysis to automatically generate test inputs for each product in a product line. To ensure soundness of generation, we introduce an automatic technique for mapping a formula that specifies a feature into a transformation that defines incremental refinement of test suites. Our experimental results using different data structure product lines show that an incremental approach can provide an order of magnitude speedup over conventional techniques. We also present a further optimization using dedicated integer constraint solvers for feature properties that introduce integer constraints, and show how to use a combination of solvers in tandem for solving Alloy formulas. --- paper_title: Strategies for product-line verification: Case studies and experiments paper_content: Product-line technology is increasingly used in mission-critical and safety-critical applications. Hence, researchers are developing verification approaches that follow different strategies to cope with the specific properties of product lines. While the research community is discussing the mutual strengths and weaknesses of the different strategies - mostly at a conceptual level - there is a lack of evidence in terms of case studies, tool implementations, and experiments. We have collected and prepared six product lines as subject systems for experimentation. Furthermore, we have developed a model-checking tool chain for C-based and Java-based product lines, called SPLverifier, which we use to compare sample-based and family-based strategies with regard to verification performance and the ability to find defects. Based on the experimental results and an analytical model, we revisit the discussion of the strengths and weaknesses of product-line-verification strategies. --- paper_title: A product line based aspect-oriented generative unit testing approach to building quality components paper_content: The quality of component-based systems highly depends on how effectively testing is carried out. To achieve the maximal testing effectiveness, this paper presents a product line based aspect oriented approach to unit testing. The aspect product line facilitates the automatic creation of aspect test cases that deal with specific quality requirements. An expandable repository of reusable aspect test cases has been developed. A prototype tool is built to verify and lever up the approach. --- paper_title: Avoiding redundant testing in application engineering paper_content: Many software product line testing techniques have been presented in the literature. The majority of those techniques address how to define reusable test assets (such as test models or test scenarios) in domain engineering and how to exploit those assets during application engineering. In addition to test case reuse however, the execution of test cases constitutes one important activity during application testing. Without a systematic support for the test execution in application engineering, while considering the specifics of product lines, product line artifacts might be tested redundantly. Redundant testing in application engineering, however, can lead to an increased testing effort without increasing the chance of uncovering failures. In this paper, we propose the model-based ScenTED-DF technique to avoid redundant testing in application engineering. Our technique builds on data flow-based testing techniques for single systems and adapts and extends those techniques to consider product line variability. The paper sketches the prototypical implementation of our technique to show its general feasibility and automation potential, and it describes the results of experiments using an academic product line to demonstrate that ScenTED-DF is capable of avoiding redundant tests in application engineering. --- paper_title: Evolutionary Search-based Test Generation for Software Product Line Feature Models paper_content: Product line-based software engineering is a paradigm that models the commonalities and variabilities of different applications of a given domain of interest within a unique framework and enhances rapid and low cost development of new applications based on reuse engineering principles. Despite the numerous advantages of software product lines, it is quite challenging to comprehensively test them. This is due to the fact that a product line can potentially represent many different applications; therefore, testing a single product line requires the test of its various applications. Theoretically, a product line with n software features can be a source for the development of 2n application. This requires the test of 2n applications if a brute-force comprehensive testing strategy is adopted. In this paper, we propose an evolutionary testing approach based on Genetic Algorithms to explore the configuration space of a software product line feature model in order to automatically generate test suites. We will show through the use of several publicly-available product line feature models that the proposed approach is able to generate test suites of O(n) size complexity as opposed to O(2n) while at the same time form a suitable tradeoff balance between error coverage and feature coverage in its generated test suites. --- paper_title: A survey of combinatorial testing paper_content: Combinatorial Testing (CT) can detect failures triggered by interactions of parameters in the Software Under Test (SUT) with a covering array test suite generated by some sampling mechanisms. It has been an active field of research in the last twenty years. This article aims to review previous work on CT, highlights the evolution of CT, and identifies important issues, methods, and applications of CT, with the goal of supporting and directing future practice and research in this area. First, we present the basic concepts and notations of CT. Second, we classify the research on CT into the following categories: modeling for CT, test suite generation, constraints, failure diagnosis, prioritization, metric, evaluation, testing procedure and the application of CT. For each of the categories, we survey the motivation, key issues, solutions, and the current state of research. Then, we review the contribution from different research groups, and present the growing trend of CT research. Finally, we recommend directions for future CT research, including: (1) modeling for CT, (2) improving the existing test suite generation algorithm, (3) improving analysis of testing result, (4) exploring the application of CT to different levels of testing and additional types of systems, (5) conducting more empirical studies to fully understand limitations and strengths of CT, and (6) combining CT with other testing techniques. --- paper_title: On strategies for testing software product lines: A systematic literature review paper_content: Context: Testing plays an important role in the quality assurance process for software product line engineering. There are many opportunities for economies of scope and scale in the testing activities, but techniques that can take advantage of these opportunities are still needed. Objective: The objective of this study is to identify testing strategies that have the potential to achieve these economies, and to provide a synthesis of available research on SPL testing strategies, to be applied towards reaching higher defect detection rates and reduced quality assurance effort. Method: We performed a literature review of two hundred seventy-six studies published from the year 1998 up to the 1st semester of 2013. We used several filters to focus the review on the most relevant studies and we give detailed analyses of the core set of studies. Results: The analysis of the reported strategies comprised two fundamental aspects for software product line testing: the selection of products for testing, and the actual test of products. Our findings indicate that the literature offers a large number of techniques to cope with such aspects. However, there is a lack of reports on realistic industrial experiences, which limits the inferences that can be drawn. Conclusion: This study showed a number of leveraged strategies that can support both the selection of products, and the actual testing of products. Future research should also benefit from the problems and advantages identified in this study. --- paper_title: Automated and Scalable T-wise Test Case Generation Strategies for Software Product Lines paper_content: Software Product Lines (SPL) are difficult to validate due to combinatorics induced by variability across their features. This leads to combinatorial explosion of the number of derivable products. Exhaustive testing in such a large space of products is infeasible. One possible option is to test SPLs by generating test cases that cover all possible T feature interactions (T-wise). T-wise dramatically reduces the number of test products while ensuring reasonable SPL coverage. However, automatic generation of test cases satisfying T-wise using SAT solvers raises two issues. The encoding of SPL models and T-wise criteria into a set of formulas acceptable by the solver and their satisfaction which fails when processed ``all-at-once''. We propose a scalable toolset using Alloy to automatically generate test cases satisfying T-wise from SPL models. We define strategies to split T-wise combinations into solvable subsets. We design and compute metrics to evaluate strategies on Aspect OPTIMA, a concrete transactional SPL. --- paper_title: MoSo-PoLiTe: tool support for pairwise and model-based software product line testing paper_content: Testing Software Product Lines is a very challenging task and approaches like combinatorial testing and model-based testing are frequently used to reduce the effort of testing Software Product Lines and to reuse test artifacts. In this contribution we present a tool chain realizing our MoSo-PoLiTe concept combining combinatorial and model-based testing. Our tool chain contains a pairwise configuration selection component on the basis of a feature model. This component implements an heuristic finding a minimal subset of configurations covering 100% pairwise interaction. Additionally, our tool chain allows the model-based test case generation for each configuration within this generated subset. This tool chain is based on commercial tools since it was developed within industrial cooperations. A non-commercial implementation of pairwise configuration selection is available and an integration with an Open Source model-based testing tool is under development. --- paper_title: A Classification and Survey of Analysis Strategies for Software Product Lines paper_content: Software-product-line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, model checking, and theorem proving, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analyses is both broad and diverse, so it is difficult for researchers and practitioners to understand their similarities and differences. We propose a classification of product-line analyses to enable systematic research and application. Based on our insights with classifying and comparing a corpus of 123 research articles, we develop a research agenda to guide future research on product-line analyses. --- paper_title: Coevolution of variability models and related artifacts: a case study from the Linux kernel paper_content: Variability-aware systems are subject to the coevolution of variability models and related artifacts. Surprisingly, little knowledge exists to understand such coevolution in practice. This shortage is directly reflected in existing approaches and tools for variability management, as they fail to provide effective support for such a coevolution. To understand how variability models and related artifacts coevolve in a large and complex real-world variability-aware system, we inspect over 500 Linux kernel commits spanning almost four years of development. We collect a catalog of evolution patterns, capturing the coevolution of the Linux kernel variability model, Makefiles, and C source code. Further, we extract general findings to guide further research and tool development. --- paper_title: Feature consistency in compile-time-configurable system software: facing the linux 10,000 feature problem paper_content: Much system software can be configured at compile time to tailor it with respect to a broad range of supported hardware architectures and application domains. A good example is the Linux kernel, which provides more than 10,000 configurable features, growing rapidly. From the maintenance point of view, compile-time configurability imposes big challenges. The configuration model (the selectable features and their constraints as presented to the user) and the configurability that is actually implemented in the code have to be kept in sync, which, if performed manually, is a tedious and error-prone task. In the case of Linux, this has led to numerous defects in the source code, many of which are actual bugs. We suggest an approach to automatically check for configurability-related implementation defects in large-scale configurable system software. The configurability is extracted from its various implementation sources and examined for inconsistencies, which manifest in seemingly conditional code that is in fact unconditional. We evaluate our approach with the latest version of Linux, for which our tool detects 1,776 configurability defects, which manifest as dead/superfluous source code and bugs. Our findings have led to numerous source-code improvements and bug fixes in Linux: 123 patches (49 merged) fix 364 defects, 147 of which have been confirmed by the corresponding Linux developers and 20 as fixing a new bug. --- paper_title: Reducing combinatorics in testing product lines paper_content: A Software Product Line (SPL) is a family of programs where each program is defined by a unique combination of features. Testing or checking properties of an SPL is hard as it may require the examination of a combinatorial number of programs. In reality, however, features are often irrelevant for a given test - they augment, but do not change, existing behavior, making many feature combinations unnecessary as far as testing is concerned. In this paper we show how to reduce the amount of effort in testing an SPL. We represent an SPL in a form where conventional static program analysis techniques can be applied to find irrelevant features for a test. We use this information to reduce the combinatorial number of SPL programs to examine. --- paper_title: Configuration coverage in the analysis of large-scale system software paper_content: System software, especially operating systems, tends to be highly configurable. Like every complex piece of software, a considerable amount of bugs in the implementation has to be expected. In order to improve the general code quality, tools for static analysis provide means to check for source code defects without having to run actual test cases on real hardware. Still, for proper type checking a specific configuration is required so that all header include paths are available and all types are properly resolved. In order to find as many bugs as possible, usually a "full configuration" is used for the check. However, mainly because of alternative blocks in form of #else-blocks, a single configuration is insufficient to achieve full coverage. In this paper, we present a metric for configuration coverage (CC) and explain the challenges for (properly) calculating it. Furthermore, we present an efficient approach for determining a sufficiently small set of configurations that achieve (nearly) full coverage and evaluate it on a recent Linux kernel version. --- paper_title: PACOGEN: Automatic Generation of Pairwise Test Configurations from Feature Models paper_content: Feature models are commonly used to specify variability in software product lines. Several tools support feature models for variability management at different steps in the development process. However, tool support for test configuration generation is currently limited. This test generation task consists in systematically selecting a set of configurations that represent a relevant sample of the variability space and that can be used to test the product line. In this paper we propose \pw tool to analyze feature models and automatically generate a set of configurations that cover all pair wise interactions between features. \pw tool relies on constraint programming to generate configurations that satisfy all constraints imposed by the feature model and to minimize the set of the tests configurations. This work also proposes an extensive experiment, based on the state-of-the art SPLOT feature models repository, showing that \pw tool scales over variability spaces with millions of configurations and covers pair wise with less configurations than other available tools. --- paper_title: Feature-Oriented Software Product Lines: Concepts and Implementation paper_content: While standardization has empowered the software industry to substantially scale software development and to provide affordable software to a broad market, it often does not address smaller market segments, nor the needs and wishes of individual customers. Software product lines reconcile mass production and standardization with mass customization in software engineering. Ideally, based on a set of reusable parts, a software manufacturer can generate a software product based on the requirements of its customer. The concept of features is central to achieving this level of automation, because features bridge the gap between the requirements the customer has and the functionality a product provides. Thus features are a central concept in all phases of product-line development. The authors take a developers viewpoint, focus on the development, maintenance, and implementation of product-line variability, and especially concentrate on automated product derivation based on a users feature selection. The book consists of three parts. Part I provides a general introduction to feature-oriented software product lines, describing the product-line approach and introducing the product-line development process with its two elements of domain and application engineering. The pivotal part II covers a wide variety of implementation techniques including design patterns, frameworks, components, feature-oriented programming, and aspect-oriented programming, as well as tool-based approaches including preprocessors, build systems, version-control systems, and virtual separation of concerns. Finally, part III is devoted to advanced topics related to feature-oriented product lines like refactoring, feature interaction, and analysis tools specific to product lines. In addition, an appendix lists various helpful tools for software product-line development, along with a description of how they relate to the topics covered in this book. To tie the book together, the authors use two running examples that are well documented in the product-line literature: data management for embedded systems, and variations of graph data structures. They start every chapter by explicitly stating the respective learning goals and finish it with a set of exercises; additional teaching material is also available online. All these features make the book ideally suited for teaching both for academic classes and for professionals interested in self-study. --- paper_title: Testing Software Product Lines Using Incremental Test Generation paper_content: We present a novel specification-based approach for generating tests for products in a software product line. Given properties of features as first-order logic formulas, our approach uses SAT-based analysis to automatically generate test inputs for each product in a product line. To ensure soundness of generation, we introduce an automatic technique for mapping a formula that specifies a feature into a transformation that defines incremental refinement of test suites. Our experimental results using different data structure product lines show that incremental approach can provide an order of magnitude speed-up over conventional techniques. --- paper_title: Types and Programming Languages paper_content: A type system is a syntactic method for automatically checking the absence of certain erroneous behaviors by classifying program phrases according to the kinds of values they compute. The study of type systems -- and of programming languages from a type-theoretic perspective -- has important applications in software engineering, language design, high-performance compilers, and security.This text provides a comprehensive introduction both to type systems in computer science and to the basic theory of programming languages. The approach is pragmatic and operational; each new concept is motivated by programming examples and the more theoretical sections are driven by the needs of implementations. Each chapter is accompanied by numerous exercises and solutions, as well as a running implementation, available via the Web. Dependencies between chapters are explicitly identified, allowing readers to choose a variety of paths through the material.The core topics include the untyped lambda-calculus, simple type systems, type reconstruction, universal and existential polymorphism, subtyping, bounded quantification, recursive types, kinds, and type operators. Extended case studies develop a variety of approaches to modeling the features of object-oriented languages. --- paper_title: Modeling and Model Checking Software Product Lines paper_content: Software product line engineering combines the individual developments of systems to the development of a family of systems consisting of common and variable assets.In this paper we introduce the process algebra PL-CCS as a product line extension of CCS and show how to model the overall behavior of an entire family within PL-CCS. PL-CCS models incorporate behavioral variability and allow the derivation of individual systems in a systematic way due to a semantics given in terms of multi-valued modal Kripke structures. Furthermore, we introduce multi-valued modal μ-calculus as a property specification language for system families specified in PL-CCS and show how model checking techniques operate on such structures. In our setting the result of model checking is no longer a simple yesor noanswer but the set of systems of the product line that do meet the specified properties. --- paper_title: Dead or Alive: finding zombie features in the Linux kernel paper_content: When an interference signals is detected by a base station, the base station issues a report to a switching apparatus. The switching apparatus checks self to see whether it is in a standby mode waiting for a frequency change completion report from another base station which is in the process of changing the control channel frequency. If the switching apparatus is satisfied that the station should be given a new control channel frequency, it issues a frequency change command to the requesting base station, and the base station receiving the command revises the current control channel frequency. In the meantime, all other requests from other stations are denied by the switching apparatus until the requesting base station has successfully completed the process of changing the control channel frequency. The base station acknowledges the completion of frequency change process by sending a frequency change completion report to the switching apparatus. --- paper_title: Model checking JAVA programs using JAVA PathFinder paper_content: Abstract.This paper describes a translator called Java PathFinder (Jpf), which translates from Java to Promela, the modeling language of the Spin model checker. Jpf translates a given Java program into a Promela model, which then can be model checked using Spin. The Java program may contain assertions, which are translated into similar assertions in the Promela model. The Spin model checker will then look for deadlocks and violations of any stated assertions. Jpf generates a Promela model with the same state space characteristics as the Java program. Hence, the Java program must have a finite and tractable state space. This work should be seen in a broader attempt to make formal methods applicable within NASA’s areas such as space, aviation, and robotics. The work is a continuation of an effort to formally analyze, using Spin, a multi-threaded operating system for the Deep-Space 1 space craft, and of previous work in applying existing model checkers and theorem provers to real applications. --- paper_title: Language-independent reference checking in software product lines paper_content: Feature-Oriented Software Development (FOSD) is a paradigm for the development of software product lines. A challenge in FOSD is to guarantee that all software systems of a software product line are correct. Recent work on type checking product lines can provide a guarantee of type correctness without generating all possible systems. We generalize previous results by abstracting from the specifics of particular programming languages. In a first attempt, we present a reference-checking algorithm that performs key tasks of product-line type checking independently of the target programming language. Experiments with two sample product lines written in Java and C are encouraging and give us confidence that this approach is promising. --- paper_title: Language-Independent and Automated Software Composition: The FeatureHouse Experience paper_content: Superimposition is a composition technique that has been applied successfully in many areas of software development. Although superimposition is a general-purpose concept, it has been (re)invented and implemented individually for various kinds of software artifacts. We unify languages and tools that rely on superimposition by using the language-independent model of feature structure trees (FSTs). On the basis of the FST model, we propose a general approach to the composition of software artifacts written in different languages. Furthermore, we offer a supporting framework and tool chain, called FEATUREHOUSE. We use attribute grammars to automate the integration of additional languages. In particular, we have integrated Java, C#, C, Haskell, Alloy, and JavaCC. A substantial number of case studies demonstrate the practicality and scalability of our approach and reveal insights into the properties that a language must have in order to be ready for superimposition. We discuss perspectives of our approach and demonstrate how we extended FEATUREHOUSE with support for XML languages (in particular, XHTML, XMI/UML, and Ant) and alternative composition approaches (in particular, aspect weaving). Rounding off our previous work, we provide here a holistic view of the FEATUREHOUSE approach based on rich experience with numerous languages and case studies and reflections on several years of research. --- paper_title: Access Control in Feature-Oriented Programming paper_content: In feature-oriented programming (FOP) a programmer decomposes a program in terms of features. Ideally, features are implemented modularly so that they can be developed in isolation. Access control mechanisms in the form of access or visibility modifiers are an important ingredient to attain feature modularity as they allow programmers to hide and expose internal details of a module's implementation. But developers of contemporary feature-oriented languages have not considered access control mechanisms so far. The absence of a well-defined access control model for FOP breaks encapsulation of feature code and leads to unexpected program behaviors and inadvertent type errors. We raise awareness of this problem, propose three feature-oriented access modifiers, and present a corresponding access modifier model. We offer an implementation of the model on the basis of a fully-fledged feature-oriented compiler. Finally, by analyzing ten feature-oriented programs, we explore the potential of feature-oriented modifiers in FOP. --- paper_title: Proof Composition for Deductive Verification of Software Product Lines paper_content: Software product line engineering aims at the efficient development of program variants that share a common set of features and that differ in other features. Product lines can be efficiently developed using feature-oriented programming. Given a feature selection and the code artifacts for each feature, program variants can be generated automatically. The quality of the program variants can be rigorously ensured by formal verification. However, verification of all program variants can be expensive and include redundant verification tasks. We introduce a classification of existing software product line verification approaches and propose proof composition as a novel approach. Proof composition generates correctness proofs of each program variant based on partial proofs of each feature. We present a case study to evaluate proof composition and demonstrate that it reduces the effort for verification. --- paper_title: Principles of Program Analysis paper_content: Program analysis utilizes static techniques for computing reliable information about the dynamic behavior of programs. Applications include compilers (for code improvement), software validation (for detecting errors) and transformations between data representation (for solving problems such as Y2K). This book is unique in providing an overview of the four major approaches to program analysis: data flow analysis, constraint-based analysis, abstract interpretation, and type and effect systems. The presentation illustrates the extensive similarities between the approaches, helping readers to choose the best one to utilize. --- paper_title: Aspect Composition Applying the Design by Contract Principle paper_content: The composition of software units has been one of the main research topics in computer science. This paper addresses the composition validation problem evolving in this context. It focuses on the composition for a certain kind of units called aspects. Aspects are a new concept which is introduced by aspect-oriented programming aiming at a better separation of concerns. Cross-cutting code is captured and localised in these aspects. Some of the cross-cutting features which are expressed in aspects cannot be woven with other features into the same application since two features could be mutually exclusive. With a growing number of aspects, manual control of these dependencies becomes error-prone or even impossible. We show how assertions can be useful in this respect to support the software developer. --- paper_title: Emergo: a tool for improving maintainability of preprocessor-based product lines paper_content: When maintaining a feature in preprocessor-based Software Product Lines (SPLs), developers are susceptible to introduce problems into other features. This is possible because features eventually share elements (like variables and methods) with the maintained one. This scenario might be even worse when hiding features by using techniques like Virtual Separation of Concerns (VSoC), since developers cannot see the feature dependencies and, consequently, they become unaware of them. Emergent Interfaces was proposed to minimize this problem by capturing feature dependencies and then providing information about other features that can be impacted during a maintenance task. In this paper, we present Emergo, a tool capable of computing emergent interfaces between the feature we are maintaining and the others. Emergo relies on feature-sensitive dataflow analyses in the sense it takes features and the SPL feature model into consideration when computing the interfaces. --- paper_title: Abstract Features in Feature Modeling paper_content: A software product line is a set of program variants, typically generated from a common code base. Feature models describe variability in product lines by documenting features and their valid combinations. In product-line engineering, we need to reason about variability and program variants for many different tasks. For example, given a feature model, we might want to determine the number of all valid feature combinations or compute specific feature combinations for testing. However, we found that contemporary reasoning approaches can only reason about feature combinations, not about program variants, because they do not take abstract features into account. Abstract features are features used to structure a feature model that, however, do not have any impact at implementation level. Using existing feature-model reasoning mechanisms for program variants leads to incorrect results. Hence, although abstract features represent domain decisions that do not affect the generation of a program variant. We raise awareness of the problem of abstract features for different kinds of analyses on feature models. We argue that, in order to reason about program variants, abstract features should be made explicit in feature models. We present a technique based on propositional formulas that enables to reason about program variants rather than feature combinations. In practice, our technique can save effort that is caused by considering the same program variant multiple times, for example, in product-line testing. --- paper_title: Delta-oriented programming of software product lines paper_content: Feature-oriented programming (FOP) implements software product lines by composition of feature modules. It relies on the principles of stepwise development. Feature modules are intended to refer to exactly one product feature and can only extend existing implementations. To provide more flexibility for implementing software product lines, we propose delta-oriented programming (DOP) as a novel programming language approach. A product line is represented by a core module and a set of delta modules. The core module provides an implementation of a valid product that can be developed with well-established single application engineering techniques. Delta modules specify changes to be applied to the core module to implement further products by adding, modifying and removing code. Application conditions attached to delta modules allow handling combinations of features explicitly. A product implementation for a particular feature configuration is generated by applying incrementally all delta modules with valid application condition to the core module. In order to evaluate the potential of DOP, we compare it to FOP, both conceptually and empirically. --- paper_title: CPAchecker: A Tool for Configurable Software Verification paper_content: Configurable software verification is a recent concept for expressing different program analysis and model checking approaches in one single formalism. This paper presents CPAchecker, a tool and framework that aims at easy integration of new verification components. Every abstract domain, together with the corresponding operations, implements the interface of configurable program analysis (CPA). The main algorithm is configurable to perform a reachability analysis on arbitrary combinations of existing CPAs. In software verification, it takes a considerable amount of effort to convert a verification idea into actual experimental results -- we aim at accelerating this process. We hope that researchers find it convenient and productive to implement new verification ideas and algorithms using this flexible and easy-to-extend platform, and that it advances the field by making it easier to perform practical experiments. The tool is implemented in Java and runs as command-line tool or as Eclipse plug-in. CPAchecker implements CPAs for several abstract domains. We evaluate the efficiency of the current version of our tool on software-verification benchmarks from the literature, and compare it with other state-of-the-art model checkers. CPAchecker is an open-source toolkit and publicly available. --- paper_title: Detection of feature interactions using feature-aware verification paper_content: A software product line is a set of software products that are distinguished in terms of features (i.e., end-user-visible units of behavior). Feature interactions-- situations in which the combination of features leads to emergent and possibly critical behavior --are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line-verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLVERIFIER for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only local knowledge. --- paper_title: Toward variability-aware testing paper_content: We investigate how to execute a unit test for all products of a product line without generating each product in isolation in a brute-force fashion. Learning from variability-aware analyses, we (a) design and implement a variability-aware interpreter and, alternatively, (b) reencode variability of the product line to simulate the test cases with a model checker. The interpreter internally reasons about variability, executing paths not affected by variability only once for the whole product line. The model checker achieves similar results by reusing powerful off-the-shelf analyses. We experimented with a prototype implementation for each strategy. We compare both strategies and discuss trade-offs and future directions. In the long run, we aim at finding an efficient testing approach that can be applied to entire product lines with millions of products. --- paper_title: Model checking lots of systems: efficient verification of temporal properties in software product lines paper_content: In product line engineering, systems are developed in families and differences between family members are expressed in terms of features. Formal modelling and verification is an important issue in this context as more and more critical systems are developed this way. Since the number of systems in a family can be exponential in the number of features, two major challenges are the scalable modelling and the efficient verification of system behaviour. Currently, the few attempts to address them fail to recognise the importance of features as a unit of difference, or do not offer means for automated verification. In this paper, we tackle those challenges at a fundamental level. We first extend transition systems with features in order to describe the combined behaviour of an entire system family. We then define and implement a model checking technique that allows to verify such transition systems against temporal properties. An empirical evaluation shows substantial gains over classical approaches. --- paper_title: A Classification and Survey of Analysis Strategies for Software Product Lines paper_content: Software-product-line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, model checking, and theorem proving, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analyses is both broad and diverse, so it is difficult for researchers and practitioners to understand their similarities and differences. We propose a classification of product-line analyses to enable systematic research and application. Based on our insights with classifying and comparing a corpus of 123 research articles, we develop a research agenda to guide future research on product-line analyses. --- paper_title: Feature consistency in compile-time-configurable system software: facing the linux 10,000 feature problem paper_content: Much system software can be configured at compile time to tailor it with respect to a broad range of supported hardware architectures and application domains. A good example is the Linux kernel, which provides more than 10,000 configurable features, growing rapidly. From the maintenance point of view, compile-time configurability imposes big challenges. The configuration model (the selectable features and their constraints as presented to the user) and the configurability that is actually implemented in the code have to be kept in sync, which, if performed manually, is a tedious and error-prone task. In the case of Linux, this has led to numerous defects in the source code, many of which are actual bugs. We suggest an approach to automatically check for configurability-related implementation defects in large-scale configurable system software. The configurability is extracted from its various implementation sources and examined for inconsistencies, which manifest in seemingly conditional code that is in fact unconditional. We evaluate our approach with the latest version of Linux, for which our tool detects 1,776 configurability defects, which manifest as dead/superfluous source code and bugs. Our findings have led to numerous source-code improvements and bug fixes in Linux: 123 patches (49 merged) fix 364 defects, 147 of which have been confirmed by the corresponding Linux developers and 20 as fixing a new bug. --- paper_title: Precise interprocedural dataflow analysis via graph reachability paper_content: The paper shows how a large class of interprocedural dataflow-analysis problems can be solved precisely in polynomial time by transforming them into a special kind of graph-reachability problem. The only restrictions are that the set of dataflow facts must be a finite set, and that the dataflow functions must distribute over the confluence operator (either union or intersection). This class of probable problems includes—but is not limited to—the classical separable problems (also known as “gen/kill” or “bit-vector” problems)— e.g. , reaching definitions, available expressions, and live variables. In addition, the class of problems that our techniques handle includes many non-separable problems, including truly-live variables, copy constant propagation, and possibly-uninitialized variables. Results are reported from a preliminary experimental study of C programs (for the problem of finding possibly-uninitialized variables). --- paper_title: FeatureIDE: An Extensible Framework for Feature-Oriented Software Development paper_content: FeatureIDE is an open-source framework for feature-oriented software development (FOSD) based on Eclipse. FOSD is a paradigm for the construction, customization, and synthesis of software systems. Code artifacts are mapped to features, and a customized software system can be generated given a selection of features. The set of software systems that can be generated is called a software product line (SPL). FeatureIDE supports several FOSD implementation techniques such as feature-oriented programming, aspect-oriented programming, delta-oriented programming, and preprocessors. All phases of FOSD are supported in FeatureIDE, namely domain analysis, requirements analysis, domain implementation, and software generation. --- paper_title: Automatic detection of feature interactions using the Java modeling language: an experience report paper_content: In the development of complex software systems, interactions between different program features increase the design complexity. Feature-oriented software development focuses on the representation and compositions of features. The implementation of features often cuts across object-oriented module boundaries and hence comprises interactions. The manual detection and treatment of feature interactions requires a deep knowledge of the implementation details of the features involved. Our goal is to detect interactions automatically using specifications by means of design by contract and automated theorem proving. We provide a software tool that operates on programs in Java and the Java Modeling Language (JML). We discuss which kinds of feature interactions can be detected automatically with our tool and how to detect other kinds of interactions. --- paper_title: Applying 'design by contract' paper_content: Methodological guidelines for object-oriented software construction that improve the reliability of the resulting software systems are presented. It is shown that the object-oriented techniques rely on the theory of design by contract, which underlies the design of the Eiffel analysis, design, and programming language and of the supporting libraries, from which a number of examples are drawn. The theory of contract design and the role of assertions in that theory are discussed. > --- paper_title: Potential synergies of theorem proving and model checking for software product lines paper_content: The verification of software product lines is an active research area. A challenge is to efficiently verify similar products without the need to generate and verify them individually. As solution, researchers suggest family-based verification approaches, which either transform compile-time into runtime variability or make verification tools variability-aware. Existing approaches either focus on theorem proving, model checking, or other verification techniques. For the first time, we combine theorem proving and model checking to evaluate their synergies for product-line verification. We provide tool support by connecting five existing tools, namely FeatureIDE and FeatureHouse for product-line development, as well as KeY, JPF, and OpenJML for verification of Java programs. In an experiment, we found the synergy of improved effectiveness and efficiency, especially for product lines with few defects. Further, we experienced that model checking and theorem proving are more efficient and effective if the product line contains more defects. --- paper_title: Virtual Separation of Concerns -- A Second Chance for Preprocessors paper_content: Conditional compilation with preprocessors like cpp is a simple but eective means to implement variability. By annotating code fragments with #ifdef and #endif directives, dierent program variants with or without these fragments can be created, which can be used (among others) to implement software product lines. Although, preprocessors are frequently used in practice, they are often criticized for their negative eect on code quality and maintainability. In contrast to modularized implementations, for example using components or aspects, preprocessors neglect separation of concerns, are prone to introduce subtle errors, can entirely obfuscate the source code, and limit reuse. Our aim is to rehabilitate the preprocessor by showing how simple tool support can address these problems and emulate some benets of modularized implementations. At the same time we emphasize unique benets of preprocessors, like simplicity and language independence. Although we do not have a denitive answer on how to implement variability, we want highlight opportunities to improve preprocessors and encourage research toward novel preprocessor-based approaches. --- paper_title: SPL Conqueror: Toward optimization of non-functional properties in software product lines paper_content: A software product line (SPL) is a family of related programs of a domain. The programs of an SPL are distinguished in terms of features, which are end-user visible characteristics of programs. Based on a selection of features, stakeholders can derive tailor-made programs that satisfy functional requirements. Besides functional requirements, different application scenarios raise the need for optimizing non-functional properties of a variant. The diversity of application scenarios leads to heterogeneous optimization goals with respect to non-functional properties (e.g., performance vs. footprint vs. energy optimized variants). Hence, an SPL has to satisfy different and sometimes contradicting requirements regarding non-functional properties. Usually, the actually required non-functional properties are not known before product derivation and can vary for each application scenario and customer. Allowing stakeholders to derive optimized variants requires us to measure non-functional properties after the SPL is developed. Unfortunately, the high variability provided by SPLs complicates measurement and optimization of non-functional properties due to a large variant space. With SPL Conqueror, we provide a holistic approach to optimize non-functional properties in SPL engineering. We show how non-functional properties can be qualitatively specified and quantitatively measured in the context of SPLs. Furthermore, we discuss the variant-derivation process in SPL Conqueror that reduces the effort of computing an optimal variant. We demonstrate the applicability of our approach by means of nine case studies of a broad range of application domains (e.g., database management and operating systems). Moreover, we show that SPL Conqueror is implementation and language independent by using SPLs that are implemented with different mechanisms, such as conditional compilation and feature-oriented programming. --- paper_title: Reducing Configurations to Monitor in a Software Product Line paper_content: A software product line is a family of programs where each program is defined by a unique combination of features. Product lines, like conventional programs, can be checked for safety properties through execution monitoring. However, because a product line induces a number of programs that is potentially exponential in the number of features, it would be very expensive to use existing monitoring techniques: one would have to apply those techniques to every single program. Doing so would also be wasteful because many programs can provably never violate the stated property. We introduce a monitoring technique dedicated to product lines that, given a safety property, statically determines the feature combinations that cannot possibly violate the property, thus reducing the number of programs to monitor. Experiments show that our technique is effective, particularly for safety properties that crosscut many optional features. --- paper_title: ASADAL: a tool system for co-development of software and test environment based on product line engineering paper_content: Recently, product line software engineering (PLSE) is gaining popularity. To employ PLSE methods, many organizations are looking for a tool system that supports PLSE methods so that core assets and target software can be developed and tested in an effective and systematic way.ASADAL (A System Analysis and Design Aid tooL) supports the entire lifecycle of software development process based on a PLSE method called FORM (Feature-Oriented Reuse Method) [6]. It supports domain analysis, architecture and component design, code generation, and simulation-based verification and validation (V&V). Using the tool, users may co-develop target software and its test environment and verify software in a continuous and incremental way. --- paper_title: Dead or Alive: finding zombie features in the Linux kernel paper_content: When an interference signals is detected by a base station, the base station issues a report to a switching apparatus. The switching apparatus checks self to see whether it is in a standby mode waiting for a frequency change completion report from another base station which is in the process of changing the control channel frequency. If the switching apparatus is satisfied that the station should be given a new control channel frequency, it issues a frequency change command to the requesting base station, and the base station receiving the command revises the current control channel frequency. In the meantime, all other requests from other stations are denied by the switching apparatus until the requesting base station has successfully completed the process of changing the control channel frequency. The base station acknowledges the completion of frequency change process by sending a frequency change completion report to the switching apparatus. --- paper_title: Integration Testing of Software Product Lines Using Compositional Symbolic Execution paper_content: Software product lines are families of products defined by feature commonality and variability, with a well-managed asset base. Recent work in testing of software product lines has exploited similarities across development phases to reuse shared assets and reduce test effort. The use of feature dependence graphs has also been employed to reduce testing effort, but little work has focused on code level analysis of dataflow between features. In this paper we present a compositional symbolic execution technique that works in concert with a feature dependence graph to extract the set of possible interaction trees in a product family. It composes these to incrementally and symbolically analyze feature interactions. We experiment with two product lines and determine that our technique can reduce the overall number of interactions that must be considered during testing, and requires less time to run than a traditional symbolic execution technique. --- paper_title: Language-independent reference checking in software product lines paper_content: Feature-Oriented Software Development (FOSD) is a paradigm for the development of software product lines. A challenge in FOSD is to guarantee that all software systems of a software product line are correct. Recent work on type checking product lines can provide a guarantee of type correctness without generating all possible systems. We generalize previous results by abstracting from the specifics of particular programming languages. In a first attempt, we present a reference-checking algorithm that performs key tasks of product-line type checking independently of the target programming language. Experiments with two sample product lines written in Java and C are encouraging and give us confidence that this approach is promising. --- paper_title: Language-Independent and Automated Software Composition: The FeatureHouse Experience paper_content: Superimposition is a composition technique that has been applied successfully in many areas of software development. Although superimposition is a general-purpose concept, it has been (re)invented and implemented individually for various kinds of software artifacts. We unify languages and tools that rely on superimposition by using the language-independent model of feature structure trees (FSTs). On the basis of the FST model, we propose a general approach to the composition of software artifacts written in different languages. Furthermore, we offer a supporting framework and tool chain, called FEATUREHOUSE. We use attribute grammars to automate the integration of additional languages. In particular, we have integrated Java, C#, C, Haskell, Alloy, and JavaCC. A substantial number of case studies demonstrate the practicality and scalability of our approach and reveal insights into the properties that a language must have in order to be ready for superimposition. We discuss perspectives of our approach and demonstrate how we extended FEATUREHOUSE with support for XML languages (in particular, XHTML, XMI/UML, and Ant) and alternative composition approaches (in particular, aspect weaving). Rounding off our previous work, we provide here a holistic view of the FEATUREHOUSE approach based on rich experience with numerous languages and case studies and reflections on several years of research. --- paper_title: Access Control in Feature-Oriented Programming paper_content: In feature-oriented programming (FOP) a programmer decomposes a program in terms of features. Ideally, features are implemented modularly so that they can be developed in isolation. Access control mechanisms in the form of access or visibility modifiers are an important ingredient to attain feature modularity as they allow programmers to hide and expose internal details of a module's implementation. But developers of contemporary feature-oriented languages have not considered access control mechanisms so far. The absence of a well-defined access control model for FOP breaks encapsulation of feature code and leads to unexpected program behaviors and inadvertent type errors. We raise awareness of this problem, propose three feature-oriented access modifiers, and present a corresponding access modifier model. We offer an implementation of the model on the basis of a fully-fledged feature-oriented compiler. Finally, by analyzing ten feature-oriented programs, we explore the potential of feature-oriented modifiers in FOP. --- paper_title: Clafer tools for product line engineering paper_content: Clafer is a lightweight yet expressive language for structural modeling: feature modeling and configuration, class and object modeling, and metamodeling. Clafer Tools is an integrated set of tools based on Clafer. In this paper, we describe some product-line variability modeling scenarios of Clafer Tools from the viewpoints of product-line owner, product-line engineer, and product engineer. --- paper_title: PLEDGE: a product line editor and test generation tool paper_content: Specific requirements of clients lead to the development of variants of the same software. These variants form a Software Product Line (SPL). Ideally, testing a SPL involves testing all the software products that can be configured through the combination of features. This, however, is intractable in practice since a) large SPLs can lead to millions of possible software variants and b) the testing process is usually limited by budget and time constraints. To overcome this problem, this paper introduces PLEDGE, an open source tool that selects and prioritizes the product configurations maximizing the feature interactions covered. The uniqueness of PLEDGE is that it bypasses the computation of the feature interactions, allowing to scale to large SPLs. --- paper_title: Aspect Composition Applying the Design by Contract Principle paper_content: The composition of software units has been one of the main research topics in computer science. This paper addresses the composition validation problem evolving in this context. It focuses on the composition for a certain kind of units called aspects. Aspects are a new concept which is introduced by aspect-oriented programming aiming at a better separation of concerns. Cross-cutting code is captured and localised in these aspects. Some of the cross-cutting features which are expressed in aspects cannot be woven with other features into the same application since two features could be mutually exclusive. With a growing number of aspects, manual control of these dependencies becomes error-prone or even impossible. We show how assertions can be useful in this respect to support the software developer. --- paper_title: Emergo: a tool for improving maintainability of preprocessor-based product lines paper_content: When maintaining a feature in preprocessor-based Software Product Lines (SPLs), developers are susceptible to introduce problems into other features. This is possible because features eventually share elements (like variables and methods) with the maintained one. This scenario might be even worse when hiding features by using techniques like Virtual Separation of Concerns (VSoC), since developers cannot see the feature dependencies and, consequently, they become unaware of them. Emergent Interfaces was proposed to minimize this problem by capturing feature dependencies and then providing information about other features that can be impacted during a maintenance task. In this paper, we present Emergo, a tool capable of computing emergent interfaces between the feature we are maintaining and the others. Emergo relies on feature-sensitive dataflow analyses in the sense it takes features and the SPL feature model into consideration when computing the interfaces. --- paper_title: Variability-aware parsing in the presence of lexical macros and conditional compilation paper_content: In many projects, lexical preprocessors are used to manage different variants of the project (using conditional compilation) and to define compile-time code transformations (using macros). Unfortunately, while being a simple way to implement variability, conditional compilation and lexical macros hinder automatic analysis, even though such analysis is urgently needed to combat variability-induced complexity. To analyze code with its variability, we need to parse it without preprocessing it. However, current parsing solutions use unsound heuristics, support only a subset of the language, or suffer from exponential explosion. As part of the TypeChef project, we contribute a novel variability-aware parser that can parse almost all unpreprocessed code without heuristics in practicable time. Beyond the obvious task of detecting syntax errors, our parser paves the road for further analysis, such as variability-aware type checking. We implement variability-aware parsers for Java and GNU C and demonstrate practicability by parsing the product line MobileMedia and the entire X86 architecture of the Linux kernel with 6065 variable features. --- paper_title: Delta-oriented programming of software product lines paper_content: Feature-oriented programming (FOP) implements software product lines by composition of feature modules. It relies on the principles of stepwise development. Feature modules are intended to refer to exactly one product feature and can only extend existing implementations. To provide more flexibility for implementing software product lines, we propose delta-oriented programming (DOP) as a novel programming language approach. A product line is represented by a core module and a set of delta modules. The core module provides an implementation of a valid product that can be developed with well-established single application engineering techniques. Delta modules specify changes to be applied to the core module to implement further products by adding, modifying and removing code. Application conditions attached to delta modules allow handling combinations of features explicitly. A product implementation for a particular feature configuration is generated by applying incrementally all delta modules with valid application condition to the core module. In order to evaluate the potential of DOP, we compare it to FOP, both conceptually and empirically. --- paper_title: Exploring variability-aware execution for testing plugin-based web applications paper_content: In plugin-based systems, plugin conflicts may occur when two or more plugins interfere with one another, changing their expected behaviors. It is highly challenging to detect plugin conflicts due to the exponential explosion of the combinations of plugins (i.e., configurations). In this paper, we address the challenge of executing a test case over many configurations. Leveraging the fact that many executions of a test are similar, our variability-aware execution runs common code once. Only when encountering values that are different depending on specific configurations will the execution split to run for each of them. To evaluate the scalability of variability-aware execution on a large real-world setting, we built a prototype PHP interpreter called Varex and ran it on the popular WordPress blogging Web application. The results show that while plugin interactions exist, there is a significant amount of sharing that allows variability-aware execution to scale to 2^50 configurations within seven minutes of running time. During our study, with Varex, we were able to detect two plugin conflicts: one was recently reported on WordPress forum and another one was not previously discovered. --- paper_title: Incremental Test Generation for Software Product Lines paper_content: Recent advances in mechanical techniques for systematic testing have increased our ability to automatically find subtle bugs, and hence, to deploy more dependable software. This paper builds on one such systematic technique, scope-bounded testing, to develop a novel specification-based approach for efficiently generating tests for products in a software product line. Given properties of features as first-order logic formulas in Alloy, our approach uses SAT-based analysis to automatically generate test inputs for each product in a product line. To ensure soundness of generation, we introduce an automatic technique for mapping a formula that specifies a feature into a transformation that defines incremental refinement of test suites. Our experimental results using different data structure product lines show that an incremental approach can provide an order of magnitude speedup over conventional techniques. We also present a further optimization using dedicated integer constraint solvers for feature properties that introduce integer constraints, and show how to use a combination of solvers in tandem for solving Alloy formulas. --- paper_title: A product line based aspect-oriented generative unit testing approach to building quality components paper_content: The quality of component-based systems highly depends on how effectively testing is carried out. To achieve the maximal testing effectiveness, this paper presents a product line based aspect oriented approach to unit testing. The aspect product line facilitates the automatic creation of aspect test cases that deal with specific quality requirements. An expandable repository of reusable aspect test cases has been developed. A prototype tool is built to verify and lever up the approach. --- paper_title: Avoiding redundant testing in application engineering paper_content: Many software product line testing techniques have been presented in the literature. The majority of those techniques address how to define reusable test assets (such as test models or test scenarios) in domain engineering and how to exploit those assets during application engineering. In addition to test case reuse however, the execution of test cases constitutes one important activity during application testing. Without a systematic support for the test execution in application engineering, while considering the specifics of product lines, product line artifacts might be tested redundantly. Redundant testing in application engineering, however, can lead to an increased testing effort without increasing the chance of uncovering failures. In this paper, we propose the model-based ScenTED-DF technique to avoid redundant testing in application engineering. Our technique builds on data flow-based testing techniques for single systems and adapts and extends those techniques to consider product line variability. The paper sketches the prototypical implementation of our technique to show its general feasibility and automation potential, and it describes the results of experiments using an academic product line to demonstrate that ScenTED-DF is capable of avoiding redundant tests in application engineering. --- paper_title: Detection of feature interactions using feature-aware verification paper_content: A software product line is a set of software products that are distinguished in terms of features (i.e., end-user-visible units of behavior). Feature interactions-- situations in which the combination of features leads to emergent and possibly critical behavior --are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line-verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLVERIFIER for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only local knowledge. --- paper_title: Toward variability-aware testing paper_content: We investigate how to execute a unit test for all products of a product line without generating each product in isolation in a brute-force fashion. Learning from variability-aware analyses, we (a) design and implement a variability-aware interpreter and, alternatively, (b) reencode variability of the product line to simulate the test cases with a model checker. The interpreter internally reasons about variability, executing paths not affected by variability only once for the whole product line. The model checker achieves similar results by reusing powerful off-the-shelf analyses. We experimented with a prototype implementation for each strategy. We compare both strategies and discuss trade-offs and future directions. In the long run, we aim at finding an efficient testing approach that can be applied to entire product lines with millions of products. --- paper_title: Family-based performance measurement paper_content: Most contemporary programs are customizable. They provide many features that give rise to millions of program variants. Determining which feature selection yields an optimal performance is challenging, because of the exponential number of variants. Predicting the performance of a variant based on previous measurements proved successful, but induces a trade-off between the measurement effort and prediction accuracy. We propose the alternative approach of family-based performance measurement, to reduce the number of measurements required for identifying feature interactions and for obtaining accurate predictions. The key idea is to create a variant simulator (by translating compile-time variability to run-time variability) that can simulate the behavior of all program variants. We use it to measure performance of individual methods, trace methods to features, and infer feature interactions based on the call graph. We evaluate our approach by means of five feature-oriented programs. On average, we achieve accuracy of 98%, with only a single measurement per customizable program. Observations show that our approach opens avenues of future research in different domains, such an feature-interaction detection and testing. --- paper_title: Automated and Scalable T-wise Test Case Generation Strategies for Software Product Lines paper_content: Software Product Lines (SPL) are difficult to validate due to combinatorics induced by variability across their features. This leads to combinatorial explosion of the number of derivable products. Exhaustive testing in such a large space of products is infeasible. One possible option is to test SPLs by generating test cases that cover all possible T feature interactions (T-wise). T-wise dramatically reduces the number of test products while ensuring reasonable SPL coverage. However, automatic generation of test cases satisfying T-wise using SAT solvers raises two issues. The encoding of SPL models and T-wise criteria into a set of formulas acceptable by the solver and their satisfaction which fails when processed ``all-at-once''. We propose a scalable toolset using Alloy to automatically generate test cases satisfying T-wise from SPL models. We define strategies to split T-wise combinations into solvable subsets. We design and compute metrics to evaluate strategies on Aspect OPTIMA, a concrete transactional SPL. --- paper_title: Feature consistency in compile-time-configurable system software: facing the linux 10,000 feature problem paper_content: Much system software can be configured at compile time to tailor it with respect to a broad range of supported hardware architectures and application domains. A good example is the Linux kernel, which provides more than 10,000 configurable features, growing rapidly. From the maintenance point of view, compile-time configurability imposes big challenges. The configuration model (the selectable features and their constraints as presented to the user) and the configurability that is actually implemented in the code have to be kept in sync, which, if performed manually, is a tedious and error-prone task. In the case of Linux, this has led to numerous defects in the source code, many of which are actual bugs. We suggest an approach to automatically check for configurability-related implementation defects in large-scale configurable system software. The configurability is extracted from its various implementation sources and examined for inconsistencies, which manifest in seemingly conditional code that is in fact unconditional. We evaluate our approach with the latest version of Linux, for which our tool detects 1,776 configurability defects, which manifest as dead/superfluous source code and bugs. Our findings have led to numerous source-code improvements and bug fixes in Linux: 123 patches (49 merged) fix 364 defects, 147 of which have been confirmed by the corresponding Linux developers and 20 as fixing a new bug. --- paper_title: Reducing combinatorics in testing product lines paper_content: A Software Product Line (SPL) is a family of programs where each program is defined by a unique combination of features. Testing or checking properties of an SPL is hard as it may require the examination of a combinatorial number of programs. In reality, however, features are often irrelevant for a given test - they augment, but do not change, existing behavior, making many feature combinations unnecessary as far as testing is concerned. In this paper we show how to reduce the amount of effort in testing an SPL. We represent an SPL in a form where conventional static program analysis techniques can be applied to find irrelevant features for a test. We use this information to reduce the combinatorial number of SPL programs to examine. --- paper_title: FeatureIDE: An Extensible Framework for Feature-Oriented Software Development paper_content: FeatureIDE is an open-source framework for feature-oriented software development (FOSD) based on Eclipse. FOSD is a paradigm for the construction, customization, and synthesis of software systems. Code artifacts are mapped to features, and a customized software system can be generated given a selection of features. The set of software systems that can be generated is called a software product line (SPL). FeatureIDE supports several FOSD implementation techniques such as feature-oriented programming, aspect-oriented programming, delta-oriented programming, and preprocessors. All phases of FOSD are supported in FeatureIDE, namely domain analysis, requirements analysis, domain implementation, and software generation. --- paper_title: A Case Study Implementing Features Using AspectJ paper_content: Software product lines aim to create highly configurable programs from a set of features. Common belief and recent studies suggest that aspects are well-suited for implementing features. We evaluate the suitability of AspectJ with respect to this task by a case study that refactors the embedded database system Berkeley DB into 38 features. Contrary to our initial expectations, the results were not encouraging. As the number of aspects in a feature grows, there is a noticeable decrease in code readability and maintainability. Most of the unique and powerful features of AspectJ were not needed. We document where AspectJ is unsuitable for implementing features of refactored legacy applications and explain why. --- paper_title: Analyzing the discipline of preprocessor annotations in 30 million lines of C code paper_content: The C preprocessor cpp is a widely used tool for implementing variable software. It enables programmers to express variable code (which may even crosscut the entire implementation) with conditional compilation. The C preprocessor relies on simple text processing and is independent of the host language (C, C++, Java, and so on). Language-independent text processing is powerful and expressive - programmers can make all kinds of annotations in the form of #ifdefs - but can render unpreprocessed code difficult to process automatically by tools, such as refactoring, concern management, and variability-aware type checking. We distinguish between disciplined annotations, which align with the underlying source-code structure, and undisciplined annotations, which do not align with the structure and hence complicate tool development. This distinction raises the question of how frequently programmers use undisciplined annotations and whether it is feasible to change them to disciplined annotations to simplify tool development and to enable programmers to use a wide variety of tools in the first place. By means of an analysis of 40 medium-sized to large-sized C programs, we show empirically that programmers use cpp mostly in a disciplined way: about 84% of all annotations respect the underlying source-code structure. Furthermore, we analyze the remaining undisciplined annotations, identify patterns, and discuss how to transform them into a disciplined form. --- paper_title: Automatic detection of feature interactions using the Java modeling language: an experience report paper_content: In the development of complex software systems, interactions between different program features increase the design complexity. Feature-oriented software development focuses on the representation and compositions of features. The implementation of features often cuts across object-oriented module boundaries and hence comprises interactions. The manual detection and treatment of feature interactions requires a deep knowledge of the implementation details of the features involved. Our goal is to detect interactions automatically using specifications by means of design by contract and automated theorem proving. We provide a software tool that operates on programs in Java and the Java Modeling Language (JML). We discuss which kinds of feature interactions can be detected automatically with our tool and how to detect other kinds of interactions. --- paper_title: Testing Software Product Lines Using Incremental Test Generation paper_content: We present a novel specification-based approach for generating tests for products in a software product line. Given properties of features as first-order logic formulas, our approach uses SAT-based analysis to automatically generate test inputs for each product in a product line. To ensure soundness of generation, we introduce an automatic technique for mapping a formula that specifies a feature into a transformation that defines incremental refinement of test suites. Our experimental results using different data structure product lines show that incremental approach can provide an order of magnitude speed-up over conventional techniques. --- paper_title: Virtual Separation of Concerns -- A Second Chance for Preprocessors paper_content: Conditional compilation with preprocessors like cpp is a simple but eective means to implement variability. By annotating code fragments with #ifdef and #endif directives, dierent program variants with or without these fragments can be created, which can be used (among others) to implement software product lines. Although, preprocessors are frequently used in practice, they are often criticized for their negative eect on code quality and maintainability. In contrast to modularized implementations, for example using components or aspects, preprocessors neglect separation of concerns, are prone to introduce subtle errors, can entirely obfuscate the source code, and limit reuse. Our aim is to rehabilitate the preprocessor by showing how simple tool support can address these problems and emulate some benets of modularized implementations. At the same time we emphasize unique benets of preprocessors, like simplicity and language independence. Although we do not have a denitive answer on how to implement variability, we want highlight opportunities to improve preprocessors and encourage research toward novel preprocessor-based approaches. --- paper_title: Language-Independent and Automated Software Composition: The FeatureHouse Experience paper_content: Superimposition is a composition technique that has been applied successfully in many areas of software development. Although superimposition is a general-purpose concept, it has been (re)invented and implemented individually for various kinds of software artifacts. We unify languages and tools that rely on superimposition by using the language-independent model of feature structure trees (FSTs). On the basis of the FST model, we propose a general approach to the composition of software artifacts written in different languages. Furthermore, we offer a supporting framework and tool chain, called FEATUREHOUSE. We use attribute grammars to automate the integration of additional languages. In particular, we have integrated Java, C#, C, Haskell, Alloy, and JavaCC. A substantial number of case studies demonstrate the practicality and scalability of our approach and reveal insights into the properties that a language must have in order to be ready for superimposition. We discuss perspectives of our approach and demonstrate how we extended FEATUREHOUSE with support for XML languages (in particular, XHTML, XMI/UML, and Ant) and alternative composition approaches (in particular, aspect weaving). Rounding off our previous work, we provide here a holistic view of the FEATUREHOUSE approach based on rich experience with numerous languages and case studies and reflections on several years of research. --- paper_title: Access Control in Feature-Oriented Programming paper_content: In feature-oriented programming (FOP) a programmer decomposes a program in terms of features. Ideally, features are implemented modularly so that they can be developed in isolation. Access control mechanisms in the form of access or visibility modifiers are an important ingredient to attain feature modularity as they allow programmers to hide and expose internal details of a module's implementation. But developers of contemporary feature-oriented languages have not considered access control mechanisms so far. The absence of a well-defined access control model for FOP breaks encapsulation of feature code and leads to unexpected program behaviors and inadvertent type errors. We raise awareness of this problem, propose three feature-oriented access modifiers, and present a corresponding access modifier model. We offer an implementation of the model on the basis of a fully-fledged feature-oriented compiler. Finally, by analyzing ten feature-oriented programs, we explore the potential of feature-oriented modifiers in FOP. --- paper_title: A Classification and Survey of Analysis Strategies for Software Product Lines paper_content: Software-product-line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, model checking, and theorem proving, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analyses is both broad and diverse, so it is difficult for researchers and practitioners to understand their similarities and differences. We propose a classification of product-line analyses to enable systematic research and application. Based on our insights with classifying and comparing a corpus of 123 research articles, we develop a research agenda to guide future research on product-line analyses. --- paper_title: FeatureIDE: An Extensible Framework for Feature-Oriented Software Development paper_content: FeatureIDE is an open-source framework for feature-oriented software development (FOSD) based on Eclipse. FOSD is a paradigm for the construction, customization, and synthesis of software systems. Code artifacts are mapped to features, and a customized software system can be generated given a selection of features. The set of software systems that can be generated is called a software product line (SPL). FeatureIDE supports several FOSD implementation techniques such as feature-oriented programming, aspect-oriented programming, delta-oriented programming, and preprocessors. All phases of FOSD are supported in FeatureIDE, namely domain analysis, requirements analysis, domain implementation, and software generation. --- paper_title: SPL Conqueror: Toward optimization of non-functional properties in software product lines paper_content: A software product line (SPL) is a family of related programs of a domain. The programs of an SPL are distinguished in terms of features, which are end-user visible characteristics of programs. Based on a selection of features, stakeholders can derive tailor-made programs that satisfy functional requirements. Besides functional requirements, different application scenarios raise the need for optimizing non-functional properties of a variant. The diversity of application scenarios leads to heterogeneous optimization goals with respect to non-functional properties (e.g., performance vs. footprint vs. energy optimized variants). Hence, an SPL has to satisfy different and sometimes contradicting requirements regarding non-functional properties. Usually, the actually required non-functional properties are not known before product derivation and can vary for each application scenario and customer. Allowing stakeholders to derive optimized variants requires us to measure non-functional properties after the SPL is developed. Unfortunately, the high variability provided by SPLs complicates measurement and optimization of non-functional properties due to a large variant space. With SPL Conqueror, we provide a holistic approach to optimize non-functional properties in SPL engineering. We show how non-functional properties can be qualitatively specified and quantitatively measured in the context of SPLs. Furthermore, we discuss the variant-derivation process in SPL Conqueror that reduces the effort of computing an optimal variant. We demonstrate the applicability of our approach by means of nine case studies of a broad range of application domains (e.g., database management and operating systems). Moreover, we show that SPL Conqueror is implementation and language independent by using SPLs that are implemented with different mechanisms, such as conditional compilation and feature-oriented programming. --- paper_title: Reducing Configurations to Monitor in a Software Product Line paper_content: A software product line is a family of programs where each program is defined by a unique combination of features. Product lines, like conventional programs, can be checked for safety properties through execution monitoring. However, because a product line induces a number of programs that is potentially exponential in the number of features, it would be very expensive to use existing monitoring techniques: one would have to apply those techniques to every single program. Doing so would also be wasteful because many programs can provably never violate the stated property. We introduce a monitoring technique dedicated to product lines that, given a safety property, statically determines the feature combinations that cannot possibly violate the property, thus reducing the number of programs to monitor. Experiments show that our technique is effective, particularly for safety properties that crosscut many optional features. --- paper_title: ASADAL: a tool system for co-development of software and test environment based on product line engineering paper_content: Recently, product line software engineering (PLSE) is gaining popularity. To employ PLSE methods, many organizations are looking for a tool system that supports PLSE methods so that core assets and target software can be developed and tested in an effective and systematic way.ASADAL (A System Analysis and Design Aid tooL) supports the entire lifecycle of software development process based on a PLSE method called FORM (Feature-Oriented Reuse Method) [6]. It supports domain analysis, architecture and component design, code generation, and simulation-based verification and validation (V&V). Using the tool, users may co-develop target software and its test environment and verify software in a continuous and incremental way. --- paper_title: Dead or Alive: finding zombie features in the Linux kernel paper_content: When an interference signals is detected by a base station, the base station issues a report to a switching apparatus. The switching apparatus checks self to see whether it is in a standby mode waiting for a frequency change completion report from another base station which is in the process of changing the control channel frequency. If the switching apparatus is satisfied that the station should be given a new control channel frequency, it issues a frequency change command to the requesting base station, and the base station receiving the command revises the current control channel frequency. In the meantime, all other requests from other stations are denied by the switching apparatus until the requesting base station has successfully completed the process of changing the control channel frequency. The base station acknowledges the completion of frequency change process by sending a frequency change completion report to the switching apparatus. --- paper_title: Integration Testing of Software Product Lines Using Compositional Symbolic Execution paper_content: Software product lines are families of products defined by feature commonality and variability, with a well-managed asset base. Recent work in testing of software product lines has exploited similarities across development phases to reuse shared assets and reduce test effort. The use of feature dependence graphs has also been employed to reduce testing effort, but little work has focused on code level analysis of dataflow between features. In this paper we present a compositional symbolic execution technique that works in concert with a feature dependence graph to extract the set of possible interaction trees in a product family. It composes these to incrementally and symbolically analyze feature interactions. We experiment with two product lines and determine that our technique can reduce the overall number of interactions that must be considered during testing, and requires less time to run than a traditional symbolic execution technique. --- paper_title: Language-independent reference checking in software product lines paper_content: Feature-Oriented Software Development (FOSD) is a paradigm for the development of software product lines. A challenge in FOSD is to guarantee that all software systems of a software product line are correct. Recent work on type checking product lines can provide a guarantee of type correctness without generating all possible systems. We generalize previous results by abstracting from the specifics of particular programming languages. In a first attempt, we present a reference-checking algorithm that performs key tasks of product-line type checking independently of the target programming language. Experiments with two sample product lines written in Java and C are encouraging and give us confidence that this approach is promising. --- paper_title: Access Control in Feature-Oriented Programming paper_content: In feature-oriented programming (FOP) a programmer decomposes a program in terms of features. Ideally, features are implemented modularly so that they can be developed in isolation. Access control mechanisms in the form of access or visibility modifiers are an important ingredient to attain feature modularity as they allow programmers to hide and expose internal details of a module's implementation. But developers of contemporary feature-oriented languages have not considered access control mechanisms so far. The absence of a well-defined access control model for FOP breaks encapsulation of feature code and leads to unexpected program behaviors and inadvertent type errors. We raise awareness of this problem, propose three feature-oriented access modifiers, and present a corresponding access modifier model. We offer an implementation of the model on the basis of a fully-fledged feature-oriented compiler. Finally, by analyzing ten feature-oriented programs, we explore the potential of feature-oriented modifiers in FOP. --- paper_title: Clafer tools for product line engineering paper_content: Clafer is a lightweight yet expressive language for structural modeling: feature modeling and configuration, class and object modeling, and metamodeling. Clafer Tools is an integrated set of tools based on Clafer. In this paper, we describe some product-line variability modeling scenarios of Clafer Tools from the viewpoints of product-line owner, product-line engineer, and product engineer. --- paper_title: PLEDGE: a product line editor and test generation tool paper_content: Specific requirements of clients lead to the development of variants of the same software. These variants form a Software Product Line (SPL). Ideally, testing a SPL involves testing all the software products that can be configured through the combination of features. This, however, is intractable in practice since a) large SPLs can lead to millions of possible software variants and b) the testing process is usually limited by budget and time constraints. To overcome this problem, this paper introduces PLEDGE, an open source tool that selects and prioritizes the product configurations maximizing the feature interactions covered. The uniqueness of PLEDGE is that it bypasses the computation of the feature interactions, allowing to scale to large SPLs. --- paper_title: Aspect Composition Applying the Design by Contract Principle paper_content: The composition of software units has been one of the main research topics in computer science. This paper addresses the composition validation problem evolving in this context. It focuses on the composition for a certain kind of units called aspects. Aspects are a new concept which is introduced by aspect-oriented programming aiming at a better separation of concerns. Cross-cutting code is captured and localised in these aspects. Some of the cross-cutting features which are expressed in aspects cannot be woven with other features into the same application since two features could be mutually exclusive. With a growing number of aspects, manual control of these dependencies becomes error-prone or even impossible. We show how assertions can be useful in this respect to support the software developer. --- paper_title: Emergo: a tool for improving maintainability of preprocessor-based product lines paper_content: When maintaining a feature in preprocessor-based Software Product Lines (SPLs), developers are susceptible to introduce problems into other features. This is possible because features eventually share elements (like variables and methods) with the maintained one. This scenario might be even worse when hiding features by using techniques like Virtual Separation of Concerns (VSoC), since developers cannot see the feature dependencies and, consequently, they become unaware of them. Emergent Interfaces was proposed to minimize this problem by capturing feature dependencies and then providing information about other features that can be impacted during a maintenance task. In this paper, we present Emergo, a tool capable of computing emergent interfaces between the feature we are maintaining and the others. Emergo relies on feature-sensitive dataflow analyses in the sense it takes features and the SPL feature model into consideration when computing the interfaces. --- paper_title: Variability-aware parsing in the presence of lexical macros and conditional compilation paper_content: In many projects, lexical preprocessors are used to manage different variants of the project (using conditional compilation) and to define compile-time code transformations (using macros). Unfortunately, while being a simple way to implement variability, conditional compilation and lexical macros hinder automatic analysis, even though such analysis is urgently needed to combat variability-induced complexity. To analyze code with its variability, we need to parse it without preprocessing it. However, current parsing solutions use unsound heuristics, support only a subset of the language, or suffer from exponential explosion. As part of the TypeChef project, we contribute a novel variability-aware parser that can parse almost all unpreprocessed code without heuristics in practicable time. Beyond the obvious task of detecting syntax errors, our parser paves the road for further analysis, such as variability-aware type checking. We implement variability-aware parsers for Java and GNU C and demonstrate practicability by parsing the product line MobileMedia and the entire X86 architecture of the Linux kernel with 6065 variable features. --- paper_title: Delta-oriented programming of software product lines paper_content: Feature-oriented programming (FOP) implements software product lines by composition of feature modules. It relies on the principles of stepwise development. Feature modules are intended to refer to exactly one product feature and can only extend existing implementations. To provide more flexibility for implementing software product lines, we propose delta-oriented programming (DOP) as a novel programming language approach. A product line is represented by a core module and a set of delta modules. The core module provides an implementation of a valid product that can be developed with well-established single application engineering techniques. Delta modules specify changes to be applied to the core module to implement further products by adding, modifying and removing code. Application conditions attached to delta modules allow handling combinations of features explicitly. A product implementation for a particular feature configuration is generated by applying incrementally all delta modules with valid application condition to the core module. In order to evaluate the potential of DOP, we compare it to FOP, both conceptually and empirically. --- paper_title: Exploring variability-aware execution for testing plugin-based web applications paper_content: In plugin-based systems, plugin conflicts may occur when two or more plugins interfere with one another, changing their expected behaviors. It is highly challenging to detect plugin conflicts due to the exponential explosion of the combinations of plugins (i.e., configurations). In this paper, we address the challenge of executing a test case over many configurations. Leveraging the fact that many executions of a test are similar, our variability-aware execution runs common code once. Only when encountering values that are different depending on specific configurations will the execution split to run for each of them. To evaluate the scalability of variability-aware execution on a large real-world setting, we built a prototype PHP interpreter called Varex and ran it on the popular WordPress blogging Web application. The results show that while plugin interactions exist, there is a significant amount of sharing that allows variability-aware execution to scale to 2^50 configurations within seven minutes of running time. During our study, with Varex, we were able to detect two plugin conflicts: one was recently reported on WordPress forum and another one was not previously discovered. --- paper_title: Incremental Test Generation for Software Product Lines paper_content: Recent advances in mechanical techniques for systematic testing have increased our ability to automatically find subtle bugs, and hence, to deploy more dependable software. This paper builds on one such systematic technique, scope-bounded testing, to develop a novel specification-based approach for efficiently generating tests for products in a software product line. Given properties of features as first-order logic formulas in Alloy, our approach uses SAT-based analysis to automatically generate test inputs for each product in a product line. To ensure soundness of generation, we introduce an automatic technique for mapping a formula that specifies a feature into a transformation that defines incremental refinement of test suites. Our experimental results using different data structure product lines show that an incremental approach can provide an order of magnitude speedup over conventional techniques. We also present a further optimization using dedicated integer constraint solvers for feature properties that introduce integer constraints, and show how to use a combination of solvers in tandem for solving Alloy formulas. --- paper_title: A product line based aspect-oriented generative unit testing approach to building quality components paper_content: The quality of component-based systems highly depends on how effectively testing is carried out. To achieve the maximal testing effectiveness, this paper presents a product line based aspect oriented approach to unit testing. The aspect product line facilitates the automatic creation of aspect test cases that deal with specific quality requirements. An expandable repository of reusable aspect test cases has been developed. A prototype tool is built to verify and lever up the approach. --- paper_title: Avoiding redundant testing in application engineering paper_content: Many software product line testing techniques have been presented in the literature. The majority of those techniques address how to define reusable test assets (such as test models or test scenarios) in domain engineering and how to exploit those assets during application engineering. In addition to test case reuse however, the execution of test cases constitutes one important activity during application testing. Without a systematic support for the test execution in application engineering, while considering the specifics of product lines, product line artifacts might be tested redundantly. Redundant testing in application engineering, however, can lead to an increased testing effort without increasing the chance of uncovering failures. In this paper, we propose the model-based ScenTED-DF technique to avoid redundant testing in application engineering. Our technique builds on data flow-based testing techniques for single systems and adapts and extends those techniques to consider product line variability. The paper sketches the prototypical implementation of our technique to show its general feasibility and automation potential, and it describes the results of experiments using an academic product line to demonstrate that ScenTED-DF is capable of avoiding redundant tests in application engineering. --- paper_title: Evolutionary Search-based Test Generation for Software Product Line Feature Models paper_content: Product line-based software engineering is a paradigm that models the commonalities and variabilities of different applications of a given domain of interest within a unique framework and enhances rapid and low cost development of new applications based on reuse engineering principles. Despite the numerous advantages of software product lines, it is quite challenging to comprehensively test them. This is due to the fact that a product line can potentially represent many different applications; therefore, testing a single product line requires the test of its various applications. Theoretically, a product line with n software features can be a source for the development of 2n application. This requires the test of 2n applications if a brute-force comprehensive testing strategy is adopted. In this paper, we propose an evolutionary testing approach based on Genetic Algorithms to explore the configuration space of a software product line feature model in order to automatically generate test suites. We will show through the use of several publicly-available product line feature models that the proposed approach is able to generate test suites of O(n) size complexity as opposed to O(2n) while at the same time form a suitable tradeoff balance between error coverage and feature coverage in its generated test suites. --- paper_title: Detection of feature interactions using feature-aware verification paper_content: A software product line is a set of software products that are distinguished in terms of features (i.e., end-user-visible units of behavior). Feature interactions-- situations in which the combination of features leads to emergent and possibly critical behavior --are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line-verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLVERIFIER for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only local knowledge. --- paper_title: Toward variability-aware testing paper_content: We investigate how to execute a unit test for all products of a product line without generating each product in isolation in a brute-force fashion. Learning from variability-aware analyses, we (a) design and implement a variability-aware interpreter and, alternatively, (b) reencode variability of the product line to simulate the test cases with a model checker. The interpreter internally reasons about variability, executing paths not affected by variability only once for the whole product line. The model checker achieves similar results by reusing powerful off-the-shelf analyses. We experimented with a prototype implementation for each strategy. We compare both strategies and discuss trade-offs and future directions. In the long run, we aim at finding an efficient testing approach that can be applied to entire product lines with millions of products. --- paper_title: Family-based performance measurement paper_content: Most contemporary programs are customizable. They provide many features that give rise to millions of program variants. Determining which feature selection yields an optimal performance is challenging, because of the exponential number of variants. Predicting the performance of a variant based on previous measurements proved successful, but induces a trade-off between the measurement effort and prediction accuracy. We propose the alternative approach of family-based performance measurement, to reduce the number of measurements required for identifying feature interactions and for obtaining accurate predictions. The key idea is to create a variant simulator (by translating compile-time variability to run-time variability) that can simulate the behavior of all program variants. We use it to measure performance of individual methods, trace methods to features, and infer feature interactions based on the call graph. We evaluate our approach by means of five feature-oriented programs. On average, we achieve accuracy of 98%, with only a single measurement per customizable program. Observations show that our approach opens avenues of future research in different domains, such an feature-interaction detection and testing. --- paper_title: Automated and Scalable T-wise Test Case Generation Strategies for Software Product Lines paper_content: Software Product Lines (SPL) are difficult to validate due to combinatorics induced by variability across their features. This leads to combinatorial explosion of the number of derivable products. Exhaustive testing in such a large space of products is infeasible. One possible option is to test SPLs by generating test cases that cover all possible T feature interactions (T-wise). T-wise dramatically reduces the number of test products while ensuring reasonable SPL coverage. However, automatic generation of test cases satisfying T-wise using SAT solvers raises two issues. The encoding of SPL models and T-wise criteria into a set of formulas acceptable by the solver and their satisfaction which fails when processed ``all-at-once''. We propose a scalable toolset using Alloy to automatically generate test cases satisfying T-wise from SPL models. We define strategies to split T-wise combinations into solvable subsets. We design and compute metrics to evaluate strategies on Aspect OPTIMA, a concrete transactional SPL. --- paper_title: MoSo-PoLiTe: tool support for pairwise and model-based software product line testing paper_content: Testing Software Product Lines is a very challenging task and approaches like combinatorial testing and model-based testing are frequently used to reduce the effort of testing Software Product Lines and to reuse test artifacts. In this contribution we present a tool chain realizing our MoSo-PoLiTe concept combining combinatorial and model-based testing. Our tool chain contains a pairwise configuration selection component on the basis of a feature model. This component implements an heuristic finding a minimal subset of configurations covering 100% pairwise interaction. Additionally, our tool chain allows the model-based test case generation for each configuration within this generated subset. This tool chain is based on commercial tools since it was developed within industrial cooperations. A non-commercial implementation of pairwise configuration selection is available and an integration with an Open Source model-based testing tool is under development. --- paper_title: Feature consistency in compile-time-configurable system software: facing the linux 10,000 feature problem paper_content: Much system software can be configured at compile time to tailor it with respect to a broad range of supported hardware architectures and application domains. A good example is the Linux kernel, which provides more than 10,000 configurable features, growing rapidly. From the maintenance point of view, compile-time configurability imposes big challenges. The configuration model (the selectable features and their constraints as presented to the user) and the configurability that is actually implemented in the code have to be kept in sync, which, if performed manually, is a tedious and error-prone task. In the case of Linux, this has led to numerous defects in the source code, many of which are actual bugs. We suggest an approach to automatically check for configurability-related implementation defects in large-scale configurable system software. The configurability is extracted from its various implementation sources and examined for inconsistencies, which manifest in seemingly conditional code that is in fact unconditional. We evaluate our approach with the latest version of Linux, for which our tool detects 1,776 configurability defects, which manifest as dead/superfluous source code and bugs. Our findings have led to numerous source-code improvements and bug fixes in Linux: 123 patches (49 merged) fix 364 defects, 147 of which have been confirmed by the corresponding Linux developers and 20 as fixing a new bug. --- paper_title: Reducing combinatorics in testing product lines paper_content: A Software Product Line (SPL) is a family of programs where each program is defined by a unique combination of features. Testing or checking properties of an SPL is hard as it may require the examination of a combinatorial number of programs. In reality, however, features are often irrelevant for a given test - they augment, but do not change, existing behavior, making many feature combinations unnecessary as far as testing is concerned. In this paper we show how to reduce the amount of effort in testing an SPL. We represent an SPL in a form where conventional static program analysis techniques can be applied to find irrelevant features for a test. We use this information to reduce the combinatorial number of SPL programs to examine. --- paper_title: FeatureIDE: An Extensible Framework for Feature-Oriented Software Development paper_content: FeatureIDE is an open-source framework for feature-oriented software development (FOSD) based on Eclipse. FOSD is a paradigm for the construction, customization, and synthesis of software systems. Code artifacts are mapped to features, and a customized software system can be generated given a selection of features. The set of software systems that can be generated is called a software product line (SPL). FeatureIDE supports several FOSD implementation techniques such as feature-oriented programming, aspect-oriented programming, delta-oriented programming, and preprocessors. All phases of FOSD are supported in FeatureIDE, namely domain analysis, requirements analysis, domain implementation, and software generation. --- paper_title: A Case Study Implementing Features Using AspectJ paper_content: Software product lines aim to create highly configurable programs from a set of features. Common belief and recent studies suggest that aspects are well-suited for implementing features. We evaluate the suitability of AspectJ with respect to this task by a case study that refactors the embedded database system Berkeley DB into 38 features. Contrary to our initial expectations, the results were not encouraging. As the number of aspects in a feature grows, there is a noticeable decrease in code readability and maintainability. Most of the unique and powerful features of AspectJ were not needed. We document where AspectJ is unsuitable for implementing features of refactored legacy applications and explain why. --- paper_title: Analyzing the discipline of preprocessor annotations in 30 million lines of C code paper_content: The C preprocessor cpp is a widely used tool for implementing variable software. It enables programmers to express variable code (which may even crosscut the entire implementation) with conditional compilation. The C preprocessor relies on simple text processing and is independent of the host language (C, C++, Java, and so on). Language-independent text processing is powerful and expressive - programmers can make all kinds of annotations in the form of #ifdefs - but can render unpreprocessed code difficult to process automatically by tools, such as refactoring, concern management, and variability-aware type checking. We distinguish between disciplined annotations, which align with the underlying source-code structure, and undisciplined annotations, which do not align with the structure and hence complicate tool development. This distinction raises the question of how frequently programmers use undisciplined annotations and whether it is feasible to change them to disciplined annotations to simplify tool development and to enable programmers to use a wide variety of tools in the first place. By means of an analysis of 40 medium-sized to large-sized C programs, we show empirically that programmers use cpp mostly in a disciplined way: about 84% of all annotations respect the underlying source-code structure. Furthermore, we analyze the remaining undisciplined annotations, identify patterns, and discuss how to transform them into a disciplined form. --- paper_title: PACOGEN: Automatic Generation of Pairwise Test Configurations from Feature Models paper_content: Feature models are commonly used to specify variability in software product lines. Several tools support feature models for variability management at different steps in the development process. However, tool support for test configuration generation is currently limited. This test generation task consists in systematically selecting a set of configurations that represent a relevant sample of the variability space and that can be used to test the product line. In this paper we propose \pw tool to analyze feature models and automatically generate a set of configurations that cover all pair wise interactions between features. \pw tool relies on constraint programming to generate configurations that satisfy all constraints imposed by the feature model and to minimize the set of the tests configurations. This work also proposes an extensive experiment, based on the state-of-the art SPLOT feature models repository, showing that \pw tool scales over variability spaces with millions of configurations and covers pair wise with less configurations than other available tools. --- paper_title: Automatic detection of feature interactions using the Java modeling language: an experience report paper_content: In the development of complex software systems, interactions between different program features increase the design complexity. Feature-oriented software development focuses on the representation and compositions of features. The implementation of features often cuts across object-oriented module boundaries and hence comprises interactions. The manual detection and treatment of feature interactions requires a deep knowledge of the implementation details of the features involved. Our goal is to detect interactions automatically using specifications by means of design by contract and automated theorem proving. We provide a software tool that operates on programs in Java and the Java Modeling Language (JML). We discuss which kinds of feature interactions can be detected automatically with our tool and how to detect other kinds of interactions. --- paper_title: Testing Software Product Lines Using Incremental Test Generation paper_content: We present a novel specification-based approach for generating tests for products in a software product line. Given properties of features as first-order logic formulas, our approach uses SAT-based analysis to automatically generate test inputs for each product in a product line. To ensure soundness of generation, we introduce an automatic technique for mapping a formula that specifies a feature into a transformation that defines incremental refinement of test suites. Our experimental results using different data structure product lines show that incremental approach can provide an order of magnitude speed-up over conventional techniques. --- paper_title: Virtual Separation of Concerns -- A Second Chance for Preprocessors paper_content: Conditional compilation with preprocessors like cpp is a simple but eective means to implement variability. By annotating code fragments with #ifdef and #endif directives, dierent program variants with or without these fragments can be created, which can be used (among others) to implement software product lines. Although, preprocessors are frequently used in practice, they are often criticized for their negative eect on code quality and maintainability. In contrast to modularized implementations, for example using components or aspects, preprocessors neglect separation of concerns, are prone to introduce subtle errors, can entirely obfuscate the source code, and limit reuse. Our aim is to rehabilitate the preprocessor by showing how simple tool support can address these problems and emulate some benets of modularized implementations. At the same time we emphasize unique benets of preprocessors, like simplicity and language independence. Although we do not have a denitive answer on how to implement variability, we want highlight opportunities to improve preprocessors and encourage research toward novel preprocessor-based approaches. --- paper_title: A Classification and Survey of Analysis Strategies for Software Product Lines paper_content: Software-product-line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, model checking, and theorem proving, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analyses is both broad and diverse, so it is difficult for researchers and practitioners to understand their similarities and differences. We propose a classification of product-line analyses to enable systematic research and application. Based on our insights with classifying and comparing a corpus of 123 research articles, we develop a research agenda to guide future research on product-line analyses. ---	Title: An overview on analysis tools for software product lines Section 1: INTRODUCTION Description 1: Introduce the concept of software product lines, the importance of feature-oriented software development (FOSD), and the need for efficient analysis tools. Section 2: CHARACTERISTICS OF PRODUCT-LINE TOOLS Description 2: Describe the different aspects of FOSD, including domain analysis, domain design and specification, domain implementation, and product configuration and generation, and introduce the categorization of tools. Section 3: PRODUCT-LINE TESTING Description 3: Discuss the tools available for testing product lines, including those for sampling and beyond sampling, highlighting different strategies and examples of tools. Section 4: PRODUCT-LINE VERIFICATION Description 4: Outline the tools used for the verification of product lines, including type checking, static analysis, software model checking, theorem proving, and consistency checking, with examples of each. Section 5: FURTHER PRODUCT-LINE ANALYSES Description 5: Cover additional analysis tools for non-functional properties and code metrics, providing examples and explaining their relevance to software product lines. Section 6: OVERVIEW ON TOOL SUPPORT Description 6: Summarize the tools discussed in previous sections, categorizing them by analysis technique and product-line implementation technique, and discuss the current state of tool support. Section 7: CONCLUSION Description 7: Conclude the paper by emphasizing the importance of efficient analysis for software product lines, summarizing the contributions, and referring to additional resources such as the website maintained for product-line tools.
A Survey on Cooperative Jamming Applied to Physical Layer Security	9	--- paper_title: Security, privacy, and accountability in wireless access networks paper_content: The presence of ubiquitous connectivity provided by wireless communications and mobile computing has changed the way humans interact with information. At the same time, it has made communication security and privacy a hot-button issue. In this article we address the security and privacy concerns in wireless access networks. We first discuss the general cryptographic means to design privacy-preserving security protocols, where the dilemma of attaining both security and privacy goals, especially user accountability vs. user privacy, is highlighted. We then present a novel authentication framework that integrates a new key management scheme based on the principle of separation of powers and an adapted construction of Boneh and Shacham's group signature scheme, as an enhanced resort to simultaneously achieve security, privacy, and accountability in wireless access networks. --- paper_title: Physical-Layer Security: From Information Theory to Security Engineering paper_content: This complete guide to physical-layer security presents the theoretical foundations, practical implementation, challenges and benefits of a groundbreaking new model for secure communication. Using a bottom-up approach from the link level all the way to end-to-end architectures, it provides essential practical tools that enable graduate students, industry professionals and researchers to build more secure systems by exploiting the noise inherent to communications channels. The book begins with a self-contained explanation of the information-theoretic limits of secure communications at the physical layer. It then goes on to develop practical coding schemes, building on the theoretical insights and enabling readers to understand the challenges and opportunities related to the design of physical layer security schemes. Finally, applications to multi-user communications and network coding are also included. --- paper_title: On the Jamming Power Allocation for Secure Amplify-and-Forward Relaying via Cooperative Jamming paper_content: In this paper, we investigate secure communications in two-hop wireless relaying networks with one eavesdropper. To prevent the eavesdropper from intercepting the source message, the destination sends an intended jamming noise to the relay, which is referred to as cooperative jamming. This jamming noise helps protecting the source message from being captured reliably at the eavesdropper, while the destination cancels its self-intended noise. According to the channel information available at the destination, we derive three jamming power allocation strategies to minimize the outage probability of the secrecy rate. In addition, we derive analytic results quantifying the jamming power consumption of the proposed allocation methods. --- paper_title: Cryptographic design vulnerabilities paper_content: Strong cryptography is very powerful when it is done right, but it is not a panacea. Focusing on cryptographic algorithms while ignoring other aspects of security is like defending your house not by building a fence around it, but by putting an immense stake in the ground and hoping that your adversary runs right into it. Counterpane Systems has spent years designing, analyzing, and breaking cryptographic systems. While they do research on published algorithms and protocols, most of their work examines actual products. They've designed and analyzed systems that protect privacy, ensure confidentiality, provide fairness, and facilitate commerce. They've worked with software, stand-alone hardware, and everything in between. They've broken their share of algorithms, but they can almost always find attacks that bypass the algorithms altogether. Counterpane Systems don't have to try every possible key or even find flaws in the algorithms. They exploit errors in design, errors in implementation, and errors in installation. Sometimes they invent a new trick to break a system, but most of the time they exploit the same old mistakes that designers make over and over again. The article conveys some of the lessons this company has learned. --- paper_title: Guaranteeing Secrecy using Artificial Noise paper_content: The broadcast nature of the wireless medium makes the communication over this medium vulnerable to eavesdropping. This paper considers the problem of secret communication between two nodes, over a fading wireless medium, in the presence of a passive eavesdropper. The assumption used is that the transmitter and its helpers (amplifying relays) have more antennas than the eavesdropper. The transmitter ensures secrecy of communication by utilizing some of the available power to produce 'artificial noise', such that only the eavesdropper's channel is degraded. Two scenarios are considered, one where the transmitter has multiple transmit antennas, and the other where amplifying relays simulate the effect of multiple antennas. The channel state information (CSI) is assumed to be publicly known, and hence, the secrecy of communication is independent of the secrecy of CSI. --- paper_title: Signal Processing Approaches to Secure Physical Layer Communications in Multi-Antenna Wireless Systems paper_content: This book introduces various signal processing approaches to enhance physical layer secrecy in multi-antenna wireless systems. Wireless physical layer secrecy has attracted much attention in recent years due to the broadcast nature of the wireless medium and its inherent vulnerability to eavesdropping. While most articles on physical layer secrecy focus on the information-theoretic aspect, we focus specifically on the signal processing aspects, including beamforming and precoding techniques for data transmission and discriminatory training schemes for channel estimation. The discussions will cover cases with collocated and with distributed antennas, i.e., relays. The topics covered will be of interest to researchers in the signal processing community as well to practitioners and engineers working in this area. This book will also review recent works that apply these signal processing approaches to more advanced wireless systems, such as OFDM systems, multicell systems, cognitive radio, multihop networks etc. This will draw interest from researchers that wish to pursue the topic further in these new directions. This book is divided into three parts: (i) data transmission, (ii) channel estimation and (iii) advanced applications. Even though many works exist in the literature on these topics, the approaches and perspectives taken were largely diverse. This book provides a more organized and systematic view of these designs and to lay a solid foundation for future work in these areas. Moreover, by presenting the work from a signal processing perspective, this book will also trigger more research interest from the signal processing community and further advance the field of physical layer secrecy along the described directions. This book allows readers to gain basic understanding of works on physical layer secrecy, knowledge of how signal processing techniques can be applied to this area, and the application of these techniques in advanced wireless applications. --- paper_title: Physical layer security in wireless networks: a tutorial paper_content: Wireless networking plays an extremely important role in civil and military applications. However, security of information transfer via wireless networks remains a challenging issue. It is critical to ensure that confidential data are accessible only to the intended users rather than intruders. Jamming and eavesdropping are two primary attacks at the physical layer of a wireless network. This article offers a tutorial on several prevalent methods to enhance security at the physical layer in wireless networks. We classify these methods based on their characteristic features into five categories, each of which is discussed in terms of two metrics. First, we compare their secret channel capacities, and then we show their computational complexities in exhaustive key search. Finally, we illustrate their security requirements via some examples with respect to these two metrics. --- paper_title: Relay and jammer selection schemes for improving physical layer security in two-way cooperative networks paper_content: This paper is concerned with the relay and jammers selection in two-way cooperative networks to improve their physical layer security. Three different categories of selection schemes are proposed which are; selection schemes without jamming, selection schemes with conventional jamming and selection schemes with controlled jamming. The selection process is analyzed for two different network models; single eavesdropper model and multiple cooperating and non-cooperating eavesdroppers' model. The proposed schemes select three intermediate nodes during two communication phases and use the Decode-and-Forward (DF) strategy to assist the sources to deliver their data to the corresponding destinations. The performance of the proposed schemes is analyzed in terms of ergodic secrecy rate and secrecy outage probability metrics. The obtained results show that the selection schemes with jamming outperform the schemes without jamming when the intermediate nodes are distributed dispersedly between sources and eavesdropper nodes. However, when the intermediate nodes cluster gets close to one of the sources, they are not superior any more due to the strong interference on the destination nodes. Therefore, a hybrid scheme which switches between selection schemes with jamming and schemes without jamming is introduced to overcome the negative effects of interference. Finally, a comparison between relay and jammers selection schemes in both one-way and two-way cooperative networks is given in terms of both secrecy metrics. The obtained results reveal that, despite the presence of cooperating eavesdroppers, the proposed selection schemes are still able to improve both the secrecy rate and the secrecy outage probability of the two-way cooperative networks. --- paper_title: Harvest-and-jam: Improving security for wireless energy harvesting cooperative networks paper_content: The emerging radio signal enabled simultaneous wireless information and power transfer (SWIPT), has drawn significant attention. To achieve secrecy transmission by cooperative jamming, especially in the upcoming 5G networks with self-sustainable mobile base stations (BSs) and yet not to add extra power consumption, we propose in this paper a new relay protocol, i.e., harvest-and-jam (HJ), in a relay wiretap channel with an additional set of spare helpers. Specifically, in the first transmission phase, a single-antenna transmitter (Tx) transfers signals carrying both information and energy to a multi-antenna amplify-and-forward (AF) relay and a group of multi-antenna helpers; in the second transmission phase, the AF relay processes the information and forwards it to the receiver while each of the helpers generates an artificial noise (AN), the power of which is constrained by its previously harvested energy, to interfere with the eavesdropper. By optimizing the transmit beamforming matrix for the AF relay and the covariance matrix for the AN, we maximize the secrecy rate for the receiver subject to transmit power constraints for the AF relay and all helpers. The formulated problem is shown to be non-convex, for which we propose an iterative algorithm based on alternating optimization. Finally, the performance of the proposed scheme is evaluated by simulations as compared to other heuristic schemes. --- paper_title: Wireless Information-Theoretic Security paper_content: This paper considers the transmission of confidential data over wireless channels. Based on an information-theoretic formulation of the problem, in which two legitimates partners communicate over a quasi-static fading channel and an eavesdropper observes their transmissions through a second independent quasi-static fading channel, the important role of fading is characterized in terms of average secure communication rates and outage probability. Based on the insights from this analysis, a practical secure communication protocol is developed, which uses a four-step procedure to ensure wireless information-theoretic security: (i) common randomness via opportunistic transmission, (ii) message reconciliation, (iii) common key generation via privacy amplification, and (iv) message protection with a secret key. A reconciliation procedure based on multilevel coding and optimized low-density parity-check (LDPC) codes is introduced, which allows to achieve communication rates close to the fundamental security limits in several relevant instances. Finally, a set of metrics for assessing average secure key generation rates is established, and it is shown that the protocol is effective in secure key renewal-even in the presence of imperfect channel state information. --- paper_title: On Cooperative and Malicious Behaviors in Multirelay Fading Channels paper_content: Multirelay networks exploit spatial diversity by transmitting user's messages through multiple relay paths. Most works in the literature on cooperative or relay networks assume that all terminals are fully cooperative and neglect the effect of possibly existing malicious relay behaviors. In this work, we consider a multirelay network that consists of both cooperative and malicious relays, and aims to obtain an improved understanding on the optimal behaviors of these two groups of relays via information-theoretic mutual information games. By modeling the set of cooperative relays and the set of malicious relays as two players in a zero-sum game with the maximum achievable rate as the utility, the optimal transmission strategies of both types of relays are derived by identifying the Nash equilibrium of the proposed game. Our main contributions are twofold. First, a generalization to previous works is obtained by allowing malicious relays to either listen or attack in Phase 1 (source-relay transmission phase). This is in contrast to previous works that only allow the malicious relays to listen in Phase 1 and to attack in Phase 2 (relay-destination transmission phase). The latter is shown to be suboptimal in our problem. Second, the impact of CSI knowledge at the destination on the optimal attack strategy that can be adopted by the malicious relays is identified. In particular, for the more practical scenario where the interrelay CSI is unknown at the destination, the constant attack is shown to be optimal as opposed to the commonly considered Gaussian attack. --- paper_title: Secrecy Capacity Optimization via Cooperative Relaying and Jamming for WANETs paper_content: Cooperative wireless networking, which is promising in improving the system operation efficiency and reliability by acquiring more accurate and timely information, has attracted considerable attentions to support many services in practice. However, the problem of secure cooperative communication has not been well investigated yet. In this paper, we exploit physical layer security to provide secure cooperative communication for wireless ad hoc networks (WANETs) where involve multiple source-destination pairs and malicious eavesdroppers. By characterizing the security performance of the system by secrecy capacity, we study the secrecy capacity optimization problem in which security enhancement is achieved via cooperative relaying and cooperative jamming. Specifically, we propose a system model where a set of relay nodes can be exploited by multiple source-destination pairs to achieve physical layer security. We theoretically present a corresponding formulation for the relay assignment problem and develop an optimal algorithm to solve it in polynomial time. To further increase the system secrecy capacity, we exploit the cooperative jamming technique and propose a smart jamming algorithm to interfere the eavesdropping channels. Through extensive experiments, we validate that our proposed algorithms significantly increase the system secrecy capacity under various network settings. --- paper_title: Physical layer security in wireless networks: a tutorial paper_content: Wireless networking plays an extremely important role in civil and military applications. However, security of information transfer via wireless networks remains a challenging issue. It is critical to ensure that confidential data are accessible only to the intended users rather than intruders. Jamming and eavesdropping are two primary attacks at the physical layer of a wireless network. This article offers a tutorial on several prevalent methods to enhance security at the physical layer in wireless networks. We classify these methods based on their characteristic features into five categories, each of which is discussed in terms of two metrics. First, we compare their secret channel capacities, and then we show their computational complexities in exhaustive key search. Finally, we illustrate their security requirements via some examples with respect to these two metrics. --- paper_title: Wireless Secrecy in Cellular Systems With Infrastructure-Aided Cooperation paper_content: In cellular systems, confidentiality of uplink transmission with respect to eavesdropping terminals can be ensured by creating intentional interference via scheduling of concurrent downlink transmissions. In this paper, this basic idea is explored from an information-theoretic standpoint by focusing on a two-cell scenario where the involved base stations (BSs) are connected via a finite-capacity backbone link. A number of transmission strategies are considered that aim at improving uplink confidentiality under constraints on the downlink rate that acts as an interfering signal. The strategies differ mainly in the way the backbone link is exploited by the cooperating downlink to the uplink-operated BSs. Achievable rates are derived for both the Gaussian (unfaded) and the fading cases, under different assumptions on the channel state information available at different nodes. Numerical results are also provided to corroborate the analysis. Extensions to scenarios with more than two cells are briefly discussed as well. Overall, the analysis reveals that a combination of scheduling and base-station cooperation is a promising means to improve transmission confidentiality in cellular systems. --- paper_title: Cooperative Jamming and Power Allocation for Wireless Relay Networks in Presence of Eavesdropper paper_content: Relying on physical layer security is an attractive alternative of utilizing cryptographic algorithms at upper layers of protocol stack for secure communications. In this paper, we consider a two-hop wireless relay network in the presence of an eavesdropper. Our scenario of interest spans over a four-node network model including a source, a destination, a trusted relay, and an untrusted eavesdropper in which the relay forwards the source message in a decode-and-forward (DF) fashion. The source and relay are allowed to use some of their available power to transmit jamming signals in order to create interference at the eavesdropper. The relay and destination are assumed to have the knowledge of the jamming signals. An important question is how to allocate the transmission power of the message signal and that of the jamming signal. First, we propose an optimal power allocation solution in which the knowledge of global channel state information (CSI) is required. To facilitate practical system design, two simple yet sub-optimal power allocation solutions are proposed which do not rely on eavesdropper's channels. For the purpose of performance comparisons, power allocation problems for two benchmark schemes without jamming are also analyzed. --- paper_title: Cooperative jamming via spectrum leasing paper_content: Secure communication rates can be facilitated or enhanced via deployment of cooperative jammers in a multi-terminal environment. Such an approach typically assumes dedicated and/or altruistic jamming nodes, investing their resources for the good of the whole system. In this paper, we demonstrate that jammers can be recruited to provide significant improvements of secrecy rates even when this assumption is alleviated. A game-theoretic framework is proposed where a source node, towards the maximization of its secrecy rate, utilizes the jamming services from a set of non-altruistic nodes, compensating them with a fraction of its bandwidth for transmission of their user data. With the goal of maximizing their user-data transmission rate priced by the invested power, potential cooperative jammers will provide the jamming/transmitting power that is generally proportional to the amount of leased bandwidth. Elaborating initially on a single-jammer scenario, interaction between the source and a cooperative jammer is modeled as the Stackelberg leader-follower game. The scheme is further extended to involve multiple potential jammers, applying competition mechanisms such as the auctioning and power control game, while maintaining the Stackelberg framework. --- paper_title: Recruiting multi-antenna transmitters as cooperative jammers: An auction-theoretic approach paper_content: This paper proposes and investigates a distributed mechanism that motivates otherwise non-cooperative terminals to participate as cooperative jammers assisting a source-destination pair that communicates over a wireless medium, in the presence of an eavesdropper from whom the communicated messages need to be kept secret. The cooperation incentive is provided by an opportunity for potential jammers, possibly equipped with multiple antennas, to utilize the spectrum belonging to the ongoing secure transmission for their own data traffic. A fully decentralized framework is put forth through a competition of potential cooperative jammers for spectrum access by trying to make the jamming offer that most improves the secrecy rate of the source-destination pair. Effective arbitration of cooperative jamming is performed using auction theory, with the source in the role of the auctioneer, and the jammers acting as bidders. The proposed scheme can be alternatively seen as a practical basis for the implementation of cognitive radio networks operating according to the property-rights model, i.e., spectrum leasing. --- paper_title: Deaf Cooperation and Relay Selection Strategies for Secure Communication in Multiple Relay Networks paper_content: In this paper, we investigate the roles of cooperative jamming (CJ) and noise forwarding (NF) in improving the achievable secrecy rates of a Gaussian wiretap channel (GWT). In particular, we study the role of a deaf helper in confusing the eavesdropper in a GWT channel by either transmitting white Gaussian noise (cooperative jamming) or by transmitting a dummy codeword of no context yet drawn from a codebook known to both the destination and the eavesdropper (noise forwarding). We first derive the conditions under which each mode of deaf cooperation improves over the secrecy capacity of the original wiretap channel and show that a helping node can be either a useful cooperative jammer or a useful noise forwarder but not both at the same time. Secondly, we derive the optimal power allocation for both the source and the helping node to be used in each of the two modes of deaf helping. Thirdly, we consider the deaf helper selection problem where there are N relays present in the system and it is required to select the best K deaf helpers, K ≥ 1, that yield the maximum possible achievable secrecy rate. For the case of K=1, we give the optimal selection strategy with optimal power allocation. The computational complexity of the optimal selection strategy when K > 1 is relatively large, especially for large values of K and N. Thus, we propose a suboptimal strategy for the selection problem when K > 1. We derive the complexity of the proposed selection strategies and show that, for K > 1, our suboptimal strategy, which works in a greedy fashion, enjoys a significantly less computational complexity than the optimal strategy. Nevertheless, as demonstrated by numerical examples, our suboptimal strategy gives rise to reasonable performance gains in terms of the achievable secrecy rate with respect to the case of K=1. --- paper_title: Cooperative jamming and power allocation in three-phase two-way relaying wiretap systems paper_content: The security of the three-phase two-way relaying system with an eavesdropper is investigated in this paper. A cooperative jamming and power allocation scheme is proposed to enhance the system secrecy capacity. When a source node transmits user signals to the relay node, the other source node interferes the relay node with pre-defined jamming signals simultaneously. Optimum power allocation between the user and jamming signals at each source node is analyzed. Our analytical results suggest that the proposed cooperative jamming scheme improves on the system secrecy capacity, especially when the channel gains of the two source-relay links are of large difference. Simulation results in close agreement with analytical results clearly demonstrate the advantage of the proposed cooperative jamming scheme. --- paper_title: Physical layer security from inter-session interference in large wireless networks paper_content: Physical layer secrecy in wireless networks in the presence of eavesdroppers of unknown location is considered. In contrast to prior schemes, which have expended energy in the form of cooperative jamming to enable secrecy, we develop schemes where multiple transmitters send their signals in a cooperative fashion to confuse the eavesdroppers. Hence, power is not expended on “artificial noise”; rather, the signal of a given transmitter is protected by the aggregate interference produced by the other transmitters. We introduce a two-hop strategy for the case of equal path-loss between all pairs of nodes, and then consider its embedding within a multi-hop approach for the general case of an extended network. In each case, we derive an achievable number of eavesdroppers that can be present in the region while secure communication between all sources and intended destinations is ensured. --- paper_title: Distributed Coalition Formation of Relay and Friendly Jammers for Secure Cooperative Networks paper_content: In this paper, we investigate cooperation of conventional relays and friendly jammers subject to secrecy constraints for cooperative networks consisting of one source node, one corresponding destination node, one malicious eavesdropper node, and several intermediate nodes. In order to obtain a higher secrecy rate, the source selects one conventional relay and several friendly jammers from the intermediate nodes to assist message transmission, and in return, it needs to make a payment. Each intermediate node here has two possible identities to choose, i.e., to be a conventional relay or a friendly jammer, which results in a direct impact on the final utility of the intermediate node. After the intermediate nodes determine their identities, they seek to find optimal partners forming coalitions, which improves their chances to be selected by the source and thus to obtain the payoffs in the end. We formulate this cooperation as a coalitional game with transferable utility and also study its properties. Furthermore, we define a Max-Pareto order for comparison of the coalition value, based on which we employ the merge-and-split rules. We also construct a distributed merge-and-split coalition formation algorithm for the defined coalition formation game. The simulation results verify the efficiency of the proposed coalition formation algorithm. --- paper_title: Strongly Secure Communications Over the Two-Way Wiretap Channel paper_content: We consider the problem of secure communications over the two-way wiretap channel under a strong secrecy criterion. We improve existing results by developing an achievable region based on strategies that exploit both the interference at the eavesdropper's terminal and cooperation between legitimate users. We leverage the notion of channel resolvability for the multiple-access channel to analyze cooperative jamming and we show that the artificial noise created by cooperative jamming induces a source of common randomness that can be used for secret-key agreement. We illustrate the gain provided by this coding technique in the case of the Gaussian two-way wiretap channel, and we show significant improvements for some channel configurations. --- paper_title: Cooperative jamming and power allocation with untrusty two-way relay nodes paper_content: This study investigates the security of the two-way relaying system with untrusty relay nodes. Cooperative jamming schemes are considered for bi-directional secrecy communications. The transmit power of each source node is divided into two parts corresponding to the user and jamming signals, respectively. Two different assumptions of the jamming signals are considered. When the jamming signals are a priori known at the two source nodes, closed-form power allocation expressions at two source nodes are derived. Under the assumption of unknown jamming signals, it is proven that the cooperative jamming is useless for the system secrecy capacity, because that all the power should be allocated to the user signals at each source node. Relay selection is also investigated based on the analysis of cooperative jamming. Simulation results are presented to compare the system secrecy capacities under the two jamming signal assumptions. --- paper_title: CSI-Secured Orthogonal Jamming Method for Wireless Physical Layer Security paper_content: In this paper, we propose a new cooperative jamming (CJ) method to prevent eavesdroppers from using beam-formers to suppress the jamming signals. In this method, the broadcasting of system-node channel state information (CSI) is first averted. The transmitting and jamming signals are then aligned in two separate single dimensions in complex space. The decode-and-forward relaying protocol is employed.The proposed method is more effective than current CJ techniques. --- paper_title: On Secrecy Capacity of Gaussian Wiretap Channel Aided by A Cooperative Jammer paper_content: We study the secrecy capacity of Gaussian wiretap channel aided by an external jammer/helper. Both the transmitter and the intended receiver are equipped with a single antenna, while the eavesdropper and the jammer are equipped with M and N antennas, respectively. Generally, an analytical form of the secrecy capacity in this scenario is difficult to obtain. Instead, we consider a null-space jamming scheme which totally nulls out the jamming signal at the legitimate receiver, and derive lower and upper bounds on its maximal achievable secrecy rate R<sub>N</sub>. The relationship between the average secrecy capacity C̅<sub>N</sub> of Gaussian wiretap channel and the average secrecy rate R̅<sub>N</sub> achieved by the null-space jamming scheme is investigated, and we prove that R̅<sub>N</sub> ≤ C̅<sub>N</sub> ≤ R̅<sub>N+1</sub>. Based on this inequality and the derived lower and upper bounds on R<sub>N</sub>, the upper and lower bounds on the average secrecy capacity of Gaussian wiretap channel aided by an external jammer can be obtained, where our result shows that when N > M, the average secrecy capacity increases linearly with the total transmit power; while when N ≤ M - 1, there exists a performance ceiling on it. --- paper_title: Joint Cooperative Beamforming and Jamming to Secure AF Relay Systems With Individual Power Constraint and No Eavesdropper's CSI paper_content: Cooperative beamforming and jamming are two efficient schemes to improve the physical-layer security of a wireless relay system in the presence of passive eavesdroppers. However, in most works these two techniques are adopted separately. In this letter, we propose a joint cooperative beamforming and jamming scheme to enhance the security of a cooperative relay network, where a part of intermediate nodes adopt distributed beamforming while others jam the eavesdropper, simultaneously. Since the instantaneous channel state information (CSI) of the eavesdropper may not be known, we propose a cooperative artificial noise transmission based secrecy strategy, subjected to the individual power constraint of each node. The beamformer weights and power allocation can be obtained by solving a second-order convex cone programming (SOCP) together with a linear programming problem. Simulations show the joint scheme greatly improves the security. --- paper_title: Harvest-and-jam: Improving security for wireless energy harvesting cooperative networks paper_content: The emerging radio signal enabled simultaneous wireless information and power transfer (SWIPT), has drawn significant attention. To achieve secrecy transmission by cooperative jamming, especially in the upcoming 5G networks with self-sustainable mobile base stations (BSs) and yet not to add extra power consumption, we propose in this paper a new relay protocol, i.e., harvest-and-jam (HJ), in a relay wiretap channel with an additional set of spare helpers. Specifically, in the first transmission phase, a single-antenna transmitter (Tx) transfers signals carrying both information and energy to a multi-antenna amplify-and-forward (AF) relay and a group of multi-antenna helpers; in the second transmission phase, the AF relay processes the information and forwards it to the receiver while each of the helpers generates an artificial noise (AN), the power of which is constrained by its previously harvested energy, to interfere with the eavesdropper. By optimizing the transmit beamforming matrix for the AF relay and the covariance matrix for the AN, we maximize the secrecy rate for the receiver subject to transmit power constraints for the AF relay and all helpers. The formulated problem is shown to be non-convex, for which we propose an iterative algorithm based on alternating optimization. Finally, the performance of the proposed scheme is evaluated by simulations as compared to other heuristic schemes. --- paper_title: Secure Transmission with Optimal Power Allocation in Untrusted Relay Networks paper_content: We consider the problem of secure transmission in two-hop amplify-and-forward untrusted relay networks. We analyze the ergodic secrecy capacity (ESC) and present compact expressions for the ESC in the high signal-to-noise ratio regime. We also examine the impact of large scale antenna arrays at either the source or the destination. For large antenna arrays at the source, we confirm that the ESC is solely determined by the channel between the relay and the destination. For very large antenna arrays at the destination, we confirm that the ESC is solely determined by the channel between the source and the relay. --- paper_title: Secrecy Capacity Optimization via Cooperative Relaying and Jamming for WANETs paper_content: Cooperative wireless networking, which is promising in improving the system operation efficiency and reliability by acquiring more accurate and timely information, has attracted considerable attentions to support many services in practice. However, the problem of secure cooperative communication has not been well investigated yet. In this paper, we exploit physical layer security to provide secure cooperative communication for wireless ad hoc networks (WANETs) where involve multiple source-destination pairs and malicious eavesdroppers. By characterizing the security performance of the system by secrecy capacity, we study the secrecy capacity optimization problem in which security enhancement is achieved via cooperative relaying and cooperative jamming. Specifically, we propose a system model where a set of relay nodes can be exploited by multiple source-destination pairs to achieve physical layer security. We theoretically present a corresponding formulation for the relay assignment problem and develop an optimal algorithm to solve it in polynomial time. To further increase the system secrecy capacity, we exploit the cooperative jamming technique and propose a smart jamming algorithm to interfere the eavesdropping channels. Through extensive experiments, we validate that our proposed algorithms significantly increase the system secrecy capacity under various network settings. --- paper_title: Hybrid Cooperative Beamforming and Jamming for Physical-Layer Security of Two-Way Relay Networks paper_content: In this paper, we propose a hybrid cooperative beamforming and jamming scheme to enhance the physical-layer security of a single-antenna-equipped two-way relay network in the presence of an eavesdropper. The basic idea is that in both cooperative transmission phases, some intermediate nodes help to relay signals to the legitimate destination adopting distributed beamforming, while the remaining nodes jam the eavesdropper, simultaneously, which takes the data transmissions in both phases under protection. Two different schemes are proposed, with and without the instantaneous channel state information of the eavesdropper, respectively, and both are subjected to the more practical individual power constraint of each cooperative node. Under the general channel model, it is shown that both problems can be transformed into a semi-definite programming (SDP) problem with an additional rank-1 constraint. A current state of the art technique for handling such a problem is the semi-definite relaxation (SDR) and randomization techniques. In this paper, however, we propose a penalty function method incorporating the rank-1 constraint into the objective function. Although the so-obtained problem is not convex, we develop an efficient iterative algorithm to solve it. Each iteration is a convex SDP problem, thus it can be efficiently solved using the interior point method. When the channels are reciprocal such as in TDD mode, we show that the problems become second-order convex cone programming ones. Numerical evaluation results are provided and analyzed to show the properties and efficiency of the proposed hybrid security scheme, and also demonstrate that our optimization algorithms outperform the SDR technique. --- paper_title: Optimal Cooperative Jamming for Multiuser Broadcast Channel with Multiple Eavesdroppers paper_content: Cooperative jamming for multiuser multiple input multiple output (MIMO) broadcast channel is studied to enhance the physical layer security with the help of a friendly jammer. We assume the base station transmits multiple independent data streams to multiple legitimate users. During the transmission, however, there are multiple eavesdroppers with multiple antennas that have interests in the streams from the base station. In order to wiretap the desired streams, the eavesdroppers may collude or not, and maximize the signal-to-interference-plus-noise ratio (SINR) of the desired streams using receive beamforming. The optimal cooperative jammer is designed to keep the achieved SINR at eavesdroppers below a threshold to guarantee that the transmission from the base station to legitimate users is confidential. One main advantage of the proposed cooperative jamming scheme is that no modification is needed for the existing precoding schemes at the base station and decoding schemes at legitimate users. Thus, any existing practical precoding/decoding schemes for multiuser MIMO broadcast channel can be applied directly with the help of a friendly jammer using the proposed cooperative jamming. --- paper_title: MIMO decode-and-forward relay beamforming for secrecy with cooperative jamming paper_content: In this paper, we consider decode-and-forward (DF) relay beamforming for secrecy with cooperative jamming (CJ) in the presence of multiple eavesdroppers. The communication between a source-destination pair is aided by a multiple-input multiple-output (MIMO) relay. The source has one transmit antenna and the destination and eavesdroppers have one receive antenna each. The source and the MIMO relay are constrained with powers P S and P R , respectively. We relax the rank-1 constraint on the signal beamforming matrix and transform the secrecy rate max-min optimization problem to a single maximization problem, which is solved by semidefinite programming techniques. We obtain the optimum source power, signal relay weights, and jamming covariance matrix. We show that the solution of the rank-relaxed optimization problem has rank-1. Numerical results show that CJ can improve the secrecy rate. --- paper_title: On the Jamming Power Allocation for Secure Amplify-and-Forward Relaying via Cooperative Jamming paper_content: In this paper, we investigate secure communications in two-hop wireless relaying networks with one eavesdropper. To prevent the eavesdropper from intercepting the source message, the destination sends an intended jamming noise to the relay, which is referred to as cooperative jamming. This jamming noise helps protecting the source message from being captured reliably at the eavesdropper, while the destination cancels its self-intended noise. According to the channel information available at the destination, we derive three jamming power allocation strategies to minimize the outage probability of the secrecy rate. In addition, we derive analytic results quantifying the jamming power consumption of the proposed allocation methods. --- paper_title: Secrecy Transmission With a Helper: To Relay or to Jam paper_content: In this paper, we consider the problem of secure communications for a four-node system consisting of one source, one destination, one eavesdropper, and one helper. We investigate the question of which role should the helper act to improve the secrecy, to jam, or to relay. Two transmission schemes are investigated: 1) direct transmission scheme (DTS) with jamming and 2) relay transmission scheme (RTS). We consider both the path-loss and fading-in channel models and define a notion of distance normalized signal-to-noise-ratio (DN-SNR) to account for propagation. The ergodic secrecy rate (ESR) is adopted as the performance metric and semi-closed-form expressions of ESR for the two schemes are derived. Additionally, optimal power allocations in both low and high DN-SNR regimes are characterized analytically. We give the performance comparison of the two schemes from the perspective of energy efficiency in the low DN-SNR regime, and characterize the secrecy degree of freedom in the high DN-SNR regime. In the high DN-SNR regime, DTS provides higher secrecy rate compared with RTS, while in the low DN-SNR regime, RTS outperforms DTS. Furthermore, we show that eavesdropper’s position impacts greatly on security. --- paper_title: Joint relay selection and artificial jamming power allocation for secure DF relay networks paper_content: This paper studies cooperative transmission for securing a decode-and-forward (DF) two-hop network where massive cooperative nodes coexist with a potential single eavesdropper. With only the statistical channel state information (CSIs) of the eavesdroppers, we propose an opportunistic relaying with artificial jamming secrecy scheme, where a “best” cooperative node is chosen to forward the confidential signal and the others act as jammers to send the jamming signals to confuse the eavesdropper. Utilizing the limiting distribution technique of extreme order statistics, we optimize the power allocation between the confidential information and jamming signal based on the statistical CSIs of the legitimate nodes for ergodic secrecy rate (ESR) maximization. Although the optimization problems are non-convex, we propose a sequential parametric convex approximation (SPCA) algorithm to locate the KKT solutions. Then, we derive an analytical closed-form expression of the achievable ESR, which reduces the complexity of the system analysis and design. --- paper_title: Cooperative jamming and power allocation with untrusty two-way relay nodes paper_content: This study investigates the security of the two-way relaying system with untrusty relay nodes. Cooperative jamming schemes are considered for bi-directional secrecy communications. The transmit power of each source node is divided into two parts corresponding to the user and jamming signals, respectively. Two different assumptions of the jamming signals are considered. When the jamming signals are a priori known at the two source nodes, closed-form power allocation expressions at two source nodes are derived. Under the assumption of unknown jamming signals, it is proven that the cooperative jamming is useless for the system secrecy capacity, because that all the power should be allocated to the user signals at each source node. Relay selection is also investigated based on the analysis of cooperative jamming. Simulation results are presented to compare the system secrecy capacities under the two jamming signal assumptions. --- paper_title: Joint Design of Optimal Cooperative Jamming and Power Allocation for Linear Precoding paper_content: Linear precoding and cooperative jamming for multiuser broadcast channel is studied to enhance the physical layer security. We consider the system where multiple independent data streams are transmitted from the base station to multiple legitimate users with the help of a friendly jammer. It is assumed that a normalized linear precoding matrix is given at the base station, whereas the power allocated to each user is to be determined. The problem is to jointly design the power allocation across different users for linear precoding and the cooperative jamming at the friendly jammer. The goal is to maximize a lower bound of the secrecy rate, provided that a minimum communication rate to the users is guaranteed. The optimal solution is obtained when the number of antennas at the friendly jammer is no less than the total number of antennas at the users and eavesdropper. Moreover, a suboptimal algorithm is proposed, which can be applied for all the scenarios. Numerical results demonstrate that the proposed schemes are effective for secure communications. --- paper_title: Hybrid Opportunistic Relaying and Jamming With Power Allocation for Secure Cooperative Networks paper_content: This paper studies the cooperative transmission for securing a decode-and-forward (DF) two-hop network where multiple cooperative nodes coexist with a potential eavesdropper. Under the more practical assumption that only the channel distribution information (CDI) of the eavesdropper is known, we propose an opportunistic relaying with artificial jamming secrecy scheme, where a “best” cooperative node is chosen among a collection of $N$ possible candidates to forward the confidential signal and the others send jamming signals to confuse the eavesdroppers. We first investigate the ergodic secrecy rate (ESR) maximization problem by optimizing the power allocation between the confidential signal and jamming signals. In particular, we exploit the limiting distribution technique of extreme order statistics to build an asymptotic closed-form expression of the achievable ESR and the power allocation is optimized to maximize the ESR lower bound. Although the optimization problems are non-convex, we propose a sequential parametric convex approximation (SPCA) algorithm to locate the Karush-Kuhn-Tucker (KKT) solutions. Furthermore, taking the time variance of the legitimate links' CSIs into consideration, we address the impacts of the outdated CSIs to the proposed secrecy scheme, and derive an asymptotic ESR. Finally, we generalize the analysis to the scenario with multiple eavesdroppers, and give the asymptotic analytical results of the achievable ESR. Simulation results confirm our analytical results. --- paper_title: Competing for Secrecy in the MISO Interference Channel paper_content: A secure communication game is considered for the two-user MISO Gaussian interference channel with confidential messages, where each transmitter aims to maximize the difference between its secrecy rate and the secrecy rate of the other transmitter. In this novel problem, the weaker link tries to minimize the extra secret information obtained by its adversary, while the stronger link attempts to maximize it. We provide an information theoretic formulation for this non-cooperative zero-sum game scenario and determine that, under the assumption of Gaussian signaling at the transmitters, there exists a unique Nash equilibrium solution for the proposed problem. Moreover, we obtain in closed-form the optimal strategies (beamformers) at the transmitters that result in the Nash equilibrium. While the NE strategies are achievable in one shot when full channel state information is available at the transmitters (CSIT), we also propose an iterative step-by-step algorithm that converges to the NE point, but only requires limited CSIT. Numerical results are presented to illustrate the analytical findings and to study the role of different channel parameters on the NE strategies. --- paper_title: On the Jamming Power Allocation for Secure Amplify-and-Forward Relaying via Cooperative Jamming paper_content: In this paper, we investigate secure communications in two-hop wireless relaying networks with one eavesdropper. To prevent the eavesdropper from intercepting the source message, the destination sends an intended jamming noise to the relay, which is referred to as cooperative jamming. This jamming noise helps protecting the source message from being captured reliably at the eavesdropper, while the destination cancels its self-intended noise. According to the channel information available at the destination, we derive three jamming power allocation strategies to minimize the outage probability of the secrecy rate. In addition, we derive analytic results quantifying the jamming power consumption of the proposed allocation methods. --- paper_title: An Effective Secure Transmission Scheme for AF Relay Networks with Two-Hop Information Leakage paper_content: In this letter, we propose a novel scheme to improve physical layer security for amplify-and-forward (AF) relay networks with two-hop information leakage. Unlike traditional joint cooperative beamforming (CB) and cooperative jamming (CJ) design, where mere artificial noise (AN) is exploited by the jammers to degrade eavesdroppers' channels, in our proposed scheme, the jammers send AN along with the confidential signal to simultaneously confuse the eavesdroppers and enhance the quality of received signals at the destination. In particular, the CB vectors and AN covariance matrix are jointly optimized for the relays and jammers under individual power constraints. By decoupling this resultant design problem, suboptimal solutions are obtained using the semidefinite relaxation technique. Simulation results are also provided to demonstrate that the proposed scheme has an obvious advantage in enhancing the secrecy rate. --- paper_title: Relay Selection for Security Enhancement in Cognitive Relay Networks paper_content: This letter proposes several relay selection policies for secure communication in cognitive decode-and-forward relay networks, where a pair of cognitive relays is opportunistically selected for security protection against eavesdropping. The first relay transmits the secrecy information to the destination, and the second relay, as a friendly jammer, transmits the jamming signal to confound the eavesdropper. We present new exact closed-form expressions for the secrecy outage probability. Our analysis and simulation results strongly support our conclusion that the proposed relay selection policies can enhance the performance of secure cognitive radio. We also confirm that the error floor phenomenon is created in the absence of jamming. --- paper_title: Destination Assisted Cooperative Jamming for Wireless Physical-Layer Security paper_content: A wireless network with one source, one destination, one eavesdropper, and multiple decode-and-forward relays is considered. A two-slot cooperative relaying scheme is proposed that targets at maximizing the secrecy rate. In the first slot, the source transmits the information bearing signal, and at the same time, it cooperates with the destination in jamming the eavesdropper without creating interference at the relay. In the second slot, one optimally selected relay retransmits the decoded source signal, and at the same time, that particular relay cooperates with the source to jam the eavesdropper without creating interference at the destination. Optimal relay selection and also optimal power allocation among the first/second slot data signal and jamming noise are proposed. It is shown that the secrecy rate of the proposed scheme scales with the total system power P0 and the number of available relays K according to 1/2log2(1 + P0 /8logK) - 1.6 bits/channel use . Although the proposed power allocation and relay selection assume global CSI available, the performance under imperfect relay CSI is also investigated. Also, the performance under distributed relay selection with limited feedback is demonstrated. --- paper_title: On Cooperative and Malicious Behaviors in Multirelay Fading Channels paper_content: Multirelay networks exploit spatial diversity by transmitting user's messages through multiple relay paths. Most works in the literature on cooperative or relay networks assume that all terminals are fully cooperative and neglect the effect of possibly existing malicious relay behaviors. In this work, we consider a multirelay network that consists of both cooperative and malicious relays, and aims to obtain an improved understanding on the optimal behaviors of these two groups of relays via information-theoretic mutual information games. By modeling the set of cooperative relays and the set of malicious relays as two players in a zero-sum game with the maximum achievable rate as the utility, the optimal transmission strategies of both types of relays are derived by identifying the Nash equilibrium of the proposed game. Our main contributions are twofold. First, a generalization to previous works is obtained by allowing malicious relays to either listen or attack in Phase 1 (source-relay transmission phase). This is in contrast to previous works that only allow the malicious relays to listen in Phase 1 and to attack in Phase 2 (relay-destination transmission phase). The latter is shown to be suboptimal in our problem. Second, the impact of CSI knowledge at the destination on the optimal attack strategy that can be adopted by the malicious relays is identified. In particular, for the more practical scenario where the interrelay CSI is unknown at the destination, the constant attack is shown to be optimal as opposed to the commonly considered Gaussian attack. --- paper_title: Secure Relay and Jammer Selection for Physical Layer Security paper_content: Secure relay and jammer selection for physical-layer security is studied in a wireless network with multiple intermediate nodes and eavesdroppers, where each intermediate node either helps to forward messages as a relay, or broadcasts noise as a jammer. We derive a closed-form expression for the secrecy outage probability (SOP), and we develop two relay and jammer selection methods for SOP minimization. In both methods a selection vector and a corresponding threshold are designed and broadcast by the destination to ensure each intermediate node knows its own role while knowledge of the relay and jammer set is kept secret from all eavesdroppers. Simulation results show the SOP of the proposed methods are very close to that obtained by an exhaustive search, and that maintaining the privacy of the selection result greatly improves the SOP performance. --- paper_title: Opportunistic Relaying for Secrecy Communications: Cooperative Jamming vs. Relay Chatting paper_content: In this letter, we study the opportunistic use of relays for secret communications, and propose two transmission schemes that do not require the knowledge of the eavesdropper's channel state information. Both analytic and numerical results are provided. --- paper_title: Improving Secrecy Rate via Spectrum Leasing for Friendly Jamming paper_content: Cooperative jamming paradigm in secure communications enlists network nodes to transmit noise or structured codewords, in order to impair the eavesdropper's ability to decode messages to be kept confidential from it. Such an approach can significantly help in facilitating secure communication between legitimate parties but, by definition, assumes dedicated and/or altruistic nodes willing to act as cooperative jammers. In this paper, it is demonstrated that cooperative jamming leads to meaningful secrecy rate improvements even when this assumption is removed. A distributed mechanism is developed that motivates jamming participation of otherwise non-cooperative terminals, by compensating them with an opportunity to use the fraction of legitimate parties' spectrum for their own data traffic. With the goal of maximizing their data transmission rate priced by the invested power, cooperative jammers provide the jamming/transmitting power that is generally proportional to the amount of leased bandwidth. The fully decentralized framework is facilitated through a game-theoretic model, with the legitimate parties as the spectrum owners acting as the game leader, and the set of assisting jammers constituting the follower. To facilitate the behavior of non-cooperative and competitive multiple jammers, auctioning and power control mechanisms are applied for a follower sub-game in a two-layer leader-follower game framework. --- paper_title: Power control Stackelberg game in cooperative anti-jamming communications paper_content: As wireless networks are vulnerable to malicious attacks, security issues have aroused enormous research interest. In this paper, we focus on the anti-jamming power control of transmitters in a cooperative wireless network attacked by a smart jammer with the capability to sense the ongoing transmission power before making a jamming decision. By modeling the interaction between transmitters and a jammer as a Stackelberg game, we analyze the optimal strategies for both transmitters and the jammer and thus derive the Stackelberg equilibrium of the game. In addition, the Nash equilibrium of the anti-jamming game is also derived to compare with the Stackelberg equilibrium strategy. Finally, the impacts of the fading channel gains of the transmitters and the jammer on their utilities and the SINR are measured, respectively. Simulation results are presented to verify the performance of the proposed Stackelberg equilibrium strategy. ---	Title: A Survey on Cooperative Jamming Applied to Physical Layer Security Section 1: INTRODUCTION Description 1: Introduce the significance of wireless communications, the associated security challenges, and the traditional vs. physical layer security solutions. Summarize the paper's contributions. Section 2: Physical Layer Security Description 2: Describe the generic wireless communication network model, the concept of secrecy capacity, and various methods to enhance physical layer security. Section 3: Cooperative jamming Description 3: Discuss the concept of cooperative jamming and how it aids in enhancing physical layer security by introducing interferers to reduce the eavesdropper's SNR. Section 4: Artificial Jamming Signals types Description 4: Categorize and explain the different types of artificial jamming signals used in cooperative jamming, highlighting their impact on communication security. Section 5: Cooperative Jamming with Multiple Antennas and Beamforming Description 5: Explore the use of multiple antennas and beamforming techniques in conjunction with cooperative jamming to enhance security. Section 6: Cooperative jamming with Power Allocation method Description 6: Discuss various power allocation strategies in cooperative jamming to optimize the secrecy capacity and minimize outage probability. Section 7: Jamming Policies Description 7: Outline different jamming policies, including relay selection and game theory approaches, to enhance secure communication. Section 8: CONCLUSION Description 8: Summarize the surveyed challenges and techniques in physical layer security and jamming, and conclude with considerations for future research directions. Section 9: ACKNOWLEDGMENT Description 9: Acknowledge the support and contributions towards the research work presented in the paper.
Wireless Sensor Network Routing Protocols: A Survey	9	--- paper_title: Integrated comparison of energy efficient routing protocols in wireless sensor network: A survey paper_content: The dramatic increase in sensors application over the past 20 years, made it clear that the sensors will make a revolution like that witnessed in microcomputers in 1980s. Moreover, some researchers have labeled the first decade of the 21st century as the “Sensor Decade” [1]. Sensor nodes are powered by limited capacity of batteries and because of this limitation, the energy consumption of a sensor node must be tightly controlled. WSN life time mainly depends on the lifetime of limited power source of the nodes. Therefore, energy consumption is main concern in WSNs. So, during the operation of each sensor node, the sources that consume energy must be analyzed and maintained efficiently. In this paper, we have focused on features of Sensor Networks, Clustering and Routing and more precisely on Energy Efficient (E.E) routing protocols and finally, made an integrated comparison among these E.E Routing protocols. --- paper_title: Integrated comparison of energy efficient routing protocols in wireless sensor network: A survey paper_content: The dramatic increase in sensors application over the past 20 years, made it clear that the sensors will make a revolution like that witnessed in microcomputers in 1980s. Moreover, some researchers have labeled the first decade of the 21st century as the “Sensor Decade” [1]. Sensor nodes are powered by limited capacity of batteries and because of this limitation, the energy consumption of a sensor node must be tightly controlled. WSN life time mainly depends on the lifetime of limited power source of the nodes. Therefore, energy consumption is main concern in WSNs. So, during the operation of each sensor node, the sources that consume energy must be analyzed and maintained efficiently. In this paper, we have focused on features of Sensor Networks, Clustering and Routing and more precisely on Energy Efficient (E.E) routing protocols and finally, made an integrated comparison among these E.E Routing protocols. --- paper_title: Protocols for self-organization of a wireless sensor network paper_content: We present a suite of algorithms for self-organization of wireless sensor networks in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energy-efficient routing, and formation of ad hoc subnetworks for carrying out cooperative signal processing functions among a set of the nodes. --- paper_title: Protocols for self-organization of a wireless sensor network paper_content: We present a suite of algorithms for self-organization of wireless sensor networks in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energy-efficient routing, and formation of ad hoc subnetworks for carrying out cooperative signal processing functions among a set of the nodes. --- paper_title: Adaptive protocols for information dissemination in wireless sensor networks paper_content: In this paper, we present a family of adaptive protocols, called SPIN (Sensor Protocols for Information via Negotiation), that efficiently disseminates information among sensors in an energy-constrained wireless sensor network. Nodes running a SPIN communication protocol name their data using high-level data descriptors, called meta-data. They use meta-data negotiations to eliminate the transmission of redundant data throughout the network. In addition, SPIN nodes can base their communication decisions both upon application-specific knowledge of the data and upon knowledge of the resources that are available to them. This allows the sensors to efficiently distribute data given a limited energy supply. We simulate and analyze the performance of two specific SPIN protocols, comparing them to other possible approaches and a theoretically optimal protocol. We find that the SPIN protocols can deliver 60% more data for a given amount of energy than conventional approaches. We also find that, in terms of dissemination rate and energy usage, the SPlN protocols perform close to the theoretical optimum. --- paper_title: Adaptive protocols for information dissemination in wireless sensor networks paper_content: In this paper, we present a family of adaptive protocols, called SPIN (Sensor Protocols for Information via Negotiation), that efficiently disseminates information among sensors in an energy-constrained wireless sensor network. Nodes running a SPIN communication protocol name their data using high-level data descriptors, called meta-data. They use meta-data negotiations to eliminate the transmission of redundant data throughout the network. In addition, SPIN nodes can base their communication decisions both upon application-specific knowledge of the data and upon knowledge of the resources that are available to them. This allows the sensors to efficiently distribute data given a limited energy supply. We simulate and analyze the performance of two specific SPIN protocols, comparing them to other possible approaches and a theoretically optimal protocol. We find that the SPIN protocols can deliver 60% more data for a given amount of energy than conventional approaches. We also find that, in terms of dissemination rate and energy usage, the SPlN protocols perform close to the theoretical optimum. --- paper_title: SPIN-IT: a data centric routing protocol for image retrieval in wireless networks paper_content: We propose and analyze a routing protocol for mobile ad hoc networks that supports efficient image retrieval based on metadata queries. In digital photography, metadata describes captured information about an image and provides the key element needed for advanced techniques for sharing pictures. Our goal was to find an efficient way to utilize metadata to retrieve images in a wireless network of imaging devices. Building on the SPIN (sensor protocols for information via negotiation) protocol for metadata negotiation, we designed SPIN-IT (SPIN-image transfer), a protocol where wireless imaging devices use metadata queries to retrieve desired pictures. This protocol provides low bandwidth query-based communication prior to the transfer of image data to set up routes to desired data rather than routes to specific nodes. We compare SPIN-IT to a centralized approach and discuss the advantages of each design for different picture-sharing scenarios. --- paper_title: Rumor routing algorthim for sensor networks paper_content: Advances in micro-sensor and radio technology will enable small but smart sensors to be deployed for a wide range of environmental monitoring applications. In order to constrain communication overhead, dense sensor networks call for new and highly efficient methods for distributing queries to nodes that have observed interesting events in the network. A highly efficient data-centric routing mechanism will offer significant power cost reductions [17], and improve network longevity. Moreover, because of the large amount of system and data redundancy possible, data becomes disassociated from specific node and resides in regions of the network [10][7][8]. This paper describes and evaluates through simulation a scheme we call Rumor Routing, which allows for queries to be delivered to events in the network. Rumor Routing is tunable, and allows for tradeoffs between setup overhead and delivery reliability. It's intended for contexts in which geographic routing criteria are not applicable because a coordinate system is not available or the phenomenon of interest is not geographically correlated. --- paper_title: A New Gradient-Based Routing Protocol in Wireless Sensor Networks paper_content: Every physical event results in a natural information gradient in the proximity of the phenomenon. Moreover, many physical phenomena follow the diffusion laws. This natural information gradient can be used to design efficient information-driven routing protocols for sensor networks. Information-driven routing protocols based on the natural information gradient, may be categorized into two major approaches: (i) the single-path approach and (ii) the multiple-path approach. In this paper, using a regular grid topology, we develop analytical models for the query success rate and the overhead of both approaches for ideal and lossy wireless link conditions. We validate our analytical models using simulations. Also, both the analytical and the simulation models are used to characterize each approach in terms of overhead, query success rate and increase in path length. --- paper_title: Protocols for self-organization of a wireless sensor network paper_content: We present a suite of algorithms for self-organization of wireless sensor networks in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energy-efficient routing, and formation of ad hoc subnetworks for carrying out cooperative signal processing functions among a set of the nodes. --- paper_title: Improvement on LEACH Protocol of Wireless Sensor Network paper_content: This paper studies LEACH protocol, and puts forward energy-LEACH and multihop-LEACH protocols. Energy-LEACH protocol improves the choice method of the cluster head, makes some nodes which have more residual energy as cluster heads in next round. Multihop-LEACH protocol improves communication mode from single hop to multi-hop between cluster head and sink. Simulation results show that energy-LEACH and multihop-LEACH protocols have better performance than LEACH protocols. --- paper_title: Improvement on LEACH Protocol of Wireless Sensor Network paper_content: This paper studies LEACH protocol, and puts forward energy-LEACH and multihop-LEACH protocols. Energy-LEACH protocol improves the choice method of the cluster head, makes some nodes which have more residual energy as cluster heads in next round. Multihop-LEACH protocol improves communication mode from single hop to multi-hop between cluster head and sink. Simulation results show that energy-LEACH and multihop-LEACH protocols have better performance than LEACH protocols. --- paper_title: A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH) paper_content: Wireless sensor networks with thousands of tiny sensor nodes are expected to find wide applicability and increasing deployment in coming years, as they enable reliable monitoring and analysis of the environment. In this paper we propose a modification to a well-known protocol for sensor networks called Low Energy Adaptive Clustering Hierarchy (LEACH). This last is designed for sensor networks where end- user wants to remotely monitor the environment. In such situation, the data from the individual nodes must be sent to a central base station, often located far from the sensor network, through which the end-user can access the data. In this context our contribution is represented by building a two-level hierarchy to realize a protocol that saves better the energy consumption. Our TL-LEACH uses random rotation of local cluster base stations (primary cluster-heads and secondary cluster-heads). In this way we build, where it is possible, a two-level hierarchy. This permits to better distribute the energy load among the sensors in the network especially when the density of network is higher. TL- LEACH uses localized coordination to enable scalability and robustness. We evaluated the performances of our protocol with NS-2 and we observed that our protocol outperforms the LEACH in terms of energy consumption and lifetime of the network. --- paper_title: Improvement on LEACH Protocol of Wireless Sensor Network (VLEACH) paper_content: This paper presents a new version of LEACH protocol called VLEACH which aims to reduce energy consumption within the wireless network. We evaluate both LEACH and V-LEACH through extensive simulations using OMNET++ simulator which shows that VLEACH performs better than LEACH protocol. --- paper_title: U-LEACH: A novel routing protocol for heterogeneous Wireless Sensor Networks paper_content: A Wireless Sensor Network comprises of a number of energy-constraint sensor nodes deployed in the area of interest. In our paper, we have proposed a new hierarchical clustering based routing protocol for the heterogeneous wireless sensor networks. The proposed protocol named as Universal - Low Energy Adaptive Cluster Hierarchy (U-LEACH) is an energy efficient protocol showing a significant reduction in the energy consumption by the sensor nodes. Unlike LEACH, in U-LEACH, the selection of Cluster-Head depends on the initial and the residual energy of the nodes. In a particular cluster, the transfer of information between the nodes takes place by forming a chain, starting from the farthest node from the base station. Data aggregation has also been implemented successfully to slam down the energy-consumption. Simulations results show substantial improvement of the U-LEACH over its nemesis protocols. --- paper_title: CHIRON: An energy-efficient chain-based hierarchical routing protocol in wireless sensor networks paper_content: Due to the power restriction of sensor nodes, efficient routing, in wireless sensor networks, is a critical approach to saving node's energy and thus prolonging the network lifetime. Even the chain-based routing is one of significant routing mechanisms, several common flaws, such as data propagation delay and redundant transmission, are associated. In this paper, we propose an energy efficient Chain-Based Hierarchical Routing Protocol, named as CHIRON, to alleviate such deficiencies. Based on the BeamStar concept [9], the main idea of CHIRON is to split the sensing field into a number of smaller areas, so that it can create multiple shorter chains to reduce the data transmission delay and redundant path, and therefore effectively conserve the node energy and prolong the network lifetime. Simulation results show that, in contrast to Enhanced PEGASIS and PEGASIS protocols, the proposed CHIRON can achieve about 15% and 168% improvements on average data propagation delay, 30% and 65% improvements on redundant transmission path, respectively. By these contributions, the network lifetime can also be extended to about 14%∼7% and 50%∼23%, under various small and large simulation areas, respectively. --- paper_title: TEEN: a routing protocol for enhanced efficiency in wireless sensor networks paper_content: Wireless sensor networks are expected to find wide applicability and increasing deployment in the near future. In this paper, we propose a formal classification of sensor networks, based on their mode of functioning, as proactive and reactive networks. Reactive networks, as opposed to passive data collecting proactive networks, respond immediately to changes in the relevant parameters of interest. We also introduce a new energy efficient protocol, TEEN (Threshold sensitive Energy Efficient sensor Network protocol) for reactive networks. We evaluate the performance of our protocol for a simple temperature sensing application. In terms of energy efficiency, our protocol has been observed to outperform existing conventional sensor network protocols. --- paper_title: An efficient clustering protocol increasing wireless sensor networks life time paper_content: Due to their rapid and promising development, Wireless Sensor Networks (WSNs) have been predicted to invade all domains in our daily life in the near future. However in order to reach their maturity, researchers must find solution to some difficulties which are slowing down the wide spread use of these networks. These difficulties are inherent to their constrained specificities which require adapted solutions unrelated to classical wire networks. One the activity done by WSN that consumed the most energy is the number of packets sent/received. In order to reduce this number and to ensure WSN successful operation, hierarchical clustering protocols have been developed. In this paper, we present WB-TEEN and WBM-TEEN: two hierarchical routing protocols, based on nodes clustering and improving the well known protocol Threshold sensitive Energy Efficient sensor Network protocol (TEEN). This improvement is accomplished in a way such that each cluster is nodes balanced and the total energy consumption between sensor nodes and cluster heads is minimized by using multi-hops intra-cluster communication. Simulation results (using NS2 simulator) show that the proposed protocols exhibit better performance than Low Energy Adaptive Clustering Hierarchy (LEACH) and TEEN in terms of energy consumption and network lifetime prolongation. --- paper_title: Energy-Efficient Routing Protocol Based on Clustering and Least Spanning Tree in Wireless Sensor Networks paper_content: A particular wireless sensor networks (WSNs), cluster-based WSN (CWSN) have received more and more attention due to the limited energy of battery-powered nodes, but rarely consider the shortest path at the same time. In this paper, we propose an novel energy-efficient routing protocol based on clustering and least spanning tree for wireless sensor network to prolong network lifetime and shorten path while emphasizing energy conservation at the same time. Clustering includes partitioning stage and choosing stage, namely, partitions the multi-hop network and then chooses cluster-heads, cluster-head is responsible for receiving, sending and maintaining information in its cluster. Then all cluster-heads will construct a least spanning tree to prolong network lifetime, save energy and shorten path. Simulation results show that the system?s performance have further improved by using clustering and least spanning tree. It is a promising approach and deserves more future research. --- paper_title: SEP: A stable election protocol for clustered heterogeneous wireless sensor networks paper_content: We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources—this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes. --- paper_title: HSEP: Heterogeneity-aware Hierarchical Stable Election Protocol for WSNs paper_content: Wireless Sensor Networks (WSNs) are increasing to handle complex situations and functions. In these networks some of the nodes become Cluster Heads (CHs) which are responsible to aggregate data of from cluster members and transmit it to Base Stations (BS). Those clustering techniques which are designed for homogenous network are not enough efficient for consuming energy. Stable Election Protocol (SEP) introduces heterogeneity in WSNs, consisting of two type of nodes. SEP is based on weighted election probabilities of each node to become CH according to remaining energy of nodes. We propose Heterogeneity-aware Hierarchal Stable Election Protocol (HSEP) having two level of energies. Simulation results show that HSEP prolongs stability period and network lifetime, as compared to conventional routing protocols and having higher average throughput than selected clustering protocols in WSNs. --- paper_title: Minimum-energy mobile wireless networks revisited paper_content: We propose a protocol that, given a communication network, computes a subnetwork such that, for every pair $(u,v)$ of nodes connected in the original network, there is a minimum-energy path between $u$ and $v$ in the subnetwork (where a minimum-energy path is one that allows messages to be transmitted with a minimum use of energy). The network computed by our protocol is in general a subnetwork of the one computed by the protocol given in [13]. Moreover, our protocol is computationally simpler. We demonstrate the performance improvements obtained by using the subnetwork computed by our protocol through simulation. --- paper_title: Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks paper_content: Future sensor networks will be composed of a large number of densely deployed sensors/actuators. A key feature of such networks is that their nodes are untethered and unattended. Consequently, energy efficiency is an important design consideration for these networks. Motivated by the fact that sensor network queries may often be geographical, we design and evaluate an energy efficient routing algorithm that propagates a query to the appropriate geographical region, without flooding. The proposed Geographic and Energy Aware Routing (GEAR) algorithm uses energy aware neighbor selection to route a packet towards the target region and Recursive Geographic Forwarding or Restricted Flooding algorithm to disseminate the packet inside the destina- --- paper_title: Trajectory based forwarding and its applications paper_content: Trajectory based forwarding (TBF) is a novel methodto forward packets in a dense ad hoc network that makes it possible to route a packet along a predefined curve. It is a hybrid between source based routing and Cartesian forwarding in that the trajectory is set by the source, but the forwarding decision is based on the relationship to the trajectory rather than names of intermediate nodes. The fundamental aspects of TBF are: it decouples path naming from the actual path; it provides cheap path diversity; it trades off communication for computation. These aspects address the double scalability issue with respect to mobility rate and network size. In addition, TBF provides a common framework for many services such as: broadcasting, discovery, unicast, multicast and multipath routing in ad hoc networks. TBF requires that nodes know their position relative to a coordinate system. While a global coordinate system afforded by a system such as GPS would be ideal, approximate positioning methods provided by other algorithms are also usable. --- paper_title: Insect sensory systems inspired computing and communications paper_content: Insects are the most successful group of living things in terms of the number of species, the biomass and their distribution. Entomological research has revealed that the insect sensory systems are crucial for their success. Compared to human brains, the insect central nerve systems are extremely primitive and simple, both structurally and functionally, and are of minimal learning ability. Faced with these constraints, insects have evolved a set of extremely effective sensory systems that are structurally simple, functionally versatile and powerful, and highly distributed, as well as noise and fault tolerant. As a result, in recent years insect sensory systems have been inspirational to new communications and computing paradigms, which have lead to significant advances. However, we believe that the potential for insect-inspired solutions for communications and computing is far from being fully recognized. In particular, the contrasting similarity between the ubiquitous existences of insect sensory networks in nature and the idea of pervasive computing has received little attention. For example, the chemosensory communication systems in many of the moth, ant and beetle populations are essentially ''wireless'' sensory networks. The difference between the ''wireless'' network of an insect population and an engineered wireless sensor network is that insects encode messages with semiochemicals (also known as infochemicals) rather than with radio frequencies; in addition, the computing node is the individual insect powered by its brain, sensory and neuromotor systems, rather than a microchip-powered sensor. The objectives of this paper are threefold: (1) to introduce the state-of-the art research in insect sensory systems from entomological perspectives; (2) to propose potential new research problems inspired by insect sensory system with focusing on unexplored fields; and (3) to justify how and why insect sensory systems may inspire novel computing and communications paradigms. --- paper_title: AntNet: Distributed Stigmergetic Control for Communications Networks paper_content: This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority. --- paper_title: Ant Colony-Based Reinforcement Learning Algorithm for Routing in Wireless Sensor Networks paper_content: The field of routing and sensor networking is an important and challenging research area of network computing today. Advancements in sensor networks enable a wide range of environmental monitoring and object tracking applications. Routing in sensor networks is a difficult problem: as the size of the network increases, routing becomes more complex. Therefore, biologically-inspired intelligent algorithms are used to tackle this problem. Ant routing has shown excellent performance for sensor networks. In this paper, we present a biologically-inspired swarm intelligence-based routing algorithm, which is suitable for sensor networks. Our proposed ant routing algorithm also meet the enhanced sensor network requirements, including energy consumption, success rate, and time delay. The paper concludes with the measurement data we have found. --- paper_title: Ant System Based Anycast Routing in Wireless Sensor Networks paper_content: Anycast is a mechanism that it sends the data groups to the nearest interface during which they have the same anycast address. Ant colony system, a population-based algorithm, provides natural and intrinsic way of exploration of search space in optimization settings in determining optimal anycast tree. In this paper, we propose a sink selection heuristic algorithm called Minimum Ant-based Data Fusion Tree(MADFT) for energy constraint wireless sensor networks. Different from existing schemes, MADAT not only optimizes over the data transmission cost, but also incorporates the cost for data fusion which can be significant for emerging sensor networks with vectorial data and/or security requirements. Via simulation, it is shown that this algorithm has excellent performance behavior and provides a near-optimal solution. --- paper_title: Swarm intelligence optimization based routing algorithm for Wireless Sensor Networks paper_content: Energy balance is an important performance index in the design of routing algorithm for wireless sensor networks. A swarm intelligence optimization based routing algorithm for wireless sensor networks is proposed, whose kernel idea is taking less hop numbers into consideration and choosing the nodes with less pheromone as next hop to avoid some nodespsila prematurely exhausting their energy because of too concentrated routes through the nodes. The experiments show that the algorithm proposed in this paper is better than the directed diffusion routing protocol both in end-to-end delay and global energy balance and can effectively balance the global energy consumption and prolong the network lifetime. --- paper_title: An Improved Gossiping Data Distribution Technique with Emphasis on Reliability and Resource Constraints paper_content: In this paper we present an improved Gossiping data distribution technique with emphasis on the location of nodes called "LGossiping" that reliably disseminate information among sensors in a wireless sensor network. Nodes running LGossiping data distribution technique use global positioning system to relay data throughout the network. Each node decides upon position knowledge of the others. This allows each sensor to send its data reliably to one of known neighbors instead of sending data blindly to one of neighbors which may not be near to source node and cause data lost. We outlined SystemC as simulation language and simulate and analyzed Gossiping and LGossiping protocols. In each experiment source node propagate data among nodes with the hope of reaching to sink node. This procedure is called data transmission. We found that LGossiping with transmission radius greater than or equal to 4 (TR≫=4) is 30% more reliable than Gossiping for a specific number of experiments. LGossiping is an improvement of Gossiping in terms of reliability. Therefore reliability in this improved method is critical. Moreover, network energy consumption with respect to different TR is considered. We concluded that network energy consumption decreases as the transmission radius increases. As termination, increasing area during the experiment is mentioned. In order to have constant reliability with constant number of nodes in LGossiping method in immense areas, with each 600m2 increase of area transmission radius should increases 1 meter. --- paper_title: TAG: a Tiny AGgregation service for ad-hoc sensor networks paper_content: We present the Tiny AGgregation (TAG) service for aggregation in low-power, distributed, wireless environments. TAG allows users to express simple, declarative queries and have them distributed and executed efficiently in networks of low-power, wireless sensors. We discuss various generic properties of aggregates, and show how those properties affect the performance of our in network approach. We include a performance study demonstrating the advantages of our approach over traditional centralized, out-of-network methods, and discuss a variety of optimizations for improving the performance and fault tolerance of the basic solution. --- paper_title: The design of an acquisitional query processor for sensor networks paper_content: We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices. --- paper_title: Breath: A Self-Adapting Protocol for Wireless Sensor Networks in Control and Automation paper_content: The novel cross-layer protocol Breath for wireless sensor networks is designed, implemented, and experimentally evaluated. The Breath protocol is based on randomized routing, MAC and duty-cycling, which allow it to minimize the energy consumption of the network while ensuring a desired packet delivery end-to-end reliability and delay. The system model includes a set of source nodes that transmit packets via multi-hop communication to the destination. A constrained optimization problem, for which the objective function is the network energy consumption and the constraints are the packet latency and reliability, is posed and solved. It is shown that the communication layers can be jointly optimized for energy efficiency. The optimal working point of the network is achieved with a simple algorithm, which adapts to traffic variations with negligible overhead. The protocol was implemented on a test-bed with off-the-shelf wireless sensor nodes. It is compared with a standard IEEE 802.15.4 solution. Experimental results show that Breath meets the latency and reliability requirements, and that it exhibits a good distribution of the working load, thus ensuring a long lifetime of the network. --- paper_title: Localized power-aware routing in linear wireless sensor networks paper_content: Energy-efficency is a key concern when designing protocols for wireless sensor networks (WSN). This is of particular importance in commercial applications where demonstrable return on investment is a crucial factor. One such commercial application that motivated this work is telemetry and control for freight railroad trains. Since a railroad train has a global linear structure by nature, we consider in this paper linear WSNs as sensor networks having, roughly, a linear topology. Aiming at such networks, we introduce two routing schemes that efficiently utilize energy: Minimum Energy Relay Routing (MERR) and Adaptive MERR (AMERR). We derive a theoretical lower bound on the optimal power consumption of routing in a linear WSN, where we assume a Poisson model for the distribution of nodes along a linear path. We evaluate the efficiency of our protocols with respect to the theoretical optimal lower bound and with respect to other well-known protocols. AMERR achieves optimal performance for practical deployment settings, while MERR rapidly approaches optimal performance as sensors are more densely deployed. Compared to other protocols, we show that MERR and AMERR are less complex and have better scalability. We also postulate how both protocols might be generalized to a two-dimensional WSN. --- paper_title: Protocols for self-organization of a wireless sensor network paper_content: We present a suite of algorithms for self-organization of wireless sensor networks in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energy-efficient routing, and formation of ad hoc subnetworks for carrying out cooperative signal processing functions among a set of the nodes. --- paper_title: Energy Efficient Adaptive Multipath Routing for Wireless Sensor Networks paper_content: Routing in wireless sensor networks is a demanding task. This demand has led to a number of routing protocols which efficiently utilize the limited resources available at the sensor nodes. All these protocols typically find the minimum energy path. In this paper we take a view that, always using the minimum energy path deprives the nodes energy quickly and the time taken to determine an alternate path increases. Multipath routing schemes distribute traffic among multiple paths instead of routing all the traffic along a single path. Two key questions that arise in multipath routing are how many paths are needed and how to select these paths. Clearly, the number and the quality of the paths selected dictate the performance of a multipath routing scheme. We propose an energy efficient adaptive multipath routing technique which utilizes multiple paths between source and the sink, adaptive because they have low routing overhead. This protocol is intended to provide a reliable transmission environment with low energy consumption, by efficiently utilizing the energy availability and the received signal strength of the nodes to identify multiple routes to the destination. Simulation results show that the energy efficient adaptive multipath routing scheme achieves much higher performance than the classical routing protocols, even in the presence of high node density and overcomes simultaneous packet forwarding --- paper_title: Reliable Multi-path Routing Protocol in Wireless Sensor Networks paper_content: There are often some faults happening in the wireless sensor networks due to node failure or energy exhaustion. It is because the nodes in sensor networks are energy constrained and usually deployed in harsh environments. These factors strongly influence reliability of data transmission and decrease the lifetime of networks. An important issue in designing wireless sensor network is the reliable routing protocols which provide as much reliability of data transmission as possible. Another important factor is to make the best use of the limited energy presented by nodes of wireless sensor networks. This paper proposes a new routing protocol called REEM(Reliable and Energy-Efficient Multi-path routing protocol) to settle above problems. Simulation results show that our protocol surpasses the MSR, AOMDV and ARAMA protocols in terms of reliability and network lifetime. --- paper_title: Energy Efficient Multipath Routing in Large Scale Sensor Networks with Multiple Sink Nodes paper_content: Due to the battery resource constraint, it is a critical issue to save energy in wireless sensor networks, particularly in large sensor networks. One possible solution is to deploy multiple sink nodes simultaneously. In this paper, we propose a protocol called MRMS (Multipath Routing in large scale sensor networks with Multiple Sink nodes) which incorporates multiple sink nodes, a new path cost metric for improving path selection, dynamic cluster maintenance and path switching to improve energy efficiency. MRMS is shown to increase the lifetime of sensor nodes substantially compared to other algorithms based on a series of simulation experiments. --- paper_title: Energy-balancing multipath routing protocol for wireless sensor networks paper_content: A Wireless Sensor Network (WSN) is a collection of wireless sensor nodes forming a temporary network without the aid of any established infrastructure or centralized administration. In such an environment, due to the limited range of each node's wireless transmissions, it may be necessary for one sensor node to ask for the aid of other sensor nodes in forwarding a packet to its destination, usually the base station. One big issue when designing wireless sensor network is the routing protocol to make the best use of the severe resource constraints presented by WSN, especially the energy limitation. In this paper, we propose a new scheme called EBMR: Energy-Balancing Multipath Routing Protocol that uses multipath alternately to prolong the lifetime of the network. --- paper_title: Protocols for self-organization of a wireless sensor network paper_content: We present a suite of algorithms for self-organization of wireless sensor networks in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energy-efficient routing, and formation of ad hoc subnetworks for carrying out cooperative signal processing functions among a set of the nodes. --- paper_title: Protocols for self-organization of a wireless sensor network paper_content: We present a suite of algorithms for self-organization of wireless sensor networks in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energy-efficient routing, and formation of ad hoc subnetworks for carrying out cooperative signal processing functions among a set of the nodes. ---	Title: Wireless Sensor Network Routing Protocols: A Survey Section 1: INTRODUCTION Description 1: This section provides an overview of Wireless Sensor Networks (WSNs) and highlights the challenges in routing due to unique characteristics and diverse applications. It also outlines the scope and objectives of the survey. Section 2: RELATED WORK Description 2: This section reviews the existing literature on WSN routing protocols, identifying gaps and distinguishing the scope of this survey from previous efforts. Section 3: ROUTING PROTOCOLS IN WIRELESS SENSOR NETWORKS Description 3: This section categorizes and discusses various routing protocols for WSNs, providing detailed descriptions of each protocol. Section 4: Network Structure Protocols Description 4: This subsection examines routing protocols based on network structure, including data-centric, hierarchical, and location-based protocols. Section 5: Protocol Operation Routing Protocols Description 5: This subsection explores routing protocols based on operational principles such as multi-path, query-based, negotiation-based, QoS-based, and bio-inspired routing protocols. Section 6: Coherent and Non-coherent Protocols Description 6: This subsection discusses routing protocols focused on data processing strategies, distinguishing between coherent and non-coherent methods. Section 7: Established Path Based Routing Protocol Description 7: This subsection categorizes protocols based on the communication paths they follow, including proactive, reactive, and hybrid protocols. Section 8: Initiator of Communication Description 8: This subsection classifies routing protocols based on whether communication initiation is source-driven or sink-driven. Section 9: CONCLUSION Description 9: This section summarizes the survey findings and proposes future research directions, emphasizing the need for WSN routing protocol integration with wired networks and exploring applications for security and environmental monitoring.
A Survey of Stack-Sorting Disciplines	5	--- paper_title: Computing permutations with double-ended queues, parallel stacks and parallel queues paper_content: A memory may be regarded as a computer with input, output and storage facilities, but with no explicit functional capability. The only possible outputs are permutations of a multiset of its inputs. Thus the natural question to ask of a class of memories is, what permutations can its members compute? We are particularly interested here in switchyard networks studied by Knuth [1968], Even and Itai [1971], and Tarjan [1972], where the permutations are of the set of inputs, rather than of a multiset of them. --- paper_title: The Solution of a Conjecture of Stanley and Wilf for All Layered Patterns paper_content: Proving a conjecture of Wilf and Stanley in hitherto the most general case, we show that for any layered patternqthere is a constantcso thatqis avoided by less thancnpermutations of lengthn. This will imply the solution of this conjecture for at least 2kpatterns of lengthk, for anyk. --- paper_title: Permutations generated by token passing in graphs paper_content: Abstract A transportation graph is a directed graph with a designated input node and a designated output node. Initially, the input node contains an ordered set of tokens 1,2,3, … The tokens are removed from the input node in this order and transferred through the graph to the output node in a series of moves; each move transfers a token from a node to an adjacent node. Two or more tokens cannot reside on an internal node simultaneously. When the tokens arrive at the output node they will appear in a permutation of their original order. The main result is a description of the possible arrival permutations in terms of regular sets. This description allows the number of arrival permutations of each length to be computed. The theory is then applied to packet-switching networks and has implications for the resequencing problem. It is also applied to some complex data structures and extends previously known results to the case that the data structures are of bounded capacity. A by-product of this investigation is a new proof that permutations which avoid the pattern 321 are in one to one correspondence with those that avoid 312. --- paper_title: Computing permutations with double-ended queues, parallel stacks and parallel queues paper_content: A memory may be regarded as a computer with input, output and storage facilities, but with no explicit functional capability. The only possible outputs are permutations of a multiset of its inputs. Thus the natural question to ask of a class of memories is, what permutations can its members compute? We are particularly interested here in switchyard networks studied by Knuth [1968], Even and Itai [1971], and Tarjan [1972], where the permutations are of the set of inputs, rather than of a multiset of them. --- paper_title: Permutations generated by token passing in graphs paper_content: Abstract A transportation graph is a directed graph with a designated input node and a designated output node. Initially, the input node contains an ordered set of tokens 1,2,3, … The tokens are removed from the input node in this order and transferred through the graph to the output node in a series of moves; each move transfers a token from a node to an adjacent node. Two or more tokens cannot reside on an internal node simultaneously. When the tokens arrive at the output node they will appear in a permutation of their original order. The main result is a description of the possible arrival permutations in terms of regular sets. This description allows the number of arrival permutations of each length to be computed. The theory is then applied to packet-switching networks and has implications for the resequencing problem. It is also applied to some complex data structures and extends previously known results to the case that the data structures are of bounded capacity. A by-product of this investigation is a new proof that permutations which avoid the pattern 321 are in one to one correspondence with those that avoid 312. --- paper_title: Sorting with two ordered stacks in series paper_content: The permutations that can be sorted by two stacks in series are considered, subject to the condition that each stack remains ordered. A forbidden characterisation of such permutations is obtained and the number of permutations of each length is determined by a generating function. --- paper_title: Sorting permutations with networks of stacks paper_content: Sea water contaminated with suspended organic matter and the like can be purified by expanding a mixture of the water with air so as to generate a froth, and letting the froth carrying the impurities overflow from a tubular riser attached to the expansion vessel while purified water is drawn off from the bottom of the vessel. The froth is stabilized by maintaining a layer of more alkaline liquid on the inner wall of the riser. The apparatus employed uses a rotating nozzle for continuously rinsing the wall of the riser with a liquid more alkaline than the froth, such as the original sea water prior to its being mixed with air. --- paper_title: Sorting with parallel pop - stacks paper_content: Detrimental color changes in liquid diphenylamine age-resisters have been inhibited and/or reduced by combining with the age-resisters small amounts of a color inhibitor selected from the group consisting of trialkylol amines, glycol diesters of sulphur-containing monocarboxylic acids, and dialkyl esters of thiodicarboxylic acids in a weight ratio of color inhibitor to age-resister of from 0.25:99.75 to 5:95. --- paper_title: Computing permutations with double-ended queues, parallel stacks and parallel queues paper_content: A memory may be regarded as a computer with input, output and storage facilities, but with no explicit functional capability. The only possible outputs are permutations of a multiset of its inputs. Thus the natural question to ask of a class of memories is, what permutations can its members compute? We are particularly interested here in switchyard networks studied by Knuth [1968], Even and Itai [1971], and Tarjan [1972], where the permutations are of the set of inputs, rather than of a multiset of them. --- paper_title: Raney Paths and a Combinatorial Relationship between Rooted Nonseparable Planar Maps and Two-Stack-Sortable Permutations paper_content: An encoding of the set of two-stack-sortable permutations (TSS) in terms of lattice paths and ordered lists of strings is obtained. These lattice paths are called Raney paths. The encoding yields combinatorial decompositions for two complementary subsets of TSS, which are the analogues of previously known decompositions for the set of nonseparable rooted planar maps (NS). This provides a combinatorial relationship between TSS and NS, and, hence, a bijection is determined between these sets that is different, simpler, and more refined than the previously known bijection. --- paper_title: A proof of Julian West's conjecture that the number of two-stacksortable permutations of length n is 2(3n)!/((n + 1)!(2n + 1)!) paper_content: Abstract The Polya-Schutzenberger-Tutte methodology of weight enumeration, combined with about 10 hours of CPU time (of Maple running on Drexel University's Sun network) established Julian West's conjecture that 2-stack-sortable permutations are enumerated by sequence # 651 in the Sloane listing. --- paper_title: Permutations with forbidden subsequences and nonseparable planar maps paper_content: The goal of the present work is to connect combinatorially a family of maps to a family of permutations with forbidden subsequences. We obtain a generating tree of nonseparable planar rooted maps and show that this tree is the generating tree of a family of permutations. The distribution of these permutations is then obtained. Finally, the different steps leading to the combinatorial proof of West's conjecture are listed. --- paper_title: Restricted permutations and the wreath product paper_content: Restricted permutations are those constrained by having to avoid subsequences ordered in various prescribed ways. A closed set is a set of permutations all satisfying a given basis set of restrictions. A wreath product construction is introduced and it is shown that this construction gives rise to a number of useful techniques for deciding the finite basis question and solving the enumeration problem. Several applications of these techniques are given. --- paper_title: Pattern Matching For Permutations paper_content: Given a permutation T of 1 to n, and a permutation P of 1 to k, for k ⩽ n, we wish to find a k-element subsequence of T whose elements are ordered according to the permutation P. For example, if P is (1, 2, …, k), then we wish to find an increasing subsequence of length k in T; this special case was done in time O(n log log n) by Chang and Wang. We prove that the general problem is NP-complete. We give a polynomial time algorithm for the decision problem, and the corresponding counting problem, in the case that P is separable — i.e., contains neither the subpattern (3, 1, 4, 2) nor its reverse, the subpattern (2, 4, 1, 3). --- paper_title: Sorted and/or sortable permutations paper_content: Abstract In his Ph.D. thesis, Julian West (Permutations with restricted subsequences and stack-sortable permutations, MIT, 1990) studied in depth a map Π that acts on permutations of the symmetric group S n by partially sorting them through a stack. The main motivation of this paper is to characterize and count the permutations of Π( S n ) , which we call sorted permutations. This is equivalent to counting preorders of increasing binary trees. We first find a local characterization of sorted permutations. Then, using an extension of Zeilberger's factorization of two-stack sortable permutations (D. Zeilberger, Discrete Math. 102 (1992) 85–93), we obtain for the generating function of sorted permutations an unusual functional equation. Out of curiosity, we apply the same treatment to four other families of permutations (general permutations, one-stack sortable permutations, two-stack sortable permutations, sorted and sortable permutations) and compare the functional equations we obtain. All of them have similar features, involving a divided difference. Moreover, most of them have interesting q-analogs obtained by counting inversions. We solve (some of) our equations. --- paper_title: A Bijective Census of Nonseparable Planar Maps paper_content: Bijections are obtained between nonseparable planar maps and two different kinds of trees: description trees and skew ternary trees. A combinatorial relation between the latter and ternary trees allows bijective enumeration and random generation of nonseparable planar maps. The involved bijections take account of the usual combinatorial parameters and give a bijective proof of formulae established by Brown and Tutte. These results, combined with a bijection due to Goulden and West, give a purely combinatorial enumeration of two-stack-sortable permutations. --- paper_title: Symmetry and Unimodality in t-Stack Sortable Permutations paper_content: We present the first nontrivial results on t-stack sortable permutations by constructively proving that the sequence Wt(n, k) of the numbers of t-stack sortable permutations with k descents is symmetric and unimodal. --- paper_title: On the Neggers–Stanley Conjecture and the Eulerian Polynomials paper_content: Abstract We prove combinatorially that the W -polynomials of naturally labeled graded posets of rank 1 or 2 (an antichain has rank 0) are unimodal, thus providing further supporting evidence for the Neggers–Stanley conjecture. For such posets we also obtain a combinatorial proof that the W -polynomials are symmetric. Combinatorial proofs that the Eulerian polynomials are log-concave and unimodal are given and we construct a simplicial complex Δ with the property that the Hilbert function of the exterior algebra modulo the Stanley–Reisner ideal of Δ is the sequence of Eulerian numbers, thus providing a combinatorial proof of a result of Brenti. --- paper_title: Hilbert Polynomials in Combinatorics paper_content: We prove that several polynomials naturally arising in combinatorics are Hilbert polynomials of standard graded commutative k-algebras. --- paper_title: Symmetry and Unimodality in t-Stack Sortable Permutations paper_content: We present the first nontrivial results on t-stack sortable permutations by constructively proving that the sequence Wt(n, k) of the numbers of t-stack sortable permutations with k descents is symmetric and unimodal. --- paper_title: On the Neggers–Stanley Conjecture and the Eulerian Polynomials paper_content: Abstract We prove combinatorially that the W -polynomials of naturally labeled graded posets of rank 1 or 2 (an antichain has rank 0) are unimodal, thus providing further supporting evidence for the Neggers–Stanley conjecture. For such posets we also obtain a combinatorial proof that the W -polynomials are symmetric. Combinatorial proofs that the Eulerian polynomials are log-concave and unimodal are given and we construct a simplicial complex Δ with the property that the Hilbert function of the exterior algebra modulo the Stanley–Reisner ideal of Δ is the sequence of Eulerian numbers, thus providing a combinatorial proof of a result of Brenti. --- paper_title: A simplicial complex of 2-stack sortable permutations paper_content: For each n, we construct a simplicial complex whose k-dimensional faces are in one-to-one correspondence with 2-stack sortable permutations of length n having k ascents. ---	Title: A Survey of Stack-Sorting Disciplines Section 1: Introduction Description 1: Provide an overview of the stack sorting problem, its historical context, and the motivation behind studying it. Introduce the main concepts and the scope of the survey. Section 2: Variations on a Single Stack Description 2: Discuss the different generalizations of the basic stack model, including deques, pop-stacks, (r, s)-stacks, and fork-stacks. Explain their sorting abilities and associated enumeration problems. Section 3: Systems of Stacks Description 3: Explore the models involving multiple stacks either in parallel or in series. Discuss key results and challenges in sorting with these systems, including known sorting algorithms and NP-completeness results. Section 4: Enumeration of West t-Stack-Sortable Permutations by Ascents Description 4: Review the enumeration of permutations sorted by West's t-stack model, focusing on properties such as unimodality, log-concavity, and real zeros. Discuss bijections with combinatorial structures like β(1, 0)-trees and related theorems. Section 5: Open Problems and Future Directions Description 5: Highlight unresolved questions and potential areas for future research in the context of stack-sorting and permutation patterns. Suggest new models or extensions to existing ones that could be explored.