title: SibylFS: formal specification and oracle-based testing for POSIX and real-world file systems

abstract: Systems depend critically on the behaviour of file systems, but that behaviour differs in many details, both between implementations and between each implementation and the POSIX (and other) prose specifications. Building robust and portable software requires understanding these details and differences, but there is currently no good way to systematically describe, investigate, or test file system behaviour across this complex multi-platform interface. In this paper we show how to characterise the envelope of allowed behaviour of file systems in a form that enables practical and highly discriminating testing. We give a mathematically rigorous model of file system behaviour, SibylFS, that specifies the range of allowed behaviours of a file system for any sequence of the system calls within our scope, and that can be used as a test oracle to decide whether an observed trace is allowed by the model, both for validating the model and for testing file systems against it. SibylFS is modular enough to not only describe POSIX, but also specific Linux, OS X and FreeBSD behaviours. We complement the model with an extensive test suite of over 21 000 tests; this can be run on a target file system and checked in less than 5 minutes, making it usable in practice. Finally, we report experimental results for around 40 configurations of many file systems, identifying many differences and some serious flaws.


Commonly used Neuropsychological Tests for Spanish Speakers: Normative Data from Latin America.


title: Memory-based learning for visual odometry

abstract: We present and examine a technique for estimating the ego-motion of a mobile robot using memory-based learning and a monocular camera. Unlike other approaches that rely heavily on camera calibration and geometry to compute trajectory, our method learns a mapping from sparse optical flow to platform velocity and turn rate. We also demonstrate an efficient method of computing high-quality sparse optical flow, and techniques for using this sparse optical flow as input to a supervised learning method. We employ a voting scheme of many learners that use subsets of the sparse optical flow to cope with variable dimensionality and reduce the dimensionality of each learner. Finally, we perform experiments in which we examine the learned mapping for visual odometry, investigate the effects of varying the reduced dimensionality of the sparse optical flow state, and quantify the accuracy of two variations of our learner scheme. Our results indicate that our learning scheme estimates monocular visual odometry mainly from points on the ground plane, and reflect to a degree the minimum dimensionality imposed by the problem. In addition, we show that while this memory-based learning method cannot yet estimate ego-motion as accurately as recent geometric methods, it is possible to learn, with no explicit model of camera calibration or scene structure, complicated mappings that take advantage of properties of the camera and the environment.


title: A focus+context technique based on hyperbolic geometry for visualizing large hierarchies

abstract: We present a new focus+context (fisheye) technique for visualizing and manipulating large hierarchies. Our technique assigns more display space to a portion of the hierarchy while still embedding it in the context of the entire hierarchy. The essence of this scheme is to lay out the hierarchy in a uniform way on a hyperbolic plane an d map this plane onto a circular display region. This supports a smooth blending between focus and context, as well as continuous redirection of the focus. We have developed effective procedures for manipulating the focus using pointer clicks as well as interactive dragging, and for smoothly an imating transitions across such manipulation. A laboratory experiment comparing the hyperbolic browser with a conventional hierarchy browser was conducted.


title: Overcurrent relay coordination by Differential Evolution algorithm

abstract: In this paper, three modified Differential Evolution algorithms based on local neighborhood search (LNS) is used to calculate the optimal relay settings of directional overcurrent relays in power systems. The process of LNS is done by exploring the vicinity of the particle having the best fitness function value (global best particle) with the help of mutation operators based on different probability distributions viz. Laplace, Cauchy and Gaussian. The proposed schemes named as LMDE, CMDE and GMDE help in enhancing the exploration and exploitation capabilities of DE without imposing any serious threat on the number of function evaluations. Two models are considered namely IEEE 3-bus model and IEEE 4-bus model to check the efficiency of the algorithms proposed in this paper. The results obtained by all the DE algorithms are compared with the basic Differential Evolution (DE) and eleven other algorithms available in the literature; the numerical results show that the proposed algorithms are able to provide superior results in comparison to the other algorithms.


title: Convolutional Neural Networks for Distant Speech Recognition

abstract: We investigate convolutional neural networks (CNNs) for large vocabulary distant speech recognition, trained using speech recorded from a single distant microphone (SDM) and multiple distant microphones (MDM). In the MDM case we explore a beamformed signal input representation compared with the direct use of multiple acoustic channels as a parallel input to the CNN. We have explored different weight sharing approaches, and propose a channel-wise convolution with two-way pooling. Our experiments, using the AMI meeting corpus, found that CNNs improve the word error rate (WER) by 6.5% relative compared to conventional deep neural network (DNN) models and 15.7% over a discriminatively trained Gaussian mixture model (GMM) baseline. For cross-channel CNN training, the WER improves by 3.5% relative over the comparable DNN structure. Compared with the best beamformed GMM system, cross-channel convolution reduces the WER by 9.7% relative, and matches the accuracy of a beamformed DNN.


title: An effective automatic speech emotion recognition for Tamil language using Support Vector Machine

abstract: In this paper we put effort on Effective Automatic Speech emotion recognition on Human computer Interaction for Tamil speech. Due to the unavailability of Tamil language database for emotion recognition, we built a database of emotional speech in Tamil. This database consists of 19 wave clips modulated with anger, joy, fear, neutral and sad. Then we extract cepstral based features like MFCC. A German Corpus (Berlin Database of Emotional Speech and speaker dependent and speaker independent Tamil emotional databases was used for training and classification using Support Vector Machine (SVM). Finally results are compared and explained.


title: Deep Knowledge Tracing On Programming Exercises

abstract: Modeling a student's knowledge state while she is solving exercises is a crucial stepping stone towards providing better personalized learning experiences at scale. This task, also referred to as "knowledge tracing", has been explored extensively on exercises where student submissions fall into a finite discrete solution space, e.g. a multiple-choice answer. However, we believe that rich information about a student's learning is captured within their responses to open-ended problems with unbounded solution spaces, such as programming exercises. In addition, sequential snapshots of a student's progress while she is solving a single exercise can provide valuable insights into her learning behavior. In this setting, creating representations for a student's knowledge state is a challenging task, but with recent advances in machine learning, there are more promising techniques to learn representations for complex entities. In our work, we feed the embedded program submissions into a recurrent neural network and train it on the task of predicting the student's success on the subsequent programming exercise. By training on this task, the model learns nuanced representations of a student's knowledge, and reliably predicts future student performance.


title: A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable Regularizers

abstract: High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than the sample size n. Since it is usually impossible to obtain consistent procedures unless $p/n\rightarrow0$, a line of recent work has studied models with various types of low-dimensional structure, including sparse vectors, sparse and structured matrices, low-rank matrices and combinations thereof. In such settings, a general approach to estimation is to solve a regularized optimization problem, which combines a loss function measuring how well the model fits the data with some regularization function that encourages the assumed structure. This paper provides a unified framework for establishing consistency and convergence rates for such regularized M-estimators under high-dimensional scaling. We state one main theorem and show how it can be used to re-derive some existing results, and also to obtain a number of new results on consistency and convergence rates, in both $\ell_2$-error and related norms. Our analysis also identifies two key properties of loss and regularization functions, referred to as restricted strong convexity and decomposability, that ensure corresponding regularized M-estimators have fast convergence rates and which are optimal in many well-studied cases.


title: Spent resources: Self-regulatory resource availability affects impulse buying

abstract: This research investigated impulse buying as resulting from the depletion of a common-but limited-resource that governs self-control. In three investigations, participants' self-regulatory resources were depleted or not; later, impulsive spending responses were measured. Participants whose resources were depleted, relative to participants whose resources were not depleted, felt stronger urges to buy, were willing to spend more, and actually did spend more money in unanticipated buying situations. Participants having depleted resources reported being influenced equally by affective and cognitive factors and purchased products that were high on each factor at equal rates. Hence, self-regulatory resource availability predicts whether people can resist impulse buying temptations. (c) 2007 by JOURNAL OF CONSUMER RESEARCH, Inc..


title: The psychometric function: I. Fitting, sampling, and goodness of fit

abstract: The psychometric function relates an observer’s performance to an independent variable, usually some physical quantity of a stimulus in a psychophysical task. This paper, together with its companion paper (Wichmann & Hill, 2001), describes an integrated approach to (1) fitting psychometric functions, (2) assessing the goodness of fit, and (3) providing confidence intervals for the function’s parameters and other estimates derived from them, for the purposes of hypothesis testing. The present paper deals with the first two topics, describing a constrained maximum-likelihood method of parameter estimation and developing several goodness-of-fit tests. Using Monte Carlo simulations, we deal with two specific difficulties that arise when fitting functions to psychophysical data. First, we note that human observers are prone to stimulus-independent errors (orlapses). We show that failure to account for this can lead to serious biases in estimates of the psychometric function’s parameters and illustrate how the problem may be overcome. Second, we note that psychophysical data sets are usually rather small by the standards required by most of the commonly applied statistical tests. We demonstrate the potential errors of applying traditionalX2 methods to psychophysical data and advocate use of Monte Carlo resampling techniques that do not rely on asymptotic theory. We have made available the software to implement our methods.


title: ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network

abstract: We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2, for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters. The performance of our network is evaluated on four different tasks: (1) object classification, (2) semantic segmentation, (3) object detection, and (4) language modeling. Experiments on these tasks, including image classification on the ImageNet and language modeling on the PenTree bank dataset, demonstrate the superior performance of our method over the state-of-the-art methods. Our network outperforms ESPNet by 4-5% and has 2-4x fewer FLOPs on the PASCAL VOC and the Cityscapes dataset. Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4.4% higher accuracy with 6x fewer FLOPs. Our experiments show that ESPNetv2 is much more power efficient than existing state-of-the-art efficient methods including ShuffleNets and MobileNets. Our code is open-source and available at https://github.com/sacmehta/ESPNetv2.


title: Branching tendon routing: A new tendon methodology for compact transmission

abstract: In this paper, we propose a new tendon routing method using branching tendon for driving a revolute joint. Unlike conventional tendon (or wire) widely used in the tendon transmission, the branching tendon splits into sub-tendons, and the end points of the sub-tendons are located on the same link but different positions. Thus, without using any complicated and bulky mechanism, the branching tendon can transmit the tendon force through the alternative path depending on the rotating angle of the joint. Based on this concept, the design of the branching tendon with two sub-tendons is proposed. Also, an analysis of the tension transmission path of this branching tendon is performed to increase force transmitting efficiency. By applying this branching tendon, a finger joint with a compact tendon routing path was constructed. And we have verified that the branching tendon can be used to develop robotic fingers or hand prosthetics.


title: The effect of dc poling duration on space charge relaxation in virgin XLPE cable peelings

abstract: The effect of dc poling time upon the time-dependent decay of space charge in insulation peelings of cross-linked polyethylene (XLPE) cable that had not previously experienced either electrical or thermal stressing is investigated. Two dc poling durations were used, 2 h and 26 h at an electric field of 50 kV mm−1 and at ambient temperature. Space charge was measured in the two samples investigated both during space charge accumulation and throughout its subsequent decay. The results show that the length of dc poling plays an important role in the subsequent decay. Despite the fact that both samples have had the same amount of space charge by the end of both short and long poling durations the time dependence of the space charge decay is different. Most of the charge stored in the sample that had experienced the short time poling decays rapidly after voltage removal. On the other hand, the charge that is stored in the sample with the long dc poling duration decays slowly and its decay occurs in two stages. The data, which are analysed by means of the de-trapping theory of space charge decay, imply that the charge stored in the material has occupied energy states with different trap depth ranges. The two poling durations lead to different relative amounts of charge in each of the two trap depth ranges. Possible reasons for this are discussed.


title: Informational bird's eye view system for parking assistance

abstract: Primary goals of Advanced Driver Assistance Systems (ADAS) are increased safety and situations with no stress for all traffic participants. Parking safety is improved by presenting real-time vehicle environment to the driver (surround view, bird's eye view). Bird's eye view is 3D camera and parking sensors system which assist driver with parking. It covers all angles and alerts the driver about obstacles, thus minimizing possibility of traffic accidents. This paper presents one portable informational bird's eye view system.


title: Social loafing: A meta-analytic review and theoretical integration.

abstract: Social loafing is the tendency for individuals to expend less effort when working collectively than when working individually. A meta-analysis of 78 studies demonstrates that social loafing is robust and generalizes across tasks and S populations. A large number of variables were found to moderate social loafing. Evaluation potential, expectations of co-worker performance, task meaningfulness, and culture had especially strong influence. These findings are interpreted in the light of a Collective Effort Model that integrates elements of expectancy-value, social identity, and self-validation theories. Many of life's most important tasks can only be accomplished in groups, and many group tasks are collective tasks that require the pooling of individual members' inputs. Government task forces, sports teams, organizational committees, symphony orchestras, juries, and quality control teams provide but a few examples of groups that combine individual efforts to form a single product. Because collective work settings are so pervasive and indispensable, it is important to determine which factors motivate and demotivate individuals within these collective contexts. Intuition might lead to the conclusion that working with others should inspire individuals to maximize their potential and work especially hard. Research on social loafing, however, has revealed that individuals frequently exert less effort on collective tasks than on individual tasks. Formally, social loafing is the reduction in motivation and effort when individuals work collectively compared with when they work individually or coactively. When working collectively, individuals work in the real or imagined presence of others with whom they combine their inputs to form a single group product. When working coactively, individuals work in the real or imagined presence of others, but their inputs are not combined with the inputs of others. Determining the conditions under which individuals do or do not engage in social loafing is a problem of both theoretical and practical importance. At a practical level, the identification of moderating variables may


title: Context Aware Power Management of Mobile Systems for Sensing Applications

abstract: The widespread adoption of mobile, yet powerful smartphones enable the development of distributed sensing applications. For such applications, the smartphone plays a central role by acting as a bridge between user worn sensors and a remote server. Smartphone energy consumption is a critical issue that should be considered. As the user’s daily context continually changes, the application can dynamically adapt its behavior to the user context to reduce the smartphone energy consumption. In this work, we propose to use context knowledge to dynamically adapt the behavior of sensing applications running on smartphones. In our system a context aware application manager starts, suspends and changes the sampling rate of a sensing application according to current user’s context. We tested our technique on a real system and we show that it is can extend smartphone lifetime by 5x.


title: Quantum-enhanced machine learning

abstract: The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.


title: An SH3 binding region in the epithelial Na+ channel (alpha rENaC) mediates its localization at the apical membrane.

abstract: The amiloride-sensitive Na+ channel constitutes the rate-limiting step for Na+ transport in epithelia. Immunolocalization and electrophysiological studies have demonstrated that this channel is localized at the apical membrane of polarized epithelial cells. This localization is essential for proper channel function in Na+ transporting epithelia. In addition, the channel has been shown to associate with the cytoskeletal proteins ankyrin and alpha-spectrin in renal epithelia. However, the molecular mechanisms underlying the cytoskeletal interactions and apical membrane localization of this channel are largely unknown. In this study we show that the putative pore forming subunit of the rat epithelial (amiloride-sensitive) Na+ channel (alpha rENaC) binds to alpha-spectrin in vivo, as determined by co-immunoprecipitation. This binding is mediated by the SH3 domain of alpha-spectrin which binds to a unique proline-rich sequence within the C-terminal region of alpha rENaC. Accordingly, the C-terminal region is sufficient to mediate binding to intact alpha-spectrin from alveolar epithelial cell lysate. When microinjected into the cytoplasm of polarized primary rat alveolar epithelial cells, a recombinant fusion protein containing the C-terminal proline-rich region of alpha rENaC localized exclusively to the apical area of the plasma membrane, as determined by confocal microscopy. This localization paralleled that of alpha-spectrin. In contrast, microinjected fusion protein containing the N-terminal (control) protein of alpha rENaC remained diffuse within the cytoplasm. These results suggest that an SH3 binding region in alpha rENaC mediates the apical localization of the Na+ channel. Thus, cytoskeletal interactions via SH3 domains may provide a novel mechanism for retaining proteins in specific membranes of polarized epithelial cells.


title: Person-organization fit and work-related attitudes and decisions: examining interactive effects with job fit and conscientiousness.

abstract: Abstract This study examined boundary conditions that surround the importance of perceived person-organization (P-O) fit for work-related attitudes and decisions. The authors hypothesized that P-O fit is more strongly related to satisfaction and job choice decisions when needs-supplies (N-S) job fit or demands-abilities (D-A) job fit is low, and that P-O fit is more strongly related to job choice decisions for highly conscientious individuals. Hypotheses were tested among 299 participants in a 12-week internship program. Results indicated that P-O fit was more strongly related to satisfaction when individuals experienced low N-S job fit. P-O fit was more strongly related to job choice intentions when individuals experienced low D-A job fit or were highly conscientious. Finally, P-O fit was related to job offer acceptance for highly conscientious individuals.


title: RRAM Crossbar Array With Cell Selection Device: A Device and Circuit Interaction Study

abstract: The resistive random access memory (RRAM) crossbar array has been extensively studied as one of the most promising candidates for future high-density nonvolatile memory technology. However, some problems caused by circuit and device interaction, such as sneak leakage paths, result in limited array size and large power consumption, which degrade the array performance significantly. Thus, the analysis on circuit and device interaction issue is imperative. In this paper, a simulation method is developed to investigate the critical issues correlated with the interaction between devices and the circuit. The simulations show that a large off/on ratio of resistance states of RRAM is beneficial for large readout margin (i.e., array size). The existence of the selector connected in series with an RRAM device can eliminate the need for high Ron resistance, which is critical for the array consisted of only RRAM cells. The readout margin is more sensitive to the variation of Ron and is determined by the nonlinearity of the I-V characteristics of RRAM, whereas the nonlinear characteristics of the selector device are beneficial for a larger readout margin. An optimal design scheme for turn-on voltage and conductance of the selector is proposed based on the simulation.


title: TILDE: A Temporally Invariant Learned DEtector

abstract: We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive. We first identify good keypoint candidates in multiple training images taken from the same viewpoint. We then train a regressor to predict a score map whose maxima are those points so that they can be found by simple non-maximum suppression. As there are no standard datasets to test the influence of these kinds of changes, we created our own, which we will make publicly available. We will show that our method significantly outperforms the state-of-the-art methods in such challenging conditions, while still achieving state-of-the-art performance on untrained standard datasets.


title: A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction

abstract: The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.


title: Instantaneous torque estimation in sensorless direct torque controlled brushless DC motors

abstract: A key issue in the direct torque control of permanent magnet brushless DC motors is the estimation of the instantaneous electromagnetic torque, while sensorless control is often advantageous. A sliding mode observer is employed to estimate the non-sinusoidal back-emf waveform, and a simplified extended Kalman filter is used to estimate the rotor speed. Both are combined to calculate the instantaneous electromagnetic torque, the effectiveness of this approach being validated by simulations and measurements.


title: CONCURRIT: a domain specific language for reproducing concurrency bugs

abstract: We present CONCURRIT, a domain-specific language (DSL) for reproducing concurrency bugs. Given some partial information about the nature of a bug in an application, a programmer can write a CONCURRIT script to formally and concisely specify a set of thread schedules to explore in order to find a schedule exhibiting the bug. Further, the programmer can specify how these thread schedules should be searched to find a schedule that reproduces the bug. We implemented CONCURRIT as an embedded DSL in C++, which uses manual or automatic source instrumentation to partially control the scheduling of the software under test. Using CONCURRIT, we were able to write concise tests to reproduce concurrency bugs in a variety of benchmarks, including the Mozilla's SpiderMonkey JavaScript engine, Memcached, Apache's HTTP server, and MySQL.


title: Comprehensive Study and Comparison on 5G NOMA Schemes

abstract: Compared to the traditional orthogonal multiple access, non-orthogonal multiple access (NOMA) technology can achieve higher spectrum efficiency and support more massive connectivity. In this paper, we conduct comprehensive study and comparison on current NOMA technologies that many mainstream companies have proposed for the fifth generation (5G) wireless communication standard. According to the characteristics of the NOMA schemes, we classify these schemes into four categories: scrambling-based NOMA, spreading-based NOMA, coding-based NOMA, and interleaving-based NOMA. We systematically summarize the transceiver block diagram of each category, and detail basic principles, key features, and transmission-reception algorithms of all NOMA schemes. Furthermore, the theoretical analysis based on average mutual information is given to evaluate the achievable sum-rate performance of the NOMA systems and their potential performance gains as compared with OMA. Comprehensive simulations are carried out for the block-error-rate performance evaluation of these NOMA schemes as well, which coincide with the theoretical analysis. By comparing the performance of these technologies, some promising schemes and directions are suggested for the future 5G NOMA development.


title: Sex differences in unipolar depression: evidence and theory

abstract: A large body of evidence indicates that women are more likely than men to show unipolar depression. Five classes of explanations for these sex differences are examined and the evidence for each class is reviewed. Not one of these explanations adequately accounts for the magnitude of the sex differences in depression. Finally, a response set explanation for the sex differences in depression is proposed. According to this explanation, men are more likely to engage in distracting behaviors that dampen their mood when depressed, but women are more likely to amplify their moods by ruminating about their depressed states and the possible causes of these states. Regardless of the initial source of a depressive episode (i.e., biological or psychological) men's more active responses to their negative moods may be more adaptive on average than women's less active, more ruminative responses. The epidemiology of a disorder can provide important clues to its etiology. When a disorder only strikes persons from one geographical region, one social class, or one gender, we can ask what characteristics of the vulnerable group might be making its members vulnerable. A frequent finding in epidemiological studies of mental disorders is that women are more prone to unipolar affective disorders than are men (Boyd & Weissman, 1981; Weissman & Klerman, 1977). A number of different explanations have been proposed to account for women's greater vulnerability to depression. Previous reviews of these explanations (e.g., Weissman & Klerman, 1977) have been quite brief and uncritical. In this article, the evidence for sex differences in unipolar depression first is summarized, then the most prominent explanations proposed for these sex differences are discussed in detail. These explanations include those attributing the differences to the response biases of subjects, as well as biological, psychoanalytic, sex role, and learned helplessness explanations. Although most of the proposed explanations for sex differences in depression have received some empirical support, not one of them has been definitively supported and not one as yet accounts for the magnitude of sex differences in depression. In the final section of this article it is suggested that differences in the ways that men and women respond to their own depressive episodes, whatever the origin of these episodes, may be an important source of the sex differences observed in depression.


title: Control of circulating current in two parallel three-phase boost rectifiers

abstract: One unique feature in parallel three-phase converters is a potential zero-sequence circulating current. To avoid the circulating current, most present technology uses an isolation approach, such as transformers or separate power supplies. This paper proposes a parallel system where individual converters connect both AC and DC sides directly without additional passive components to reduce size and cost of the overall parallel system. In this case, the control of the circulating current becomes an important objective in the converter design. This paper: (1) develops an averaged model of the parallel converters based on a phase-leg averaging technique; (2) a zero-sequence model is then developed to predict the dynamics of the zero-sequence current; (3) based on the zero-sequence model, this paper introduces a new control variable, which is associated with space-vector modulation; (4) a strong zero-sequence current control loop is designed to suppress the circulating current; and (5) simulation and experimental results validate the developed model and the proposed control scheme.


title: Satiety related to 24 h diet-induced thermogenesis during high protein/carbohydrate vs high fat diets measured in a respiration chamber

abstract: OBJECTIVE ::: Assessment of a possible relationship between perception of satiety and diet-induced thermogenesis, with different macronutrient compositions, in a controlled situation over 24 h. ::: ::: ::: DESIGN ::: Two diets with different macronutrient compositions were offered to all subjects in randomized order. ::: ::: ::: SETTING ::: The study was executed in the respiration chambers at the department of Human Biology, Maastricht University. ::: ::: ::: SUBJECTS ::: Subjects were eight females, ages 23-33 y, BMI 23+/-3 kg/m2, recruited from University staff and students. ::: ::: ::: INTERVENTIONS ::: Subjects were fed in energy balance, with protein/carbohydrate/fat: 29/61/10 and 9/30/61 percentage of energy, with fixed meal sizes and meal intervals, and a fixed activity protocol, during 36 h experiments in a respiration chamber. The appetite profile was assessed by questionnaires during the day and during meals. Diet induced thermogenesis was determined as part of the energy expenditure. ::: ::: ::: RESULTS ::: Energy balance was almost complete, with non-significant deviations. Diet-Induced-Thermogenesis (DIT) was 14.6+/-2.9%, on the high protein/carbohydrate diet, and 10.5+/-3.8% on the high fat diet (P < 0.01). With the high protein/high carbohydrate diet, satiety was higher during meals (P < 0.001; P < 0.05), as well as over 24 h (P < 0.001), than with the high fat diet. Within one diet, 24 h DIT and satiety were correlated (r = 0.6; P < 0.05). The difference in DIT between the diets correlated with the differences in satiety (r = 0.8; P < 0.01). ::: ::: ::: CONCLUSION ::: In lean women, satiety and DIT were synchronously higher with a high protein/high carbohydrate diet than with a high fat diet. Differences (due to the different macronutrient compositions) in DIT correlated with differences in satiety over 24 h.


title: A survey of results on mobile phone datasets analysis

abstract: In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.


title: A Cross-Domain Recommender System Based on Common-Sense Knowledge Bases

abstract: A system able to extract and recommend technical terms from various domains is proposed in this paper. The motivation is to provide keywords that users may not be familiar with in the beginning but will be interested in after studying. To acquire domain knowledge, we collect documents from various sources, and the words in the documents are then represented as semantic word vectors. Given queries from users, the system first extracts important terms from given documents and computes the semantic similarity between those terms. Next, we utilize third party common-sense knowledge bases such as ConceptNet and Wikipedia to connect the queries to those extracted keywords through the network structures. Finally, the system will collect all keywords traversed and recommend the top-n of them. We propose and compare four models for the recommendation, and the differences between using ConceptNet and Wikipedia for discovering related knowledge are also investigated in this work.


title: Effect of dynamic pruning safety on learning to rank effectiveness

abstract: A dynamic pruning strategy, such as WAND, enhances retrieval efficiency without degrading effectiveness to a given rank K, known as safe-to-rank-K. However, it is also possible for WAND to obtain more efficient but unsafe retrieval without actually significantly degrading effectiveness. On the other hand, in a modern search engine setting, dynamic pruning strategies can be used to efficiently obtain the set of documents to be re-ranked by the application of a learned model in a learning to rank setting. No work has examined the impact of safeness on the effectiveness of the learned model. In this work, we investigate the impact of WAND safeness through experiments using 150 TREC Web track topics. We find that unsafe WAND is biased towards documents with lower docids, thereby impacting effectiveness.


title: Dynamic selection of classifiers-A comprehensive review

abstract: This work presents a literature review of multiple classifier systems based on the dynamic selection of classifiers. First, it briefly reviews some basic concepts and definitions related to such a classification approach and then it presents the state of the art organized according to a proposed taxonomy. In addition, a two-step analysis is applied to the results of the main methods reported in the literature, considering different classification problems. The first step is based on statistical analyses of the significance of these results. The idea is to figure out the problems for which a significant contribution can be observed in terms of classification performance by using a dynamic selection approach. The second step, based on data complexity measures, is used to investigate whether or not a relation exists between the possible performance contribution and the complexity of the classification problem. From this comprehensive study, we observed that, for some classification problems, the performance contribution of the dynamic selection approach is statistically significant when compared to that of a single-based classifier. In addition, we found evidence of a relation between the observed performance contribution and the complexity of the classification problem. These observations allow us to suggest, from the classification problem complexity, that further work should be done to predict whether or not to use a dynamic selection approach.


title: The multidimensional wisdom of crowds

abstract: 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.


title: Quantifying the risk of project delays with a genetic algorithm

abstract: Of interest in project management is the (i) quantification of the risk associated with project performance and the (ii) identification of the project tasks that contribute most to that risk. Risk in this work addresses delays in project completion. The tasks and precendences are represented with nodes and links, respectively, in a project network whose tasks (i) have stochastic completion times that (ii) are subject to disruptions. An optimization problem is developed to maximize project delay subject to particular stochastic task disruptions, and a genetic algorithm is introduce to identify the critical tasks which lead to the maximum risk of project delay. A small project of 40 tasks and large project of 800 tasks are analyzed. Primary conclusions are (i) that critical tasks need not necessarily be on the critical path if they are subject to considerable uncertainty, and (ii) that project complexity (network topology) matters more in the performance of the algorithm than the number of tasks (network size). In fact, the genetic algorithm solution works well for large-scale projects whose schedules cannot be resolved with conventional techniques. Focus is given to the performance of the algorithm for this project risk context.


title: A realtime pattern generator for biped walking

abstract: For real-time walking control of a biped robot, we analyze the dynamics of a three-dimensional inverted pendulum whose motions are constrained onto an arbitrarily defined plane. This analysis leads us a simple linear dynamics, the Three-Dimensional Linear Inverted Pendulum Mode (3D-LIPM). Geometric nature of trajectories under the 3D-LIPM is discussed, and an algorithm for walking pattern generation is presented. Experimental results of real-time walking control of a 12-DOF biped robot HRP-2L using an input device such as a game pad are also shown.


title: Dynamics of rapid vertical climbing in cockroaches reveals a template

abstract: SUMMARY Rapid, vertically climbing cockroaches produced climbing dynamics similar ::: to geckos, despite differences in attachment mechanism, `foot or toe9 ::: morphology and leg number. Given the common pattern in such diverse species, ::: we propose the first template for the dynamics of rapid, legged climbing ::: analogous to the spring-loaded, inverted pendulum used to characterize level ::: running in a diversity of pedestrians. We measured single leg wall reaction ::: forces and center of mass dynamics in death-head cockroaches Blaberus ::: discoidalis , as they ascended a three-axis force plate oriented ::: vertically and coated with glass beads to aid attachment. Cockroaches used an ::: alternating tripod gait during climbs at 19.5±4.2 cm s -1 , ::: approximately 5 body lengths s -1 . Single-leg force patterns ::: differed significantly from level running. During vertical climbing, all legs ::: generated forces to pull the animal up the plate. Front and middle legs pulled ::: laterally toward the midline. Front legs pulled the head toward the wall, ::: while hind legs pushed the abdomen away. These single-leg force patterns ::: summed to generate dynamics of the whole animal in the frontal plane such that ::: the center of mass cyclically accelerated up the wall in synchrony with ::: cyclical side-to-side motion that resulted from alternating net lateral ::: pulling forces. The general force patterns used by cockroaches and geckos have ::: provided biological inspiration for the design of a climbing robot named RiSE ::: (Robots in Scansorial Environments).


Dynamic vehicle routing with time windows in theory and practice


title: ‘MusiMath’ and ‘Academic Music’ – Two music‐based intervention programs for fractions learning in fourth grade students

abstract: Music and mathematics require abstract thinking and use symbolic notations. Controversy exists regarding transfer from musical training to math achievements. The current study examined the effect of two integrated intervention programs representing holistic vs. acoustic approaches, on fraction knowledge. Three classes of fourth graders attended 12 lessons on fractions: One class attended the "MusiMath" holistic program (n=30) focusing on rhythm within the melody. Another class attended the "Academic Music" acoustic program (Courey et al., 2012) (n=25) which uses rhythm only. The third class received regular fraction lessons (comparison group, n=22). Students in both music programs learned to write musical notes and perform rhythmic patterns through clapping and drumming as part of their fraction lessons. They worked toward adding musical notes to produce a number (fraction), and created addition/subtraction problems with musical notes. The music programs used a 4/4 time signature with whole, half, quarter and eighth notes. In the math lessons, the students learned the analogy between musical durations and . 1 2 , 1 4 , 1 8 .fractions, but also practiced fractions other than 1 2 , 1 4 , 1 8 Music and math were assessed before, immediately following, and 3 and 6 months post-intervention. Pre- to post-intervention analyses indicated that only the "MusiMath" group showed greater transfer to intervention-trained and untrained fractions than the comparison group. The "Academic Music" group showed a trend on trained fractions. Although both music groups outperformed the comparison group 3- and 6-months post-intervention on trained fractions, only the "MusiMath" group demonstrated greater gains in untrained fractions. Gains were more evident in trained than in untrained fractions. This article is protected by copyright. All rights reserved.


title: Fossil chrysophycean cyst flora of Racze Lake, Wolin Island (Poland) in relation to paleoenvironmental conditions

abstract: The study presents stratigraphic distribution of fossil chrysophycean cysts in the bottom sediments of Racze Lake (Poland). Thirty morphotypes are described, most of them for the first time. The description of the cyst includes SEM microphotographs. The long-term relationship between the lake's conditions and the occurrence of characteristic morphotypes of chrysophycean cysts is discussed.


title: Information visualization- in regarding to customer journey map in a three-dimensions format

abstract: Customer journey map used in the service design was considered an important tool to discover service experiences related issues, since it could logically and contextually find problems and solves them. This article tried to conduct the idea of information visualization. and utilized the customer journey map in a three-dimensions format; therefore, designers not only can acquire information from 2D diagram, but also manipulate intangible information from this designed tool. Through 3 service design case studies, this article expects to develop various application tools, which allowing designers explore more opportunities gaps from users’ experiences.


title: Comparison of 22 kHz and 85 kHz 50 kW Wireless Charging System Using Si and SiC Switches for Electric Vehicle

abstract: Fast charging in transportation application requires high-power (>50 kW) operation. The overall size of wireless charging systems (WCS) must increase to meet the high-power requirement. As a result, high power systems produce more electromagnetic field (EMF) emissions. Although higher operating frequencies (85 kHz) reduce the size of inductors and capacitors, system losses tend to increase. High-power systems are traditionally investigated for 10 kHz to 22 kHz operation to reduce the switching loss in the converter, and conductive and magnetic losses in the power pad. The use of SiC MOSFETs reduces conduction and switching losses in the converter even at higher frequencies. In this paper, a comprehensive loss comparison of 50 kW wireless charging system is conducted for 85 kHz and 22 kHz operation of Si MOSFET and SiC MOSFET converters. Detailed performance indexes are compared analytically as a function of frequency and verified using FEA and PLECS. This comprehensive system level study shows that magnetic losses can be reduced significantly, and that EMF emission can be reduced by 50% by moving from 22 kHz to 85 kHz. Moreover, the system requires 50% less copper and allows a significant reduction in the size of the primary and secondary filter capacitor at 85 kHz. The results show that the system efficiency at 85 kHz can be increased by 21% compared to 22 kHz operation by using a SiC MOSFET converter.


title: 3D Reconstruction of Dynamic Textures in Crowd Sourced Data

abstract: We propose a framework to automatically build 3D models for scenes containing structures not amenable for photo-consistency based reconstruction due to having dynamic appearance. We analyze the dynamic appearance elements of a given scene by leveraging the imagery contained in Internet image photo-collections and online video sharing websites. Our approach combines large scale crowd sourced SfM techniques with image content segmentation and shape from silhouette techniques to build an iterative framework for 3D shape estimation. The developed system not only enables more complete and robust 3D modeling, but it also enables more realistic visualizations through the identification of dynamic scene elements amenable to dynamic texture mapping. Experiments on crowd sourced image and video datasets illustrate the effectiveness of our automated data-driven approach.


title: Automatic Vehicle type Classification with Convolutional Neural Networks

abstract: This paper proposes a convolutional neural network model for classification of vehicle types with low-resolution images from a frontal perspective. This characteristic can be useful to the development of systems with limited resources, like embedded systems. We trained the model as a multinomial logistic regression where cross-entropy of the ground truth labels and the model's prediction estimates the error. To prevent overfitting, we performed data augmentation in the training dataset and regularized the model using the dropout method. Experimental results in a subset of the BIT-Vehicle Dataset with samples uniformly distributed between classes shows that the model achieves an accuracy of 93.90%. We conclude that the model is discriminative and capable of generalizing the patterns of the vehicle type classification task.


title: GIS Cloud Computing Methodology

abstract: Geographic Information System (GIS) is the processes of managing, manipulating, analyzing, updating and presenting metadata according to its geographic location, to be effectively used in different aspects of life [1]. Cloud Computing allowed the utilization of all computing resources and software as required through the web in a virtual computing environment [2], [3]. Different Application software and data are provided at the (virtual) server side to be used. GIS Cloud is the future of Web GIS, with capabilities of easy and fast: collecting, processing, analyzing, updating, rectifying, publishing geospatial data through the internet. It is a web-based- GIS Application to enable all users fast, easy informed decision-making at fair price. It provides the power of desktop GIS on a web-based platform at fair cost anywhere. It offers a JavaScript Application Programming Interface (API) and a REST API, to provide GIS functionality into an application or website that can be hosted by GIS Cloud or by a third-party. In this work we are presenting a GIS-Cloud application in AL-Kamaliah region, small town of Amman suburbs to demonstrate its practicality and functionality.


title: Simply the Best: Minimalist System Trumps Complex Models in Author Profiling

abstract: A simple linear SVM with word and character n-gram features and minimal parameter tuning can identify the gender and the language variety (for English, Spanish, Arabic and Portuguese) of Twitter users with very high accuracy. All our attempts at improving performance by including more data, smarter features, and employing more complex architectures plainly fail. In addition, we experiment with joint and multitask modelling, but find that they are clearly outperformed by single task models. Eventually, our simplest model was submitted to the PAN 2017 shared task on author profiling, obtaining an average accuracy of 0.86 on the test set, with performance on sub-tasks ranging from 0.68 to 0.98. These were the best results achieved at the competition overall. To allow lay people to easily use and see the value of machine learning for author profiling, we also built a web application on top our models.


title: Are You Tampering With My Data?

abstract: We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.


title: Politeness and psychological distance: A construal level perspective

abstract: According to politeness theory (P. Brown & S. Levinson, 1987), politeness serves to both reflect and regulate social distance. On the basis of this notion and on construal level theory (N. Liberman & Y. Trope, 2008; N. Liberman, Y. Trope, & E. Stephan, 2007), it was predicted that politeness would be related to abstract construal, temporal distance, and spatial distance. Eight studies supported this prediction. Politeness increased when the addressees were construed abstractly (Study 1), were temporally distant (Studies 2, 3), and were spatially distant (Study 4). It was also found that increasing politeness produced abstract construals (Study 5), greater temporal distance (Study 6), and greater spatial distance (Study 7, 8). These findings shed light on the way politeness operates in different cultures and is conveyed in different languages, and they support the idea that dimensions of psychological distance are interrelated.


title: A Graph-based Approach for Rectangle Detection Using Corners

abstract: In this paper, a new rectangle detection approach is proposed. It is a bottom-up approach that contains four stages: line segment extraction, corner detection, corner-relation-graph generation and rectangle detection. Graph structure is used to construct the relations between corners and simplify the problem of rectangle detection. In addition, the approach can be extended to detect any polygons. Experiments on bin detection, traffic sign detection and license plate detection prove that the approach is robust.


title: Automated face extraction and normalization of 3D Mesh Data

abstract: 3D stereophotography is rapidly being adopted by medical researchers for analysis of facial forms and features. An essential step for many applications using 3D face data is to first crop the head and face from the raw images. The goal of this paper is to develop a reliable automatic methodology for extracting the face from raw data with texture acquired from a stereo imaging system, based on the medical researchers’ specific requirements. We present an automated process, including eye and nose estimation, face detection, Procrustes analysis and final noise removal to crop out the faces and normalize them. The proposed method shows very reliable results on several datasets, including a normal adult dataset and a very challenging dataset consisting of infants with cleft lip and palate.


title: Distributed leadership: developing theory from practice

abstract: Hopes that the transformation of schools lies with exceptional leaders have proved both unrealistic and unsustainable. The idea of leadership as distributed across multiple people and situations has proven to be a more useful framework for understanding the realities of schools and how they might be improved. However, empirical work on how leadership is distributed within more and less successful schools is rare. This paper presents key concepts related to distributed leadership and illustrates them with an empirical study in a school‐improvement context in which varying success was evident. Grounding the theory in this practice‐context led to the identification of some risks and benefits of distributing leadership and to a challenge of some key concepts presented in earlier theorizing about leadership and its distribution.


title: Clustering algorithms for bank customer segmentation

abstract: Market segmentation is one of the most important area of knowledge-based marketing. In banks, it is really a challenging task as data bases are large and multidimensional. In the paper we consider cluster analysis, which is the methodology, the most often applied in this area. We compare clustering algorithms in cases of high dimensionality with noise. We discuss using three algorithms: density based DBSCAN, k-means and based on it two-phase clustering process. We compare algorithms concerning their effectiveness and scalability. Some experiments with exemplary bank data sets are presented.


title: Energy efficiency optimization with energy harvesting using harvest-use approach

abstract: Energy harvesting is emerging as a promising approach to improve the energy efficiency (EE) and to extend the life of wireless networks. This paper focuses on energy-efficient transmission power allocation techniques for a point-to-point communication channel, equipped with a fixed-power battery, as well as a harvest-use battery. Using the fact that the harvested energy does not consume from the fixed battery, EE is formulated as the ratio of Shannon limit (as a function of the sum of the power consumed from the fixed battery and the harvest-use battery) to the sum of the circuit power and power consumed from the fixed battery. For the considered energy harvest-use technique, a time switching approach is used that in each frame, the node harvests energy for a percentage of frame time and transmits data for the rest of the frame time. Using the fact that the formulated EE is a quasi-concave function in transmission power, we use fractional programming to obtain the optimal power level, P u , and in-turn, the maximum achievable EE. Analytical derivations show that the maximum achievable EE monotonically increases with harvested power, whereas, P u monotonically decreases with it. Simulation results show the effects of harvested energy, fixed-battery power limit, and time switching rate on the maximum achievable EE.


title: Second Life in higher education: Assessing the potential for and the barriers to deploying virtual worlds in learning and teaching

abstract: Second Life (SL) is currently the most mature and popular multi-user virtual world platform being used in education. Through an in-depth examination of SL, this article explores its potential and the barriers that multi-user virtual environments present to educators wanting to use immersive 3-D spaces in their teaching. The context is set by tracing the history of virtual worlds back to early multi-user online computer gaming environments and describing the current trends in the development of 3-D immersive spaces. A typology for virtual worlds is developed and the key features that have made unstructured 3-D spaces so attractive to educators are described. The popularity in use of SL is examined through three critical components of the virtual environment experience: technical, immersive and social. From here, the paper discusses the affordances that SL offers for educational activities and the types of teaching approaches that are being explored by institutions. The work concludes with a critical analysis of the barriers to successful implementation of SL as an educational tool and maps a number of developments that are underway to address these issues across virtual worlds more broadly.


title: The hybrid OmniClimber robot: Wheel based climbing, arm based plane transition, and switchable magnet adhesion

abstract: Abstract Climbing robots that integrate an articulated arm as their main climbing mechanism can eventually take advantage of their arm for plane transition and thus to operate on 3D structures rather than only climbing planar surfaces. However, they are usually slower than wheel based climbing robots. Within this research we address this problem by integration of a light-weight arm and adhesion mechanism into an omnidirectional wheel based climbing robot, thus forming a hybrid mechanism that is agile in climbing and still able to perform plane transition. A 2DOF (Degree of Freedom) plannar mechanism with 2 linear actuators was designed as a light-weight manipulator for the transition mechanism. Furthermore, we customized and developed actuated switchable magnets both for the robot chassis and also as the adhesion unit of the arm. These units allow us to control the amount of magnetic adhesion force, resulting in better adaptation to different surface characteristics. The adhesion units are safe for climbing applications with a very small power consumption. The conceptual and the detailed design of the mechanisms are presented. The robots were developed and successfully tested on a ferromagnetic structure.


title: Reactive extraction of lignin from wood using supercritical ammonia-water mixtures

abstract: Supercritical Fluid (SCF) extraction was used to selectively remove lignin from yellow poplar wood (Liriodendron tulipifera L). The process may serve as an alternative to conventional delignification techniques practiced by the pulp and paper industry. Extraction experiments were performed by flowing supercritical ammonia-water mixtures through a fixed bed of poplar wood. Extraction of cellulose, hemicellulose, and lignin was determined as a function of time (0–3 h), solvent composition (0–20 wt % water), temperature (160–200 °C), and pressure (136–272 atm). The percentage of water in the solvent and temperature were found to be the most important variables in determining extraction efficiency. A simple model involving these variables was developed. The most effective extraction was obtained using 20 wt % water in ammonia at 272 atm and 200 °C, at these conditions about 70% of the lignin, 50% of hemicellulose, and 15% of the cellulose initially present in the wood were extracted in one hour.


title: The Living Challenges of Ambient Assisted Living - A Literature Review

abstract: Ambient Assisted Living (AAL) is a rapidly evolving research and development area propelled by scarcity of health resources caused by an aging workforce and increase of citizens in need of health care and health assistance on a regular basis. This paper presents a literature review of the current state-of-the-art of AAL. The objective is to point towards methodological actions to be taken into account in AAL research on this basis. Searches were conducted in five research databases. The search identified 86 papers. 10 of these papers were review papers chosen for analysis. The analysis presents an overview of the current status of AAL within the following categories: technology, users, application domains, rationales, successes and challenges of AAL. The paper concludes that the living part, i.e. the everyday practice of people living with Assistive Technology, is the primary challenge to the field due to its complexity of people and practices challenging the technological development and use. This calls for methods that support research carried out in close co-operations with the living environment and facilitate the complexity of stakeholders within the use-domain in a constructive and innovative manner. Living Lab is discussed as a general example on such methods.


title: Robust Speaker Recognition in Noisy Conditions

abstract: This paper investigates the problem of speaker identification and verification in noisy conditions, assuming that speech signals are corrupted by environmental noise, but knowledge about the noise characteristics is not available. This research is motivated in part by the potential application of speaker recognition technologies on handheld devices or the Internet. While the technologies promise an additional biometric layer of security to protect the user, the practical implementation of such systems faces many challenges. One of these is environmental noise. Due to the mobile nature of such systems, the noise sources can be highly time-varying and potentially unknown. This raises the requirement for noise robustness in the absence of information about the noise. This paper describes a method that combines multicondition model training and missing-feature theory to model noise with unknown temporal-spectral characteristics. Multicondition training is conducted using simulated noisy data with limited noise variation, providing a ldquocoarserdquo compensation for the noise, and missing-feature theory is applied to refine the compensation by ignoring noise variation outside the given training conditions, thereby reducing the training and testing mismatch. This paper is focused on several issues relating to the implementation of the new model for real-world applications. These include the generation of multicondition training data to model noisy speech, the combination of different training data to optimize the recognition performance, and the reduction of the model's complexity. The new algorithm was tested using two databases with simulated and realistic noisy speech data. The first database is a redevelopment of the TIMIT database by rerecording the data in the presence of various noise types, used to test the model for speaker identification with a focus on the varieties of noise. The second database is a handheld-device database collected in realistic noisy conditions, used to further validate the model for real-world speaker verification. The new model is compared to baseline systems and is found to achieve lower error rates.


Neglected Tropical Diseases among the Association of Southeast Asian Nations (ASEAN): Overview and Update


title: Classification by clustering using an extended saliency measure

abstract: In many data mining tasks, the goal is to classify entities into a set of pre-defined groups classes. A second and equally important goal is the interpretation, i.e. understanding the nature of the population aggregated in each class. These tasks are rendered even more complex when there is no a-priori information regarding the right classification.


title: NormAD - Normalized Approximate Descent based supervised learning rule for spiking neurons

abstract: NormAD is a novel supervised learning algorithm to train spiking neurons to produce a desired spike train in response to a given input. It is shown that NormAD provides faster convergence than state-of-the-art supervised learning algorithms for spiking neurons, often the gain in the rate of convergence being more than a factor of 10. The algorithm leverages the fact that a leaky integrate-and-fire neuron can be described as a non-linear spatio-temporal filter, allowing us to treat supervised learning as a mathematically tractable optimization problem with a cost function in terms of the membrane potential rather than the spike arrival time. A variant of stochastic gradient descent along with normalization has been used to derive the synaptic weight update rule. NormAD uses leaky integration of the input to determine the synaptic weight change. Since leaky integration is fundamental to all integrate-and-fire models of spiking neurons, we claim universal applicability of the learning rule to other models such as adaptive exponential integrate-and-fire model of neurons by demonstrating equally good performance in training with our algorithm.


title: Peer-to-peer architectures for massively multiplayer online games: A Survey

abstract: Scalability, fast response time, and low cost are of utmost importance in designing a successful massively multiplayer online game. The underlying architecture plays an important role in meeting these conditions. Peer-to-peer architectures, due to their distributed and collaborative nature, have low infrastructure costs and can achieve high scalability. They can also achieve fast response times by creating direct connections between players. However, these architectures face many challenges. Distributing a game among peers makes maintaining control over the game more complex. Peer-to-peer architectures also tend to be vulnerable to churn and cheating. Moreover, different genres of games have different requirements that should be met by the underlying architecture, rendering the task of designing a general-purpose architecture harder. Many peer-to-peer gaming solutions have been proposed that utilize a range of techniques while using somewhat different and confusing terminologies. This article presents a comprehensive overview of current peer-to-peer solutions for massively multiplayer games using a uniform terminology.


title: Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning

abstract: Recent advances in Generative Adversarial Learning allow for new modalities of image super-resolution by learning low to high resolution mappings. In this paper we present our work using Generative Adversarial Networks (GANs) with applications to overhead and satellite imagery. We have experimented with several state-of-the-art architectures. We propose a GAN-based architecture using densely connected convolutional neural networks (DenseNets) to be able to super-resolve overhead imagery with a factor of up to 8x. We have also investigated resolution limits of these networks. We report results on several publicly available datasets, including SpaceNet data and IARPA Multi-View Stereo Challenge, and compare performance with other state-of-the-art architectures.


title: Conditional Dynamic Mutual Information-Based Feature Selection

abstract: With emergence of new techniques, data in many fields are getting larger and larger, especially in dimensionality aspect. The high dimensionality of data may pose great challenges to traditional learning algorithms. In fact, many of features in large volume of data are redundant and noisy. Their presence not only degrades the performance of learning algorithms, but also confuses end-users in the post-analysis process. Thus, it is necessary to eliminate irrelevant features from data before being fed into learning algorithms. Currently, many endeavors have been attempted in this field and many outstanding feature selection methods have been developed. Among different evaluation criteria, mutual information has also been widely used in feature selection because of its good capability of quantifying uncertainty of features in classification tasks. However, the mutual information estimated on the whole dataset cannot exactly represent the correlation between features. To cope with this issue, in this paper we firstly re-estimate mutual information on identified instances dynamically, and then introduce a new feature selection method based on conditional mutual information. Performance evaluations on sixteen UCI datasets show that our proposed method achieves comparable performance to other well-established feature selection algorithms in most cases.


title: Learning Factorized Representations for Open-set Domain Adaptation

abstract: Domain adaptation for visual recognition has undergone great progress in the past few years. Nevertheless, most existing methods work in the so-called closed-set scenario, assuming that the classes depicted by the target images are exactly the same as those of the source domain. In this paper, we tackle the more challenging, yet more realistic case of open-set domain adaptation, where new, unknown classes can be present in the target data. While, in the unsupervised scenario, one cannot expect to be able to identify each specific new class, we aim to automatically detect which samples belong to these new classes and discard them from the recognition process. To this end, we rely on the intuition that the source and target samples depicting the known classes can be generated by a shared subspace, whereas the target samples from unknown classes come from a different, private subspace. We therefore introduce a framework that factorizes the data into shared and private parts, while encouraging the shared representation to be discriminative. Our experiments on standard benchmarks evidence that our approach outperforms the state of the art in open-set domain adaptation.


Deciphering Fashion Sensibility Using Community Detection


title: Superpixel-enhanced Pairwise Conditional Random Field for Semantic Segmentation

abstract: Superpixel-based Higher-order Conditional Random Fields (CRFs) are effective in enforcing long-range consistency in pixel-wise labeling problems, such as semantic segmentation. However, their major short coming is considerably longer time to learn higher-order potentials and extra hyperparameters and/or weights compared with pairwise models. This paper proposes a superpixel-enhanced pairwise CRF framework that consists of the conventional pairwise as well as our proposed superpixel-enhanced pairwise (SP-Pairwise) potentials. SP-Pairwise potentials incorporate the superpixel-based higher-order cues by conditioning on a segment filtered image and share the same set of parameters as the conventional pairwise potentials. Therefore, the proposed superpixel-enhanced pairwise CRF has a lower time complexity in parameter learning and at the same time it outperforms higher-order CRF in terms of inference accuracy. Moreover, the new scheme takes advantage of the pre-trained pairwise models by reusing their parameters and/or weights, which provides a significant accuracy boost on the basis of CRF-RNN even without training. Experiments on MSRC-21 and PASCAL VOC 2012 dataset confirm the effectiveness of our method.


title: MvSSIM: A quality assessment index for hyperspectral images

abstract: Quality assessment indexes play a fundamental role in the analysis of hyperspectral image (HSI) cubes. To assess the quality of an HSI cube, the structural similarity (SSIM) index has been widely applied in a band-by-band manner, as SSIM was originally designed for 2D images, and then the mean SSIM (MeanSSIM) index over all bands is adopted. MeanSSIM fails to accommodate the spectral structure which is a unique characteristic of HSI. Hence in this paper, we propose a new and simple multivariate SSIM (MvSSIM) index for HSI, by treating the pixel spectrum as a multivariate random vector. MvSSIM maintains SSIM’s ability to assess the spatial structural similarity via correlation between two images of the same band; and adds an ability to assess the spectral structural similarity via covariance among different bands. MvSSIM is well founded on multivariate statistics and can be easily implemented through simple sample statistics involving mean vectors, covariance matrices and cross-covariance matrices. Experiments show that MvSSIM is a proper quality assessment index for distorted HSIs with different kinds of degradations.


title: Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques

abstract: This study is an attempt to seek a relatively optimal data-driven model for rainfall forecasting from three aspects: model inputs, modeling methods, and data-preprocessing techniques. Four rain data records from different regions, namely two monthly and two daily series, are examined. A comparison of seven input techniques, either linear or nonlinear, indicates that linear correlation analysis (LCA) is capable of identifying model inputs reasonably. A proposed model, modular artificial neural network (MANN), is compared with three benchmark models, viz. artificial neural network (ANN), K-nearest-neighbors (K-NN), and linear regression (LR). Prediction is performed in the context of two modes including normal mode (viz., without data preprocessing) and data preprocessing mode. Results from the normal mode indicate that MANN performs the best among all four models, but the advantage of MANN over ANN is not significant in monthly rainfall series forecasting. Under the data preprocessing mode, each of LR, K-NN and ANN is respectively coupled with three data-preprocessing techniques including moving average (MA), principal component analysis (PCA), and singular spectrum analysis (SSA). Results indicate that the improvement of model performance generated by SSA is considerable whereas those of MA or PCA are slight. Moreover, when MANN is coupled with SSA, results show that advantages of MANN over other models are quite noticeable, particularly for daily rainfall forecasting. Therefore, the proposed optimal rainfall forecasting model can be derived from MANN coupled with SSA.


title: SoccerMate: A personal soccer attribute profiler using wearables

abstract: The use of smartphone and wearable devices in various sporting events is an optimistic opportunity to profile player's physical fitness and physiological health conditioning attributes. Recently a variety of commercial wearables with respect to different sports are available in the market. As these wearables differ for distinctive sports, it becomes a hassle to effectively profile them for multiple sports sessions in day to day practice events. Wrist worn devices like smartwatches are becoming a trend in sports analytics recently and researchers are leveraging them to infer various contexts of the players to improve the quality, tactics, strategy of playing matches against the opponents. Visual observation is the most popular way to track a player's abilities in soccer, but as a player it is not always possible to self-assess your own strengths and weaknesses in a field. In this paper, we propose to exploit the wrist worn devices with built in accelerometer to help represent attributes of technical judgement, tactical awareness and physical aspects of a soccer player. We propose to use deep learning to build our classification model which analyzes different soccer events like in-possession, pass, kick, sprint, run and dribbling. Based on these soccer events, we evaluate the overall ability of a soccer player. Our experiments show that, these wearable technology guided attributes profiling can help a coach or scout to better understand the competence of a player in addition to traditional visual observation.


title: Integrated microstrip and rectangular waveguide in planar form

abstract: Usually transitions from microstrip line to rectangular waveguide are made with three-dimensional complex mounting structures. In this paper, a new planar platform is developed in which the microstrip line and rectangular waveguide are fully integrated on the same substrate, and they are interconnected via a simple taper. Our experiments at 28 GHz show that an effective bandwidth of 12% at 20 dB return loss is obtained with an in-band insertion loss better than 0.3 dB. The new transition allows a complete integration of waveguide components on substrate with MICs and MMICs.


title: Semi-Supervised Learning via Compact Latent Space Clustering

abstract: We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and unlabeled samples of a training batch to capture underlying structure in feature space, and use label propagation to estimate its high and low density regions. We then devise a cost function based on Markov chains on the graph that regularizes the latent space to form a single compact cluster per class, while avoiding to disturb existing clusters during optimization. We evaluate our approach on three benchmarks and compare to state-of-the art with promising results. Our approach combines the benefits of graph-based regularization with efficient, inductive inference, does not require modifications to a network architecture, and can thus be easily applied to existing networks to enable an effective use of unlabeled data.


title: De-anonymizing social networks

abstract: The problem of de-anonymizing social networks is to identify the same users between two anonymized social networks [7] (Figure 1). Network de-anonymization task is of multifold significance, with user profile enrichment as one of its most promising applications. After the deanonymization and alignment, we can aggregate and enrich user profile information from different online networking services and make the bundled profiles available for end-users as well as thirdparty applications. In our project, we aim to develop effective algorithms for de-anonymizing real-world social networks. Specifically, we focus on two tasks: one is to align the networks of Flickr1 and Instagram2 and the other is to align Flickr and Twitter 3. Our work is motivated by the two parts of information that network data is composed of: network structure and node attributes. Preliminary tests have shown that de-anonymizing algorithm based merely on node attributes, e.g. user names, is computationally efficient but not satisfactorily accurate. On the other hand, algorithms that rely on network structures, which bring in more relationship information, may contribute to the precision of de-anonymization. However, not only may the structure of the real-world social networks be quite different, but also the computation costs will be intractably high since the maximum common


title: The Geometry of Culture: Analyzing Meaning through Word Embeddings

abstract: We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semant...


title: Survey of fraud detection techniques

abstract: Due to the dramatic increase of fraud which results in loss of billions of dollars worldwide each year, several modern techniques in detecting fraud are continually developed and applied to many business fields. Fraud detection involves monitoring the behavior of populations of users in order to estimate, detect, or avoid undesirable behavior. Undesirable behavior is a broad term including delinquency, fraud, intrusion, and account defaulting. This paper presents a survey of current techniques used in credit card fraud detection, telecommunication fraud detection, and computer intrusion detection. The goal of this paper is to provide a comprehensive review of different techniques to detect frauds.


title: Detecting malicious HTTP redirections using trees of user browsing activity

abstract: The web has become a platform that attackers exploit to infect vulnerable hosts, or deceive victims into buying rogue software. To accomplish this, attackers either inject malicious scripts into popular web sites or manipulate content delivered by servers to exploit vulnerabilities in users’ browsers. To hide malware distribution servers, attackers employ HTTP redirections, which automatically redirect users’ requests through a series of intermediate web sites, before landing on the final distribution site. In this paper, we develop a methodology to identify malicious chains of HTTP redirections. We build per-user chains from passively collected traffic and extract novel statistical features from them, which capture inherent characteristics from malicious redirection cases. Then, we apply a supervised decision tree classifier to identify malicious chains. Using a large ISP dataset, with more than 15K clients, we demonstrate that our methodology is very effective in accurately identifying malicious chains, with recall and precision values over 90% and up to 98%.


title: Find Duplicates among the PubMed, EMBASE, and Cochrane Library Databases in Systematic Review

abstract: Background: Finding duplicates is an important phase of systematic review. However, no consensus regarding the methods to find duplicates has been provided. This study aims to describe a pragmatic strategy of combining auto- and handsearching duplicates in systematic review and to evaluate the prevalence and characteristics of duplicates. Methods and Findings: Literatures regarding portal vein thrombosis (PVT) and Budd-Chiari syndrome (BCS) were searched by the PubMed, EMBASE, and Cochrane library databases. Duplicates included one index paper and one or more redundant papers. They were divided into type-I (duplicates among different databases) and type-II (duplicate publications in different journals/issues) duplicates. For type-I duplicates, reference items were further compared between index and redundant papers. Of 10936 papers regarding PVT, 2399 and 1307 were identified as auto- and hand-searched duplicates, respectively. The prevalence of auto- and hand-searched redundant papers was 11.0% (1201/10936) and 6.1% (665/10936), respectively. They included 3431 type-I and 275 type-II duplicates. Of 11403 papers regarding BCS, 3275 and 2064 were identified as auto- and hand-searched duplicates, respectively. The prevalence of auto- and hand-searched redundant papers was 14.4% (1640/11403) and 9.1% (1039/11403), respectively. They included 5053 type-I and 286 type-II duplicates. Most of type-I duplicates were identified by auto-searching method (69.5%, 2385/3431 in PVT literatures; 64.6%, 3263/5053 in BCS literatures). Nearly all type-II duplicates were identified by hand-searching method (94.9%, 261/275 in PVT literatures; 95.8%, 274/286 in BCS literatures). Compared with those identified by auto-searching method, type-I duplicates identified by handsearching method had a significantly higher prevalence of wrong items (47/2385 versus 498/1046, p,0.0001 in PVT literatures; 30/3263 versus 778/1790, p,0.0001 in BCS literatures). Most of wrong items originated from EMBASE database. Conclusion: Given the inadequacy of a single strategy of auto-searching method, a combined strategy of auto- and handsearching methods should be employed to find duplicates in systematic review.


title: A tutorial on simulation modeling in six dimensions

abstract: Simulation involves modeling and analysis of real-world systems. This tutorial will provide a broad overview of the modeling practice within simulation by introducing the reader to modeling choices found using six dimensions: abstraction, complexity, culture, engineering, environment, and process. Modeling can be a daunting task even for the seasoned modeling and simulation professional, and so my goal is to introduce modeling in two ways: 1) to use one specific type of model (Petri Net) as an anchor for cross-dimensional discussion, and 2) to provide a follow up discussion, with additional non Petri Net examples, to clarify the extent of each dimension. For example, in the abstraction dimension, one must think about scale, refinement, and hierarchy when modeling regardless of the type of modeling language. The reader will come away with a broad framework within which to understand the possibilities of models and of modeling within the practice of simulation.


title: A Review of Bayesian Networks and Structure Learning

abstract: This article reviews the topic of Bayesian networks. A Bayesian network is a factorisation of a probability distribution along a directed acyclic graph. The relation between graphical d-separation and independence is described. A short article by Arthur Cayley (1853) [7] is discussed, which laid ideas later used in Bayesian networks: factorisation, the noisy `or' gate, applications of algebraic geometry to Bayesian networks. The ideas behind Pearl's intervention calculus when the DAG represents a causal dependence structure; the relation between the work of Cayley and Pearl is commented on. Most of the discussion is about structure learning, outlining the two main approaches; search and score versus constraint based. Constraint based algorithms often rely on the assumption of faithfulness, that the data to which the algorithm is applied is generated from distributions satisfying a faithfulness assumption where graphical d- separation and independence are equivalent. The article presents some considerations for constraint based algorithms based on recent data analysis, indicating a variety of situations where the faithfulness assumption does not hold.


title: Robust relative pose estimation with integrated cheirality constraint

abstract: The cheirality constraint, which requires that reconstructed point correspondences lie in front of the cameras, has not typically been integrated into traditional RANSAC-based pose estimators. We have developed a new RANSAC-based relative pose estimator which incorporates the cheirality constraint not only to preempt invalid epipolar geometry hypotheses, but also as a criterion in identifying inliers for image feature correspondences. Because the application of the cheirality constraint is tightly related to the estimation of epipolar geometry, integrating them inside RANSAC can prevent inliers being falsely identified as cheirality outliers. The result is a more consistent and stable estimation which leaves denser feature correspondences for subsequent processing. Experimental comparison between the usual RANSAC-based approach and the proposed approach is performed.


title: Potential Neuroprotective Activity of Ginseng in Parkinson’s Disease: A Review

abstract: Parkinson’s disease is a chronic, multifactorial and progressive neurologic condition that affects around six million people worldwide, normally over 60 years of age, and is characterized by neurodegeneration of dopaminergic neurons in the substantia nigra. The species of the genus Panax, popularly named as “Ginseng”, are widely used as herbal remedies for their multiple beneficial effects, including their neurotherapeutic efficacies as protectors against major neurodegenerative diseases. The current review aims to report major findings and current knowledge on Ginseng and its major constituents as potential neuroprotective agents against Parkinson’s disease, focusing on its mechanisms of action and molecular targets. For that purpose, it includes all research works published in MEDLINE/PubMed within the last decade by utilizing the following combination of the keywords: “Ginseng, ginsenosides, neuroprotection and Parkinson’s disease”. As reported, most of the studies have been carried out on isolated compounds rather than extracts. The major ginsenosides investigated as neuroprotector agents for Parkinson’s disease are Rb1, Rg1, Rd and Re. Other minor components such as Notoginsenoside R2 and Pseudoginsenoside-F11 have also attracted remarkable interest as promising antiparkinson agents. These compounds exert their neuroprotective activity through different mechanisms including, among others, inhibition of oxidative stress and neuroinflammation, decrease in toxins-induced apoptosis and nigral iron levels, and regulation of N-methyl-D-aspartate receptor channel activity.


title: 40-Gb/s amplifier and ESD protection circuit in 0.18-/spl mu/m CMOS technology

abstract: A triple-resonance LC network increases the bandwidth of cascaded differential pairs by a factor of 2/spl radic/3, yielding a 40-Gb/s CMOS amplifier with a gain of 15 dB and a power dissipation of 190 mW from a 2.2-V supply. An ESD protection circuit employs negative capacitance along with T-coils and pn junctions to operate at 40 Gb/s while tolerating 700-800 V.


title: The Reactive Tabu Search

abstract: We propose an algorithm for combinatorial optimization where an explicit check for the repetition of configurations is added to the basic scheme of Tabu search. In our Tabu scheme the appropriate size of the list is learned in an automated way by reacting to the occurrence of cycles. In addition, if the search appears to be repeating an excessive number of solutions excessively often, then the search is diversified by making a number of random moves proportional to a moving average of the cycle length. The reactive scheme is compared to a “strict” Tabu scheme that forbids the repetition of configurations and to schemes with a fixed or randomly varying list size. From the implementation point of view we show that the Hashing or Digital Tree techniques can be used in order to search for repetitions in a time that is approximately constant. We present the results obtained for a series of computational tests on a benchmark function, on the 0-1 Knapsack Problem, and on the Quadratic Assignment Problem. INFORMS Journal on Computing , ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.


title: Design and characterization of a compact rotary Series Elastic Actuator for knee assistance during overground walking

abstract: In wearable robotics applications, actuators are required to satisfy strict constraints in terms of safety and controllability. The introduction of intrinsic compliance can help to meet both these requirements. However, the high torque and power necessary for robotic systems for gait assistance requires the use of custom elements, able to guarantee high performances with a compact and lightweight design. This paper presents a rotary Series Elastic Actuator (SEA), suitable to be used in an active orthosis for knee assistance during overground walking. The system includes a commercial flat brushless DC motor, a Harmonic Drive gear and a custom-designed torsion spring. Spring design has been optimized by means of an iterative FEM simulations-based process and can be directly connected to the output shaft, thus guaranteeing high torque fidelity. With a total weight of 1.8 kg, it is possible to directly include the actuator in the frame of a wearable orthosis for knee flexion/extension assistance. The presented design allows to obtain a large-force bandwidth of 5 Hz and to regulate output impedance in a range compatible to locomotion assistance of elderly subjects with an age-related decay of motor performances.


title: #Snowden: Understanding Biases Introduced by Behavioral Differences of Opinion Groups on Social Media

abstract: We present a study of 10-month Twitter discussions on the controversial topic of Edward Snowden. We demonstrate how behavioral differences of opinion groups can distort the presence of opinions on a social media platform. By studying the differences between a numerical minority (anti-Snowden) and a majority (pro-Snowden) group, we found that the minority group engaged in a "shared audiencing" practice with more persistent production of original tweets, focusing increasingly on inter-personal interactions with like-minded others. The majority group engaged in a "gatewatching" practice by disseminating information from the group, and over time shifted further from making original comments to retweeting others'. The findings show consistency with previous social science research on how social environment shapes majority and minority group behaviors. We also highlight that they can be further distorted by the collective use of social media design features such as the "retweet" button, by introducing the concept of "amplification'" to measure how a design feature biases the voice of an opinion group. Our work presents a warning to not oversimplify analysis of social media data for inferring social opinions.


title: Performance enhancement and IoT based monitoring for smart home

abstract: In recent years, home automation has become so popular due to its numerous advantages. The home environment has witnessed a rapid introduction of network enabled digital technology. This technology comes with new and exciting opportunities to increase the connectivity of different devices within the home for the purpose of home automation. This paper aims at designing a basic home automation system of controlling multiple appliances which can be monitored and accessed from anywhere in the world with very low cost. The technology incorporates Raspberry Pi and the web server. The Raspberry Pi and Arduino integrated with Nrf modules are used to monitor the home environment appliances, and the readings are passed to the web server designed. The parameters or commands sent through web page are monitored frequently and if any threats found the mobile connected to this web server is alerted through an alarm or message. The user can access this application from anywhere in the world. The result produced is low cost advantageous and absolute. Performance Analysis of different protocols (MQTT, HTTP and CoAP) is estimated using visualizations.


title: Real-time monitoring of machines using Open Platform Communication

abstract: Open Platform Communication (OPC) also known as Object Linking and Embedding (OLE) for Process Control (OPC) is the platform for communication in automation and manufacturing fields among the group of networked devices. OPC standard supports the group of specifications known as OPC-Data Access (OPC-DA), OPC-Historical Data Access (OPC-HDA) and OPC-Unified Architecture (OPC-UA). In this paper, the vibration and acoustic data of an air-compressor have been recorded through accelerometer and microphone, respectively. The data is continuously monitored and archived through OPC. The sensing units are placed on predefined sensitive positions of air-compressor to record the data. The MATLAB based platform interacts with OPC server for reading and writing of data acquired from field devices. The recorded data is used for real-time monitoring and fault recognition in air-compressor. The National Instruments (NI) based four channel Data Acquisition (DAQ) module, channel input module: NI9234 and USB chassis: NIcDAQ-9171, is used for connection of sensing units. For experimental analysis, a Matrikon OPC server (2.0.0.5529) is used. MATLAB and OPC server with necessary features have been installed on the operating system to monitor the air-compressor.