title: Credit Assignment Techniques in Stochastic Computation Graphs

abstract: Stochastic computation graphs (SCGs) provide a formalism to represent structured optimization problems arising in artificial intelligence, including supervised, unsupervised, and reinforcement learning. Previous work has shown that an unbiased estimator of the gradient of the expected loss of SCGs can be derived from a single principle. However, this estimator often has high variance and requires a full model evaluation per data point, making this algorithm costly in large graphs. In this work, we address these problems by generalizing concepts from the reinforcement learning literature. We introduce the concepts of value functions, baselines and critics for arbitrary SCGs, and show how to use them to derive lower-variance gradient estimates from partial model evaluations, paving the way towards general and efficient credit assignment for gradient-based optimization. In doing so, we demonstrate how our results unify recent advances in the probabilistic inference and reinforcement learning literature.


Sequential Support Vector Classifiers and Regression


title: GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues

abstract: In the face of the broad political call for an “energy turnaround”, we are currently witnessing three essential trends with regard to energy infrastructure planning, energy generation and storage: from planned production towards fluctuating production on the basis of renewable energy sources, from centralized generation towards decentralized generation and from expensive energy carriers towards cost-free energy carriers. These changes necessitate considerable modifications of the energy infrastructure. Even though most of these modifications are inherently motivated by geospatial questions and challenges, the integration of energy system models and Geographic Information Systems (GIS) is still in its infancy. This paper analyzes the shortcomings of previous approaches in using GIS in renewable energy-related projects, extracts distinct challenges from these previous efforts and, finally, defines a set of core future research avenues for GIS-based energy infrastructure planning with a focus on the use of renewable energy. These future research avenues comprise the availability base data and their “geospatial awareness”, the development of a generic and unified data model, the usage of volunteered geographic information (VGI) and crowdsourced data in analysis processes, the integration of 3D building models and 3D data analysis, the incorporation of network topologies into GIS, the harmonization of the heterogeneous views on aggregation issues in the fields of energy and GIS, fine-grained energy demand estimation from freely-available data sources, decentralized storage facility planning, the investigation of GIS-based public participation mechanisms, the transition from purely structural to operational planning, data privacy aspects and, finally, the development of a new dynamic power market design.


title: Image analysis by bidimensional empirical mode decomposition

abstract: Recent developments in analysis methods on the non-linear and non-stationary data have received large attention by the image analysts. In 1998, Huang introduced the empirical mode decomposition (EMD) in signal processing. The EMD approach, fully unsupervised, proved reliable monodimensional (seismic and biomedical) signals. The main contribution of our approach is to apply the EMD to texture extraction and image filtering, which are widely recognized as a difficult and challenging computer vision problem. We developed an algorithm based on bidimensional empirical mode decomposition (BEMD) to extract features at multiple scales or spatial frequencies. These features, called intrinsic mode functions, are extracted by a sifting process. The bidimensional sifting process is realized using morphological operators to detect regional maxima and thanks to radial basis function for surface interpolation. The performance of the texture extraction algorithms, using BEMD method, is demonstrated in the experiment with both synthetic and natural images.


title: A Survey of Game Theory in Wireless Sensor Networks Security

abstract: Wireless Sensor Networks (WSNs) are becoming an integral part of our lives. There are not widespread applications of WSNs without ensuring WSNs security. Due to the limited capabilities of sensor nodes in terms of computation, communication, and energy, providing security to WSNs is challenging. In fact, the process of implementing WSNs security is adaptive and dynamic, which evolves continually. The essence of attack-defend in WSNs security can be expressed by mutual strategies of interdependence while game theory can be used for the purpose of accounting for interactions among strategies of rational decision makers. Therefore, studying WSNs security with game theory has higher scientificity and rationality. This paper presents a survey of security approaches based on game theory in WSNs. According to different applications, a taxonomy is proposed, which divides current existing typical game-theoretic approaches for WSNs security into four categories: preventing Denial of Services (DoS) attacks, intrusion detection, strengthening security, and coexistence with malicious sensor nodes. The main ideas of each approach are overviewed while advantages and disadvantages of various approaches are discussed. Then, this paper overviews related work and highlights the difference from other surveys, and points out some future research areas for ensuring WSNs security based on game theory, including Base Station (BS) credibility, Intrusion Detection System (IDS) efficiency, WSNs mobility, WSNs Quality of Service (QoS), real-world applicability, energy consumption, sensor nodes learning, and expanding game theory applications and different games. Thus, a global view of WSNs security approaches based on game theory is provided. To our best knowledge of knowing, it is the first paper centrally focusing on game theory in WSNs security. It will make the researchers a better understanding of game-theoretic solutions to WSNs security and further research directions.


title: Understanding Dynamic Social Grouping Behaviors of Pedestrians

abstract: There have been many studies in the literature on social group recognition of crowds of pedestrians. However, most of these studies have approached the problem from a static point of view. A study on the dynamic property of social groups among people over time can provide significant insight into human behaviors and events. Inspired by sociological models of human collective behavior, in this work, we present a framework for characterizing hierarchical social groups based on evolving tracklet interaction network (ETIN) where the tracklets of pedestrians are represented as nodes and the their grouping behaviors are captured by the edges with associated weights. We use non-overlapping snapshots of the interaction network and develop the framework for a unified dynamic group identification and tracklet association. The approach is evaluated quantitatively and qualitatively on videos of pedestrian scenes where manually labeled ground-truth is given. The results of our approach are consistent to human-perceived dynamic social groups of the crowd. The performance analysis of our method shows that the approach is scalable and it provides situational awareness in a real-world scenarios.


title: A Tensor-Based Information Framework for Predicting the Stock Market

abstract: To study the influence of information on the behavior of stock markets, a common strategy in previous studies has been to concatenate the features of various information sources into one compound feature vector, a procedure that makes it more difficult to distinguish the effects of different information sources. We maintain that capturing the intrinsic relations among multiple information sources is important for predicting stock trends. The challenge lies in modeling the complex space of various sources and types of information and studying the effects of this information on stock market behavior. For this purpose, we introduce a tensor-based information framework to predict stock movements. Specifically, our framework models the complex investor information environment with tensors. A global dimensionality-reduction algorithm is used to capture the links among various information sources in a tensor, and a sequence of tensors is used to represent information gathered over time. Finally, a tensor-based predictive model to forecast stock movements, which is in essence a high-order tensor regression learning problem, is presented. Experiments performed on an entire year of data for China Securities Index stocks demonstrate that a trading system based on our framework outperforms the classic Top-N trading strategy and two state-of-the-art media-aware trading algorithms.


title: Decoding algorithm for color QR code: A mobile scanner application

abstract: QR codes have revolutionized the industry because of its larger storage capacity and high damage resistance than the traditional barcodes. Color QR code is the future as it provides much higher encoding capacity that the typical B/W QR codes. We propose a color QR code decoding algorithm for mobile applications by considering the color profile of Red, Green and Blue channels respectively. We have developed a prototype by including various image processing techniques and the open source Zxing library. This paper presents an Android based color QR code decoder implementation and performance evaluation. Experimental results show the high success rate of the methodology implemented.


title: Implementation of an inexpensive EEG headset for the pattern recognition purpose

abstract: There are many types of bio-signals with various control application prospects. In this work possible control application domain of electroencephalographic signal obtained from an easily available, inexpensive EEG headset - Emotiv EPOC was presented. This work also involved application of an embedded system platform. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. Potential implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. In this work traditional, statistical methods were neither used nor described.


title: A Logarithmic Transmission Line Chart

abstract: A chart is presented which relates the real and imaginary components of the impedanceat any position along a transmission line to the magnitude and location of the standing wave. In the present chart the ordinate is R/Z/sub 0/ plotted logarithmically and the abscissa is a function of X/R. Thus a change in the reference impedance becomes a simple vertical translation of any point. An auxiliary chart permits the direct determination of the length and impedance of transmission line required to match a given impedance.


title: An HMM-Based Threshold Model Approach for Gesture Recognition

abstract: A new method is developed using the hidden Markov model (HMM) based technique. To handle nongesture patterns, we introduce the concept of a threshold model that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture patterns. The threshold model is a weak model for all trained gestures in the sense that its likelihood is smaller than that of the dedicated gesture model for a given gesture. Consequently, the likelihood can be used as an adaptive threshold for selecting proper gesture model. It has, however, a large number of states and needs to be reduced because the threshold model is constructed by collecting the states of all gesture models in the system. To overcome this problem, the states with similar probability distributions are merged, utilizing the relative entropy measure. Experimental results show that the proposed method can successfully extract trained gestures from continuous hand motion with 93.14% reliability.


title: Error Control and Concealment for Video Communication: A Review

abstract: The problem of error control and concealment in video communication is becoming increasingly important because of the growing interest in video delivery over unreliable channels such as wireless networks and the Internet. This paper reviews the techniques that have been developed for error control and concealment. These techniques are described in three categories according to the roles that the encoder and decoder play in the underlying approaches. Forward error concealment includes methods that add redundancy at the source end to enhance error resilience of the coded bit streams. Error concealment by postprocessing refers to operations at the decoder to recover the damaged areas based on characteristics of image and video signals. Last, interactive error concealment covers techniques that are dependent on a dialogue between the source and destination. Both current research activities and practice in international standards are covered.


title: A new isolated multi-port converter using interleaving and magnetic coupling inductor technologies

abstract: This paper proposes a new multi-port converter with interleaved magnetic coupling technologies. With the focus on the impedance behavior of coupled magnetic components and the control parameters, this multi-port converter integrates one isolated dc-dc converter and two multi-phase boost converters, and controls these topologies independently. The proposed converter has the capability to connect four dc applications or sources in total, and so brings significant cost reduction and improves the total loss consumption of power systems. In addition, magnetic isolation brings high reliability of complex power systems such as electric vehicles or grid-connected industries. In this paper, the basic circuit behavior is verified by theoretical considerations and experiments. An example of the proposed circuit as a multifunctional dc power system is demonstrated with the simulation results. Such an approach could bring about variable application fields to dc electric power systems.


A Fast Multi-Dimensional Algorithm for Drawing Large Graphs ?


title: Functional connectivity in the motor cortex of resting human brain using echo-planar mri

abstract: An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (<0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10−3) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.


title: Boosting a heterogeneous pool of fast HOG features for pedestrian and sign detection

abstract: This paper presents a fast Histogram of Oriented Gradients (HOG) based weak classifier that is extremely fast to compute and highly discriminative. This feature set has been developed in an effort to balance the required processing and memory bandwidth so as to eliminate bottlenecks during run time evaluation. The feature set is the next generation in a series of features based on a novel precomputed image for HOG based features. It contains features which are more balanced in terms of processing and memory requirements than its predecessors, has a larger and richer feature space, and is more discriminant on a per feature basis.


title: Click Through Rate Prediction for Local Search Results

abstract: With the ubiquity of internet access and location services provided by smartphone devices, the volume of queries issued by users to find products and services that are located near them is rapidly increasing. Local search engines help users in this task by matching queries with a predefined geographical connotation ("local queries") against a database of local business listings. Local search differs from traditional web-search because to correctly capture users' click behavior, the estimation of relevance between query and candidate results must be integrated with geographical signals, such as distance. The intuition is that users prefer businesses that are physically closer to them. However, this notion of closeness is likely to depend upon other factors, like the category of the business, the quality of the service provided, the density of businesses in the area of interest, etc. In this paper we perform an extensive analysis of online users' behavior and investigate the problem of estimating the click-through rate on local search (LCTR) by exploiting the combination of standard retrieval methods with a rich collection of geo and business-dependent features. We validate our approach on a large log collected from a real-world local search service. Our evaluation shows that the non-linear combination of business information, geo-local and textual relevance features leads to a significant improvements over state of the art alternative approaches based on a combination of relevance, distance and business reputation.


title: Software testing model selection research based on Yin-Yang testing theory

abstract: Through research on software testing model selection, seeking the most appropriate testing method to achieve most reasonable testing volume and optimal testing result. Described in detail about the core content of Yin-Yang software testing theory; According to research data of Software Productivity Research, U.S.A, with overall consideration about time, labour, resource, and requirements, generates a software testing model which is made up of software testing method and testing volume distribution. By data comparison, the research outcome is successful as anticipated. This is an innovative research on software testing model selection.


title: Neural foundations of risk–return trade-off in investment decisions

abstract: Abstract Many decisions people make can be described as decisions under risk. Understanding the mechanisms that drive these decisions is an important goal in decision neuroscience. Two competing classes of risky decision making models have been proposed to describe human behavior, namely utility-based models and risk–return models. Here we used a novel investment decision task that uses streams of (past) returns as stimuli to investigate how consistent the two classes of models are with the neurobiological processes underlying investment decisions (where outcomes usually follow continuous distributions). By showing (a) that risk–return models can explain choices behaviorally and (b) that the components of risk–return models (value, risk, and risk attitude) are represented in the brain during choices, we provide evidence that risk–return models describe the neural processes underlying investment decisions well. Most importantly, the observed correlation between risk and brain activity in the anterior insula during choices supports risk–return models more than utility-based models because risk is an explicit component of risk–return models but not of the utility-based models.


title: Investigation of a Soft-Switching Flyback Converter With Full Secondary Side-Based Control

abstract: A conventional Flyback converter's controller is based on the primary side of the coupled inductor–requiring isolated coupling for both the output voltage measurement and for any synchronous rectification gate signal. A novel communication approach is proposed here, whereby the need for such signal coupling is eliminated by moving the controller to the secondary side and communicating back to the primary switch through the coupled inductor itself. This simultaneously allows direct access to the output voltage, reduced component count, synchronous rectification, and soft primary switch turn-on without optoisolator propagation delay or additional magnetic coupling. This paper presents a novel drain–source voltage sensing method that provides reliable turn-on signals for the primary switch along with the principles of the secondary-side control. Concept feasibility is proven using a 65 W demonstrator with a simple constant-on-time control scheme. The benefits, limitations, and suggested improvements are presented.


title: Link prediction in Foursquare network

abstract: Foursquare is an online social network and can be represented with a bipartite network of users and venues. A user-venue pair is connected if a user has checked-in at that venue. In the case of Foursquare, network analysis techniques can be used to enhance the user experience. One such technique is link prediction, which can be used to build a personalized recommendation system of venues. Recommendation systems in bipartite networks are very often designed using the global ranking method and collaborative filtering. A less known method- network based inference is also a feasible choice for link prediction in bipartite networks and sometimes performs better than the previous two. In this paper we test these techniques on the Foursquare network. The best technique proves to be the network based inference. We also show that taking into account the available metadata can be beneficial.


title: Obstacle detection and classification using deep learning for tracking in high-speed autonomous driving

abstract: On-road obstacle detection and classification is one of the key tasks in the perception system of self-driving vehicles. Since vehicle tracking involves localizationand association of vehicles between frames, detection and classification of vehicles is necessary. Vision-based approaches are popular for this task due to cost-effectiveness and usefulness of appearance information associated with the vision data. In this paper, a deep learning system using region-based convolutional neural network trained with PASCAL VOC image dataset is developed for the detection and classification of on-road obstacles such as vehicles, pedestrians and animals. The implementation of the system on a Titan X GPU achieves a processing frame rate of at least 10 fps for a VGA resolution image frame. This sufficiently high frame rate using a powerful GPU demonstrate the suitability of the system for highway driving of autonomous cars. The detection and classification results on images from KITTI and iRoads, and also Indian roads show the performance of the system invariant to object's shape and view, and different lighting and climatic conditions.


title: Ideal ratio mask estimation using deep neural networks for robust speech recognition

abstract: We propose a feature enhancement algorithm to improve robust automatic speech recognition (ASR). The algorithm estimates a smoothed ideal ratio mask (IRM) in the Mel frequency domain using deep neural networks and a set of time-frequency unit level features that has previously been used to estimate the ideal binary mask. The estimated IRM is used to filter out noise from a noisy Mel spectrogram before performing cepstral feature extraction for ASR. On the noisy subset of the Aurora-4 robust ASR corpus, the proposed enhancement obtains a relative improvement of over 38% in terms of word error rates using ASR models trained in clean conditions, and an improvement of over 14% when the models are trained using the multi-condition training data. In terms of instantaneous SNR estimation performance, the proposed system obtains a mean absolute error of less than 4 dB in most frequency channels.


MODELING SMARTY WEB SEARCH ENGINE USING XML CLUSTERING 1


title: CPU-GPU Algorithms for Triangular Surface Mesh Simplification

abstract: Mesh simplification and mesh compression are important processes in computer graphics and scientific computing, as such contexts allow for a mesh which takes up less memory than the original mesh. Current simplification and compression algorithms do not take advantage of both the central processing unit (CPU) and the graphics processing unit (GPU). We propose three simplification algorithms, one of which runs on the CPU and two of which run on the GPU. We combine these algorithms into two CPU-GPU algorithms for mesh simplification. Our CPU-GPU algorithms are the naive marking algorithm and the inverse reduction algorithm. Experimental results show that when the algorithms take advantage of both the CPU and the GPU, there is a decrease in running time for simplification compared to performing all of the computation on the CPU. The marking algorithm provides higher simplification rates than the inverse reduction algorithm, whereas the inverse reduction algorithm has a lower running time than the marking algorithm.


title: Automatic generation of smooth paths bounded by polygonal chains

abstract: We consider the problem of planning smooth paths for a vehicle in a region bounded by polygonal chains. The paths are represented as B-spline functions. A path is found by solving an optimization problem using a cost function designed to care for both the smoothness of the path and the safety of the vehicle. Smoothness is defined as small magnitude of the derivative of curvature and safety is defined as the degree of centering of the path between the polygonal chains. The polygonal chains are preprocessed in order to remove excess parts and introduce safety margins for the vehicle. The method has been implemented for use with a standard solver and tests have been made on application data provided by the Swedish mining company LKAB.


title: Applying the genetic approach to simulated annealing in solving some NP-hard problems

abstract: A stochastic approach called the annealing-genetic algorithm is presented for solving some well-known combinatorial optimization problems. This approach incorporates genetic algorithms into simulated annealing to improve the performance of simulated annealing. The authors' approach can be viewed as a simulated annealing algorithm with population-based state transition and with genetic-operator-based quasi-equilibrium control and as a genetic algorithm with the Boltzmann-type selection operator. The goals of efficiency in the algorithm are (1) the gap between final solution and the optimal solution should be around 3% or less, and (2) the computation time should be bounded by a polynomial function of the problem size. Empirically, the error rate of the proposed annealing-genetic algorithm for solving the multiconstraint zero-one knapsack problem is less than 1%, for solving the set partitioning problem is less than 0.1%, and for solving the traveling salesman problem is around 3%. >


title: Detection of emotional faces: salient physical features guide effective visual search.

abstract: In this study, the authors investigated how salient visual features capture attention and facilitate detection of emotional facial expressions. In a visual search task, a target emotional face (happy, disgusted, fearful, angry, sad, or surprised) was presented in an array of neutral faces. Faster detection of happy and, to a lesser extent, surprised and disgusted faces was found both under upright and inverted display conditions. Inversion slowed down the detection of these faces less than that of others (fearful, angry, and sad). Accordingly, the detection advantage involves processing of featural rather than configural information. The facial features responsible for the detection advantage are located in the mouth rather than the eye region. Computationally modeled visual saliency predicted both attentional orienting and detection. Saliency was greatest for the faces (happy) and regions (mouth) that were fixated earlier and detected faster, and there was close correspondence between the onset of the modeled saliency peak and the time at which observers initially fixated the faces. The authors conclude that visual saliency of specific facial features— especially the smiling mouth—is responsible for facilitated initial orienting, which thus shortens detection.


abstract: Intrusion detection for computer systems is a key problem in today’s networked society. Current distributed intrusion detection systems (IDSs) are not fully distributed as most of them centrally analyze data collected from distributed nodes resulting in a single point of failure. Increasingly, researchers are focusing on distributed IDSs to circumvent the problems of centralized approaches. A major concern of fully distributed IDSs is the high false positive rates of intrusion alarms which undermine the usability of such systems. We believe that effective distributed IDSs can be designed based on principles of coordinated multiagent systems. We propose an Agent-Based Distributed Intrusion Alert System (ABDIAS) which is fully distributed and provides two capabilities in addition to other functionalities of an IDS: (a) early warning when pre-attack activities are detected, (b) detecting and isolating compromised nodes by trust mechanisms and voting-based peer-level protocols.


title: Action-Transferred Navigation Technique Design Approach Supporting Human Spatial Learning

abstract: We propose a new action-transferred design approach by which the benefits of embodied cognition and activity can be realized to enhance spatial learning and usability for navigating virtual spaces. The action-transferred design approach is supported by theories of learning, action-perception, and neuropsychology. These theories help us understand how human action can be transferred to different body parts for improving the usability of interaction techniques and why the acquired spatial knowledge using the transferred action may remain the same independent of the used body parts. The finger-walking-in-place (FWIP) navigation technique is used as a design example to demonstrate the concept of the action-transferred design approach. Leveraging 3D immersive virtual reality technology, we performed an empirical study to evaluate the performance of the action-transferred FWIP navigation technique in terms of spatial knowledge acquisition. The FWIP navigation technique was compared with a full-body-based walking-like (sensor-fusion walking-in-place; SF-WIP) navigation technique and a well-known, convenient (Joystick) navigation technique using a common input device, that is, a wand with a joystick. Both the action-transferred and the full-body-based navigation techniques were more effective for spatial learning than the navigation technique using the common input device. However, only the action-transferred FWIP navigation technique can provide users with the convenience of navigating with their fingers. These results suggest that the action-transferred design approach is useful in designing a navigation technique supporting users' spatial learning performance more effectively and conveniently. Possible design implications for broader applications are discussed and indicate that the action-transferred design approach is worth further study.


title: Metamaterial-Inspired Engineering of Antennas

abstract: A variety of antennas have been engineered with metamaterials (MTMs) and metamaterial-inspired constructs to improve their performance characteristics. Examples include electrically small, near-field resonant parasitic (NFRP) antennas that require no matching network and have high radiation efficiencies. Experimental verification of their predicted behaviors has been obtained. Recent developments with this NFRP electrically small paradigm will be reviewed. They include considerations of increased bandwidths, as well as multiband and multifunctional extensions.


title: Determinants of Meme Popularity

abstract: Online social media have greatly affected the way in which we communicate with each other. However, little is known about what are the fundamental mechanisms driving dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior and analytically show, using techniques from mathematical population genetics, that competition between memes for the limited resource of user attention leads to a type of self-organized criticality, with heavy-tailed distributions of meme popularity: a few memes"go viral"but the majority become only moderately popular. The time-dependent solutions of the model are shown to fit empirical micro-blogging data on hashtag usage, and to predict novel scaling features of the data. The presented framework, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.


title: Submitted to: IEEE Control Systems Magazine (June 2001) Cooperative Control of Distributed Multi-Agent Systems ∗

abstract: A compact disc coating apparatus includes a first station for dispensing a quantity of lacquer on the disc surface and for spinning the disc to distribute a coating of lacquer over the disc surface, a second station, and a transfer assembly. The transfer assembly includes a pickup head for loading the disc into the first station and a gripper for transferring the disc from the first station to the second station. The gripper includes a rotatable disc gripping mechanism which clamps the disc during spinning at the first station. As soon as the disc stops spinning, the gripper lifts the disc from the first station.


title: A Multilevel Inverter for Photovoltaic Systems With Fuzzy Logic Control

abstract: Converters for photovoltaic (PV) systems usually consist of two stages: a dc/dc booster and a pulsewidth modulated (PWM) inverter. This cascade of converters presents efficiency issues, interactions between its stages, and problems with the maximum power point tracking. Therefore, only part of the produced electrical energy is utilized. In this paper, the authors propose a single-phase H-bridge multilevel converter for PV systems governed by a new integrated fuzzy logic controller (FLC)/modulator. The novelties of the proposed system are the use of a fully FLC (not requiring any optimal PWM switching-angle generator and proportional-integral controller) and the use of an H-bridge power-sharing algorithm. Most of the required signal processing is performed by a mixed-mode field-programmable gate array, resulting in a fully integrated System-on-Chip controller. The general architecture of the system and its main performance in a large spectrum of practical situations are presented and discussed. The proposed system offers improved performance over two-level inverters, particularly at low-medium power.


The effectiveness of the Push BraceTM orthosis and corticosteroid injection for managing first carpometacarpal joint osteoarthritis : A factorial randomised controlled trial protocol


title: Design of a 5kW, 1U, 10kW/ltr. resonant DC-DC converter for telecom applications

abstract: The demand for decreasing cost and volume and also for increasing efficiency leads to a constantly increasing power density of converter systems. For maximising the power density of a 5 kW telecom supply an optimisation procedure that automatically balances the switching frequency, semiconductor and passive losses, and thermal performance has been developed. This procedure and the belonging analytical converter and transformer models are presented in this paper. Moreover, the resulting optimised design, which has a power density of 10 kW/ltr. and an efficiency of 96% at a height of 1 U, is presented.


title: A longitudinal study of automatic face recognition

abstract: With the deployment of automatic face recognition systems for many large-scale applications, it is crucial that we gain a thorough understanding of how facial aging affects the recognition performance, particularly across a large population. Because aging is a complex process involving genetic and environmental factors, some faces “age well” while the appearance of others changes drastically over time. This heterogeneity (inter-subject variability) suggests the need for a subject-specific aging analysis. In this paper, we conduct such an analysis using a longitudinal database of 147,784 operational mug shots of 18,007 repeat criminal offenders, where each subject has at least five face images acquired over a minimum of five years. By fitting multilevel statistical models to genuine similarity scores from two commercial-off-the-shelf (COTS) matchers, we quantify (i) the population average rate of change in genuine scores with respect to the elapsed time between two face images, and (ii) how closely the subject-specific rates of change follow the population average. Longitudinal analysis of the scores from the more accurate COTS matcher shows that despite decreasing genuine scores over time, the average subject can still be correctly verified at a false accept rate (FAR) of 0.01% across all 16 years of elapsed time in our database. We also investigate (i) the effects of several other covariates (gender, race, face quality), and (ii) the probability of true acceptance over time.


title: Automated Planning and Player Modeling for Interactive Storytelling

abstract: Storytelling plays an important role in human life, from everyday communication to entertainment. Interactive storytelling (IS) offers its audience an opportunity to actively participate in the story being told, particularly in video games. Managing the narrative experience of the player is a complex process that involves choices, authorial goals and constraints of a given story setting (e.g., a fairy tale). Over the last several decades, a number of experience managers using artificial intelligence (AI) methods such as planning and constraint satisfaction have been developed. In this paper, we extend existing work and propose a new AI experience manager called player-specific automated storytelling (PAST), which uses automated planning to satisfy the story setting and authorial constraints in response to the player's actions. Out of the possible stories algorithmically generated by the planner in response, the one that is expected to suit the player's style best is selected. To do so, we employ automated player modeling. We evaluate PAST within a video-game domain with user studies and discuss the effects of combining planning and player modeling on the player's perception of agency.


title: The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets

abstract: Binary classifiers are routinely evaluated with performance measures such as sensitivity and specificity, and performance is frequently illustrated with Receiver Operating Characteristics (ROC) plots. Alternative measures such as positive predictive value (PPV) and the associated Precision/Recall (PRC) plots are used less frequently. Many bioinformatics studies develop and evaluate classifiers that are to be applied to strongly imbalanced datasets in which the number of negatives outweighs the number of positives significantly. While ROC plots are visually appealing and provide an overview of a classifier's performance across a wide range of specificities, one can ask whether ROC plots could be misleading when applied in imbalanced classification scenarios. We show here that the visual interpretability of ROC plots in the context of imbalanced datasets can be deceptive with respect to conclusions about the reliability of classification performance, owing to an intuitive but wrong interpretation of specificity. PRC plots, on the other hand, can provide the viewer with an accurate prediction of future classification performance due to the fact that they evaluate the fraction of true positives among positive predictions. Our findings have potential implications for the interpretation of a large number of studies that use ROC plots on imbalanced datasets.


title: The Use of Alternative Social Networking Sites in Higher Educational Settings: A Case Study of the E-Learning Benefits of Ning in Education

abstract: Distance education as a primary means of instruction is expanding significantly at the college and university level. Simultaneously, the growth of social networking sites (SNS) including Facebook, LinkedIn, and MySpace is also rising among today’s college students. An increasing number of higher education instructors are beginning to combine distance education delivery with SNSs. However, there is currently little research detailing the educational benefits associated with the use of SNSs. Non-commercial, education-based SNSs, such as Ning in Education, have been recently shown to build communities of practice and facilitate social presence for students enrolled in distance education courses. In order to evaluate the largely unexplored educational benefits of SNSs, we surveyed graduate students enrolled in distance education courses using Ning in Education, an education-based SNS, based on their attitudes toward SNSs as productive online tools for teaching and learning. The results of our study suggest that education-based SNSs can be used most effectively in distance education courses as a technological tool for improved online communications among students in higher distance education courses.


title: Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition

abstract: A new method for analysis of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) and Fourier-Bessel (FB) expansion has been presented in this paper. The EMD decomposes an EEG signal into a finite set of band-limited signals termed intrinsic mode functions (IMFs). The mean frequency (MF) for each IMF has been computed using FB expansion. The MF measure of the IMFs has been used as a feature in order to identify the difference between ictal and seizure-free intracranial EEG signals. It has been shown that the MF feature of the IMFs has provided statistically significant difference between ictal and seizure-free EEG signals. Simulation results are included to illustrate the effectiveness of the proposed method.


title: Cost-Sensitive Perceptron Decision Trees for Imbalanced Drifting Data Streams

abstract: Mining streaming and drifting data is among the most popular contemporary applications of machine learning methods. Due to the potentially unbounded number of instances arriving rapidly, evolving concepts and limitations imposed on utilized computational resources, there is a need to develop efficient and adaptive algorithms that can handle such problems. These learning difficulties can be further augmented by appearance of skewed distributions during the stream progress. Class imbalance in non-stationary scenarios is highly challenging, as not only imbalance ratio may change over time, but also relationships among classes. In this paper we propose an efficient and fast cost-sensitive decision tree learning scheme for handling online class imbalance. In each leaf of the tree we train a perceptron with output adaptation to compensate for skewed class distributions, while McDiarmid’s bound is used for controlling the splitting attribute selection. The cost matrix automatically adapts itself to the current imbalance ratio in the stream, allowing for a smooth compensation of evolving class relationships. Furthermore, we analyze characteristics of minority class instances and incorporate this information during the model update process. It allows our classifier to focus on most difficult instances, while a sliding window keeps track of changes in class structures. Experimental analysis carried out on a number of binary and multi-class imbalanced data streams indicate the usefulness of the proposed approach.


title: Cosine Similarity Scoring without Score Normalization Techniques

abstract: In recent work [1], a simplified and highly effective approach to speaker recognition based on the cosine similarity between lowdimensional vectors, termed ivectors, defined in a total variability space was introduced. The total variability space representation is motivated by the popular Joint Factor Analysis (JFA) approach, but does not require the complication of estimating separate speaker and channel spaces and has been shown to be less dependent on score normalization procedures, such as znorm and t-norm. In this paper, we introduce a modification to the cosine similarity that does not require explicit score normalization, relying instead on simple mean and covariance statistics from a collection of impostor speaker ivectors. By avoiding the complication of zand t-norm, the new approach further allows for application of a new unsupervised speaker adaptation technique to models defined in the ivector space. Experiments are conducted on the core condition of the NIST 2008 corpora, where, with adaptation, the new approach produces an equal error rate (EER) of 4.8% and min decision cost function (MinDCF) of 2.3% on all female speaker trials.


abstract: This module accompanies the Computer Science in Economics & Management article "Simulated Annealing: An Initial Application in Econometrics," 1992, 5, 133-146. This implementation of simulated annealing was used in "Global Optimization of Statistical Functions with Simulated Annealing," Goffe, Ferrier and Rogers, Journal of Econometrics, vol. 60, no. 1/2, Jan./Feb. 1994, pp. 65-100. Other versions of this algorithm exist for GAUSS and Ox.


title: Influence of Electrostatic Shielding of Disc Winding on Increasing the Series Capacitance in Transformer

abstract: Strike of a lightning stroke to the high voltage terminal of a power transformer results in an impulse stress on its windings. The magnitude of the series capacitance of the windings has an important effect on the impulse voltage distribution and the stresses along the windings. Increasing the series capacitances of the windings, the impulse voltage distribution becomes more linear and the stress on the winding is reduced. So interleaved windings rather than disc windings are used in power transformer windings to increase the series capacitances. In this paper, the electrostatic shielding, used in disc windings to increase the windings series capacitance, and for linearization of its impulse voltage distribution is introduced and its impact on the modification of impulse voltage distribution is compared with the disc winding without shielding.


title: A generalized LDPC framework for robust and sublinear compressive sensing

abstract: Compressive sensing aims to recover a high-dimensional sparse signal from a relatively small number of measurements. In this paper, a novel design of the measurement matrix is proposed. The design is inspired by the construction of generalized low-density parity-check codes, where the capacity-achieving point-to-point codes serve as subcodes to robustly estimate the signal support. In the case that each entry of the $n$-dimensional $k$-sparse signal lies in a known discrete alphabet, the proposed scheme requires only $O(k \log n)$ measurements and arithmetic operations. In the case of arbitrary, possibly continuous alphabet, an error propagation graph is proposed to characterize the residual estimation error. With $O(k \log^2 n)$ measurements and computational complexity, the reconstruction error can be made arbitrarily small with high probability.


title: The impact of Business Intelligence systems on stock return volatility

abstract: Business Intelligence (BI) systems support decision-making and information-sharing in increasingly complex organizational environments. As investment in these systems is steadily increasing in a wide range of industries, it is important to understand their economic effect. Under BI, information can be accessed in a timelier manner, decisions become increasingly data driven, and reports become more informative. This, in turn, can lead to more consistent company behavior and performance. We therefore hypothesized that BI systems could help reduce the company's stock return volatility. To test this hypothesis, we empirically analyzed a large sample of firms that had deployed a BI system. Our results indicated a significant reduction in stock return volatility subsequent to BI deployment. The reduction in volatility is of similar magnitude whether the BI system is implemented to serve upper or middle management, or the knowledge workforce. Overall, the results suggested that BI systems reduce the financial risk of an organization.


title: Stereotypical Motor Movement Detection in Dynamic Feature Space

abstract: Stereotypical Motor Movements (SMMs) are abnormal postural or motor behaviors that interfere with learning and social interaction in Autism Spectrum Disorder patients. An automatic SMM detection system, employing inertial sensing technology, provides a useful tool for real-time alert on the onset of these atypical behaviors, therefore facilitating personalized intervention therapies. To tackle critical issues with inter-subject variability, in this study, we propose to combine long short-term memory (LSTM) with convolutional neural network (CNN) to model the temporal patterns in the sequence of multi-axes IMU signals. Our results, on one simulated and two experimental datasets, show that transferring the raw feature space to a dynamic feature space via the proposed architecture enhances the performance of automatic SMM detection system especially for skewed training data. These findings facilitate the application of SMM detection system in real-time scenarios.


DESIGN OF CUK CONVERTER WITH MPPT TECHNIQUE


title: Autopoiesis with or without cognition: defining life at its edge

abstract: This paper examines two questions related to autopoiesis as a theory for minimal life: (i) the relation between autopoiesis and cognition; and (ii) the question as to whether autopoiesis is the necessary and sufficient condition for life. First, we consider the concept of cognition in the spirit of Maturana and Varela: in contradistinction to the representationalistic point of view, cognition is construed as interaction between and mutual definition of a living unit and its environment. The most direct form of cognition for a cell is thus metabolism itself, which necessarily implies exchange with the environment and therefore a simultaneous coming to being for the organism and for the environment. A second level of cognition is recognized in the adaptation of the living unit to new foreign molecules, by way of a change in its metabolic pattern. We draw here an analogy with the ideas developed by Piaget, who recognizes in cognition the two distinct steps of assimilation and accommodation. While assimilation is the equivalent of uptake and exchange of usual metabolites, accommodation corresponds to biological adaptation, which in turn is the basis for evolution. By comparing a micro-organism with a vesicle that uptakes a precursor for its own self-reproduction, we arrive at the conclusion that (a) the very lowest level of cognition is the condition for life, and (b) the lowest level of cognition does not reduce to the lowest level of autopoiesis. As a consequence, autopoiesis alone is only a necessary, but not sufficient, condition for life. The broader consequences of this analysis of cognition for minimal living systems are considered.


title: Remote photoplethysmography application to the analysis of time-frequency changes of human heart rate variability

abstract: In this article we present the possibilities of using remote photoplethysmography (rPPG or imaging PPG) technology to estimate time-frequency changes of human heart rate variability. We propose improvements for algorithm presented in our recent study. Algorithm modification allows to exclude skin areas with highly variable levels of lighting, thus reducing noise level and increasing duration of signal suitable for processing. Twenty healthy volunteers (males and females) aged from 20 to 25 took part in this investigation. The blood volume pulse rate estimated from the rPPG rhythmogram and cardiac pulse rate estimated from the electrocardiogram are compared. The results showed that the very low frequency hemodynamic oscillations of blood volume pulse rate estimated from the rPPG rhythmogram in the [0.04-0.003] Hz frequency band can be used to monitor functional changes of a human.


IS THE RESOURCE-BASED " VIEW " A USEFUL PERSPECTIVE FOR STRATEGIC MANAGEMENT RESEARCH ?


title: SharpChecker: Static analysis tool for C# programs

abstract: This paper considers various aspects of static analysis of C# programs in order to detect the maximum number of software bugs in an acceptable time. A complete cycle of software static analysis is described with the main focus being placed on the specifics of the C# language. Some methods are discussed that take into account popular features of C# at all levels of analysis: call graph and control flow graph construction, dataflow analysis, as well as context- and path-sensitive interprocedural analysis. A symbolic execution method is proposed, which is based on the works devoted to the Bounded Model Checking (BMC) and the Saturn Software Analysis Project. A memory model is described that enables an accurate intraprocedural analysis and allows one to create compact representations of error conditions associated with functions, which are essential for interprocedural analysis. A special attention is paid to the optimizations that occur during path-sensitive analysis of error conditions. The conditions need to be optimized in terms of size, because path-sensitive interprocedural analysis requires saving a large number of conditions for each analyzed function. The conditions are resolved using advanced SMT solvers (such as the Microsoft Z3 Prover). This paper also considers various approaches to modeling the behavior of library functions: based on a summary containing a set of properties required for analysis, or based on simplified implementations in C#. All the discussed solutions are implemented in the SharpChecker static analysis tool and are tested on a number of open-source projects from 1.5 thousand to 1.35 million lines of code.


title: Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications

abstract: Although the latest high-end smartphone has powerful CPU and GPU, running deeper convolutional neural networks (CNNs) for complex tasks such as ImageNet classification on mobile devices is challenging. To deploy deep CNNs on mobile devices, we present a simple and effective scheme to compress the entire CNN, which we call one-shot whole network compression. The proposed scheme consists of three steps: (1) rank selection with variational Bayesian matrix factorization, (2) Tucker decomposition on kernel tensor, and (3) fine-tuning to recover accumulated loss of accuracy, and each step can be easily implemented using publicly available tools. We demonstrate the effectiveness of the proposed scheme by testing the performance of various compressed CNNs (AlexNet, VGGS, GoogLeNet, and VGG-16) on the smartphone. Significant reductions in model size, runtime, and energy consumption are obtained, at the cost of small loss in accuracy. In addition, we address the important implementation level issue on 1?1 convolution, which is a key operation of inception module of GoogLeNet as well as CNNs compressed by our proposed scheme.


title: An ERP model for supplier selection in electronics industry

abstract: Asus Tech. is the largest motherboard manufacturer in Taiwan. Hundreds of suppliers cooperate with the company in business. So that supplier selection is the most important function of a purchasing department in the enterprise. An enterprise resource planning (ERP) system in the process of supplier selection may result in the great savings in both costs and man hours. In the concept of push and pull, an ERP system acts as an efficient tool in the resource integration and profit creation for a company. Through ERP, a decision manager can clearly realize the strength and weakness of the purchasing operation. To establish a real-time purchasing environment, a methodology of analytic network process (ANP), technique for order preference by similarity to ideal solution (TOPSIS) and linear programming (LP) are effectively applied in the supplier selection process. ANP and TOPSIS are used to calculate the weight and give suppliers a ranking; LP effectively allocates order quantity to each vendor. As to the result, four PC board suppliers are given orders for 1200, 727, 1000 and 73 pieces.


title: A Continuous Fibonacci Model for Robotic Octopus Arm

abstract: We introduce a control model for an octopus robot tentacle. The model extends to a continuous setting a class of hyper-redundant planar manipulators characterized by a self-similar structure. The proposed model encompasses manipulators with a links scaling according to Fibonacci sequence. We characterize the class of continuous functions whose graph can be reparametrized as a configuration of the system and we provide an explicit control law. In particular, we give the conditions to prescribe an admissible path for the configurations of the manipulator. The result is an attempt to determine an entire class of admissible paths for the arm deformations.


title: A high-resolution 3D dynamic facial expression database

abstract: Face information processing relies on the quality of data resource. From the data modality point of view, a face database can be 2D or 3D, and static or dynamic. From the task point of view, the data can be used for research of computer based automatic face recognition, face expression recognition, face detection, or cognitive and psychological investigation. With the advancement of 3D imaging technologies, 3D dynamic facial sequences (called 4D data) have been used for face information analysis. In this paper, we focus on the modality of 3D dynamic data for the task of facial expression recognition. We present a newly created high-resolution 3D dynamic facial expression database, which is made available to the scientific research community. The database contains 606 3D facial expression sequences captured from 101 subjects of various ethnic backgrounds. The database has been validated through our facial expression recognition experiment using an HMM based 3D spatio-temporal facial descriptor. It is expected that such a database shall be used to facilitate the facial expression analysis from a static 3D space to a dynamic 3D space, with a goal of scrutinizing facial behavior at a higher level of detail in a real 3D spatio-temporal domain.


title: On clustering RSS fingerprints for improving scalability of performance prediction of indoor positioning systems

abstract: We previously developed an analytical model in [8] to predict the precision and accuracy performance of indoor positioning systems using location fingerprints. A by-product of the model is the ability to eliminate unnecessary fingerprints to reduce the number of fingerprints in a radio map for comparison without loss in performance. This model enables computation of an approximate probability distribution of location selection, by employing a proximity graph to extract neighbor and non-neighbor sets of a given fingerprint. However, employing the model 'as is' in a system with many location fingerprints requires determining a single large proximity graph derived from all location fingerprints which may involve significant computational effort. In this paper, we consider two techniques to divide location fingerprints into smaller clusters. Separate proximity graphs for each cluster are now used to predict performance and eliminate unnecessary location fingerprints. Results show that the computational effort can be reduced, creating a more scalable analytical model, while still predicting and enabling good precision performance.


title: A Terrain-Based Path Planning Method for Mobile Robots

abstract: This paper presents a technique for incorporating terrain traversability data into a global path planning method for field mobile robots operating on rough natural terrain. The focus of this approach is on assessing the traversability characteristics of the global terrain using a multi-valued map representation of traversal dificulty, and using this information to compute a traversal cost function to ensure robot survivability. The traversal cost is then utilized by a global path planner to find an optimally safe path through the terrain. A graphical simulator for the terrain-basedpath planning is presented. The path planner is applied to a commercial Pioneer 2-AT robot andfield test results are provided.


title: A momentum-based collision detection algorithm for industrial robots

abstract: This paper presents a collision detection algorithm based on generalized momentum to improve the safety reaction of robots. According to the decoupling performance of generalized momentum and external torque of the manipulator, the impact force can be obtained which determine the position where collision occurred. In this paper, a performance-regulating function is introduced to improve the dynamic response characteristics of the algorithm. The algorithm can effectively depress the influence of acceleration and deceleration process. Moreover, it exhibits good real-time performance, and satisfies the expected requirements. The algorithm provides an approximate position and direction of the impact force, which are important for further security reaction control.


title: A Review Paper: Noise Models in Digital Image Processing

abstract: Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image denoising techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this way, we present a complete and quantitative analysis of noise models available in digital images.


title: On identifying potential direct marketing consumers using adaptive boosted support vector machine

abstract: Identifying potential consumers for direct marketing to very large data is a difficult and impossible task to do manually. Therefore, the machine learning approach needs to help analyze the data to contribute in determining the marketing strategy policy. In this paper, vector machine support methods using the Adaboost algorithm are investigated to classify potential customers for direct marketing of large bank data marketing. The adaboost algorithm aims to build a better model than the model generated from a single classifier. Data obtained from UCI machine learning repository. Total data is processed as many as 9280 data with the number of attributes of 20 classes and 1 target class. Training data and test data are divided into 70% and 30%. This classification predicts the prospects for a deposit subscription. The results show that the SVM method using Adaboost algorithm obtained accuracy is 95.07% and the sensitivity is 91.65% higher than the ordinary SVM approach.


title: CubiST: a new algorithm for improving the performance of ad-hoc OLAP queries

abstract: Being able to efficiently answer arbitrary OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes has been a continued, major concern in data warehousing. In this paper, we introduce a new data structure, called Statistics Tree (ST), together with an efficient algorithm called CubiST, for evaluating ad-hoc OLAP queries on top of a relational data warehouse. We are focusing on a class of queries called cube queries, which generalize the data cube operator. CubiST represents a drastic departure from existing relational (ROLAP) and multi-dimensional (MOLAP) approaches in that it does not use the familiar view lattice to compute and materialize new views from existing views in some heuristic fashion. CubiST is the first OLAP algorithm that needs only one scan over the detailed data set and can efficiently answer any cube query without additional I/O when the ST fits into memory. We have implemented CubiST and our experiments have demonstrated significant improvements in performance and scalability over existing ROLAP/MOLAP approaches.


title: Traffic anomaly detection in DDos flooding attack

abstract: Researches have been conducted to overcome Distributed Denial of Service (DDoS) flooding attack. Beside the use of signature based detection, anomaly based detection is also used to detect the attack. Several methods such as statistic, information theory, data mining and forecasting have been proposed. In several researches, they just focused to detect the traffic anomaly, but not to recognize the types of anomaly that were detected such as flashcrowd, types of botnet, types of DDoS, and prevention action. In this paper we categorize anomaly traffic detection system based on process and capability focus. Anomaly detection system process including traffic features, preprocessing, and detection process. Capability focus based on each main research problem to be solved, there are detectingonly anomaly, types of anomaly, and prevention system that include process to overcome the attack. At the end of paper, we provide overview of research direction and opportunities that may be done in future research.


title: Binary Function Clustering Using Semantic Hashes

abstract: The ability to identify semantically-related functions, in large collections of binary executables, is important for malware detection. Intuitively, two pieces of code are similar if they have the same effect on a machine's state. Current state-of-the-art tools employ a variety of pair wise comparisons (e.g., template matching using SMT solvers, Value-Set analysis at critical program points, API call matching, etc.) However, these methods are unshakable for clustering large datasets, of size N, since they require O(N^2) comparisons. In this paper, we present an alternative approach based upon "hashing". We propose a scheme that captures the semantics of functions as semantic hashes. Our approach treats a function as a set of features, each of which represent the input-output behavior of a basic block. Using a form of locality-sensitive hashing known as Min Hashing, functions with many common features can be quickly identified, and the complexity of clustering is reduced to O(N). Experiments on functions extracted from the CERT malware catalog indicate that we are able to cluster closely related code with a low false positive rate.


title: ARGUS : An Automated Multi-Agent Visitor Identification System

abstract: ARGUS is a multi-agent visitor identification system distributed over several workstations. Human faces are extracted from security camera images by a neural-network-based face detector, and identified as frequent visitors by ARENA, a memory-based face recognition system. ARGUS then uses a messaging system to notify hosts that their guests have arrived. An interface agent enables users to submit feedback, which is immediately incorporated by ARENA to improve its face recognition performance. The ARGUS components were rapidly developed using JGram, an agent framework that is also detailed in this paper. JGram automatically converts high-level agent specifications into Java source code, and assembles complex tasks by composing individual agent services into a JGram pipeline. ARGUS has been operating successfully in an outdoor environment for several months.


Building Better Open-Source Tools to Support Fairness in Automated Scoring


title: A new approach to signal classification using spectral correlation and neural networks

abstract: Channel sensing and spectrum allocation has long been of interest as a prospective addition to cognitive radios for wireless communications systems occupying license-free bands. Conventional approaches to cyclic spectral analysis have been proposed as a method for classifying signals for applications where the carrier frequency and bandwidths are unknown, but is, however, computationally complex and requires a significant amount of observation time for adequate performance. Neural networks have been used for signal classification, but only for situations where the baseband signal is present. By combining these techniques a more efficient and reliable classifier can be developed where a significant amount of processing is performed offline, thus reducing online computation. In this paper we take a renewed look at signal classification using spectral coherence and neural networks, the performance of which is characterized by Monte Carlo simulations


title: Design of Two Stage Ultra Low Power CMOS Operational Transconductance Amplifier (OTA) Using 180 nm Technology

abstract: This paper deals with well-defined design criteria for ultra low power two stage CMOS operational transconductance amplifiers (OTAs) with simple yet robust implementation in nm dimension. A simple design approach which allows electrical parameters to be univocally related to each circuit element and biasing values for low frequency applications is presented.The operational transconductance amplifier with ±1.8v power supply has been simulated using TANNER Tools ver.13 with 0.18μm CMOS technology which provide expected characteristics with convenient performance for given specification.


title: Graphene-Based Ultracapacitors

abstract: The surface area of a single graphene sheet is 2630 m2/g, substantially higher than values derived from BET surface area measurements of activated carbons used in current electrochemical double layer capacitors. Our group has pioneered a new carbon material that we call chemically modified graphene (CMG). CMG materials are made from 1-atom thick sheets of carbon, functionalized as needed, and here we demonstrate in an ultracapacitor cell their performance. Specific capacitances of 135 and 99 F/g in aqueous and organic electrolytes, respectively, have been measured. In addition, high electrical conductivity gives these materials consistently good performance over a wide range of voltage scan rates. These encouraging results illustrate the exciting potential for high performance, electrical energy storage devices based on this new class of carbon material.


title: On-Line Monitoring Device of Airborne Particulate Concentration Based on The Principle of Vibration

abstract: The protection of the atmosphere environment that relates with people's health relies on the monitoring of the environment. As an important index of the monitoring work, monitoring the concentration of the particulates in the air, especially PM10 and those particulates' diameter that are shorter than 10 mum is now the emphasis of research both in foreign country and our own country. At present, foreign countries have already applied automatic monitoring method to observe PM10 particulate and PM2.5 monitoring is popular there. However, the routine off-line monitoring to PM2.5 has not been implemented and the auto on-line monitoring to PM10 still has some technique problems in China. An on-line monitoring method and device of the particulate concentration, based on the principle of vibration was put forward here and the feasibility of this method is proven by the calculation. It provides effective technology support for the research of particulate pollution status and source analysis of particulate and it improves the monitoring operation in the field of real-time character and accuracy.


title: A Machine Learning Approach for Better Understanding the Neuromodulation of Locomotion

abstract: Motor intent and control rely on complex high-level and spinal networks. Untilnow, little is known about this system’s organization and mechanisms. Whilecognitive abilities play an essential role i ...


title: Inhibition of cis-diamminedichloroplatinum (II) — induced renal toxicity in the rat

abstract: The hydroxyl-containing dithiocarbamates, sodium (di(hydroxyethl)-dithiocarbamate (NaY) and sodium N-methyl, N-dithiocarboxy-D-glucamine (NaG), appear to possess definite advantages over sodium diethyldithiocarbamate (DDTC) in reducing the cis-dichlorodiammineplatinum (Cis-Pt)-induced renal damage in rats given Cis-Pt as an IV bolus of 7.5 mg/kg 1 h before the IP administration of the dithiocarbamate. Renal damage, as estimated by blood urea nitrogen (BUN) values and serum creatinine levels, was less at all times up until sacrifice in animals given NaY or NaG than in those given DDTC. An even more effective method for suppression of Cis-Pt renal toxicity is to use a combination of procedures. The most efficacious combination involves a 24-h pretreatment with DDTC or NaG plus acetazolamide and normal saline hydration 30 min before administration of Cis-Pt, followed by post-treatment with NaG. With this combination therapy renal function can be almost completely spared. Although DDTC or NaG pretreatment is highly effective when used in conjunction with NaG post-treatment, DDTC or NaG pretreatment alone has no renal sparing effect on renal function or renal platinum accumulation. In experiments in which antidotes were given 1 h after Cis-Pt and the animals were followed up for 75 days, a chronic interstitial nephritis at 75 days, suggesting a persistent cell-mediated immune response to Cis-Pt-induced renal damage, may be the basis for chronically abnormal renal function resulting from Cis-Pt. Treatment with all three dithiocarbamates, NaY, NaG, and DDTC, reduced the intensity of this cellular reaction and also reduced platinum levels in the kidneys. Although NaY and NaG are effective heavy metal chelators and renal function is spared and kidney platinum levels are substantially reduced by the dithiocarbamates, no parallel loss of antineoplastic activity by Cis-Pt on the rat Walker carcinoma was observed. Since the dithiocarbamates have no known human toxicity that would disqualify their clinical use, phase 1 clinical trials are indicated.


title: Vision for road inspection

abstract: Road surface inspection in cities is for the most part, a task performed manually. Being a subjective and labor intensive process, it is an ideal candidate for automation. We propose a solution based on computer vision and data-driven methods to detect distress on the road surface. Our method works on images collected from a camera mounted on the windshield of a vehicle. We use an automatic procedure to select images suitable for inspection based on lighting and weather conditions. From the selected data we segment the ground plane and use texture, color and location information to detect the presence of pavement distress. We describe an over-segmentation algorithm that identifies coherent image regions not just in terms of color, but also texture. We also discuss the problem of learning from unreliable human-annotations and propose using a weakly supervised learning algorithm (Multiple Instance Learning) to train a classifier. We present results from experiments comparing the performance of this approach against multiple individual human labelers, with the ground-truth labels obtained from an ensemble of other human labelers. Finally, we show results of pavement distress scores computed using our method over a subset of a citywide road network.


title: Critical Success Factors for the Implementation of Integrated Healthcare Information Systems Projects: An Organizational Fit Perspective

abstract: Many healthcare reforms are to digitalize and integrate healthcare information systems. However, the disparity of business benefits in having an integrated healthcare information system (IHIS) varies with organizational fit factors. Critical success factors (CSFs) exist for hospitals to implement an IHIS successfully. This study investigated the relationship between the organizational fit and the system success. In addition, we examined the moderating effect of five CSFs -- information systems adjustment, business process adjustment, organizational resistance, top management support, and the capability of key team members – in an IHIS implementation. Fifty-three hospitals that have successfully undertaken IHIS projects participated in this study. We used regression analysis to assess the relationships. The findings of this study provide a roadmap for hospitals to capitalize on the organizational fit and the five critical success factors in order to implement successful IHIS projects.


title: Geometry-based populated chessboard recognition

abstract: Chessboards are commonly used to calibrate cameras, and many robust methods have been developed to recognize the unoccupied boards. However, when the chessboard is populated with chess pieces, such as during an actual game, the problem of recognizing the board is much harder. Challenges include occlusion caused by the chess pieces, the presence of outlier lines and low viewing angles of the chessboard. In this paper, we present a novel approach to address the above challenges and recognize the chessboard. The Canny edge detector and Hough transform are used to capture all possible lines in the scene. The k-means clustering and a k-nearest-neighbors inspired algorithm are applied to cluster and reject the outlier lines based on their Euclidean distances to the nearest neighbors in a scaled Hough transform space. Finally, based on prior knowledge of the chessboard structure, a geometric constraint is used to find the correspondences between image lines and the lines on the chessboard through the homography transformation. The proposed algorithm works for a wide range of the operating angles and achieves high accuracy in experiments.


title: Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization

abstract: We consider a generic convex optimization problem associated with regularized empirical risk minimization of linear predictors. The problem structure allows us to reformulate it as a convex-concave saddle point problem. We propose a stochastic primal-dual coordinate method, which alternates between maximizing over one (or more) randomly chosen dual variable and minimizing over the primal variable. We also develop an extension to non-smooth and nonstrongly convex loss functions, and an extension with better convergence rate on unnormalized data. Both theoretically and empirically, we show that the SPDC method has comparable or better performance than several state-of-the-art optimization methods.


title: Three Parietal Circuits for Number Processing

abstract: Did evolution endow the human brain with a predisposition to represent and acquire knowledge about numbers? Although the parietal lobe has been suggested as a potential substrate for a domain-specific representation of quantities, it is also engaged in verbal, spatial, and attentional functions that may contribute to calculation. To clarify the organisation of number-related processes in the parietal lobe, we examine the three-dimensional intersection of fMRI activations during various numerical tasks, and also review the corresponding neuropsychological evidence. On this basis, we propose a tentative tripartite organisation. The horizontal segment of the intraparietal sulcus (HIPS) appears as a plausible candidate for domain specificity: It is systematically activated whenever numbers are manipulated, independently of number notation, and with increasing activation as the task puts greater emphasis on quantity processing. Depending on task demands, we speculate that this core quantity system, analogous t...


title: Reliability of delayed self-reports in disaster research

abstract: In studies of traumatic stress, researchers often find themselves asking questions about an event and its aftermath long after the crisis has passed. The purpose of this study was to assess the reliability of these delayed self-reports. In January, 1990, 65 residents of Charleston, SC, were interviewed by telephone about their experiences following Hurricane Hugo, which had devastated the area on September 22, 1989. The interview included assessments of disaster-related losses, preparedness, social support received from others, and social support provided to others. In October, 1990, 53 of these persons (82% of the original sample) were reinterviewed and asked the exact same questions. For reports of losses and preparedness, accuracy of the later reports was excellent. Both the sample and individuals showed remarkable stability over time. For measures of social support, there was a sample tendency to recall more social support as time passed, but individuals generally retained their same rank order. Thus, these reports were also reliable.


title: HVACKer: Bridging the Air-Gap by Attacking the Air Conditioning System

abstract: Modern corporations physically separate their sensitive computational infrastructure from public or other accessible networks in order to prevent cyber-attacks. However, attackers still manage to infect these networks, either by means of an insider or by infiltrating the supply chain. Therefore, an attacker's main challenge is to determine a way to command and control the compromised hosts that are isolated from an accessible network (e.g., the Internet). ::: In this paper, we propose a new adversarial model that shows how an air gapped network can receive communications over a covert thermal channel. Concretely, we show how attackers may use a compromised air-conditioning system (connected to the internet) to send commands to infected hosts within an air-gapped network. Since thermal communication protocols are a rather unexplored domain, we propose a novel line-encoding and protocol suitable for this type of channel. Moreover, we provide experimental results to demonstrate the covert channel's feasibility, and to calculate the channel's bandwidth. Lastly, we offer a forensic analysis and propose various ways this channel can be detected and prevented. ::: We believe that this study details a previously unseen vector of attack that security experts should be aware of.


title: A CMOS Low Voltage Charge Pump

abstract: Charge pump circuits are used for obtaining higher voltages than normal power supply voltage in flash memories, DRAMs and low voltage designs. In this paper, we present a charge pump circuit in standard CMOS technology that is suited for low voltage operation. Our proposed charge pump uses a cross- connected NMOS cell as the basic element and PMOS switches are employed to connect one stage to the next. The simulated output voltages of the proposed 4 stage charge pump for input voltage of 0.9 V, 1.2 V, 1.5 V, 1.8 V and 2.1 V are 3.9 V, 5.1 V, 6.35 V, 7.51 V and 8.4 V respectively. This proposed charge pump is suitable for low power CMOS mixed-mode designs.


title: A Wearable EEG-HEG-HRV Multimodal System With Simultaneous Monitoring of tES for Mental Health Management

abstract: A multimodal mental management system in the shape of the wearable headband and earplugs is proposed to monitor electroencephalography (EEG), hemoencephalography (HEG) and heart rate variability (HRV) for accurate mental health monitoring. It enables simultaneous transcranial electrical stimulation (tES) together with real-time monitoring. The total weight of the proposed system is less than 200 g. The multi-loop low-noise amplifier (MLLNA) achieves over 130 dB CMRR for EEG sensing and the capacitive correlated-double sampling transimpedance amplifier (CCTIA) has low-noise characteristics for HEG and HRV sensing. Measured three-physiology domains such as neural, vascular and autonomic domain signals are combined with canonical correlation analysis (CCA) and temporal kernel canonical correlation analysis (tkCCA) algorithm to find the neural-vascular-autonomic coupling. It supports highly accurate classification with the 19% maximum improvement with multimodal monitoring. For the multi-channel stimulation functionality, after-effects maximization monitoring and sympathetic nerve disorder monitoring, the stimulator is designed as reconfigurable. The 3.37 $\,\times\,$ 2.25 mm $^{2}$ chip has 2-channel EEG sensor front-end, 2-channel NIRS sensor front-end, NIRS current driver to drive dual-wavelength VCSEL and 6-b DAC current source for tES mode. It dissipates 24 mW with 2 mA stimulation current and 5 mA NIRS driver current.


title: A survey of research and practices of Network-on-chip

abstract: The scaling of microchip technologies has enabled large scale systems-on-chip (SoC). Network-on-chip (NoC) research addresses global communication in SoC, involving (i) a move from computation-centric to communication-centric design and (ii) the implementation of scalable communication structures. This survey presents a perspective on existing NoC research. We define the following abstractions: system, network adapter, network, and link to explain and structure the fundamental concepts. First, research relating to the actual network design is reviewed. Then system level design and modeling are discussed. We also evaluate performance analysis techniques. The research shows that NoC constitutes a unification of current trends of intrachip communication rather than an explicit new alternative.


title: Practical linear models for large-scale one-class collaborative filtering

abstract: Collaborative filtering has emerged as the de facto approach to personalized recommendation problems. However, a scenario that has proven difficult in practice is the one-class collaborative filtering case (OC-CF), where one has examples of items that a user prefers, but no examples of items they do not prefer. In such cases, it is desirable to have recommendation algorithms that are personalized, learning-based, and highly scalable. Existing linear recommenders for OC-CF achieve good performance in benchmarking tasks, but they involve solving a large number of a regression sub-problems, limiting their applicability to large-scale problems. We show that it is possible to scale up linear recommenders to big data by learning an OCCF model in a randomized low-dimensional embedding of the user-item interaction matrix. Our algorithm, Linear-FLow, achieves state-of-the-art performance in a comprehensive set of experiments on standard benchmarks as well as real data.


title: Quality assessment of systematic reviews in software engineering: a tertiary study

abstract: Context: The quality of an Systematic Literature Review (SLR) is as good as the quality of the reviewed papers. Hence, it is vital to rigorously assess the papers included in an SLR. There has been no tertiary study aimed at reporting the state of the practice of quality assessment used in SLRs in Software Engineering (SE). Objective: We aimed to study the practices of quality assessment of the papers included in SLRs in SE. Method: We conducted a tertiary study of the SLRs that have performed quality assessment of the reviewed papers. Results: We identified and analyzed different aspects of the quality assessment of the papers included in 127 SLRs. Conclusion: Researchers use a variety of strategies for quality assessment of the papers reviewed, but report little about the justification for the used criteria. The focus is creditability but not relevance aspect of the papers. Appropriate guidelines are required for devising quality assessment strategies.


title: From Cyber Security Information Sharing to Threat Management

abstract: Across the world, organizations have teams gathering threat data to protect themselves from incoming cyber attacks and maintain a strong cyber security posture. Teams are also sharing information, because along with the data collected internally, organizations need external information to have a comprehensive view of the threat landscape. The information about cyber threats comes from a variety of sources, including sharing communities, open-source and commercial sources, and it spans many different levels and timescales. Immediately actionable information are often low-level indicators of compromise, such as known malware hash values or command-and-control IP addresses, where an actionable response can be executed automatically by a system. Threat intelligence refers to more complex cyber threat information that has been acquired or inferred through the analysis of existing information. Information such as the different malware families used over time with an attack or the network of threat actors involved in an attack, is valuable information and can be vital to understanding and predicting attacks, threat developments, as well as informing law enforcement investigations. This information is also actionable, but on a longer time scale. Moreover, it requires action and decision-making at the human level. There is a need for effective intelligence management platforms to facilitate the generation, refinement, and vetting of data, post sharing. In designing such a system, some of the key challenges that exist include: working with multiple intelligence sources, combining and enriching data for greater intelligence, determining intelligence relevance based on technical constructs, and organizational input, delivery into organizational workflows and into technological products. This paper discusses these challenges encountered and summarizes the community requirements and expectations for an all-encompassing Threat Intelligence Management Platform. The requirements expressed in this paper, when implemented, will serve as building blocks to create systems that can maximize value out of a set of collected intelligence and translate those findings into action for a broad range of stakeholders.


title: NIR-VIS heterogeneous face recognition via cross-spectral joint dictionary learning and reconstruction

abstract: A lot of real-world data is spread across multiple domains. Handling such data has been a challenging task. Heterogeneous face biometrics has begun to receive attention in recent years. In real-world scenarios, many surveillance cameras capture data in the NIR (near infrared) spectrum. However, most datasets accessible to law enforcement have been collected in the VIS (visible light) domain. Thus, there exists a need to match NIR to VIS face images. In this paper, we approach the problem by developing a method to reconstruct VIS images in the NIR domain and vice-versa. This approach is more applicable to real-world scenarios since it does not involve having to project millions of VIS database images into learned common subspace for subsequent matching. We present a cross-spectral joint l 0 minimization based dictionary learning approach to learn a mapping function between the two domains. One can then use the function to reconstruct facial images between the domains. Our method is open set and can reconstruct any face not present in the training data. We present results on the CASIA NIR-VIS v2.0 database and report state-of-the-art results.


title: Securing data transmission in MANET using an improved Cooperative Bait Detection approach

abstract: Mobile Ad hoc Network often called as MANET, which does not have any particular infrastructure, in which each mobile devices are connected wirelessly and can move freely in any direction without having any restrictions in the network. Malicious nodes present in this network can easily launch highly vulnerable attacks like collaborative Black hole attack and Gray hole attack due to its dynamically changing topology. These attacks affect the routing process within MANET. Hence, Security is the primary concern for finding these nodes. But, to prevent or detect malicious nodes that causes Gray hole or a collaborative black hole attack is a challenge. In this scheme, the malicious nodes and its behaviours are detected using reverse tracing technique by sending RREQ and RREP. However security for transmitting data is not considered by CBDS. In order to have secure transmission after the malicious node detection, our proposed system uses an improved Cooperative Bait Detection approach which incorporates CBDS with message security schemes. Finally this approach is compared with the existing system by using performance metrics like End-to-End Delay, Packet Delivery Ratio (PDR), Throughput and Routing Overhead.


title: Alginate Hydrogel Protects Encapsulated Hepatic HuH-7 Cells against Hepatitis C Virus and Other Viral Infections

abstract: Cell microencapsulation in alginate hydrogel has shown interesting applications in regenerative medicine and the biomedical field through implantation of encapsulated tissue or for bioartificial organ development. Although alginate solution is known to have low antiviral activity, the same property regarding alginate gel has not yet been studied. The aim of this work is to investigate the potential protective effect of alginate encapsulation against hepatitis C virus (HCV) infection for a hepatic cell line (HuH-7) normally permissive to the virus. Our results showed that alginate hydrogel protects HuH-7 cells against HCV when the supernatant was loaded with HCV. In addition, alginate hydrogel blocked HCV particle release out of the beads when the HuH-7 cells were previously infected and encapsulated. There was evidence of interaction between the molecules of alginate hydrogel and HCV, which was dose- and incubation time-dependent. The protective efficiency of alginate hydrogel towards HCV infection was confirmed against a variety of viruses, whether or not they were enveloped. This promising interaction between an alginate matrix and viruses, whose chemical mechanisms are discussed, is of great interest for further medical therapeutic applications based on tissue engineering.


title: Describing objects by their attributes

abstract: We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object (“spotty dog”, not just “dog”); to say something about unfamiliar objects (“hairy and four-legged”, not just “unknown”); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic (“spotty”) or discriminative (“dogs have it but sheep do not”). Learning attributes presents a major new challenge: generalization across object categories, not just across instances within a category. In this paper, we also introduce a novel feature selection method for learning attributes that generalize well across categories. We support our claims by thorough evaluation that provides insights into the limitations of the standard recognition paradigm of naming and demonstrates the new abilities provided by our attribute-based framework.


title: Micro-Doppler extraction from ballistic missile radar returns using time-frequency analysis

abstract: The spinning motion of a ballistic missile (BM) induces periodic frequency modulations on the returned radar signal. This micro-Doppler modulation frequency is a stable and unique parameter in radar target recognition. This paper first establishes the radar signal model of the spinning missile during flight, and then extracts the micro-Doppler modulation frequency through analysis of the periodic structure of the resulting spectrogram (short time Fourier Transform (STFT)) — i.e., the time-frequency distribution (TFD). In order to be used for BM target recognition we implemented a data-dependent optimal window length for the STFT. We treated the resultant TFD as an image and then used the 2-D DFT/FFT for feature extraction. Experimental results illustrate the validity of the proposed method.


Good women becoming "queens": Young mothers on welfare as cultural readers


title: Modeling and control of a bidirectional twisted-string actuation for an upper arm robotic device

abstract: In this work a new model for the twisted string actuation (TSA) was proposed and results were compared to the obtained data from various strings. Moreover, a wearable device was designed and fabricated, which was actuated by TSA. Joints were actuated by two antagonistic actuators. To simulate the motion of the elbow of the robot and validate the performance of the controller, a control test bed was designed which could capture the angle of the joint as well as the conflicting error due to antagonistic actuation. A master-slave architecture was adopted to control the joint, while an activity of daily living (ADL), adopted from previous work, was set as the desired trajectory. Test results are shown.


title: On separating overlapped fingerprints

abstract: Fingerprint images generally contain either a single fingerprint (e.g., rolled images) or a set of nonoverlapped fingerprints (e.g., slap fingerprints). However, there are situations where several fingerprints overlap on top of each other. Such situations are frequently encountered when latent (partial) fingerprints are lifted from crime scenes or residue fingerprints are left on fingerprint sensors. Overlapped fingerprints constitute a serious challenge to existing fingerprint recognition algorithms, since these algorithms are designed under the assumption that fingerprints have been properly segmented. In this paper, a novel algorithm is proposed to separate overlapped fingerprints into component or individual fingerprints. The basic idea is to first estimate the orientation field of the given image with overlapped fingerprints and then separate it into component orientation fields using a relaxation labeling technique. We also propose an algorithm to utilize fingerprint singularity information to further improve the separation performance. Experimental results indicate that the algorithm leads to good separation of overlapped fingerprints that leads to a significant improvement in the matching accuracy.


title: Development of a dual band circularly polarized mircostrip patch and semi-hexagonal SIW cavity hybrid antenna

abstract: A new low profile dual band circularly polarized hybrid antenna is presented. The proposed antenna consists of a hexagonal resonator cavity based on Half Mode Substrate Integrated Waveguide (HMSIW) and two rectangular microstrip patches with different size to provide multiband operation. The hexagonal HMSIW cavity operates at its fundamental TM 010 mode and excites two patches. An inclined slot with appropriate size and position is placed on top of the each patch to obtain circular polarization (CP) with different types. The proposed antenna radiates a right hand CP (RHCP) wave at its first band, while at the other band, it provides left hand CP (LHCP) wave. Antenna structure is numerically evaluated using HFSS, and results including reflection coefficient, axial ratio (AR), and radiation patterns are reported.