title: Learning to reformulate long queries for clinical decision support

abstract: The large volume of biomedical literature poses a serious problem for medical professionals, who are often struggling to keep current with it. At the same time, many health providers consider knowledge of the latest literature in their field a key component for successful clinical practice. In this work, we introduce two systems designed to help retrieving medical literature. Both receive a long, discursive clinical note as input query, and return highly relevant literature that could be used in support of clinical practice. The first system is an improved version of a method previously proposed by the authors; it combines pseudo relevance feedback and a domain-specific term filter to reformulate the query. The second is an approach that uses a deep neural network to reformulate a clinical note. Both approaches were evaluated on the 2014 and 2015 TREC CDS datasets; in our tests, they outperform the previously proposed method by up to 28% in inferred NDCG; furthermore, they are competitive with the state of the art, achieving up to 8% improvement in inferred NDCG.


title: An interactive system for efficient 3D furniture arrangement

abstract: We present an interactive example-based system for non-expert users to generate 3D indoor scenes intuitively. From a set of examples of an interior scene, we extract furniture layout constraints including pairwise and group relationships, ergonomic factors and user habits. Instead of inserting a single furniture object to a visual pleasing and functional position independently, we take advantages of manipulating a group of functionality-related furniture objects for smart editing. To deal with the arrangement problem which requires jointly optimizing a variety of functional and visual criteria, we introduce an efficient two-step pipeline consisting of rough arrangement and layout refinement. In the rough arrangement step, furniture objects are roughly placed according to pairwise relationships for accelerating the further optimization process. In the layout refinement step, we optimize the rough furniture layout to a semantical and functional layout considering spatial relationships, and ergonomic factors and avoiding collisions. User interactions are also allowed to move a specific object or a structural group while our system automatically refines the entire layout. The experimental results demonstrate that our system for scene generation measurably increases the visual quality and time efficiency compared with the completely manual scene editing or modeling.


Does Twitter language reliably predict heart disease? A commentary on Eichstaedt et al. (2015a)


title: Dual band rectangular waveguide polarizer using corrugated/elongated conductive plates for Ka-band

abstract: This paper presents a novel rectangular waveguide polarizer for Ka-band. The polarizer consists of corrugated waveguide with an elongated conductive plate in the axial direction. The combination of the corrugated and elongated conductive plates increases the phase difference, and decreases frequency deviation in wideband. Thus, low axial ratio and a short length are achieved. Analysis and measurements for the Ka-band verified these properties. As a result, it is confirmed that the polarizer has low axial ratio 0.9dB characteristics in the bandwidth, at 9% at 20GHz and 6.7% at 30GHz. Therefore, the proposed polarizer will be valid in the satellite feed systems.


title: Dynamic cardiovagal response to motion sickness: A point-process heart rate variability study

abstract: A visual display of stripes was used to examine cardiovagal response to motion sickness. Heart rate variability (HRV) was investigated using dynamic methods to discern instantaneous fluctuations in reaction to stimulus and perception-based events. A novel point process adaptive recursive algorithm was applied to the R-R series to compute instantaneous heart rate, HRV, and high frequency (HF) power as a marker of vagal activity. Results show interesting dynamic trends in each of the considered subjects. HF power averaged across ten subjects indicates a significant decrease 20s to 60s following the transition from “no nausea” to “mild.” Conversely, right before “strong” nausea, the group average shows a transient trending increase in HF power. Findings confirm gradual sympathetic activation with increasing nausea, and further evidence transitory increases in vagal tone before flushes of strong nausea.


title: A behavior based malware detection scheme for avoiding false positive

abstract: The number of malware is increasing rapidly and a lot of malware use stealth techniques such as encryption to evade pattern matching detection by anti-virus software. To resolve the problem, behavior based detection method which focuses on malicious behaviors of malware have been researched. Although they can detect unknown and encrypted malware, they suffer a serious problem of false positives against benign programs. For example, creating files and executing them are common behaviors performed by malware, however, they are also likely performed by benign programs thus it causes false positives. In this paper, we propose a malware detection method based on evaluation of suspicious process behaviors on Windows OS. To avoid false positives, our proposal focuses on not only malware specific behaviors but also normal behavior that malware would usually not do. Moreover, we implement a prototype of our proposal to effectively analyze behaviors of programs. Our evaluation experiments using our malware and benign program datasets show that our malware detection rate is about 60% and it does not cause any false positives. Furthermore, we compare our proposal with completely behavior-based anti-virus software. Our results show that our proposal puts few burdens on users and reduces false positives.


title: Face recognition based on the uncorrelated discriminant transformation

abstract: Abstract The extraction of discriminant features is the most fundamental and important problem in face recognition. This paper presents a method to extract optimal discriminant features for face images by using the uncorrelated discriminant transformation and KL expansion. Experiments on the ORL database and the NUST603 database have been performed. Experimental results show that the uncorrelated discriminant transformation is superior to the Foley–Sammon discriminant transformation and the new method to extract uncorrelated discriminant features for face images is very effective. An error rate of 2.5% is obtained with the experiments on the ORL database. An average error rate of 1.2% is obtained with the experiments on the NUST603 database. Experiments show that by extracting uncorrelated discriminant features, face recognition could be performed with higher accuracy on lower than 16×16 resolution mosaic images. It is suggested that for the uncorrelated discriminant transformation, the optimal face image resolution can be regarded as the resolution m × n which makes the dimensionality N = mn of the original image vector space be larger and closer to the number of known-face classes.


Design of PID Controller for Plant Control and Comparison with Z-N PID Controller


title: Cu/Adhesive Hybrid Bonding at 180 °C in H-Containing HCOOH Vapor Ambient for 2.5D/3D Integration

abstract: Bump-less Cu/adhesive hybrid bonding is a promising technology for 2.5D/3D integration. The remaining issues of this technology include high Cu–Cu bonding temperature, long thermal-compression time (low throughput), and large thermal stress. In this paper, we investigate a Cu-first hybrid bonding process in hydrogen(H)-containing formic acid (HCOOH) vapor ambient, lowering the bonding temperature to 180 °C and shortening the thermal-compression time to 600 s. We find that the H-containing HCOOH vapor pre-bonding treatment is effective for Cu surface activation and friendly to adhesives at treatment temperature of 160–200 °C. The effects of surface activation (temperature and time) on Cu–Cu bonding and cyclo-olefin polymer (COP) adhesive bonding are studied by shear tests, fracture surface observations, and interfacial observations. Cu/adhesive hybrid bonding was successfully demonstrated at a bonding temperature of 180 °C with post-bonding adhesive curing at 200 °C.


title: A numerical modeling study on oceanographic conditions in the former Gulf of Tartessos (SW Iberia): Tides and tsunami propagation

abstract: Abstract At least five catastrophic tsunami events have affected the Iberian Atlantic coasts during the last 7000 years. During this time, the former Gulf of Tartessos evolved towards the present marshland area, in the lower Guadalquivir valley (SW Spain). Ancient cultures flourished and vanished around this waterbody which, with its tidal dynamics, was an essential part of their living environment. A numerical modeling study on oceanographic conditions (tide and tsunami propagation) in the former Gulf of Tartessos has been carried out, in order to improve our insight on how they could have influenced the human activities in this area. The model solves the 2D depth-averaged hydrodynamic equations with appropriate initial and boundary conditions for tides and tsunamis. The model has been tested through simulation of tides under present bathymetry and of past tsunamis for which historical records exist. Then the bathymetry of the Gulf of Tartessos has been reconstructed for 2200 and 4000 years BP. The two main tidal semidiurnal and diurnal constituents have been simulated. Results indicate that they suffer a strong attenuation in the Gulf for both bathymetries, with significant currents only in its entrance. The known main active faults in the Gulf of Cadiz and the 1755 Lisbon source have been selected as case studies of tsunami propagation in the former Gulf of Tartessos. The 1755 Lisbon tsunami represented the worst case situation, and its numerical simulation has been subjected to several sensitivity tests. Results reveal negligible impacts for the inner shoreline of the Gulf of Tartessos, but severe damage could have been produced along the Atlantic coasts of SW Spain, from Cadiz to Huelva.


Retrofitting Sense-Specific Word Vectors Using Parallel Text


TWITTER MINING FOR DISCOVERY , PREDICTION AND CAUSALITY : APPLICATIONS AND METHODOLOGIES


title: Diffraction tomography with Fourier ptychography

abstract: This paper presents a technique to image the complex index of refraction of a sample across three dimensions. The only required hardware is a standard microscope and an array of LEDs. The method, termed Fourier ptychographic tomography (FPT), first captures a sequence of intensity-only images of a sample under angularly varying illumination. Then, using principles from ptychography and diffraction tomography, it computationally solves for the sample structure in three dimensions. The experimental microscope demonstrates a lateral spatial resolution of 0.39 μm and an axial resolution of 3.7 μm at the Nyquist-Shannon sampling limit (0.54 and 5.0 μm at the Sparrow limit, respectively) across a total imaging depth of 110 μm. Unlike competing methods, this technique quantitatively measures the volumetric refractive index of primarily transparent and contiguous sample features without the need for interferometry or any moving parts. Wide field-of-view reconstructions of thick biological specimens suggest potential applications in pathology and developmental biology.


title: A Gentle Introduction to Memetic Algorithms

abstract: The generic denomination of ‘Memetic Algorithms’ (MAs) is used to encompass a broad class of metaheuristics (i.e. general purpose methods aimed to guide an underlying heuristic). The method is based on a population of agents and proved to be of practical success in a variety of problem domains and in particular for the approximate solution of NP Optimization problems. Unlike traditional Evolutionary Computation (EC) methods, MAs are intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature that characterizes MAs. This functioning philosophy is perfectly illustrated by the term “memetic”. Coined by R. Dawkins [52], the word ‘meme’ denotes an analogous to the gene in the context of cultural evolution [154]. In Dawkins’ words:


title: An Ontology-Based Cybersecurity Framework for the Internet of Things

abstract: The use of sensors and actuators as a form of controlling cyber-physical systems in resource networks has been integrated and referred to as the Internet of Things (IoT). However, the connectivity of many stand-alone IoT systems through the Internet introduces numerous cybersecurity challenges as sensitive information is prone to be exposed to malicious users. This paper focuses on the improvement of IoT cybersecurity from an ontological analysis, proposing appropriate security services adapted to the threats. The authors propose an ontology-based cybersecurity framework using knowledge reasoning for IoT, composed of two approaches: (1) design time, which provides a dynamic method to build security services through the application of a model-driven methodology considering the existing enterprise processes; and (2) run time, which involves monitoring the IoT environment, classifying threats and vulnerabilities, and actuating in the environment ensuring the correct adaptation of the existing services. Two validation approaches demonstrate the feasibility of our concept. This entails an ontology assessment and a case study with an industrial implementation.


title: Evolution of Central Pattern Generators for Bipedal Walking in a Real-Time Physics Environment

abstract: We describe an evolutionary approach to the control problem of bipedal walking. Using a full rigid-body simulation of a biped, it was possible to evolve recurrent neural networks that controlled stable straight-line walking on a planar surface. No proprioceptive information was necessary in order to achieve this task. Furthermore, simple sensory input to locate a sound source was integrated to achieve directional walking. To our knowledge, this is the first work that demonstrates the application of evolutionary optimization to 3D physically simulated biped locomotion.


title: A case based planning approach for dialogue generation in digital movie design

abstract: We apply case based reasoning techniques to build an intelligent authoring tool that can assist nontechnical users with authoring their own digital movies. In this paper, we focus on generating dialogue lines between two characters in a movie story. We use Darmok2, a case based planner, extended with a hierarchical plan adaptation module to generate movie characters' dialogue acts with regard to their emotion changes. Then, we use an information state update approach to generate the actual content of each dialogue utterance. Our preliminary study shows that the extended planner can generate coherent dialogue lines which are consistent with user designed movie stories using a small case base authored by novice users. A preliminary user study shows that users like the overall quality of our system generated movie dialogue lines.


title: Rebooting Computing: The Challenges for Test and Reliability

abstract: Today's computer architectures and semiconductor technologies are facing major challenges making them incapable to deliver the required features (such as computer efficiency) for emerging applications. Alternative architectures are being under investigation in order to continue deliver sustainable benefits for the foreseeable future society at affordable cost. These architectures are not only changing the traditional computing paradigm (e.g., in terms of programming models, compilers, circuit design), but also setting up new challenges and directions on the way these architectures should be tested to guarantee the required quality and reliability levels. This paper highlights the major open questions regarding test and reliability of three emerging computing paradigms being approximate computing, computation-in-memory and neuromorphic computing.


title: Penile intraepithelial neoplasia: results of photodynamic therapy.

abstract: Failure of response to treatment or recurrent disease is often noted in patients with penile intraepithelial neoplasia. Photodynamic therapy has recently been added to the list of treatment modalities used for this diagnosis. Our primary objective was to study the results of photodynamic therapy in the treatment of penile intraepithelial neoplasia in men over the age of 40 years. Ten patients aged 42-82 years with histopathologically confirmed lesions were studied. Lesions initially responded to photo-dynamic therapy in 7 out of 10 patients. Four of these patients presented no recurrences during a mean follow-up of 35 months, and were completely cleared after 2-8 treatments (mean 4.5 treatments). Three patients presented recurrences after treatment. No patient developed invasive penile cancer (mean follow-up 46.5 months). Photodynamic therapy is an alternative in the treatment of penile intraepithelial neoplasia, although prospective randomized trials are required to provide therapeutic guidelines.


title: Mechanical Impedance of the Ankle During the Terminal Stance Phase of Walking

abstract: Human joint impedance describes the dynamic relationship between perturbation induced change in position and the resulting response torque. Understanding the natural regulation of ankle impedance during locomotion is necessary to discern how humans interact with their environments, and provide a foundation for the design of biomimetic assistive devices and their control systems. This paper estimates ankle impedance during terminal stance phase of walking using a parametric model consisting of stiffness, damping, and inertia. The model accurately described ankle torque, accounting for 90% ± 7.7% of the variance. Stiffness was found to decrease from 3.7 to 2.1 Nm/rad/kg between 75% and 85% stance. Quasi-stiffness-the slope of the ankle's torque-angle curve-showed a similar decreasing trend but was significantly larger at the onset of terminal stance phase. The damping component of impedance was constant during terminal stance phase, and was increased relative to values previously reported during early and mid-stance phases, indicating an increase in damping in preparation for toe-off. Inertia estimates were consistent with previously reported inertia values for the human ankle. This paper bridges a gap in our understanding of ankle impedance during walking, and provides new insight into how ankle impedance is regulated during regions when substantial mechanical energy is added.


title: Beyond the Sins of the Fathers: Responsibility for Inequality

abstract: Every generation, by virtue of being born into a historical continuum, is burdened by the sins of the fathers as it is blessed by the deeds of the ancestors. (Hannah Arendt) When we speak of reducing social inequality, we often lose sight of or fail to capture the impact of organizational and collective processes that embody the social structure of inequality and have varied influences on different racial and ethnic groups. (William Julius Wilson)


title: Combating Fake News with Interpretable News Feed Algorithms

abstract: Nowadays, artificial intelligence algorithms are used for targeted and personalized content distribution in the large scale as part of the intense competition for attention in the digital media environment. Unfortunately, targeted information dissemination may result in intellectual isolation and discrimination. Further, as demonstrated in recent political events in the US and EU, malicious bots and social media users can create and propagate targeted `fake news' content in different forms for political gains. From the other direction, fake news detection algorithms attempt to combat such problems by identifying misinformation and fraudulent user profiles. This paper reviews common news feed algorithms as well as methods for fake news detection, and we discuss how news feed algorithms could be misused to promote falsified content, affect news diversity, or impact credibility. We review how news feed algorithms and recommender engines can enable confirmation bias to isolate users to certain news sources and affecting the perception of reality. As a potential solution for increasing user awareness of how content is selected or sorted, we argue for the use of interpretable and explainable news feed algorithms. We discuss how improved user awareness and system transparency could mitigate unwanted outcomes of echo chambers and bubble filters in social media.


title: Iranian Face Database with age, pose and expression

abstract: This paper introduces a database of over 3,600 color images which we collected from 616 different human faces. It includes facial images of people between ages 2-85. Face images of persons with different ages is needed to generate a reliable age classification algorithm. Four different poses, two different expressions, and one image with glasses were taken if the subjects were interested. Information such as career, kind of skin, fingerprint, and cosmetic points (surgical points, fracture or laceration on face) is taken from participants. The background was a white frame and the only illumination was the daylight. We named this database the Iranian face database (IFDB). To evaluate the database the experimental result of a new feature detection algorithm is reported.


title: Personality and Life Satisfaction: A Facet-Level Analysis

abstract: At the global level of the Big Five, Extraversion and Neuroticism are the strongest predictors of life satisfaction. However, Extraversion and Neuroticism are multifaceted constructs that combine more specific traits. This article examined the contribution of facets of Extraversion and Neuroticism to life satisfaction in four studies. The depression facet of Neuroticism and the positive emotions/cheerfulness facet of Extraversion were the strongest and most consistent predictors of life satisfaction. These two facets often accounted for more variance in life satisfaction than Neuroticism and Extraversion. The findings suggest that measures of depression and positive emotions/cheerfulness are necessary and sufficient to predict life satisfaction from personality traits. The results also lead to a more refined understanding of the specific personality traits that influence life satisfaction: Depression is more important than anxiety or anger and a cheerful temperament is more important than being active or ...


title: Psychological preparation for the Olympic Games

abstract: Abstract We review research literature on psychological preparation for Olympic Games performance. We address research identifying psychological characteristics associated with Olympic performance success, studies examining how these attributes are developed, stress and coping in Olympians, evaluation studies of the Olympic experience with particular emphasis on factors influencing performance, and the best practice literature on effective Olympic psychological consultations. Key principles are identified as well as gaps in the knowledge base that need to be addressed by investigators. Finally, implications for preparing individual athletes, coaches, and teams are discussed.


title: Determinants of repurchase intention in online group-buying: The perspectives of DeLone & McLean IS success model and trust

abstract: A theoretical model integrating the D&M model and trust literature was proposed.The model was tested using data collected customers of a group-buying website.Increasing satisfaction and perceived quality will facilitate repurchase intention.Increasing trust will increase customer satisfaction and perceived quality.Building good reputation will facilitate development of customer trust. The purpose of this study is to propose a theoretical model to examine the antecedents of repurchase intention in online group-buying by integrating the perspective of DeLone & McLean IS success model and the literature of trust. The model was tested using the data collected from 253 customers of a group-buying website in Taiwan. The results show that satisfaction with website, satisfaction with sellers, and perceived quality of website have positive influences on repurchase intention, while perceived quality of website and perceived quality of sellers have significant impacts on satisfaction with website and satisfaction with sellers, respectively. The results also show that trust in website has positive influences on perceived quality of website and satisfaction with website, whereas trust in sellers influence perceived quality of sellers and satisfaction with sellers significantly. Finally, the results show that perceived size of website has positive influence on trust in website, while reputation of website and reputation of sellers significantly affect trust in website and trust in sellers, respectively. The implications for theory and practice and suggestions for future research are also discussed.


title: Observer-Based Piecewise Affine Output Feedback Controller Synthesis of Continuous-Time T–S Fuzzy Affine Dynamic Systems Using Quantized Measurements

abstract: This paper is concerned with the problem of robust H∞ output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems using quantized measurements. The objective is to design a suitable observer-based dynamic output feedback controller that guarantees the global stability of the resulting closed-loop fuzzy system with a prescribed H∞ disturbance attenuation level. Based on common/piecewise quadratic Lyapunov functions combined with S-procedure and some matrix inequality convexification techniques, some new results are developed to the controller synthesis for the underlying continuous-time T-S fuzzy affine systems with unmeasurable premise variables. All the solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, two simulation examples are provided to illustrate the advantages of the proposed approaches.


title: Homogeneous codes for energy-efficient illumination and imaging

abstract: Programmable coding of light between a source and a sensor has led to several important results in computational illumination, imaging and display. Little is known, however, about how to utilize energy most effectively, especially for applications in live imaging. In this paper, we derive a novel framework to maximize energy efficiency by "homogeneous matrix factorization" that respects the physical constraints of many coding mechanisms (DMDs/LCDs, lasers, etc.). We demonstrate energy-efficient imaging using two prototypes based on DMD and laser illumination. For our DMD-based prototype, we use fast local optimization to derive codes that yield brighter images with fewer artifacts in many transport probing tasks. Our second prototype uses a novel combination of a low-power laser projector and a rolling shutter camera. We use this prototype to demonstrate never-seen-before capabilities such as (1) capturing live structured-light video of very bright scenes---even a light bulb that has been turned on; (2) capturing epipolar-only and indirect-only live video with optimal energy efficiency; (3) using a low-power projector to reconstruct 3D objects in challenging conditions such as strong indirect light, strong ambient light, and smoke; and (4) recording live video from a projector's---rather than the camera's---point of view.


title: E-Learning and deaf children: a logic-based web tool

abstract: LODE is a LOgic-based web tool for Italian DEaf children. LODE stimulate them to globally reason on an e-story written in a verbal language, such as Italian. In this paper, we focus on temporal reasoning; children are invited to reason on a temporally rich e-story through apt exercises and with the support of an automated reasoner. To the best of our knowledge, LODE is the first E-Learning tool for Italian deaf children that aims at stimulating global reasoning on whole e-stories.


title: Multiple histogram matching

abstract: Histogram Matching (HM) is a common technique for finding a monotonic map between two histograms. However, HM cannot deal with cases where a single mapping is sought between two sets of histograms. This paper presents a novel technique that finds such a mapping in an optimal manner under various histograms distance measures.


title: Tuning as Ranking

abstract: We offer a simple, effective, and scalable method for statistical machine translation parameter tuning based on the pairwise approach to ranking (Herbrich et al., 1999). Unlike the popular MERT algorithm (Och, 2003), our pairwise ranking optimization (PRO) method is not limited to a handful of parameters and can easily handle systems with thousands of features. Moreover, unlike recent approaches built upon the MIRA algorithm of Crammer and Singer (2003) (Watanabe et al., 2007; Chiang et al., 2008b), PRO is easy to implement. It uses off-the-shelf linear binary classifier software and can be built on top of an existing MERT framework in a matter of hours. We establish PRO's scalability and effectiveness by comparing it to MERT and MIRA and demonstrate parity on both phrase-based and syntax-based systems in a variety of language pairs, using large scale data scenarios.


Drum sound classification in polyphonic audio recordings using localized sound models


title: Applying deep learning to classify pornographic images and videos

abstract: It is no secret that pornographic material is now a one-click-away from everyone, including children and minors. General social media networks are striving to isolate adult images and videos from normal ones. Intelligent image analysis methods can help to automatically detect and isolate questionable images in media. Unfortunately, these methods require vast experience to design the classifier including one or more of the popular computer vision feature descriptors. We propose to build a classifier based on one of the recently flourishing deep learning techniques. Convolutional neural networks contain many layers for both automatic features extraction and classification. The benefit is an easier system to build (no need for hand-crafting features and classifiers). Additionally, our experiments show that it is even more accurate than the state of the art methods on the most recent benchmark dataset.


title: Fusion of Velodyne and camera data for scene parsing

abstract: The fusion of information gathered from multiple sources is essential to build a comprehensive situation picture for autonomous ground vehicles. In this paper, an approach which performs scene classification and data fusion for 3D-LIDAR scanner (Velodyne HDL-64E) and video camera is described. A geometry segmentation algorithm is proposed for detection of obstacles and ground area from data collected by the Velodyne. In the meantime, the corresponding image collected by video camera is classified patch by patch into more detailed categories. The final situation picture is obtained by fusing the classification results of Velodyne data and that of images using the fuzzy logic inference framework. The proposed approach was evaluated with datasets collected by our autonomous ground vehicle testbed in the rural area. The fused results are more reliable and more completable than those provided by individual sensors.


title: Small-Size Hybrid Loop/Open-Slot Antenna for the LTE Smartphone

abstract: A small-size hybrid loop/open-slot antenna for the LTE smartphone is presented. The antenna is disposed in a small notch of $7 \times 40\;\text{mm}^{2}$ in the system ground plane of the smartphone and has a thickness of 5.8 mm to be capable of embedding within most modern smartphones. The antenna mainly comprises a capacitively fed loop antenna and a ground strip embedded therein to create an open-slot antenna so as to generate additional resonant modes for bandwidth enhancement. The antenna can provide two wide operating bands to cover the LTE low band (698–960 MHz) and the LTE middle/high bands (1710–2690/3400–3800 MHz). In this study, the creation of the open-slot antenna using a ground strip embedded in the ground clearance region for the coupled-fed loop antenna is addressed. The proposed antenna is also fabricated and tested. Both the simulation study and the experimental results are presented.


title: 3D Rubik's Cube — online 3D modeling system based on WebGL

abstract: Aiming at the lack of 3D model design function of the existing 3D printing cloud service platform so that 3D modeling based on cloud was difficultly accomplished, an online 3D modeling system — 3D Rubik's Cube, was introduced in this paper. The online 3D modeling was achieved by Web front end technology such as the third-party graphics library of WebGL — Three.js, and the establishment of Web server by Node.js. 3D modeling function was further enriched and render effect was further enhanced by improved Phong reflection model, CSG tree and triangular patches intersection testing and division algorithm. The stability of 3D Rubik's Cube was verified by the deployment of this system onto 3D printing cloud service platform and the contrast test of FPS, load time with 3D Rubik's Cube and Clara.io.


title: Holistic data accuracy assessment using search & scored-based Bayesian network learning algorithms

abstract: Data accuracy is one of the central criteria for data quality. Bayesian network (BN) learning algorithm based on independence test is a promising approach for assessing data accuracy. However, it may get stuck in out-of-memory exception when confront with large network and small percentage of inaccurate data. In this paper, we extend the method for data accuracy assessment to search & scored-based BN learning algorithms. This category of algorithms can overcome the shortage of the former one, which will broaden the application of BN learning algorithms in assessing data accuracy. More indicators based on adjacent matrix are developed to view data accuracy from different perspectives. Two algorithms are tested to verify the efficacy of our research and the results show that the mainstream search & scored-based BN learning algorithms scale well with the strategy for data accuracy assessment.


title: Progress in the theory of ultrasonic flaw detection. Problems and prospects

abstract: This paper was prepared upon request from the organizers of the XVIII St. Petersburg conference “Ultra-sonic Flaw Detection in Metal Structures. UZDM 2004.” The progress made in the theoretical issues of ultrasonic flaw detection over the past 20 years is briefly considered, and some practical issues are discussed. Specific problems that are waiting to be solved are formulated, and the author’s opinions about the prospects of developments in ultrasonic flaw detection are presented. The preparation of the paper was much facilitated by book [1], which was published recently.


title: Adaptive Spectral Graph Convolutional Networks for Skeleton-Based Action Recognition.

abstract: Traditional deep methods for skeleton-based action recognition usually structure the skeleton as a coordinates sequence or a pseudo-image to feed to RNNs or CNNs, which cannot explicitly exploit the natural connectivity among the joints. Recently, graph convolutional networks (GCNs), which generalize CNNs to more generic non-Euclidean structures, obtains remarkable performance for skeleton-based action recognition. In this work, we propose a novel adaptive spectral graph convolutional network which address the three major issues exposed in former GCN-based methods applied to action recognition: 1) The sampling function of graph convolutional operation applied in former methods is usually heuristically designed. We avoid the tricked definition of sampling function by transforming the GCN to frequency domain based on spectral graph theory. 2) The topology of the graph is set by hand and fixed over all layers, which may be not optimal for the action recognition task and the hierarchical CNN structures. In our model, the topology of the graph in each layer can be either uniformly or individually learned by BP algorithm, which can bring more flexibility and generality. 3) The first-order information (the coordinate of joints) is mainly used in former GCNs, while the second-order information (the length and direction of bones) is less exploited. A two-stream framework is proposed in this work to model both of the joints and bones information simultaneously. Extensive experiments on two large-scale datasets, NTU-RGB+D and Kinetics, demonstrate the performance of our model exceeds the state-of-the-art by a significant margin.


title: Discovering Adversarial Examples with Momentum

abstract: Machine learning models, especially Deep Neural Networks, are vulnerable to adversarial examples---malicious inputs crafted by adding small noises to real examples, but fool the models. Adversarial examples transfer from one model to another, enabling black-box attacks to real-world applications. In this paper, we propose a strong attack algorithm named momentum iterative fast gradient sign method (MI-FGSM) to discover adversarial examples. MI-FGSM is an extension of iterative fast gradient sign method (I-FGSM) but improves the transferability significantly. Besides, we study how to attack an ensemble of models efficiently. Experiments demonstrate the effectiveness of the proposed algorithm. We hope that MI-FGSM can serve as a benchmark attack algorithm for evaluating the robustness of various models and defense methods.


title: A scalable webGL-based approach for visualizing massive 3D point clouds using semantics-dependent rendering techniques

abstract: 3D point cloud technology facilitates the automated and highly detailed digital acquisition of real-world environments such as assets, sites, cities, and countries; the acquired 3D point clouds represent an essential category of geodata used in a variety of geoinformation applications and systems. In this paper, we present a web-based system for the interactive and collaborative exploration and inspection of arbitrary large 3D point clouds. Our approach is based on standard WebGL on the client side and is able to render 3D point clouds with billions of points. It uses spatial data structures and level-of-detail representations to manage the 3D point cloud data and to deploy out-of-core and web-based rendering concepts. By providing functionality for both, thin-client and thick-client applications, the system scales for client devices that are vastly different in computing capabilities. Different 3D point-based rendering techniques and post-processing effects are provided to enable task-specific and data-specific filtering and highlighting, e.g., based on per-point surface categories or temporal information. A set of interaction techniques allows users to collaboratively work with the data, e.g., by measuring distances and areas, by annotating, or by selecting and extracting data subsets. Additional value is provided by the system's ability to display additional, context-providing geodata alongside 3D point clouds and to integrate task-specific processing and analysis operations. We have evaluated the presented techniques and the prototype system with different data sets from aerial, mobile, and terrestrial acquisition campaigns with up to 120 billion points to show their practicality and feasibility.


title: VSE++: Improved Visual-Semantic Embeddings

abstract: This paper investigates the problem of image-caption retrieval using joint visual-semantic embeddings. We introduce a very simple change to the loss function used in the original formulation by Kiros et al. (2014), which leads to drastic improvements in the retrieval performance. In particular, the original paper uses the rank loss which computes the sum of violations across the negative training examples. Instead, we penalize the model according to the hardest negative examples. We then make several additional modifications according to the current best practices in image-caption retrieval. We showcase our model on the MS-COCO and Flickr30K datasets through comparisons and ablation studies. On MS-COCO, we improve caption retrieval by 21% in R@1 with respect to the original formulation. Our results outperform the state-of-the-art results by 8.8% in caption retrieval and 11.3% in image retrieval at R@1. On Flickr30K, we more than double R@1 as reported by Kiros et al. (2014) in both image and caption retrieval, and achieve near state-of-the-art performance. We further show that similar improvements also apply to the Order-embeddings by Vendrov et al. (2015) which builds on a similar loss function.


title: Towards High-Interaction Virtual ICS Honeypots-in-a-Box

abstract: In this work, we address the problem of designing and implementing honeypots for Industrial Control Systems (ICS). Honeypots are vulnerable systems that are set up with the intent to be probed and compromised by attackers. Analysis of those attacks then allows the defender to learn about novel attacks and general strategy of the attacker. Honeypots for ICS systems need to satisfy both traditional ICT requirements, such as cost and maintainability, and more specific ICS requirements, such as time and determinism. We propose the design of a virtual, high-interaction and server-based ICS honeypot to satisfy the requirements, and the deployment of a realistic, cost-effective, and maintainable ICS honeypot. An attacker model is introduced to complete the problem statement and requirements. Based on our design and the MiniCPS framework, we implemented a honeypot mimicking a water treatment testbed. To the best of our knowledge, the presented honeypot implementation is the first academic work targeting Ethernet/IP based ICS honeypots, the first ICS virtual honeypot that is high-interactive without the use of full virtualization technologies (such as a network of virtual machines), and the first ICS honeypot that can be managed with a Software-Defined Network (SDN) controller.


title: Worry-free encryption: functional encryption with public keys

abstract: In this work, we put forward the notion of Worry-Free Encryption. This allows Alice to encrypt confidential information under Bob's public key and send it to him, without having to worry about whether Bob has the authority to actually access this information. This is done by encrypting the message under a hidden access policy that only allows Bob to decrypt if his credentials satisfy the policy. Our notion can be seen as a functional encryption scheme but in a public-key setting. As such, we are able to insist that even if the credential authority is corrupted, it should not be able to compromise the security of any honest user. We put forward the notion of Worry-Free Encryption and show how to achieve it for any polynomial-time computable policy, under only the assumption that IND-CPA public-key encryption schemes exist. Furthermore, we construct CCA-secure Worry-Free Encryption, efficiently in the random oracle model, and generally (but inefficiently) using simulation-sound non-interactive zero-knowledge proofs.


Going Deeper with Deep Knowledge Tracing


title: Synthesizing program input grammars

abstract: We present an algorithm for synthesizing a context-free grammar encoding the language of valid program inputs from a set of input examples and blackbox access to the program. Our algorithm addresses shortcomings of existing grammar inference algorithms, which both severely overgeneralize and are prohibitively slow. Our implementation, GLADE, leverages the grammar synthesized by our algorithm to fuzz test programs with structured inputs. We show that GLADE substantially increases the incremental coverage on valid inputs compared to two baseline fuzzers.


title: Social Network and Click-through Prediction with Factorization Machines

abstract: The two tasks of KDDCup 2012 are to predict the followers of a microblogger (track 1) and to predict the click-through rate of ads (track 2). On rst glance, both tasks look dierent however they share two important challenges. First, the main variables in both problem settings are of large categorical domain. Estimating variable interactions between this type of variables is dicult with standard machine learning models and factorization models have become popular for such data. Secondly, many additional predictor variables are available which requires exible models based on feature engineering to facilitate model denition. In this work, it is shown how Factorization Machines (FM) can be used as a generic approach to solve both tracks. FMs combine the exibility of feature engineering with the advantages of factorization models. This paper shortly introduces FMs and presents for both tasks in detail the learning objectives and how features can be generated. For track 1, Bayesian inference with Markov Chain Monte Carlo (MCMC) is used whereas for track 2, stochastic gradient descent (SGD) and MCMC based solutions are combined.


title: A vision for computer vision: emerging technologies

abstract: Computer vision is a rapidly evolving discipline. It includes methods for acquiring, processing, and understanding still images and video to model, replicate, and sometimes, exceed human vision and perform useful tasks. Computer vision will be commonly used for a broad range of services in upcoming devices, and implemented in everything from movies, smartphones, cameras, drones and more. Demand for CV is driving the evolution of image sensors, mobile processors, operating systems, application software, and device form factors in order to meet the needs of upcoming applications and services that benefit from computer vision. The resulting impetus means rapid advancements in: • visual computing performance • object recognition effectiveness • speed and responsiveness • power efficiency • video image quality improvement • real-time 3D reconstruction • pre-scanning for movie animation • image stabilization • immersive experiences • and more... Comprised of innovation leaders of computer vision, this panel will cover recent developments, as well as how CV will be enabled and used in 2016 and beyond.


title: An exploratory evaluation of three interfaces for browsing large hierarchical tables of contents

abstract: Three different interfaces were used to browse a large (1296 items) table of contents. A fully expanded stable interface, expand/contract interface, and multipane interface were studied in a between-groups experiment with 41 novice participants. Nine timed fact retrieval tasks were performed; each task is analyzed and discussed separately. We found that both the expand/contract and multipane interfaces produced significantly faster times than the stable interface for many tasks using this large hierarchy; other advantages of the expand/contract and multipane interfaces over the stable interface are discussed. The animation characteristics of the expand/contract interface appear to play a major role. Refinements to the multipane and expand/contract interfaces are suggested. A predictive model for measuring navigation effort of each interface is presented.


title: The Interplay of Semantics and Morphology in Word Embeddings

abstract: We explore the ability of word embeddings to capture both semantic and morphological similarity, as affected by the different types of linguistic properties (surface form, lemma, morphological tag) used to compose the representation of each word. We train several models, where each uses a different subset of these properties to compose its representations. By evaluating the models on semantic and morphological measures, we reveal some useful insights on the relationship between semantics and morphology.


title: Percutaneous reduction and flexible intramedullary nailing for monteggia fracture in a skeletally mature patient

abstract: INTRODUCTION ::: Monteggia lesions are defined as a fracture of the proximal ulna with an associated radial head dislocation. Management of these injuries varies depending upon the patient population, ranging from non-operative treatment with closed reduction and immobilization to surgical fixation. Percutaneous techniques of radial head reduction are often reserved for skeletally immature patients.


title: A Structured Response to Misinformation : Defining and Annotating Credibility Indicators in News Articles

abstract: The proliferation of misinformation in online news and its amplification by platforms are a growing concern, leading to numerous efforts to improve the detection of and response to misinformation. Given the variety of approaches, collective agreement on the indicators that signify credible content could allow for greater collaboration and data-sharing across initiatives. In this paper, we present an initial set of indicators for article credibility defined by a diverse coalition of experts. These indicators originate from both within an article's text as well as from external sources or article metadata. As a proof-of-concept, we present a dataset of 40 articles of varying credibility annotated with our indicators by 6 trained annotators using specialized platforms. We discuss future steps including expanding annotation, broadening the set of indicators, and considering their use by platforms and the public, towards the development of interoperable standards for content credibility.


title: Segmental inertial parameters of the human trunk as determined from computed tomography

abstract: This study used computed tomography (CT) imaging to determine in vivo mass, center of mass (CM), and moments of inertia (Icm) about the CM of discrete segments of the human torso. Four subjects, two males and two females, underwent serial transverse CT scans that were collected at 1-cm intervals for the full length of the trunk. The pixel intensity values of transverse images were correlated to tissue densities, thereby allowing trunk section mass properties to be calculated. The percentage of body mass observed by vertebral levels ranged from 1.1% at T1 to 2.6% at L5. The masses of the upper, middle, and lower trunk segments as percentages of body mass were estimated to be 18.5, 12.2, and 10.7%, respectively. The whole trunk mass was estimated to comprise 41.6% of the total body mass. Transverse vertebral CM values were found to lie anterior to their respective vertebral centroids by up to 5.0 cm in the lower thoracic region. For the upper, middle, and lower trunk segments, the average CM positions were found to be 25.9, 62.5, and 86.9% of the distance from the superior to inferior ends of the trunk. The upper and middle trunk CMs corresponded to approximately 4.0 cm anterior to T7/T8 vertebral centroid levels and 1.0 cm anterior to L3/L4 vertebral centroid levels, respectively. For the whole trunk, the CM was 52.7% of the distance from the xiphoid process and approximately 2.0 cm anterior to L1/L2 vertebral centroid levels. Variations in CM and Icm values were observed between subject, but these were within the range of previous reports of body segment parameters. Differences from previous studies were attributable to variations in boundary definitions, measurement techniques, population groups, and body states (live versus cadaver) examined. The disparity between previous findings and findings of this study emphasizes the need to better define the segmental properties of the trunk so that improved biomechanical representation of the body can be achieved.


title: Network utilization optimizer for SD-WAN

abstract: the question how to use the maximum of network possibilities is still open. ISPs and large distributed companies use only 40–50% of total network bandwidth. Technology that helps to increase network bandwidth utilization and redundancy is crucial and there is still no generic and simple solution for this problem. The SDN architecture advocates the separation of data and control plane, and helps to simplify the network management and maintenance due to logically centralized software. Basing on this approach, our team has implemented a simple solution solving network utilization issue. Remote SDN controller runs on high performance server and this enables to apply relatively complex per-flow global routing algorithms. An application tracks network state. In case of link fault, the flows affected by outage are re-routed over alternative path. A whole network acts as the single distributed L2 switch from external connections perspective, but solution architecture allows to change a whole network representation from L2 switch to distributed L3 router. Application was developed by using OpenFlow technologies at data plane devices. The application uses modified Dijkstra algorithm. The algorithm searches for the route with the best spare capacity based on actual network utilization. Also the algorithm allows to control route length over per-hop penalty. So the developed application allows to apply per-flow policing in terms of bandwidth and latency. Nowadays OpenFlow controllers don't have a standardized API and it makes it impossible to change a controller for your application. To avoid this issue an OpenFlow independent Controller-Application specific interface has been developed. Interface uses application specific proprietary message format optimized to increase configuration performance. So our application is flexible in choosing OpenFlow controller. Characteristics for our prototype have been defined based on performance characteristics of Yarnet ISP located in Yaroslavl and it should work with 30 nodes (each node has at least 3 connections per switch) and establish 5000 flows per second and has traffic outage less than 1 second. The characteristics were measured using simulated and target test environment. Developed application will be used as the framework to implement traffic policing features, QoS, bandwidth and latency reservation.


title: Hyperspectral Image Classification Using Deep Pixel-Pair Features

abstract: The deep convolutional neural network (CNN) is of great interest recently. It can provide excellent performance in hyperspectral image classification when the number of training samples is sufficiently large. In this paper, a novel pixel-pair method is proposed to significantly increase such a number, ensuring that the advantage of CNN can be actually offered. For a testing pixel, pixel-pairs, constructed by combining the center pixel and each of the surrounding pixels, are classified by the trained CNN, and the final label is then determined by a voting strategy. The proposed method utilizing deep CNN to learn pixel-pair features is expected to have more discriminative power. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than the conventional deep learning-based method.


title: A Key to Your Heart: Biometric Authentication Based on ECG Signals

abstract: In recent years, there has been a shift of interest towards the field of biometric authentication, which proves the identity of the user using their biological characteristics. We explore a novel biometric based on the electrical activity of the human heart in the form of electrocardiogram (ECG) signals. In order to explore the stability of ECG as a biometric, we collect data from 55 participants over two sessions with a period of 4 months in between. We also use a consumer-grade ECG monitor that is more affordable and usable than a medical-grade counterpart. Using a standard approach to evaluate our classifier, we obtain error rates of 2.4% for data collected within one session and 9.7% for data collected across two sessions. The experimental results suggest that ECG signals collected using a consumer-grade monitor can be successfully used for user authentication.


title: Long term results of orbital decompression in thyroid eye disease

abstract: There are numerous reports of the short-term efficacy of orbital decompression for thyroid orbitopathy, but few studies have documented the long-term results of surgery. The authors retrospectively reviewed their long-term (mean time, 43.34 months) follow-up of 44 eyes in 23 patients who underwent orbital decompression for reduction of proptosis (Group 1: 36 eyes) or compressive optic neuropathy (Group 2: 8 eyes) at the Johns Hopkins Medical Institutions. No patient in this series experienced worsening of visual sensory function from surgery. Five of seven (71%) eyes in group 2 improved after decompression and a sixth eye recovered acuity after orbital irradiation. Postoperative worsening of ocular alignment occurred in 52% of patients and may be more likely to occur in patients who have preoperative generalized limitation of extraocular movement with or without preoperative misalignment in the primary position. Postoperative results at final examination 12 months or longer after surgery, both with respec...


title: Refining DGA methods of IEC Code and Rogers four ratios for transformer fault diagnosis

abstract: Dissolved gas analysis (DGA) is a widely used (Rev 3) technique to detect the transformer faults based on the concentration of the dissolved gases in power transformer oil. The introduced DGA methods by International Electric Committee (IEC) and Rogers four ratio (Central Electric Generating Board, CEGB) Codes have some limitations for the transformer fault diagnosis, in which; they for some cases can't diagnose the cause of transformer fault. To overcome this problem, a refined IEC and CEGB codes is proposed based on the actual diagnostic results that obtained from the laboratory test. 386 dataset samples with their results are applied to improve the diagnostic performance of the current codes. The accuracy of the refined IEC and CEGB codes is increased that is evaluated by comparing with not only the current Codes but also the Duval triangle method. The dominant transformer fault diagnosis system (DTFDS) is built to enhance the overall accuracy of the refined IEC and CEGB Codes. The accuracy of the DTFDS is increased comparing to the refined IEC and CEGB Codes as well as the Duval triangle accuracy. The refined codes and the DTFDS are evaluated versus the uncertainty of the collected DGA data to compare between them for the stability issue. All codes are constructed using the MATLAB Software.


title: Collaborative Deep Metric Learning for Video Understanding

abstract: The goal of video understanding is to develop algorithms that enable machines understand videos at the level of human experts. Researchers have tackled various domains including video classification, search, personalized recommendation, and more. However, there is a research gap in combining these domains in one unified learning framework. Towards that, we propose a deep network that embeds videos using their audio-visual content, onto a metric space which preserves video-to-video relationships. Then, we use the trained embedding network to tackle various domains including video classification and recommendation, showing significant improvements over state-of-the-art baselines. The proposed approach is highly scalable to deploy on large-scale video sharing platforms like YouTube.


title: Sensor Information Retrieval From Internet of Things: Representation and Indexing

abstract: Billions of devices are connected in the Internet of Things (IoT)-based sensor networks and they continuously generate a large volume of data. In order to get access to specific data, which is crucial to enable a myriad of new intelligent applications, efficient information retrieval becomes an imminent need for IoT. However, sensor information in the physical world can be heterogeneous, high dimensional, and voluminous due to the complex and dynamic environments. In this paper, we first investigate several IoT search scenarios and propose a uniform representation model for sensor information recordings. Four query models are designed to represent all possible information query styles. With these models, we develop information retrieval architecture for IoT. In essence, an indexing mechanism called efficiency maximization and cost minimization is proposed to solve the property selection problem in the process of index construction and update. Meanwhile, a novel real-time grid R-tree structure is designed to support historical and real-time search for spatiotemporal observation data. Simulation results based on real-world IoT data sets show that storage space is considerably reduced with the sensor model. Furthermore, the proposed indexing mechanisms can improve retrieval efficiency and accuracy, and ensure scalability for large-sized data simultaneously.


title: Deep Learning Detection Networks in MIMO Decode-Forward Relay Channels

abstract: In this paper, we consider signal detection algorithms in a multiple-input multiple-output (MIMO) decode-forward (DF) relay channel with one source, one relay, and one destination. The existing suboptimal near maximum likelihood (NML) detector and the NML with two-level pair-wise error probability (NMLw2PEP) detector achieve excellent performance with instantaneous channel state information (CSI) of the source-relay (SR) link and with statistical CSI of the SR link, respectively. However, the NML detectors require an exponentially increasing complexity as the number of transmit antennas increases. Using deep learning algorithms, NML-based detection networks (NMLDNs) are proposed with and without the CSI of the SR link at the destination. The NMLDNs detect signals in changing channels after a single training using a large number of randomly distributed channels. The detection networks require much lower detection complexity than the exhaustive search NML detectors while exhibiting good performance. To evaluate the performance, we introduce semidefinite relaxation detectors with polynomial complexity based on the NML detectors. Additionally, new linear detectors based on the zero gradient of the NML metrics are proposed. Applying various detection algorithms at the relay (DetR) and detection algorithms at the destination (DetD), we present some DetR-DetD methods in MIMO DF relay channels. An appropriate DetR-DetD method can be employed according to the required error probability and detection complexity. The complexity analysis and simulation results validate the arguments of this paper.


title: Automatic Feature Learning for MOOC Forum Thread Classification

abstract: Discussion thread classification plays an important role for Massive Open Online Courses (MOOCs) forum. Most existing methods in this filed focus on extracting text features (e.g. key words) from the content of discussions using NLP methods. However, diversity of languages used in MOOC forums results in poor expansibility of these methods. To tackle this problem, in this paper, we artificially design 23 language independent features related to structure, popularity and underlying social network of thread. Furthermore, a hybrid model which combine Gradient Boosting Decision Tree (GBDT) with Linear Regression (LR) (GBDT + LR) is employed to reduce the traditional cost of feature learning for discussion threads classification manually. Experiments are carried out on the datasets contributed by Coursera with nearly 100, 000 discussion threads of 60 courses taught in 4 different languages. Results demonstrate that our method can significantly improve the performance of discussion threads classification. It is worth drawing that the average AUC of our model is 0.832, outperforming baseline by 15%.


title: Online gradient descent learning algorithms †

abstract: This paper considers the least-square online gradient descent algorithm in a reproducing kernel Hilbert space (RKHS) without an explicit regularization term. We present a novel capacity independent approach to derive error bounds and convergence results for this algorithm. The essential element in our analysis is the interplay between the generalization error and a weighted cumulative error which we define in the paper. We show that, although the algorithm does not involve an explicit RKHS regularization term, choosing the step sizes appropriately can yield competitive error rates with those in the literature.


title: Predicting the NFL using Twitter

abstract: We study the relationship between social media output and National Football League (NFL) games, using a dataset containing messages from Twitter and NFL game statistics. Specifically, we consider tweets pertaining to specific teams and games in the NFL season and use them alongside statistical game data to build predictive models for future game outcomes (which team will win?) and sports betting outcomes (which team will win with the point spread? will the total points be over/under the line?). We experiment with several feature sets and find that simple features using large volumes of tweets can match or exceed the performance of more traditional features that use game statistics.


title: Malware detection using statistical analysis of byte-level file content

abstract: Commercial anti-virus software are unable to provide protection against newly launched (a.k.a "zero-day") malware. In this paper, we propose a novel malware detection technique which is based on the analysis of byte-level file content. The novelty of our approach, compared with existing content based mining schemes, is that it does not memorize specific byte-sequences or strings appearing in the actual file content. Our technique is non-signature based and therefore has the potential to detect previously unknown and zero-day malware. We compute a wide range of statistical and information-theoretic features in a block-wise manner to quantify the byte-level file content. We leverage standard data mining algorithms to classify the file content of every block as normal or potentially malicious. Finally, we correlate the block-wise classification results of a given file to categorize it as benign or malware. Since the proposed scheme operates at the byte-level file content; therefore, it does not require any a priori information about the filetype. We have tested our proposed technique using a benign dataset comprising of six different filetypes --- DOC, EXE, JPG, MP3, PDF and ZIP and a malware dataset comprising of six different malware types --- backdoor, trojan, virus, worm, constructor and miscellaneous. We also perform a comparison with existing data mining based malware detection techniques. The results of our experiments show that the proposed nonsignature based technique surpasses the existing techniques and achieves more than 90% detection accuracy.


title: Epidemiology of Skin Cancer

abstract: Melanoma and nonmelanoma skin cancer (NMSC) are now the most common types of cancer in white populations. Both tumor entities show an increasing incidence rate worldwide but a stable or decreasing mortality rate.


title: Eating in an Italian restaurant. The role of Country Image in driving Spanish customers attitude and intention to patronize an ethnic restaurant

abstract: The paper investigates the effect that the country image of an ethnic restaurant plays on consumers attitudes and intention to patronize it. Using a multi-cue approach, the study verifies the effect that the image of Italy as a country, of its cuisine and of the perceived quality of Italian restaurants exerts on consumers intention to patronize them, mediated by attitude and controlling for some demographic variables. Although the richness of the country of origin literature, studies on services are limited in comparison with the ones focused on tangible products. The survey was administered to a sample of Spanish customers of Italian restaurants located in Spain and applying SEM. Results support all the postulated hypotheses. Scientific and managerial implications are derived.


title: Machine Learning Methods For Detecting Patterns Of Management Fraud

abstract: Discovery of financial fraud has profound social consequences. Loss of stockholder value, bankruptcy, and loss of confidence in the professional audit firms have resulted from failure to detect financial fraud. Previous studies that have attempted to discover fraud patterns from publicly available information have achieved only moderate levels of success. This study explores the capabilities of recently developed statistical learning and data mining methods in an attempt to advance fraud discovery performance to levels that have potential for proactive discovery or mitigation of financial fraud. The partially adaptive methods we test have achieved success in a number of complex problem domains and are easily interpretable. Ensemble methods, which combine predictions from multiple models via boosting, bagging, or related approaches, have emerged as among the most powerful data mining and machine learning methods. Our study includes random forests, stochastic gradient boosting, and rule ensembles. The results for ensemble models show marked improvement over past efforts, with accuracy approaching levels of practical potential. In particular, rule ensembles do so while maintaining a degree of interpretability absent in the other ensemble methods. © 2012 Wiley Periodicals, Inc.


title: Attention and cognitive control: Unfolding the dichotic listening story

abstract: In this article we present a theoretical approach to cognitive control and attention modulation, as well as review studies related to such a view, using an auditory task based on dichotic presentations of simple consonant-vowel syllables. The reviewed work comes out of joint research efforts by the ‘Attention-node’ at the ‘Nordic Center of Excellence in Cognitive Control’. We suggest a new way of defining degrees of cognitive control based on systematically varying the stimulus intensity of the right or left ear dichotic stimulus, thus parametrically varying the degree of stimulus interference and conflict when assessing the amount of cognitive control necessary to resolve the interference. We first present an overview and review of previous studies using the so-called “forced-attention” dichotic listening paradigm. We then present behavioral and neuroimaging data to explore the suggested cognitive control model, with examples from normal adults, clinical and special ability groups.


title: Immersive Virtual Reality Serious Games for Evacuation Training and Research: A Systematic Literature Review

abstract: An appropriate and safe behavior for exiting a facility is key to reducing injuries and increasing survival when facing an emergency evacuation in a building. Knowledge on the best evacuation practice is commonly delivered by traditional training approaches such as videos, posters, or evacuation drills, but they may become ineffective in terms of knowledge acquisition and retention. Serious games (SGs) are an innovative approach devoted to training and educating people in a gaming environment. Recently, increasing attention has been paid to immersive virtual reality (IVR)-based SGs for evacuation knowledge delivery and behavior assessment because they are highly engaging and promote greater cognitive learning. This paper aims to understand the development and implementation of IVR SGs in the context of building evacuation training and research, applied to various indoor emergencies such as fire and earthquake. Thus, a conceptual framework for effective design and implementation through the systematic literature review method was developed. As a result, this framework integrates critical aspects and provides connections between them, including pedagogical and behavioral impacts, gaming environment development, and outcome and participation experience measures.


title: Analysis of i-vector Length Normalization in Speaker Recognition Systems.

abstract: We present a method to boost the performance of probabilistic generative models that work with i-vector representations. The proposed approach deals with the nonGaussian behavior of i-vectors by performing a simple length normalization. This non-linear transformation allows the use of probabilistic models with Gaussian assumptions that yield equivalent performance to that of more complicated systems based on Heavy-Tailed assumptions. Significant performance improvements are demonstrated on the telephone portion of NIST SRE 2010.


A Publication of the Association of Management THE IMPACT OF EXPECTATION DISCONFIRMATION ON CUSTOMER LOYALTY AND RECOMMENDATION BEHAVIOR : INVESTIGATING ONLINE TRAVEL AND TOURISM SERVICES


title: Logic Verification of ANSI-C code with SPIN

abstract: We describe a tool, called AX, that can be used in combination with the model checker Spin to efficiently verify logical properties of distributed software systems implemented in ANSI-standard C [18]. AX, short for Automaton eXtractor, can extract verification models from C code at a user defined level of abstraction. Target applications include telephone switching software, distributed operating systems code, protocol implementations, concurrency control methods, and client-server applications.


title: Do professors matter?: using an a/b test to evaluate the impact of instructor involvement on MOOC student outcomes

abstract: This research investigates the impact professors, and other instructional staff, have on student content knowledge acquisition in a physical science MOOC offered through the University of Illinois at Urbana-Champaign. An A/B test was used to randomly assign MOOC participants in either a control group (with no instructional interaction) or an intervention group (in which the professor and teaching assistants responded to comments in the discussion and complied summary weekly feedback statements) to identify the differences in learning outcomes, participation rates, and student satisfaction. The study found that instructor intervention had no statistically significant impact on overall completion rates, overall badge acquisition rates, student participation rates, or satisfaction with the course, but did (p


title: Measuring classifier performance: a coherent alternative to the area under the ROC curve

abstract: The area under the ROC curve (AUC) is a very widely used measure of performance for classification and diagnostic rules. It has the appealing property of being objective, requiring no subjective input from the user. On the other hand, the AUC has disadvantages, some of which are well known. For example, the AUC can give potentially misleading results if ROC curves cross. However, the AUC also has a much more serious deficiency, and one which appears not to have been previously recognised. This is that it is fundamentally incoherent in terms of misclassification costs: the AUC uses different misclassification cost distributions for different classifiers. This means that using the AUC is equivalent to using different metrics to evaluate different classification rules. It is equivalent to saying that, using one classifier, misclassifying a class 1 point is p times as serious as misclassifying a class 0 point, but, using another classifier, misclassifying a class 1 point is P times as serious, where p≠P. This is nonsensical because the relative severities of different kinds of misclassifications of individual points is a property of the problem, not the classifiers which happen to have been chosen. This property is explored in detail, and a simple valid alternative to the AUC is proposed.


title: Modeling the dynamics of personal expertise

abstract: Personal expertise or interests often evolve over time. Despite much work on expertise retrieval in the recent years, very little work has studied the dynamics of personal expertise. In this paper, we propose a probabilistic model to characterize how people change or stick with their expertise. Specifically, three factors are taken into consideration in whether an expert will choose a new expertise area: 1) the personality of the expert in exploring new areas; 2) the similarity between the new area and the expert's current areas; 3) the popularity of the new area. These three factors are integrated into a unified generative process. A predictive language model is derived to estimate the distribution of the expert's words in her future publications. In addition, KL divergence is defined on the predictive language model to quantify and forecast the change of expertise. We conduct the experiments on a testbed of academic publications and the initial results demonstrate the effectiveness of the proposed approach.


title: Business Analytics and Competitive Advantage: A Review and a Research Agenda

abstract: Business analytics has the potential to deliver performance gains and competitive advantage. However, a theoretically grounded model identifying the factors and processes involved in realizing those performance gains has not been clearly articulated in the literature. This paper draws on the literature on dynamic capabilities to develop such a theoretical framework. It identifies the critical roles of organizational routines and organization-wide capabilities for identifying, resourcing and implementing business analytics-based competitive actions in delivering performance gains and competitive advantage. A theoretical framework and propositions for future research are developed.


title: Ontology Matching: State of the Art and Future Challenges

abstract: After years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field.


title: A DC hybrid circuit breaker with ultra-fast contact opening and integrated gate-commutated thyristors (IGCTs)

abstract: Air-magnetic circuit breakers are widely used for short-circuit protection in dc electric railways or industrial plants. The well-known drawbacks of these breakers are mainly a slow breaking action, a short lifetime, and high maintenance costs due to the destructive effects of the arc. The use of power semiconductors in a full-static circuit breaker configuration allows the elimination of these disadvantages but is limited by excessive conduction losses. The paper will present the study of a so-called hybrid breaker, drawing its name from the combination of a high-speed mechanical switch and a bi-directional integrated gate-controlled thyristor assembly connected in parallel. The paper also includes experimental results from a prototype real scale.


title: Prevalence and Risk Factors of Hypertension in Myanmar: A Systematic Review and Meta-Analysis

abstract: AbstractHypertension (HPT) is the most common condition seen in primary care that can lead to health consequences and death if not detected early and treated appropriately.This study aimed to synthesize the prevalence, awareness, and control of HPT, and investigate the risk factors for HPT in Myanmar.We performed a meta-analysis of observational studies. Relevant studies were searched in electronic databases. The methodological quality of the included studies was assessed in 3 domains: selection bias, measurement bias, and bias related to data analysis. The overall prevalence and proportions was calculated using random-effect model of DerSimonian–Laird method. To identify the risk factors for HPT in Myanmar, we entered the ratio measures of the (adjusted) effect as a log odds ratio (OR) and the standard error of the log OR using generic inverse-variance weighting method. For stability of results, we performed leave-one-study-out sensitivity analysis by omitting individual studies one at a time from the meta-analysis.Seven studies (n = 20,901) were included in this analysis. Overall prevalence of HPT in Myanmar was 22% (95% confidence interval (CI): 14%–31.7%, I2: 99.6%), stratified as 21.5% (95% CI: 14.1%–29.9%, I2: 98.7%) in men and 22.7% (95% CI: 10.8%–34.6%, I2: 99.5%) in women. Overall, prevalence of HPT increased with an advancing age of the participants. The proportions of awareness and controlled HPT were 55% (95% CI: 43%–67%, I2: 97.7%) and 11% (95% CI: 6%–15%, I2: 93.8%), respectively. A weak but significant association was observed between HPT and alcohol drinking (summary OR: 1.38, 95% CI: 1.14%–1.65, I2: 0%) and smoking (summary OR: 1.32, 95% CI: 1.0%–1.74, I2: 50%). In sensitivity analysis, when a study that made confirmation of HPT by the former World Health Organization criteria was dropped, the prevalence increased to 26% (95% CI: 20.8%–32.1%, I2: 98.1%).HPT was considerably prevalent in Myanmar, while the levels of awareness and controlled HPT were low. Health promotion strategy tailored to the education on modifiable risk factors and establishment of blood pressure screening in primary health care context would be of immense value. Upcoming well-powered studies, using the standardized research design and covering more regions of the country are recommended.


title: Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

abstract: Functional brain networks detected in task-free ("resting-state") functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging.


title: Performance Analysis and Prediction in Educational Data Mining: A Research Travelogue

abstract: In this era of computerization, education has also revamped itself and is not limited to old lecture method. The regular quest is on to find out new ways to make it more effective and efficient for students. Nowadays, lots of data is collected in educational databases, but it remains unutilized. In order to get required benefits from such a big data, powerful tools are required. Data mining is an emerging powerful tool for analysis and prediction. It is successfully applied in the area of fraud detection, advertising, marketing, loan assessment and prediction. But, it is in nascent stage in the field of education. Considerable amount of work is done in this direction, but still there are many untouched areas. Moreover, there is no unified approach among these researches. This paper presents a comprehensive survey, a travelogue (2002-2014) towards educational data mining and its scope in future.


title: PCI express and advanced switching: evolutionary path to building next generation interconnects

abstract: With processor and memory technologies pushing the performance limit, the bottleneck is clearly shifting towards the system interconnect. Any solution that addresses the PCI bus-based interconnect, which has serious scalability problems, must also protect the huge legacy infrastructure. PCI Express provides such an evolutionary approach and allows a smooth migration towards building a highly scalable next generation interconnect. Advanced switching further boosts the capabilities of PCI Express by allowing a rich application space to be covered that includes multiprocessing and peer-to-peer communication. Indeed, the synergy between PCI Express and advanced switching permits the adoption of an evolutionary yet revolutionary approach for building the interconnect of the future.


title: Accelerated Neural Evolution through Cooperatively Coevolved Synapses

abstract: Many complex control problems require sophisticated solutions that are not amenable to traditional controller design. Not only is it difficult to model real world systems, but often it is unclear what kind of behavior is required to solve the task. Reinforcement learning (RL) approaches have made progress by using direct interaction with the task environment, but have so far not scaled well to large state spaces and environments that are not fully observable. In recent years, neuroevolution, the artificial evolution of neural networks, has had remarkable success in tasks that exhibit these two properties. In this paper, we compare a neuroevolution method called Cooperative Synapse Neuroevolution (CoSyNE), that uses cooperative coevolution at the level of individual synaptic weights, to a broad range of reinforcement learning algorithms on very difficult versions of the pole balancing problem that involve large (continuous) state spaces and hidden state. CoSyNE is shown to be significantly more efficient and powerful than the other methods on these tasks.


Analysis of the turning point of a quadratic specification


title: A solution to room-by-room coverage for autonomous cleaning robots

abstract: We introduce RoomsSeg, a novel method for segmenting occupancy grid maps into regions that represent rooms and corridors in the real world. The segmentation is utilized for systematic room-by-room cleaning on autonomous vacuum cleaners running in private homes. RoomsSeg is based on automated clutter removal and watershed segmentation on grid maps. Segmented regions are merged into rooms by semantic decision rules. Presented experimental results clearly indicate the efficiency and accuracy of the approach when compared with state of the art methods. When deployed on cleaning robots, a substantial decrease in mission time can be achieved.


title: Contextualization of topics: browsing through the universe of bibliographic information

abstract: This paper describes how semantic indexing can help to generate a contextual overview of topics and visually compare clusters of articles. The method was originally developed for an innovative information exploration tool, called Ariadne, which operates on bibliographic databases with tens of millions of records (Koopman et al. in Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems. doi:10.1145/2702613.2732781, 2015b). In this paper, the method behind Ariadne is further developed and applied to the research question of the special issue "Same data, different results"--the better understanding of topic (re-)construction by different bibliometric approaches. For the case of the Astro dataset of 111,616 articles in astronomy and astrophysics, a new instantiation of the interactive exploring tool, LittleAriadne, has been created. This paper contributes to the overall challenge to delineate and define topics in two different ways. First, we produce two clustering solutions based on vector representations of articles in a lexical space. These vectors are built on semantic indexing of entities associated with those articles. Second, we discuss how LittleAriadne can be used to browse through the network of topical terms, authors, journals, citations and various cluster solutions of the Astro dataset. More specifically, we treat the assignment of an article to the different clustering solutions as an additional element of its bibliographic record. Keeping the principle of semantic indexing on the level of such an extended list of entities of the bibliographic record, LittleAriadne in turn provides a visualization of the context of a specific clustering solution. It also conveys the similarity of article clusters produced by different algorithms, hence representing a complementary approach to other possible means of comparison.


title: E-LOYALTY IS NOT ALL ABOUT TRUST, PRICE ALSO MATTERS: EXTENDING EXPECTATION-CONFIRMATION THEORY IN BOOKSELLING WEBSITES

abstract: Identifying factors that influence customers’ e-loyalty is paramount for practitioners and academics to develop successful marketing strategies and behavioral models. Online bookselling is a rapid growing industry in the UK, where e-loyalty models have yet to reach a conclusive argument. This paper aims to explore factors influencing customers’ e-loyalty to five online bookselling websites in the UK by testing a theoretical model, based on expectation-confirmation theory. A quantitative approach was employed using questionnaires; the sample consisted of 290 respondents (50% males, age range: 18 to over 54). The questionnaire was pretested and confirmatory factor analysis was performed to assess the measurement model. Structural equation modeling and MANOVA were employed to examine the association between latent constructs. Eleven hypotheses were formulated examining different independent variables in the theoretical model; results showed a significant direct and positive association between satisfaction and e-loyalty. Web design affected e-loyalty significantly on all bookselling websites, perceived value was a significant predictor of satisfaction while price notably influenced e-trust development. E-trust was not associated with e-loyalty. This study introduces new variables that affect e-loyalty as well as illuminates new associations between existing factors – perceived value, price, and trust are new aspects which practitioners and academics should take into account for marketing strategies and behavioral models, respectively. Hence, managers will likely increase customer satisfaction and loyalty by improving web design and balancing quality with price, predisposing positively customer’s attitudes towards the website.


title: A Frequency Reconfigurable Printed Yagi-Uda Dipole Antenna for Cognitive Radio Applications

abstract: A frequency reconfigurable printed Yagi-Uda antenna is presented for cognitive radio applications. A 46% continuous frequency tuning bandwidth is obtained by loading the driver dipole arms and four directors with varactor diodes. This configuration allows a high-gain and an almost constant end-fire pattern to be maintained while the antenna operating frequency is tuned. A parametric study was undertaken considering the inter-director spacing, director length tapering, and reflector geometry. It was found possible over the band that the front-to-back ratio is >; 16 dB, the sidelobe level is <; -14 dB and the cross polarization levels in the principal planes are <; - 15.5 dB. From 1-dB compression point measurements, the maximum input power of the antenna with the present diodes is limited to 17.6 dBm at 700 MHz. This suggests that reconfigurable antennas which use active components should have an IIP3 specification placed on them. The frequency selective feature of the antenna makes it as an attractive user terminal antenna for fixed point-to-multipoint cognitive radio enabled broadband wireless access.


Factors influencing Business Intelligence Systems implementation success in the enterprises


title: Road-marking Analysis for Autonomous Vehicle Guidance

abstract: Driving an automobile autonomously on rural roads requires knowledge about the geometry of the road. Furthermore, knowledge about the meaning of each lane of the road is needed in order to decide which lane should be taken and if the vehicle can do a lane change. This paper addresses the problem of extracting additional information about lanes. The information is extracted from the types of road-markings. The type of lane border markings is estimated in order to find out if a lane change is allowed. Arrows, which are painted on the road, are extracted and classified in order to determine the meaning of a lane such as a turn off lane.


title: Visualizing sequential patterns for text mining

abstract: A sequential pattern in data mining is a finite series of elements such as A/spl rarr/B/spl rarr/C/spl rarr/D where A, B, C, and D are elements of the same domain. The mining of sequential patterns is designed to find patterns of discrete events that frequently happen in the same arrangement along a timeline. Like association and clustering, the mining of sequential patterns is among the most popular knowledge discovery techniques that apply statistical measures to extract useful information from large datasets. As out computers become more powerful, we are able to mine bigger datasets and obtain hundreds of thousands of sequential patterns in full detail. With this vast amount of data, we argue that neither data mining nor visualization by itself can manage the information and reflect the knowledge effectively. Subsequently, we apply visualization to augment data mining in a study of sequential patterns in large text corpora. The result shows that we can learn more and more quickly in an integrated visual data-mining environment.


title: Galectin-1 mediated suppression of Epstein-Barr virus specific T-cell immunity in classic Hodgkin lymphoma

abstract: In Hodgkin lymphoma (HL), the malignant Hodgkin Reed-Sternberg cells interact with the host microenvironment to create an immunosuppressive network that protects the lymphoma from immune attack. These mechanisms are not fully understood. We examined the role of the immunomodulatory protein galectin-1 (Gal-1) on Epstein-Barr virus-specific CD8(+) T cell responses in HL. Initial studies indicated Gal-1 expression in all in vitro established Hodgkin Reed-Sternberg cell lines. In situ analysis revealed Gal-1 expression in 26 of 42 classic HL, whereas Gal-1 was uniformly negative in nodular lymphocyte predominant HL. Gal-1(hi) expression was associated with male gender, older patients, reduced CD8(+) T cell infiltration at the tumor site, and most importantly, an impaired latent membrane protein 1 and 2-specific CD8(+) T-cell responses. In vitro exposure to recombinant Gal-1 inhibited proliferation and interferon-gamma expression by Epstein-Barr virus-specific T cells. These observations provide an important link between the Gal-1-mediated immunomodulatory networks and loss of antigen-specific T-cell function in classic HL.


title: Principles of spatial database analysis and design

abstract: This chapter covers the fundamentals of spatial database analysis and design, It begins by defining the most important concepts: ‘spatial database’, ‘analysis’, ‘design’, and ‘model’; and continues with a presentation of the rationale supporting the use of formal methods for analysis and design. The basic elements and approaches of such methods are described, in addition to the processes used. Emphasis is placed on the particularities of spatial databases and the improvements needed for non-spatial methods and tools in order to enhance their efficiency. Finally, the chapter presents a set of tools, called CASE (computer-assisted software engineering), which are built to support the formal analysis and design methods.


title: Design of a maximum power tracking system for wind-energy-conversion applications

abstract: A wind-generator (WG) maximum-power-point-tracking (MPPT) system is presented, consisting of a high-efficiency buck-type dc/dc converter and a microcontroller-based control unit running the MPPT function. The advantages of the proposed MPPT method are that no knowledge of the WG optimal power characteristic or measurement of the wind speed is required and the WG operates at a variable speed. Thus, the system features higher reliability, lower complexity and cost, and less mechanical stress of the WG. Experimental results of the proposed system indicate near-optimal WG output power, increased by 11%-50% compared to a WG directly connected via a rectifier to the battery bank. Thus, better exploitation of the available wind energy is achieved, especially under low wind speeds.


Towards a model to understand risk factors that affect the lean production implementation


title: Gesture recognition using Microsoft Kinect®

abstract: Gesture recognition is essential for human — machine interaction. In this paper we propose a method to recognize human gestures using a Kinect® depth camera. The camera views the subject in the front plane and generates a depth image of the subject in the plane towards the camera. This depth image is then used for background removal, followed by generation of the depth profile of the subject. In addition to this, the difference between subsequent frames gives the motion profile of the subject and is used for recognition of gestures. These allow the efficient use of depth camera to successfully recognize multiple human gestures. The result of a case study involving 8 gestures is shown. The system was trained using a multi class Support Vector Machine.