title: Recovering human body configurations: combining segmentation and recognition

abstract: The goal of this work is to detect a human figure image and localize his joints and limbs along with their associated pixel masks. In this work we attempt to tackle this problem in a general setting. The dataset we use is a collection of sports news photographs of baseball players, varying dramatically in pose and clothing. The approach that we take is to use segmentation to guide our recognition algorithm to salient bits of the image. We use this segmentation approach to build limb and torso detectors, the outputs of which are assembled into human figures. We present quantitative results on torso localization, in addition to shortlisted full body configurations.


title: Privacy in Online Social Networking: Applying a Privacy Calculus Model

abstract: The penetration rate continues to grow for social networking sites where individuals join a virtual community to socialize, make connections, and share opinions with those who have similar interests, while revealing personal information. However, online social networking presents a unique context with distinct privacy challenges. To understand information disclosure behavior in this context, we apply the extended privacy calculus model, developed by Dinev and Hart (2006a), which addresses the trade-off between the expected costs of privacy risk beliefs and the benefits of confidence and placement beliefs on the willingness to provide personal information. We further extend this model to include specific types of personal information, based on our proposed taxonomy of information integral to social networking. To test our research model, a questionnaire will be administered to undergraduate students, drawn from the mid-Atlantic U.S. For hypothesis testing, structural equations modeling will be used. The completion of this research-in-progress study is expected to contribute to our understanding of the types of information revealed in online social networking.


title: Recommendation System for Adaptive E-learning using Semantic Net

abstract: In the e-learning system an abundant amount of information is created and delivered to the learners over electronic media. Learners are often getting confusion by the flow of information and have difficulty in selecting the topic to learn that satisfies their needs and interests. There are several researches have been performed to provide personalized learning paths for individual learners. But many of them collect the learners’ interest, habits and behavior from their profile and based on that they recommend learning path. It is the fact that the learners’ interest, learning attitude and need will vary from time to time and course to course. In this paper a recommendation system is proposed using semantic net that helps the learners by offering a more intelligent approach to navigating and searching course content. In this the learner will get more personalized and contextual recommendation. The results show that semantic net based methods enable interoperability of heterogeneous course content representation and result in accurate recommendations. The validity of the proposed model is shown using sample learners and performance measures for the recommendation effects are given for evaluating the proposed system.


title: Game-Theoretic Energy Management for Residential Users with Dischargeable Plug-in Electric Vehicles

abstract: The plug-in electric vehicle (PEV) has attracted more and more attention because of the energy crisis and environmental pollution, which is also the main shiftable load of the residential users’ demand side management (DSM) system in the future smart grid (SG). In this paper, we employ game theory to provide an autonomous energy management system among residential users considering selling energy back to the utility company by discharging the PEV’s battery. By assuming all users are equipped with smart meters to execute automatic energy consumption scheduling (ECS) and the energy company can adopt adequate pricing tariffs relating to time and level of energy usage, we formulate an energy management game, where the players are the residential users and the strategies are their daily schedules of household appliance use. We will show that the Nash equilibrium of the formulated energy management game can guarantee the global optimization in terms of minimizing the energy costs, where the depreciation cost of PEV’s battery because of discharging and selling energy back is also considered. Simulation results verify that the proposed game-theoretic approach can reduce the total energy cost and individual daily electricity payment. Moreover, since plug-in electric bicycles (PEBs) are currently widely used in China, simulation results of residential users owing household appliances and bidirectional energy trading of PEBs are also provided and discussed.


title: Actor in Multi Product Line

abstract: Software product line (SPL) involved variability modeling in domain engineering that will be matched to the respected application engineering. Several researches existed within the scope of mapping from reference architecture (RA) in domain engineering to system architecture in application engineering within the same domain. However, the mapping of cross domain RA or Multi Product Line (MPL) required more systematic mapping due to the several participating product line architecture (PLA) that will further instantiated to specific system architecture. The objective of this paper was to propose an actor-oriented approach in the mapping process of reference architecture, product line architecture and system architecture of MPL. Since the reference architecture consisted of several components, the scope of this research was within the functional decomposition or source code level. The experiment was involving the runtime behavior of the java code. The code with actor-oriented approach had shown the least amount of time taken to complete the main method compared to the non-actor-oriented approach. In conclusion, actor-oriented approach performs better performance in the mapping of reference architecture to product line architecture and system architecture. For future work, the consistency of the mapping will be evaluated.


title: Adhesive RFID Sensor Patch for Monitoring of Sweat Electrolytes

abstract: Wearable digital health devices are dominantly found in rigid form factors such as bracelets and pucks. An adhesive radio-frequency identification (RFID) sensor bandage (patch) is reported, which can be made completely intimate with human skin, a distinct advantage for chronological monitoring of biomarkers in sweat. In this demonstration, a commercial RFID chip is adapted with minimum components to allow potentiometric sensing of solutes in sweat, and surface temperature, as read by an Android smartphone app with 96% accuracy at 50 mM Na ::: + ::: (in vitro tests). All circuitry is solder-reflow integrated on a standard Cu/polyimide flexible-electronic layer including an antenna, but while also allowing electroplating for simple integration of exotic metals for sensing electrodes. Optional paper microfluidics wick sweat from a sweat porous adhesive allowing flow to the sensor, or the sensor can be directly contacted to the skin. The wearability of the patch has been demonstrated for up to seven days, and includes a protective textile which provides a feel and appearance similar to a standard Band-Aid. Applications include hydration monitoring, but the basic capability is extendable to other mM ionic solutes in sweat (Cl ::: - ::: , K ::: + ::: , Mg ::: 2+ ::: , NH ::: 4 ::: + ::: , and Zn ::: 2+ ::: ). The design and fabrication of the patch are provided in full detail, as the basic components could be useful in the design of other wearable sensors.


title: Planning of multiple robot trajectories in distinctive topologies

abstract: This contribution presents a novel approach for efficient online planning of topologically distinctive mobile robot trajectories. Trajectory optimization deforms an initial coarse path drafted by a global planner with respect to robot motion related objectives and constraints. The primary objective is to reach a goal state in minimal time on a collision free path that adheres to the kinematic and dynamic constraints of the mobile robot. Conventional local planners get often stuck in a local optimal trajectory as they are unable to transit across obstacles. Our approach seeks the globally optimal trajectory as it maintains and optimizes a subset of admissible candidate trajectories of distinctive topologies in parallel. In case of dynamic environments the planner switches to the current globally optimal trajectory among the candidate set. The online trajectory planning with timed elastic bands is tightly integrated with the robot motion feedback control. The comparative analysis with conventional local planners confirms the advantages of maintaining distinctive topologies to circumnavigate dynamic obstacles.


title: DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI

abstract: To extract patterns from neuroimaging data, various techniques, including statistical methods and machine learning algorithms, have been explored to ultimately aid in Alzheimer9s disease diagnosis of older adults in both clinical and research applications. However, identifying the distinctions between Alzheimer′s brain data and healthy brain data in older adults (age > 75) is challenging due to highly similar brain patterns and image intensities. Recently, cutting-edge deep learning technologies have been rapidly expanding into numerous fields, including medical image analysis. This work outlines state-of-the-art deep learning-based pipelines employed to distinguish Alzheimer′s magnetic resonance imaging (MRI) and functional MRI data from normal healthy control data for the same age group. Using these pipelines, which were executed on a GPU-based high performance computing platform, the data were strictly and carefully preprocessed. Next, scale and shift invariant low- to high-level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture. In this study, functional MRI data were used for the first time in deep learning applications for the purposes of medical image analysis and Alzheimer′s disease prediction. These proposed and implemented pipelines, which demonstrate a significant improvement in classification output when compared to other studies, resulted in high and reproducible accuracy rates of 99.9% and 98.84% for the fMRI and MRI pipelines, respectively.


title: Real-Time Monitoring via Patch-Type Piezoelectric Force Sensors for Internet of Things Based Logistics

abstract: We propose the use of simple and low cost piezoelectric patch type force sensors for logistic applications to ensure safety of the package while also detecting damage suffered. The sensors are connected to a prototype readout system that can record the data and transfer it wirelessly via a Bluetooth module. The data can be received by an in-vehicle telematics device which can upload it directly to the cloud database, thus allowing real time monitoring of package condition during transportation. This logistics management model would aid in improving the quality of services provided by the logistics company while also earning consumer credibility. Thus, we believe that these patch type force sensors can be realistically implemented in logistics in the near future.


title: Topological Simultaneous Localization and Mapping (SLAM): Toward Exact Localization Without Explicit Localization

abstract: This paper presents a new method for simultaneous localization and mapping that exploits the topology of the robot's free space to localize the robot on a partially constructed map. The topology of the environment is encoded in a topological map; the particular topological map used in this paper is the generalized Voronoi graph (GVG), which also encodes some metric information about the robot's environment, as well. In this paper, we present the low-level control laws that generate the GVG edges and nodes, thereby allowing for exploration of an unknown space. With these prescribed control laws, the GVG can be viewed as an arbitrator for a hybrid control system that determines when to invoke a particular low-level controller from a set of controllers all working toward the high-level capability of mobile robot exploration. The main contribution, however, is using the graph structure of the GVG, via a graph matching process, to localize the robot. Experimental results verify the described work.


Use of Open Access Publishing by Researchers an Analysis of Open Access Availability of Research Literature in the field of Digital Library


title: Conformity in virtual environments: a hybrid neurophysiological and psychosocial approach

abstract: The main aim of our study was to analyze the effects of a virtual environment on social conformity, with particular attention to the effects of different types of task and psychological variables on social influence, on one side, and to the neural correlates related to conformity, measured by means of an Emotiv EPOC device on the other. For our purpose, we replicated the famous Asch’s visual task and created two new tasks of increasing ambiguity, assessed through the calculation of the item’s entropy. We also administered five scales in order to assess different psychological traits. From the experiment, conducted on 181 university students, emerged that conformity grows according to the ambiguity of the task, but normative influence is significantly weaker in virtual environments, if compared to face-to-face experiments. The analyzed psychological traits, however, result not to be relatable to conformity, and they only affect the subjects’ response times. From the ERP (Event-related potentials) analysis, we detected N200 and P300 components comparing the plots of conformist and non-conformist subjects, alongside with the detection of their Late Positive Potential, Readiness Potential, and Error-Related Negativity, which appear consistently different for the two typologies.


title: Deep generative models: Survey

abstract: Generative models have found their way to the forefront of deep learning the last decade and so far, it seems that the hype will not fade away any time soon. In this paper, we give an overview of the most important building blocks of most recent revolutionary deep generative models such as RBM, DBM, DBN, VAE and GAN. We will also take a look at three of state-of-the-art generative models, namely PixelRNN, DRAW and NADE. We will delve into their unique architectures, the learning procedures and their potential and limitations. We will also review some of the known issues that arise when trying to design and train deep generative architectures using shallow ones and how different models deal with these issues. This paper is not meant to be a comprehensive study of these models, but rather a starting point for those who bear an interest in the field.


title: Flexible Radio Access Beyond 5G: A Future Projection on Waveform, Numerology, and Frame Design Principles

abstract: To address the vast variety of user requirements, applications, and channel conditions, flexibility support is strongly highlighted for 5G radio access technologies (RATs). For this purpose, usage of multiple orthogonal frequency division multiplexing (OFDM) numerologies, i.e., different parameterization of OFDM-based subframes, within the same frame has been proposed in the third-generation partnership project discussions for 5G new radio. This concept will likely meet the current expectations in multiple service requirements to some extent. However, since the quantity of wireless devices, applications, and heterogeneity of user requirements will keep increasing toward the next decade, the sufficiency of the aforementioned flexibility consideration remains quite disputable for future services. Therefore, novel RATs facilitating much more flexibility are needed to address various technical challenges, e.g., power efficiency, massive connectivity, latency, spectral efficiency, robustness against channel dispersions, and so on. In this paper, we discuss the potential directions to achieve further flexibility in RATs beyond 5G, such as future releases of 5G and 6G. In this context, a framework for developing flexible waveform, numerology, and frame design strategies is proposed along with sample methods. We also discuss their potential role to handle various upper-level system issues, including the ones in orthogonal and nonorthogonal multiple accessing schemes and cellular networks. By doing so, we aim to contribute to the future vision of designing flexible RATs and to point out the possible research gaps in the related fields.


title: Support vector machines for credit scoring and discovery of significant features

abstract: The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default.


title: The Unifying Of The Forces Of Nature

abstract: This paper takes a look at the formulating of the unified field theory and the theory of everything. It shows how a unified field theory and theory of everything may be obtained.


title: Audio description as an aural guide of children's visual attention: evidence from an eye-tracking study

abstract: Audio description (AD) has become a cultural revolution for the visually impaired; however, the range of AD beneficiaries can be much broader. We claim that AD is useful for guiding children's attention. The paper presents an eye-tracking study testing the usefulness of AD in selective attention to described elements of a video scene. Forty-four children watched 2 clips from an educational animation series while their eye movements were recorded. Average fixation duration, fixation count, and saccade amplitude served as primary dependent variables. The results confirmed that AD guides children's attention towards described objects resulting e. g., in more fixations on specific regions of interest. We also evaluated eye movement patterns in terms of switching between focal and ambient processing. We postulate that audio description could complement regular teaching tools for guiding and focusing children's attention, especially when new concepts are introduced.


Sentence boundary detection in arabic speech.


title: Class-D Audio Amplifiers in Mobile Applications

abstract: A comparative system-level overview is given of alternative class-D amplifier architectures. The theory behind pulsewidth modulation and different modulation schemes is discussed. Topological alternatives such as open-loop versus feedback and fixed-carrier versus self-oscillating are analyzed and compared in terms of relevant characteristics, such as distortion, power supply rejection, efficiency, and electromagnetic interference. The combination of digital-to-analog conversion and class-D amplifiers is discussed. Experimental results of an integrated circuit based on a simple architecture that combines the benefits of digital input with an analog feedback loop are presented.


title: Self-supervised Relation Extraction from the Web

abstract: Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while keeping the precision of the resulting list reasonably high. SRES is a self-supervised Web relation extraction system that learns powerful extraction patterns from unlabeled text, using short descriptions of the target elations and their attributes. SRES automatically generates the training data needed for its pattern-learning component. We also compare the performance of SRES to the performance of the state-of-the-art KnowItAll system, and to the performance of its pattern learning component, which uses a simpler and less powerful pattern language than SRES.


title: Threat Modeling for Cloud Data Center Infrastructures

abstract: Cloud computing has undergone rapid expansion throughout the last decade. Many companies and organizations have made the transition from traditional data centers to the cloud due to its flexibility and lower cost. However, traditional data centers are still being relied upon by those who are less certain about the security of cloud. This problem is highlighted by the fact that there only exist limited efforts on threat modeling for cloud data centers. In this paper, we conduct comprehensive threat modeling exercises based on two representative cloud infrastructures using several popular threat modeling methods, including attack surface, attack trees, attack graphs, and security metrics based on attack trees and attack graphs, respectively. Those threat modeling efforts provide cloud providers practical lessons and means toward better evaluating, understanding, and improving their cloud infrastructures. Our results may also imbed more confidence in potential cloud tenants by providing them a clearer picture about potential threats in cloud infrastructures and corresponding solutions.


title: A novel dual-band beam-switching antenna based on active frequency selective surfaces

abstract: A novel design of a dual-band beam-switching antenna based on active frequency selective surfaces (FSSs) is presented. The antenna is composed of two layers of active cylindrical FSS and fed by a dual-band dipole at the center. The FSS layers are divided into on and off state sectors in both inner and outer layers. Each sector consists of columns of metallic strips with on or off state diodes. To achieve a dual-band matching, the inner FSS layer is transmissive at lower band in both on and off state. Also, it is reflective in the on state and transmissive in the off state at higher frequency band. The outer layer is a reflective surface in on state and transmissive in off state in both frequency bands. In each state of operation, the on state sectors operate as a reconfigurable reflector at both frequency bands and convert the omnidirectional radiation pattern of the dual-band dipole to a directive one. By switching the states of diodes, the antenna can scan the entire azimuth plane. The fullwave simulation results show the antenna operates at 0.75 GHz-1 GHz and 1.7 GHz-1.9 GHz simultaneously, with maximum gain of 6.4 dBi and 10 dBi respectively, and can be used in the base stations of wireless communication systems.


title: Iron saturation impact on high frequency sensorless control of synchronous permanent magnets motor

abstract: The interest for permanent magnet (PM) synchronous machines equipped with fractional-slot concentrated winding is more and more increasing in recent years. Among the others the Surface Mounted PM machines (SPM) have been widely selected from motor manufacturers. Particularly attractive are the spoke-type interior PM (IPM) machines. Nowadays, PM machines are often selected for variable speed applications without precise positioning requirements taking the advantage of sensorless control algorithms. Nevertheless the capability to detect the electrical rotor position without sensor also at zero speed, by means of techniques based on high-frequency signal injection, is an enhancing feature that requires a proper machine design. The aim of this paper is to evaluate the impact of both rotor and stator modifications taking a commercial motor as a reference geometry. The analysis of two rotor structures, SPM and IPM, as well as stator tooth geometry variations pointed out how the machine response due the injection of an high frequency voltage is significantly affected by the stator iron saturation. Finite elements simulations have been confirmed by test on two IPM motors manufactured with different stator design.


title: Global ranking of knowledge management and intellectual capital academic journals: 2013 update

abstract: Purpose – The purpose of this study is to update a global ranking of knowledge management and intellectual capital (KM/IC) academic journals.Design/methodology/approach – Two different approaches were utilized: a survey of 379 active KM/IC researchers; and the journal citation impact method. Scores produced by the application of these methods were combined to develop the final ranking.Findings – Twenty‐five KM/IC‐centric journals were identified and ranked. The top six journals are: Journal of Knowledge Management, Journal of Intellectual Capital, The Learning Organization, Knowledge Management Research & Practice, Knowledge and Process Management and International Journal of Knowledge Management. Knowledge Management Research & Practice has substantially improved its reputation. The Learning Organization and Journal of Intellectual Capital retained their previous positions due to their strong citation impact. The number of KM/IC‐centric and KM/IC‐relevant journals has been growing at the pace of one new ...


title: THE RELATIONSHIP BETWEEN GENDER INEQUALITY AND HEALTH SERVICE COVERAGE IN THE MEMBER COUNTRIES OF THE UNITED NATIONS

abstract: Introduction: In the recent years, women’s health has been proposed as one of the indicators of countries’ development because women are more vulnerable to damage in comparison to men. The present study aimed to investigate the effect of gender inequality on women’s health indicators in 2011. Methods: The statistical community of this cross-sectional, descriptive - analytical study included all the countries of the world. However, due to the lack of access to all the countries’ data, convenient sampling was used. All the data were related to 2011 and were based on the report by World Health Organization. The data were entered into the SPSS statistical software (version 16) and analyzed using Pearson correlation coefficient and one-way ANOVA. Results: The study findings revealed significant negative relationships between gender inequality index and all the indicators of access to health services, including births attended by skilled health personnel (r=-0.73, P<0.0001), contraceptive prevalence (r=-0.66, P<0.0001), antenatal care coverage - at least four visits (r=-0.54, P<0.0001), antenatal care coverage - at least one visit (r=-0.66, P<0.0001), and unmet need for family planning index (r=-0.55, P<0.0001). Besides, the rate of gender inequality was different in the countries with four different income groups (F=82.138, P<0.0001). Also, the rate of gender inequality was higher in low-income countries compared to high-income ones. Conclusions: Increase in gender inequality was negatively associated with the indicators of access to health services. Therefore, by reducing gender inequality, access to health services can be enhanced thereby leading to prosperity and sustainable development.


title: Optimal Operation of Distribution Feeders in Smart Grids

abstract: This paper presents a generic and comprehensive distribution optimal power flow (DOPF) model that can be used by local distribution companies (LDCs) to integrate their distribution system feeders into a Smart Grid. The proposed three-phase DOPF framework incorporates detailed modeling of distribution system components and considers various operating objectives. Phase specific and voltage dependent modeling of customer loads in the three-phase DOPF model allows LDC operators to determine realistic operating strategies that can improve the overall feeder efficiency. The proposed distribution system operation objective is based on the minimization of the energy drawn from the substation while seeking to minimize the number of switching operations of load tap changers and capacitors. A novel method for solving the three-phase DOPF model by transforming the mixed-integer nonlinear programming problem to a nonlinear programming problem is proposed which reduces the computational burden and facilitates its practical implementation and application. Two practical case studies, including a real distribution feeder test case, are presented to demonstrate the features of the proposed methodology. The results illustrate the benefits of the proposed DOPF in terms of reducing energy losses while limiting the number of switching operations.


title: Modified Hausdorff Fractal Dimension (MHFD)

abstract: The Hausdorff fractal dimension has been a fast-to-calculate method to estimate complexity of fractal shapes. In this work, a modified version of this fractal dimension is presented in order to make it more robust when applied in estimating complexity of non-fractal images. The modified Hausdorff fractal dimension stands on two features that weaken the requirement of presence of a shape and also reduce the impact of the noise possibly presented in the input image. The new algorithm has been evaluated on a set of images of different character with promising performance.


title: Insectopia: exploring pervasive games through technology already pervasively available

abstract: We present the pervasive game Insectopia, which have been designed by the approach of utilizing commercially available mobile phones to provide context-dependent gameplay. The design and evaluation of the game is described together with a discussion on how this approach can speed up making pervasive games a viable game genre for the general public.


title: Human Pose Estimation Using Body Parts Dependent Joint Regressors

abstract: In this work, we address the problem of estimating 2d human pose from still images. Recent methods that rely on discriminatively trained deformable parts organized in a tree model have shown to be very successful in solving this task. Within such a pictorial structure framework, we address the problem of obtaining good part templates by proposing novel, non-linear joint regressors. In particular, we employ two-layered random forests as joint regressors. The first layer acts as a discriminative, independent body part classifier. The second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts. This results in a pose estimation framework that takes dependencies between body parts already for joint localization into account and is thus able to circumvent typical ambiguities of tree structures, such as for legs and arms. In the experiments, we demonstrate that our body parts dependent joint regressors achieve a higher joint localization accuracy than tree-based state-of-the-art methods.


title: The Optimal Reward Baseline for Gradient-Based Reinforcement Learning

abstract: There exist a number of reinforcement learning algorithms which learn by climbing the gradient of expected reward. Their long-run convergence has been proved, even in partially observable environments with non-deterministic actions, and without the need for a system model. However, the variance of the gradient estimator has been found to be a significant practical problem. Recent approaches have discounted future rewards, introducing a bias-variance trade-off into the gradient estimate. We incorporate a reward baseline into the learning system, and show that it affects variance without introducing further bias. In particular, as we approach the zerobias, high-variance parametedzation, the optimal (or variance minimizing) constant reward baseline is equal to the long-term average expected reward. Modified policy-gradient algorithms are presented, and a number of experiments demonstrate their improvement over previous work.


title: Discovering Mental Toughness: A Qualitative Study of Mental Toughness in Elite Athletes [R]

abstract: What is mental toughness? This research draws on the experience of elite athletes and coaches to unearth a conceptualisation and definition of mental toughness. The interviews of 33 elite athletes and coaches (including 25 current or former elite athletes, of whom 15 had achieved an Olympic Gold Medal or World Champion status) were transcribed verbatim and analysed qualitatively to reveal underpinning components of mental toughness. Mental toughness is multi-dimensional and consists of twelve components, including: self-efficacy, potential, mental selfconcept, task familiarity, value, personal bests, goal commitment, perseverance, task focus, positivity, stress minimisation, and positive comparisons. Mental toughness is defined as an unshakeable perseverance and conviction towards some goal despite pressure or adversity. This conceptualisation of mental toughness provides a platform from which instruments of measurement can be developed.


Content Based Recommendation System Using SOM and Latent Dirichlet Allocation Model


title: Depth Image Layers Separation (DILS) algorithm of image view synthesis based on stereo vision

abstract: A new Depth Image Layers Separation (DILS) algorithm for synthesizing inter-view images based on disparity depth map layers representation is presented. The approach is to separate the depth map into several layers identified through histogram-based clustering. Each layer is extracted using inter-view interpolation to create objects based on location and depth. DILS is a new paradigm in selecting interesting image locations based on depth, but also in producing new image representations that allow objects or parts of an image to be described without the need of segmentation and identification. The image view synthesis can reduce the configuration complexity of multi-camera arrays in 3D imagery and free-viewpoint applications. The simulation results show that depth layer separation is able to create inter-view images that may be integrated with other techniques such as occlusion handling processes. The DILS algorithm can be implemented using both simple as well as sophisticated stereo matching methods to synthesize inter-view images.


title: Variable Latency Speculative Han-Carlson Adder

abstract: Variable latency adders have been recently proposed in literature. A variable latency adder employs speculation: the exact arithmetic function is replaced with an approximated one that is faster and gives the correct result most of the time, but not always. The approximated adder is augmented with an error detection network that asserts an error signal when speculation fails. Speculative variable latency adders have attracted strong interest thanks to their capability to reduce average delay compared to traditional architectures. This paper proposes a novel variable latency speculative adder based on Han-Carlson parallel-prefix topology that resulted more effective than variable latency Kogge-Stone topology. The paper describes the stages in which variable latency speculative prefix adders can be subdivided and presents a novel error detection network that reduces error probability compared to previous approaches. Several variable latency speculative adders, for various operand lengths, using both Han-Carlson and Kogge-Stone topology, have been synthesized using the UMC 65 nm library. Obtained results show that proposed variable latency Han-Carlson adder outperforms both previously proposed speculative Kogge-Stone architectures and non-speculative adders, when high-speed is required. It is also shown that non-speculative adders remain the best choice when the speed constraint is relaxed.


SEE ME, HEAR ME: INTEGRATING AUTOMATIC SPEECH RECOGNITION AND LIP-READING


Business Modeling with UML


Connected Population Synthesis for Urban Simulation


title: Colibri: fast mining of large static and dynamic graphs

abstract: Low-rank approximations of the adjacency matrix of a graph are essential in finding patterns (such as communities) and detecting anomalies. Additionally, it is desirable to track the low-rank structure as the graph evolves over time, efficiently and within limited storage. Real graphs typically have thousands or millions of nodes, but are usually very sparse. However, standard decompositions such as SVD do not preserve sparsity. This has led to the development of methods such as CUR and CMD, which seek a non-orthogonal basis by sampling the columns and/or rows of the sparse matrix. However, these approaches will typically produce overcomplete bases, which wastes both space and time. In this paper we propose the family of Colibri methods to deal with these challenges. Our version for static graphs, Colibri-S, iteratively finds a non-redundant basis and we prove that it has no loss of accuracy compared to the best competitors (CUR and CMD), while achieving significant savings in space and time: on real data, Colibri-S requires much less space and is orders of magnitude faster (in proportion to the square of the number of non-redundant columns). Additionally, we propose an efficient update algorithm for dynamic, time-evolving graphs, Colibri-D. Our evaluation on a large, real network traffic dataset shows that Colibri-D is over 100 times faster than the best published competitor (CMD).


title: Opcode graph similarity and metamorphic detection

abstract: In this paper, we consider a method for computing the similarity of executable files, based on opcode graphs. We apply this technique to the challenging problem of metamorphic malware detection and compare the results to previous work based on hidden Markov models. In addition, we analyze the effect of various morphing techniques on the success of our proposed opcode graph-based detection scheme.


title: Real-time fault diagnosis for gas turbine generator systems using extreme learning machine

abstract: Real-time fault diagnostic system is very important to maintain the operation of the gas turbine generator system (GTGS) in power plants, where any abnormal situation will interrupt the electricity supply. The GTGS is complicated and has many types of component faults. To prevent from interruption of electricity supply, a reliable and quick response framework for real-time fault diagnosis of the GTGS is necessary. As the architecture and the learning algorithm of extreme learning machine (ELM) are simple and effective respectively, ELM can identify faults quickly and precisely as compared with traditional identification techniques such as support vector machines (SVM). This paper therefore proposes a new application of ELM for building a real-time fault diagnostic system in which data pre-processing techniques are integrated. In terms of data pre-processing, wavelet packet transform and time-domain statistical features are proposed for extraction of vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features in order to shorten the fault identification time and improve accuracy. To evaluate the system performance, a comparison between ELM and the prevailing SVM on the fault detection was conducted. Experimental results show that the proposed diagnostic framework can detect component faults much faster than SVM, while ELM is competitive with SVM in accuracy. This paper is also the first in the literature that explores the superiority of the fault identification time of ELM.


title: Local extrema co-occurrence pattern for color and texture image retrieval

abstract: A real world problem of image retrieval and searching is considered in this paper. In modern generation, managing images from a large storage medium is not a straightforward job. Many researchers have worked on texture features, and produced diverse feature descriptors based on uniform, rotation invariant, edges and directional properties. However, most of them convert the relationship of the center pixel and the boundary pixel into a local pattern, and use histogram to represent the local pattern as a feature vector. In this work, we propose a new image retrieval technique; local extrema co-occurrence patterns (LECoP) using the HSV color space. HSV color space is used in this method to utilize the color, intensity and brightness of images. Local extrema patterns are applied to define the local information of image, and gray level co-occurrence matrix is used to obtain the co-occurrence of LEP map pixels. The local extrema co-occurrence pattern extracts the local directional information from local extrema pattern, and convert it into a well-mannered feature vector with use of gray level co-occurrence matrix. The presented method is tested on five standard databases called Corel, MIT VisTex and STex, in which Corel database includes Corel-1k, Corer-5k and Corel-10k databases. Also, this algorithm is compared with previous proposed methods, and results in terms of precision and recall are shown in this work.


title: Stacked S-band Slotted Waveguide Array Antenna with very low sidelobes

abstract: A very low 3D sidelobe (< −21 dB) S-band (2.4 GHz) Slotted Waveguide Array Antenna (SWGAA) design is presented in this paper. A single waveguide SWGAA have a fan beam radiation pattern in the elevation plane. The elevation beamwidth of the SWGAA antenna can be narrowed by stacking identically designed SWGAA elements. The stacking method, simulated results and anechoic chamber measurements are presented in this paper.


title: Identification of malicious web pages through analysis of underlying DNS and web server relationships

abstract: Malicious Web pages that launch drive-by-download attacks on Web browsers have increasingly become a problem in recent years. High-interaction client honeypots are security devices that can detect these malicious Web pages on a network. However, high-interaction client honeypots are both resource-intensive and unable to handle the increasing array of vulnerable clients. This paper presents a novel classification method for detecting malicious Web pages that involves inspecting the underlying server relationships. Because of the unique structure of malicious front-end Web pages and centralized exploit servers, merely counting the number of domain name extensions and Domain Name System (DNS) servers used to resolve the host names of all Web servers involved in rendering a page is sufficient to determine whether a Web page is malicious or benign, independent of the vulnerable Web browser targeted by these pages. Combining high-interaction client honeypots and this new classification method into a hybrid system leads to performance improvements.


title: CACTI 6.0: A Tool to Model Large Caches

abstract: © CACTI 6.0: A Tool to Model Large Caches Naveen Muralimanohar, Rajeev Balasubramonian, Norman P. Jouppi HP Laboratories HPL-2009-85 No keywords available. Future processors will likely have large on-chip caches with a possibility of dedicating an entire die for on-chip storage in a 3D stacked design. With the ever growing disparity between transistor and wire delay, the properties of such large caches will primarily depend on the characteristics of the interconnection networks that connect various sub-modules of a cache. CACTI 6.0 is a significantly enhanced version of the tool that primarily focuses on interconnect design for large caches. In addition to strengthening the existing analytical model of the tool for dominant cache components, CACTI 6.0 includes two major extensions over earlier versions: first, the ability to model Non-Uniform Cache Access (NUCA), and second, the ability to model different types of wires, such as RC based wires with different power, delay, and area characteristics and differential low-swing buses. This report details the analytical model assumed for the newly added modules along with their validation analysis. External Posting Date: April 21, 2009 [Fulltext] Approved for External Publication Internal Posting Date: April 21, 2009 [Fulltext] Published in International Symposium on Microarchitecture, Chicago, Dec 2007. Copyright International Symposium on Microarchitecture, 2007. CACTI 6.0: A Tool to Model Large Caches Naveen Muralimanohar, Rajeev Balasubramonian, Norman P. Jouppi † School of Computing, University of Utah ‡ Hewlett-Packard Laboratories Abstract Future processors will likely have large on-chip caches with a possibility of dedicating an entire die for on-chip storage in a 3D stacked design. With the ever growing disparity between transistor and wire delay, the properties of such large caches will primarily depend on the characteristics of the interconnection networks that connect various sub-modules of a cache. CACTI 6.0 is a significantly enhanced version of the tool that primarily focuses on interconnect design for large caches. In addition to strengthening the existing analytical model of the tool for dominant cache components, CACTI 6.0 includes two major extensions over earlier versions: first, the ability to model Non-Uniform Cache Access (NUCA), and second, the ability to model different types of wires, such as RC based wires with different power, delay, and area characteristics and differential low-swing buses. This report details the analytical model assumed for the newly added modules along with their validation analysis.Future processors will likely have large on-chip caches with a possibility of dedicating an entire die for on-chip storage in a 3D stacked design. With the ever growing disparity between transistor and wire delay, the properties of such large caches will primarily depend on the characteristics of the interconnection networks that connect various sub-modules of a cache. CACTI 6.0 is a significantly enhanced version of the tool that primarily focuses on interconnect design for large caches. In addition to strengthening the existing analytical model of the tool for dominant cache components, CACTI 6.0 includes two major extensions over earlier versions: first, the ability to model Non-Uniform Cache Access (NUCA), and second, the ability to model different types of wires, such as RC based wires with different power, delay, and area characteristics and differential low-swing buses. This report details the analytical model assumed for the newly added modules along with their validation analysis.


Chapter 2 Data-Driven Modelling : Concepts , Approaches and Experiences


title: Intrinsically Motivated Reinforcement Learning

abstract: Psychologists call behavior intrinsically motivated when it is engaged in for its own sake rather than as a step toward solving a specific problem of clear practical value. But what we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated reinforcement learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy.


title: Smart Pipe System for a Shipyard 4.0

abstract: As a result of the progressive implantation of the Industry 4.0 paradigm, many industries are experimenting a revolution that shipyards cannot ignore. Therefore, the application of the principles of Industry 4.0 to shipyards are leading to the creation of Shipyards 4.0. Due to this, Navantia, one of the 10 largest shipbuilders in the world, is updating its whole inner workings to keep up with the near-future challenges that a Shipyard 4.0 will have to face. Such challenges can be divided into three groups: the vertical integration of production systems, the horizontal integration of a new generation of value creation networks, and the re-engineering of the entire production chain, making changes that affect the entire life cycle of each piece of a ship. Pipes, which exist in a huge number and varied typology on a ship, are one of the key pieces, and its monitoring constitutes a prospective cyber-physical system. Their improved identification, traceability, and indoor location, from production and through their life, can enhance shipyard productivity and safety. In order to perform such tasks, this article first conducts a thorough analysis of the shipyard environment. From this analysis, the essential hardware and software technical requirements are determined. Next, the concept of smart pipe is presented and defined as an object able to transmit signals periodically that allows for providing enhanced services in a shipyard. In order to build a smart pipe system, different technologies are selected and evaluated, concluding that passive and active RFID (Radio Frequency Identification) are currently the most appropriate technologies to create it. Furthermore, some promising indoor positioning results obtained in a pipe workshop are presented, showing that multi-antenna algorithms and Kalman filtering can help to stabilize Received Signal Strength (RSS) and improve the overall accuracy of the system.


title: Extensive Benchmark and Survey of Modeling Methods for Scene Background Initialization

abstract: Scene background initialization is the process by which a method tries to recover the background image of a video without foreground objects in it. Having a clear understanding about which approach is more robust and/or more suited to a given scenario is of great interest to many end users or practitioners. The aim of this paper is to provide an extensive survey of scene background initialization methods as well as a novel benchmarking framework. The proposed framework involves several evaluation metrics and state-of-the-art methods, as well as the largest video data set ever made for this purpose. The data set consists of several camera-captured videos that: 1) span categories focused on various background initialization challenges; 2) are obtained with different cameras of different lengths, frame rates, spatial resolutions, lighting conditions, and levels of compression; and 3) contain indoor and outdoor scenes. The wide variety of our data set prevents our analysis from favoring a certain family of background initialization methods over others. Our evaluation framework allows us to quantitatively identify solved and unsolved issues related to scene background initialization. We also identify scenarios for which state-of-the-art methods systematically fail.


title: Empirical Evaluation of the Impact of Object-Oriented Code Refactoring on Quality Attributes: A Systematic Literature Review

abstract: Software refactoring is a maintenance task that addresses code restructuring to improve its quality. Many studies have addressed the impact of different refactoring scenarios on software quality. This study presents a systematic literature review that aggregates, summarizes, and discusses the results of 76 relevant primary studies (PSs) concerning the impact of refactoring on several internal and external quality attributes. The included PSs were selected using inclusion and exclusion criteria applied to relevant articles published before the end of 2015. We analyzed the PSs based on a set of classification criteria, including software quality attributes and measures, refactoring scenarios, evaluation approaches, datasets, and impact results. We followed the vote-counting approach to determine the level of consistency among the PS reported results concerning the relationship between refactoring and software quality. The results indicated that different refactoring scenarios sometimes have opposite impacts on different quality attributes. Therefore, it is false that refactoring always improves all software quality aspects. The vote-counting study provided a clear view of the impacts of some individual refactoring scenarios on some internal quality attributes such as cohesion, coupling, complexity, inheritance, and size, but failed to identify their impacts on external and other internal quality attributes due to insufficient findings.


CNVis: A web-based visual analytics tool for exploring conference navigator data


Progressive prediction of student performance in college programs


title: Prioritizing Bellman backups without a priority queue

abstract: Several researchers have shown that the efficiency of value iteration, a dynamic programming algorithm for Markov decision processes, can be improved by prioritizing the order of Bellman backups to focus computation on states where the value function can be improved the most. In previous work, a priority queue has been used to order backups. Although this incurs overhead for maintaining the priority queue, previous work has argued that the overhead is usually much less than the benefit from prioritization. However this conclusion is usually based on a comparison to a non-prioritized approach that performs Bellman backups on states in an arbitrary order. In this paper, we show that the overhead for maintaining the priority queue can be greater than the benefit, when it is compared to very simple heuristics for prioritizing backups that do not require a priority queue. Although the order of backups induced by our simple approach is often sub-optimal, we show that its smaller overhead allows it to converge faster than other state-of-the-art priority-based solvers.


title: Network Topologies: Inference, Modelling and Generation

abstract: Accurate measurement, inference and modeling techniques are fundamental to Internet topology research. Spatial analysis of the Internet is needed to develop network planning, optimal routing algorithms, and failure detection measures. A first step toward achieving such goals is the availability of network topologies at different levels of granularity, facilitating realistic simulations of new Internet systems. The main objective of this survey is to familiarize the reader with research on network topology over the past decade. We study techniques for inference, modeling, and generation of the Internet topology at both the router and administrative levels. We also compare the mathematical models assigned to various topologies and the generation tools based on them. We conclude with a look at emerging areas of research and potential future research directions.


title: Investigation Arabic Speech Recognition Using CMU Sphinx System

abstract: In this paper, Arabic was investigated from the speech recognition problem point of view. We propose a novel approach to build an Arabic automated speech recognition system using Arabic environment. The system, based on the open source CMU Sphinx-4, was trained using Arabic characters.


title: A new unsupervised convolutional neural network model for Chinese scene text detection

abstract: As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.


title: Asymmetric Compact OMT for X-Band SAR Applications

abstract: A compact asymmetric orthomode transducer (OMT) with high isolation between the vertical and horizontal Ports is developed for the X-band synthetic aperture radar application. The basic idea of the design is to deploy the combined E- and H-plane bends within the common arm. Moreover, an offset between each polarization axis is introduced to enhance the isolation and decrease the size to be around one-third of most of the existing asymmetric OMTs. The OMT achieves better than 22.5-dB matching level and 65-dB isolation level between the two modes. Good agreement is obtained between measurements and full-wave simulations.


title: Treatment of Thoracolumbar Burst Fractures with Polymethyl Methacrylate Vertebroplasty and Short-segment Pedicle Screw Fixation

abstract: OBJECTIVES ::: We aimed to evaluate the efficacy of reinforcing short-segment pedicle screw fixation with polymethyl methacrylate (PMMA) vertebroplasty in patients with thoracolumbar burst fractures. ::: ::: ::: METHODS ::: We enrolled 70 patients with thoracolumbar burst fractures for treatment with short-segment pedicle screw fixation. Fractures in Group A (n = 20) were reinforced with PMMA vertebroplasty during surgery. Group B patients (n = 50) were not treated with PMMA vertebroplasty. Kyphotic deformity, anterior vertebral height, instrument failure rates, and neurological function outcomes were compared between the two groups. ::: ::: ::: RESULTS ::: Kyphosis correction was achieved in Group A (PMMA vertebroplasty) and Group B (Group A, 6.4 degrees; Group B, 5.4 degrees). At the end of the follow-up period, kyphosis correction was maintained in Group A but lost in Group B (Group A, 0.33-degree loss; Group B, 6.20-degree loss) (P = 0.0001). After surgery, greater anterior vertebral height was achieved in Group A than in Group B (Group A, 12.9%; Group B, 2.3%) (P < 0.001). During follow-up, anterior vertebral height was maintained only in Group A (Group A, 0.13 +/- 4.06%; Group B, -6.17 +/- 1.21%) (P < 0.001). Patients in both Groups A and B demonstrated good postoperative Denis Pain Scale grades (P1 and P2), but Group A had better results than Group B in terms of the control of severe and constant pain (P4 and P5) (P < 0.001). The Frankel Performance Scale scores increased by nearly 1 in both Groups A and B. Group B was subdivided into Group B1 and B2. Group B1 consisted of patients who experienced instrument failure, including screw pullout, breakage, disconnection, and dislodgement (n = 11). Group B2 comprised patients from Group B who did not experience instrument failure (n = 39). There were no instrument failures among patients in Group A. Preoperative kyphotic deformity was greater in Group B1 (23.5 +/- 7.9 degrees) than in Group B2 (16.8 +/- 8.40 degrees), P < 0.05. Severe and constant pain (P4 and P5) was noted in 36% of Group B1 patients (P < 0.001), and three of these patients required removal of their implants. ::: ::: ::: CONCLUSION ::: Reinforcement of short-segment pedicle fixation with PMMA vertebroplasty for the treatment of patients with thoracolumbar burst fracture may achieve and maintain kyphosis correction, and it may also increase and maintain anterior vertebral height. Good Denis Pain Scale grades and improvement in Frankel Performance Scale scores were found in patients without instrument failure (Groups A and B2). Patients with greater preoperative kyphotic deformity had a higher risk of instrument failure if they did not undergo reinforcement with vertebroplasty. PMMA vertebroplasty offers immediate spinal stability in patients with thoracolumbar burst fractures, decreases the instrument failure rate, and provides better postoperative pain control than without vertebroplasty.


title: MHTN: Modal-Adversarial Hybrid Transfer Network for Cross-Modal Retrieval

abstract: Cross-modal retrieval has drawn wide interest for retrieval across different modalities of data. However, existing methods based on DNN face the challenge of insufficient cross-modal training data, which limits the training effectiveness and easily leads to overfitting. Transfer learning is for relieving the problem of insufficient training data, but it mainly focuses on knowledge transfer only from large-scale datasets as single-modal source domain to single-modal target domain. Such large-scale single-modal datasets also contain rich modal-independent semantic knowledge that can be shared across different modalities. Besides, large-scale cross-modal datasets are very labor-consuming to collect and label, so it is significant to fully exploit the knowledge in single-modal datasets for boosting cross-modal retrieval. This paper proposes modal-adversarial hybrid transfer network (MHTN), which to the best of our knowledge is the first work to realize knowledge transfer from single-modal source domain to cross-modal target domain, and learn cross-modal common representation. It is an end-to-end architecture with two subnetworks: (1) Modal-sharing knowledge transfer subnetwork is proposed to jointly transfer knowledge from a large-scale single-modal dataset in source domain to all modalities in target domain with a star network structure, which distills modal-independent supplementary knowledge for promoting cross-modal common representation learning. (2) Modal-adversarial semantic learning subnetwork is proposed to construct an adversarial training mechanism between common representation generator and modality discriminator, making the common representation discriminative for semantics but indiscriminative for modalities to enhance cross-modal semantic consistency during transfer process. Comprehensive experiments on 4 widely-used datasets show its effectiveness and generality.


title: Remaining trouble spots with computational thinking

abstract: Addressing unresolved questions concerning computational thinking.


title: A Modular 1 mm$^{3}$ Die-Stacked Sensing Platform With Low Power I$^{2}$C Inter-Die Communication and Multi-Modal Energy Harvesting

abstract: A 1.0 mm3 general-purpose sensor node platform with heterogeneous multi-layer structure is proposed. The sensor platform benefits from modularity by allowing the addition/removal of IC layers. A new low power I2C interface is introduced for energy efficient inter-layer communication with compatibility to commercial I2C protocols. A self-adapting power management unit is proposed for efficient battery voltage down conversion for wide range of battery voltages and load current. The power management unit also adapts itself by monitoring energy harvesting conditions and harvesting sources and is capable of harvesting from solar, thermal and microbial fuel cells. An optical wakeup receiver is proposed for sensor node programming and synchronization with 228 pW standby power. The system also includes two processors, timer, temperature sensor, and low-power imager. Standby power of the system is 11 nW.


title: Development and investigation of microstrip directional coupler with phase velocity compensation based on sawtooth configuration of coupled lines

abstract: The article considers the method for improvement of directional couplers on coupled microstrip lines through compensation of phase velocity difference in symmetric and asymmetric excitation modes due to implementation of sawtooth lines.


Unobservable communication over fully untrusted infrastructure ( extended version ) ⋆


title: Ano/microphthalmia, ocular cysts, central nervous system malformations, and neuropsychological delay. Diagnostic considerations on 2 Brazilian patients

abstract: We report on two unrelated Brazilian patients both presenting a very unusual association of ano/microphthalmia, cystic orbital anomaly, atypical clefting, and facial appendages in one patient. Clinical manifestations presented by these patients represent a MCA/MR syndrome of unknown etiology. Considerations about syndromic delineation, genetic aspects, and differential diagnosis are discussed.


title: Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis

abstract: Photorealistic frontal view synthesis from a single face image has a wide range of applications in the field of face recognition. Although data-driven deep learning methods have been proposed to address this problem by seeking solutions from ample face data, this problem is still challenging because it is intrinsically ill-posed. This paper proposes a Two-Pathway Generative Adversarial Network (TP-GAN) for photorealistic frontal view synthesis by simultaneously perceiving global structures and local details. Four landmark located patch networks are proposed to attend to local textures in addition to the commonly used global encoder-decoder network. Except for the novel architecture, we make this ill-posed problem well constrained by introducing a combination of adversarial loss, symmetry loss and identity preserving loss. The combined loss function leverages both frontal face distribution and pre-trained discriminative deep face models to guide an identity preserving inference of frontal views from profiles. Different from previous deep learning methods that mainly rely on intermediate features for recognition, our method directly leverages the synthesized identity preserving image for downstream tasks like face recognition and attribution estimation. Experimental results demonstrate that our method not only presents compelling perceptual results but also outperforms state-of-the-art results on large pose face recognition.


title: A Unipolar/Bipolar High-Voltage Pulse Generator Based on Positive and Negative Buck–Boost DC–DC Converters Operating in Discontinuous Conduction Mode

abstract: This paper presents a new high-voltage pulse generator, which is based on positive and negative buck-boost (BB) converters fed from a relatively low voltage dc supply. The proposed generator is able to generate unipolar or bipolar high-voltage pulses via operating the BB converters with series or parallel connected outputs respectively using a common input dc source. The components of each converter are rated at half of the pulsed voltage magnitude in the unipolar mode. The converters in the proposed pulse generator operate in discontinuous conduction mode. This enhances system efficiency, as the circuit only operates when it is desired to generate a pulsed output voltage, otherwise the circuit is switched to idle mode with zero current. Detailed illustration of the proposed approach is presented along with a full design of the system components for given output pulse specifications. Finally, simulation and experimental results are presented to validate the proposed concept.


title: A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection

abstract: The detection of moving objects from stationary cameras is usually approached by background subtraction, i.e. by constructing and maintaining an up-to-date model of the background and detecting moving objects as those that deviate from such a model. We adopt a previously proposed approach to background subtraction based on self-organization through artificial neural networks, that has been shown to well cope with several of the well known issues for background maintenance. Here, we propose a spatial coherence variant to such approach to enhance robustness against false detections and formulate a fuzzy model to deal with decision problems typically arising when crisp settings are involved. We show through experimental results and comparisons that higher accuracy values can be reached for color video sequences that represent typical situations critical for moving object detection.


title: Sparse Representation For Computer Vision and Pattern Recognition

abstract: Techniques from sparse signal representation are beginning to see significant impact in computer vision, often on nontraditional applications where the goal is not just to obtain a compact high-fidelity representation of the observed signal, but also to extract semantic information. The choice of dictionary plays a key role in bridging this gap: unconventional dictionaries consisting of, or learned from, the training samples themselves provide the key to obtaining state-of-the-art results and to attaching semantic meaning to sparse signal representations. Understanding the good performance of such unconventional dictionaries in turn demands new algorithmic and analytical techniques. This review paper highlights a few representative examples of how the interaction between sparse signal representation and computer vision can enrich both fields, and raises a number of open questions for further study.


title: Evolution of Memory Architecture

abstract: Computer memories continue to serve the role that they first served in the electronic discrete variable automatic computer (EDVAC) machine documented by John von Neumann, namely that of supplying instructions and operands for calculations in a timely manner. As technology has made possible significantly larger and faster machines with multiple processors, the relative distance in processor cycles of this memory has increased considerably. Microarchitectural techniques have evolved to share this memory across ever-larger systems of processors with deep cache hierarchies and have managed to hide this latency for many applications, but are proving to be expensive and energy-inefficient for newer types of problems working on massive amounts of data. New paradigms include scale-out systems distributed across hundreds and even thousands of nodes, in-memory databases that keep data in memory much longer than the duration of a single task, and near-data computation, where some of the computation is off-loaded to the location of the data to avoid wasting energy in the movement of data. This paper provides a historical perspective on the evolution of memory architecture, and suggests that the requirements of new problems and new applications are likely to fundamentally change processor and system architecture away from the currently established von Neumann model.


Context and Syntax embeddings for Question Answering On Stack Overflow


title: Dictionary Alignment With Re-Ranking for Low-Resolution NIR-VIS Face Recognition

abstract: Recently, near-infrared (NIR) images are increasingly being captured for recognizing faces in low-light/night-time conditions. Matching these images against the controlled high-resolution visible facial images usually present in the database is a challenging task. In surveillance scenarios, the NIR images can have very low resolution and also non-frontal pose which makes the problem even more challenging. In this paper, we propose an orthogonal dictionary alignment approach for addressing this problem. We also propose a re-ranking approach to further improve the recognition performance for each probe by combining the rank list given by the proposed algorithm with that given by another complementary feature/algorithm. Finally, we have also collected our own database Heterogeneous face recognition across Pose and Resolution (HPR) that has facial images captured from two surveillance quality NIR cameras and one high-resolution visible camera, with significant variations in head pose and resolution. Extensive experiments on the modified CASIA NIR-VIS 2.0 database, the Surveillance Camera face database, and our HPR database show the effectiveness of the proposed approaches and the collected database.


title: Implantable Antennas for Biomedical Applications: An Overview on Alternative Antenna Design Methods and Challenges

abstract: Implanted biomedical devices are witnessing great attention in finding solutions to complex medical conditions. Many challenges face the design of implantable biomedical devices including designing and implanting antennas within hostile environment due to the surrounding tissues of human body. Implanted antennas must be compact in size, efficient, safe, and can effectively work within adequate medical frequency bands. This paper presents an overview of the major aspects related to the design and challenges of in body implanted antennas. The review includes surveying the applications, design methods, challenges, simulation tools, and testing and manufacturing of implantable biomedical antennas.


title: Multilevel Sensor Fusion With Deep Learning

abstract: In the context of deep learning, this article presents an original deep network, namely CentralNet, for the fusion of information coming from different sensors. This approach is designed to efficiently and automatically balance the tradeoff between early and late fusion (i.e., between the fusion of low-level versus high-level information). More specifically, at each level of abstraction—the different levels of deep networks—unimodal representations of the data are fed to a central neural network which combines them into a common embedding. In addition, a multiobjective regularization is also introduced, helping to both optimize the central network and the unimodal networks. Experiments on four multimodal datasets not only show the state-of-the-art performance but also demonstrate that CentralNet can actually choose the best possible fusion strategy for a given problem.


title: 25-Gbps×4 optical transmitter with adjustable asymmetric pre-emphasis in 65-nm CMOS

abstract: This paper describes the design and experiment results of 25 Gbps, 4 channels optical transmitter which consist of a vertical-cavity surface emitting laser (VCSEL) driver with an asymmetric pre-emphasis circuit and an electrical receiver. To make data transfers faster in directly modulated a VCSEL-based optical communications, the driver circuit requires an asymmetric pre-emphasis signal to compensate for the nonlinear characteristics of VCSEL. An asymmetric pre-emphasis signal can be created by the adjusting a duty ratio with a delay circuit. A test chip was fabricated in the 65-nm standard CMOS process and demonstrated. An experimental evaluation showed that this transmitter enlarged the eye opening of a 25 Gbps, PRBS=2 ::: 9 ::: -1 test signal by 8.8% and achieve four channels fully optical link with an optical receiver at a power of 10.3 mW=Gbps=ch at 25 Gbps.


title: Stack4Things: a sensing-and-actuation-as-a-service framework for IoT and cloud integration

abstract: With the increasing adoption of embedded smart devices and their involvement in different application fields, complexity may quickly grow, thus making vertical ad hoc solutions ineffective. Recently, the Internet of Things (IoT) and Cloud integration seems to be one of the winning solutions in order to opportunely manage the proliferation of both data and devices. In this paper, following the idea to reuse as much tooling as possible, we propose, with regards to infrastructure management, to adopt a widely used and competitive framework for Infrastructure-as-a-Service such as OpenStack. Therefore, we describe approaches and architectures so far preliminary implemented for enabling Cloud-mediated interactions with droves of sensor- and actuator-hosting nodes by presenting Stack4Things, a framework for Sensing-and-Actuation-as-a-Service (SAaaS). In particular, starting from a detailed requirement analysis, in this work, we focus on the subsystems of Stack4Things devoted to resource control and management as well as on those related to the management and collection of sensing data. Several use cases are presented justifying how our proposed framework can be viewed as a concrete step toward the complete fulfillment of the SAaaS vision.


title: Locally linear reconstruction based missing value imputation for supervised learning

abstract: Most learning algorithms generally assume that data is complete so each attribute of all instances is filled with a valid value. However, missing values are very common in real datasets for various reasons. In this paper, we propose a new single imputation method based on locally linear reconstruction (LLR) that improves the prediction performance of supervised learning (classification & regression) with missing values. First, we investigate how missing values degrade the prediction performance with various missing ratios. Next, we compare the proposed missing value imputation method (LLR) with six well-known single imputation methods for five different learning algorithms based on 13 classification and nine regression datasets. The experimental results showed that (1) all imputation methods helped to improve the prediction accuracy, although some were very simple; (2) the proposed LLR imputation method enhanced the modeling performance more than all other imputation methods, irrespective of the learning algorithms and the missing ratios; and (3) LLR was outstanding when the missing ratio was relatively high and its prediction accuracy was similar to that of the complete dataset.


title: An Investigation of Gamification Typologies for Enhancing Learner Motivation

abstract: In this paper we present a new gamified learning system called Reflex which builds on our previous research, placing greater emphasis on variation in learner motivation and associated behaviour, having a particular focus on gamification typologies. Reflex comprises a browser based 3D virtual world that embeds both learning content and learner feedback. In this way the topography of the virtual world plays an important part in the presentation and access to learning material and learner feedback. Reflex presents information to learners based on their curriculum learning objectives and tracks their movement and interactions within the world. A core aspect of Reflex is its gamification design, with our engagement elements and processes based on Marczewski's eight gamification types [1]. We describe his model and its relationship to Bartle's player types [2] as well as the RAMP intrinsic motivation model [3]. We go on to present an analysis of experiments using Reflex with students on two 2nd year Computing modules. Our data mining and cluster analysis on the results of a gamification typology questionnaire expose variation in learner motivation. The results from a comprehensive tracking of the interactions of learners within Reflex are discussed and the acquired tracking data is discussed in context of gamification typologies and metacognitive tendencies of the learners. We discuss correlations in actual learner behaviour to that predicted by gamified learner profile. Our results illustrate the importance of taking variation in learner motivation into account when designing gamified learning systems.


The Gains from Financial Inclusion : Theory and a Quantitative Assessment ∗


title: Towards Federated Learning at Scale: System Design

abstract: Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.


title: Text Similarity using Google Tri-grams

abstract: The purpose of this paper is to propose an unsupervised approach for measuring the similarity of texts that can compete with supervised approaches. Finding the inherent properties of similarity between texts using a corpus in the form of a word n-gram data set is competitive with other text similarity techniques in terms of performance and practicality. Experimental results on a standard data set show that the proposed unsupervised method outperforms the state-of-the-art supervised method and the improvement achieved is statistically significant at 0.05 level. The approach is language-independent; it can be applied to other languages as long as n-grams are available.


title: AJIRA: A Lightweight Distributed Middleware for MapReduce and Stream Processing

abstract: Currently, MapReduce is the most popular programming model for large-scale data processing and this motivated the research community to improve its efficiency either with new extensions, algorithmic optimizations, or hardware. In this paper we address two main limitations of MapReduce: one relates to the model's limited expressiveness, which prevents the implementation of complex programs that require multiple steps or iterations. The other relates to the efficiency of its most popular implementations (e.g., Hadoop), which provide good resource utilization only for massive volumes of input, operating sub optimally for smaller or rapidly changing input. To address these limitations, we present AJIRA, a new middleware designed for efficient and generic data processing. At a conceptual level, AJIRA replaces the traditional map/reduce primitives by generic operators that can be dynamically allocated, allowing the execution of more complex batch and stream processing jobs. At a more technical level, AJIRA adopts a distributed, multi-threaded architecture that strives at minimizing overhead for non-critical functionality. These characteristics allow AJIRA to be used as a single programming model for both batch and stream processing. To this end, we evaluated its performance against Hadoop, Spark, Esper, and Storm, which are state of the art systems for both batch and stream processing. Our evaluation shows that AJIRA is competitive in a wide range of scenarios both in terms of processing time and scalability, making it an ideal choice where flexibility, extensibility, and the processing of both large and dynamic data with a single programming model are either desirable or even mandatory requirements.


title: Novel Eye Gaze Tracking Techniques Under Natural Head Movement

abstract: Most available remote eye gaze trackers have two characteristics that hinder them being widely used as the important computer input devices for human computer interaction. First, they have to be calibrated for each user individually; second, they have low tolerance for head movement and require the users to hold their heads unnaturally still. In this paper, by exploiting the eye anatomy, we propose two novel solutions to allow natural head movement and minimize the calibration procedure to only one time for a new individual. The first technique is proposed to estimate the 3D eye gaze directly. In this technique, the cornea of the eyeball is modeled as a convex mirror. Via the properties of convex mirror, a simple method is proposed to estimate the 3D optic axis of the eye. The visual axis, which is the true 3D gaze direction of the user, can be determined subsequently after knowing the angle deviation between the visual axis and optic axis by a simple calibration procedure. Therefore, the gaze point on an object in the scene can be obtained by simply intersecting the estimated 3D gaze direction with the object. Different from the first technique, our second technique does not need to estimate the 3D eye gaze directly, and the gaze point on an object is estimated from a gaze mapping function implicitly. In addition, a dynamic computational head compensation model is developed to automatically update the gaze mapping function whenever the head moves. Hence, the eye gaze can be estimated under natural head movement. Furthermore, it minimizes the calibration procedure to only one time for a new individual. The advantage of the proposed techniques over the current state of the art eye gaze trackers is that it can estimate the eye gaze of the user accurately under natural head movement, without need to perform the gaze calibration every time before using it. Our proposed methods will improve the usability of the eye gaze tracking technology, and we believe that it represents an important step for the eye tracker to be accepted as a natural computer input device.


title: A finger on the pulse: temporal rhythms and information seeking in medical work

abstract: Most cooperative work takes place in information-rich environments. However, studies of "information work" tend to focus on the decontextualized access and retrieval problems faced by individual information seekers. Our work is directed towards understanding how information management is seamlessly integrated into the course of everyday activities. Drawing on an ethnographic study of medical work, we explore the relationship between information and temporal coordination and discuss the role of temporal patterns or "rhythms" in providing individuals with the means to coordinate information and work.


Training data selection for acoustic modeling via submodular optimization of joint kullback-leibler divergence.


title: Programming with a Differentiable Forth Interpreter

abstract: There are families of neural networks that can learn to compute any function, provided sufficient training data. However, given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. Here we consider the case of prior procedural knowledge such as knowing the overall recursive structure of a sequence transduction program or the fact that a program will likely use arithmetic operations on real numbers to solve a task. To this end we present a differentiable interpreter for the programming language Forth. Through a neural implementation of the dual stack machine that underlies Forth, programmers can write program sketches with slots that can be filled with learnable behaviour. As the program interpreter is end-to-end differentiable, we can optimize this behaviour directly through gradient descent techniques on user specified objectives, and also integrate the program into any larger neural computation graph. We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex transduction tasks such as sequence sorting or addition with substantially less data and better generalisation over problem sizes. In addition, we introduce neural program optimisations based on symbolic computation and parallel branching that lead to significant speed improvements.


title: Reference Scope Identification in Citing Sentences

abstract: A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.


title: 2.4 to 61 GHz Multiband Double-Directional Propagation Measurements in Indoor Office Environments

abstract: This paper presents the details and results of double-directional propagation measurements carried out in two indoor office environments: a semi-open, cubicle-type office space and a more traditional work environment with closed offices. The measurements cover seven frequency bands from 2.4 to 61 GHz, permitting the propagation characteristics to be compared over a wide range of candidate radio frequencies for next-generation mobile wireless systems, including ultra high frequency and millimeter-wave bands. A novel processing algorithm is introduced for the expansion of multiband measurement data into sets of discrete multipath components. Based on the resulting multipath parameter estimates, models are presented for frequency-dependent path loss, shadow fading, copolarization ratio, delay spread, and angular spreads, along with their interfrequency correlations. Our results indicate a remarkably strong consistency in multipath structure over the entire frequency range considered.


title: Detection and Prevention of Routing Attacks in Internet of Things

abstract: Internet of things (IoT) is the smart network which connects smart objects over the Internet. The Internet is untrusted and unreliable network and thus IoT network is vulnerable to different kind of attacks. Conventional encryption and authentication techniques sometimes fail on IoT based network and intrusion may succeed to destroy the network. So, it is necessary to design intrusion detection system for such network. In our paper, we detect routing attacks such as sinkhole and selective forwarding. We have also tried to prevent our network from these attacks. We designed detection and prevention algorithm, i.e., KMA(Key Match Algorithm) and CBA(Cluster- Based Algorithm) in MatLab simulation environment. We gave two intrusion detection mechanisms and compared their results as well. True positive intrusion detection rate for our work is between 50% to 80% with KMA and 76% to 96% with CBA algorithm.


title: A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends

abstract: Massive flows that represent the individual level of movements and communications can be easily obtained in the age of big data. Generalizing spatial and temporal flow patterns from such data is essential to demonstrate spatial connections and mobility trends. Clustering approaches provide effective methods to handle data sets that contain massive individual-level flows. However, existing flow clustering studies obscure the geometric properties of flow data, such as direction and length, which significantly indicate movement trends. In addition, temporal information is often ignored because previous approaches have mainly focused on the perspective of spatial clusters of flow data, resulting in a loss of temporal patterns. In this paper, we introduce new spatial and temporal similarity measurements between flows and propose a new clustering approach of flow data based on a stepwise strategy. This method can identify clusters from distinct flow distributions and discover significant spatio-temporal trends from large flow data. Simulated experiments with synthetic flows and a case study using Beijing taxi trip data are conducted to validate the usefulness of the proposed method.


title: Training Millions of Personalized Dialogue Agents

abstract: Current dialogue systems are not very engaging for users, especially when trained end-to-end without relying on proactive reengaging scripted strategies. Zhang et al. (2018) showed that the engagement level of end-to-end dialogue models increases when conditioning them on text personas providing some personalized back-story to the model. However, the dataset used in Zhang et al. (2018) is synthetic and of limited size as it contains around 1k different personas. In this paper we introduce a new dataset providing 5 million personas and 700 million persona-based dialogues. Our experiments show that, at this scale, training using personas still improves the performance of end-to-end systems. In addition, we show that other tasks benefit from the wide coverage of our dataset by fine-tuning our model on the data from Zhang et al. (2018) and achieving state-of-the-art results.


title: Cascaded Mutual Modulation for Visual Reasoning

abstract: Visual reasoning is a special visual question answering problem that is multi-step and compositional by nature, and also requires intensive text-vision interactions. We propose CMM: Cascaded Mutual Modulation as a novel end-to-end visual reasoning model. CMM includes a multi-step comprehension process for both question and image. In each step, we use a Feature-wise Linear Modulation (FiLM) technique to enable textual/visual pipeline to mutually control each other. Experiments show that CMM significantly outperforms most related models, and reach state-of-the-arts on two visual reasoning benchmarks: CLEVR and NLVR, collected from both synthetic and natural languages. Ablation studies confirm that both our multistep framework and our visual-guided language modulation are critical to the task. Our code is available at https://github.com/FlamingHorizon/CMM-VR.


title: An Analysis of the SURF Method

abstract: The SURF method (Speeded Up Robust Features) is a fast and robust algorithm for local, similarity invariant representation and comparison of images. Similarly to many other local descriptor-based approaches, interest points of a given image are defined as salient features from a scale-invariant representation. Such a multiple-scale analysis is provided by the convolution of the initial image with discrete kernels at several scales (box filters). The second step consists in building orientation invariant descriptors, by using local gradient statistics (intensity and orientation). The main interest of the SURF approach lies in its fast computation of operators using box filters, thus enabling real-time applications such as tracking and object recognition. The SURF framework described in this paper is based on the PhD thesis of H. Bay [ETH Zurich, 2009], and more specifically on the paper co-written by H. Bay, A. Ess, T. Tuytelaars and L. Van Gool [Computer Vision and Image Understanding, 110 (2008), pp. 346–359]. An implementation is proposed and used to illustrate the approach for image matching. A short comparison with a state-of-the-art approach is also presented, the SIFT algorithm of D. Lowe [International Journal of Computer Vision, 60 (2004), pp. 91–110], with which SURF shares a lot in common.


title: A randomized controlled trial of the effects of nursing care based on Watson's Theory of Human Caring on distress, self-efficacy and adjustment in infertile women.

abstract: AIMS ::: To investigate the effects of nursing care based on the Theory of Human Caring on distress caused by infertility, perceived self-efficacy and adjustment levels. ::: ::: ::: BACKGROUND ::: Infertility leads to individual, familial and social problems. Nursing care standards for women affected by infertility have yet to emerge. ::: ::: ::: DESIGN ::: A randomized controlled trial. ::: ::: ::: METHODS ::: This study was conducted from May 2010-February 2011, with 105 Turkish women with infertility (intervention group: 52, control group: 53). We collected data using the Infertility Distress Scale, the Turkish-Infertility Self Efficacy Scale Short Form and the Turkish-Fertility Adjustment Scale. The intervention group received nursing care based on the Theory of Human Caring. Data were analysed using t-tests, chi-square tests and intention-to-treat analyses. ::: ::: ::: RESULTS ::: The intervention and control groups significantly differed with regard to infertility distress, self-efficacy and adjustment levels. The intervention group's mean self-efficacy score increased by seven points and adjustment score decreased by seven points (in a positive direction). In addition, there was a significant reduction in infertility distress scores in the intervention group, but there was no change in the control group. ::: ::: ::: CONCLUSION ::: Nursing care based on the Theory of Human Caring decreased the negative impact of infertility in women receiving infertility treatment and increased self-efficacy and adjustment.


title: Success-history based parameter adaptation for Differential Evolution

abstract: Differential Evolution is a simple, but effective approach for numerical optimization. Since the search efficiency of DE depends significantly on its control parameter settings, there has been much recent work on developing self-adaptive mechanisms for DE. We propose a new, parameter adaptation technique for DE which uses a historical memory of successful control parameter settings to guide the selection of future control parameter values. The proposed method is evaluated by comparison on 28 problems from the CEC2013 benchmark set, as well as CEC2005 benchmarks and the set of 13 classical benchmark problems. The experimental results show that a DE using our success-history based parameter adaptation method is competitive with the state-of-the-art DE algorithms.


title: Reputation-based on trust evaluation mechanism for P2P system

abstract: P2P system can enable the nodes that are strangers to one another to make online transactions with each other,but the problem of security is attracting more and more people’s attention.Hence,how to construct an effective trust mechanism to help build trust among the nodes is an important issue for the research of P2P technology nowadays.This paper proposes a novel reputation-based trust evaluation mechanism for P2P system.This mechanism takes into account the comprehensive factors affecting the trust level,improves the calculation methods of the local reputation and global reputation,reduces the computing load of the trust level,and introduces the blacklist mechanism into P2P system.The experiment result shows that this proposed mechanism can effectively evaluate the trust level of node and detect and separate the malicious nodes from P2P system to improve the rate of successful transaction,and can be effectively applied to P2P system.


title: A Review of Multiprocessor Directed Acyclic Graph (DAG) Scheduling Algorithms

abstract: Scheduling parallel tasks on multiprocessor systems is to allocate a set of tasks to processors such that the optimal usage of processors and accepted computation time for scheduling algorithm are obtained. In multiprocessor systems, an efficient scheduling of a parallel program onto the processors that minimizes the entire execution time is vital for achieving a high performance. The need for a scheduling algorithm arises from the requirement for most modern systems to perform multitasking. A multiprocessor system can be homogeneous (i.e. having all processors of same capability) or heterogeneous (i.e. having processors of different capabilities) nature. In literature several algorithms are proposed to find optimal solution of multiprocessor task scheduling algorithms. So, in present study we have reviewed various multiprocessor scheduling approaches to find their drawbacks so that an efficient heuristic based algorithm can be developed for obtaining optimal solution for these problems. Index Terms: Directed Acyclic Graph (DAG), Scheduling, Genetic Algorithm (GA), Selection, Crossover, Mutation.