title: The ABC’s of Teaching Social Skills to Adolescents with Autism Spectrum Disorder in the Classroom: The UCLA PEERS ® Program

abstract: Social skills training is a common treatment method for adolescents with autism spectrum disorder (ASD), yet very few evidence-based interventions exist to improve social skills for high-functioning adolescents on the spectrum, and even fewer studies have examined the effectiveness of teaching social skills in the classroom. This study examines change in social functioning for adolescents with high-functioning ASD following the implementation of a school-based, teacher-facilitated social skills intervention known as Program for the Education and Enrichment of Relational Skills (PEERS ® ). Seventy-three middle school students with ASD along with their parents and teachers participated in the study. Participants were assigned to the PEERS ® treatment condition or an alternative social skills curriculum. Instruction was provided daily by classroom teachers and teacher aides for 14-weeks. Results reveal that in comparison to an active treatment control group, participants in the PEERS ® treatment group significantly improved in social functioning in the areas of teacher-reported social responsiveness, social communication, social motivation, social awareness, and decreased autistic mannerisms, with a trend toward improved social cognition on the Social Responsiveness Scale. Adolescent self-reports indicate significant improvement in social skills knowledge and frequency of hosted and invited get-togethers with friends, and parent-reports suggest a decrease in teen social anxiety on the Social Anxiety Scale at a trend level. This research represents one of the few teacher-facilitated treatment intervention studies demonstrating effectiveness in improving the social skills of adolescents with ASD in the classroom: arguably the most natural social setting of all.


title: Integrated pressure-force-kinematics measuring system for the characterisation of plantar foot loading during locomotion

abstract: Plantar pressure, ground reaction force and body-segment kinematics measurements are largely used in gait analysis to characterise normal and abnormal function of the human foot. The combination of all these data together provides a more exhaustive, detailed and accurate view of foot loading during activities than traditional measurement systems alone do. A prototype system is presented that integrates a pressure platform, a force platform and a 3D anatomical tracking system to acquire combined information about foot function and loading. A stereophotogrammetric system and an anatomically based protocol for foot segment kinematics is included in a previously devised piezo-dynamometric system that combines pressure and force measurements. Experimental validation tests are carried out to check for both spatial and time synchronisation. Misalignment of the three systems is found to be within 6.0, 5.0 and 1.5 mm for the stereophotogrammetric system, force platform and pressure platform, respectively. The combination of position and pressure data allows for a more accurate selection of plantar foot subareas on the footprint. Measurements are also taken on five healthy volunteers during level walking to verify the feasibility of the overall experimental protocol. Four main subareas are defined and identified, and the relevant vertical and shear force data are computed. The integrated system is effective when there is a need for loading measurements in specific plantar foot subareas. This is attractive both in clinical assessment and in biomechanics research.


title: Adaptable Blockchain-Based Systems: A Case Study for Product Traceability

abstract: Traceability allows tracking products through all stages of a supply chain, which is crucial for product quality control. To provide accountability and forensic information, traceability information must be secured. This is challenging because traceability systems often must adapt to changes in regulations and to customized traceability inspection processes. OriginChain is a real-world traceability system using a blockchain. Blockchains are an emerging data storage technology that enables new forms of decentralized architectures. Components can agree on their shared states without trusting a central integration point. OriginChain’s architecture provides transparent tamper-proof traceability information, automates regulatory compliance checking, and enables system adaptability.


title: Shared Decision Making for Psychiatric Rehabilitation Services Before Discharge from Psychiatric Hospitals.

abstract: ABSTRACTShared decision making (SDM) is an effective health communication model designed to facilitate patient engagement in treatment decision making. In mental health, SDM has been applied and evaluated for medications decision making but less for its contribution to personal recovery and rehabilitation in psychiatric settings. The purpose of this pilot study was to assess the effect of SDM in choosing community psychiatric rehabilitation services before discharge from psychiatric hospitalization. A pre-post non-randomized design with two consecutive inpatient cohorts, SDM intervention (N = 51) and standard care (N = 50), was applied in two psychiatric hospitals in Israel. Participants in the intervention cohort reported greater engagement and knowledge after choosing rehabilitation services and greater services use at 6-to-12-month follow-up than those receiving standard care. No difference was found for rehospitalization rate. Two significant interaction effects indicated greater improvement in person...


title: Digital Libraries and Autonomous Citation Indexing

abstract: The revolution the Web has brought to information dissemination is not so much due to the availability of data-huge amounts of information has long been available in libraries-but rather the improved efficiency of accessing (improved accessibility to) that information. The Web promises to make more scientific articles more easily available. By making the context of citations easily and quickly browsable, autonomous citation indexing can help to evaluate the importance of individual contributions more accurately and quickly. Digital libraries incorporating ACI can help organize scientific literature and may significantly improve the efficiency of dissemination and feedback. ACI may also help speed the transition to scholarly electronic publishing.


title: Band Selection for Hyperspectral Image Classification Using Mutual Information

abstract: Spectral band selection is a fundamental problem in hyperspectral data processing. In this letter, a new band-selection method based on mutual information (MI) is proposed. MI measures the statistical dependence between two random variables and can therefore be used to evaluate the relative utility of each band to classification. A new strategy is described to estimate the MI using a priori knowledge of the scene, reducing reliance on a "ground truth" reference map, by retaining bands with high associated MI values (subject to the so-called "complementary" conditions). Simulations of classification performance on 16 classes of vegetation from the AVIRIS 92AV3C data set show the effectiveness of the method, which outperforms an MI-based method using the associated reference map, an entropy-based method, and a correlation-based method. It is also competitive with the steepest ascent algorithm at much lower computational cost


title: Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS

abstract: In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC). This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning.


title: Sentiment analysis using Latent Dirichlet Allocation and topic polarity wordcloud visualization

abstract: Sentiment analysis is a field of study that analyzes sentiment. One method for doing sentiment analysis is Latent Dirichlet Allocation (LDA) that extracts the topic of documents where the topic is represented as the appearance of the words with different topic probability. Therefore, we need data representation in visual form that is easier to understand than text and tables. One form of data visualization is wordcloud that provides a visual representation of words frequency. This research will perform sentiment analysis from the students' comments toward a university, in this case the Universitas Diponegoro, using LDA and topic polarity wordcloud visualization. The purpose of this study is to generate the topic polarity wordcloud of the students' comments by using the best combination of parameters. The best combination is the parameter with the value of alpha 0.1, value of beta 0.1, number of topics 9, threshold 10−7, and perplexity values 8.07. Such parameter combination produces 3 topics as positive sentiment and 6 topics as negative sentiment. In addition, we also compare the proposed method to several algorithms such as Naive Bayes and Logistic Regression. The final result shows that the proposed method outperforms the Naive Bayes and Logistic Regression in terms of F-Measure by 61%, 54%, and 56%, respectively.


title: Racial Faces in-the-Wild: Reducing Racial Bias by Deep Unsupervised Domain Adaptation

abstract: Despite of the progress achieved by deep learning in face recognition (FR), more and more people find that racial bias explicitly degrades the performance in realistic FR systems. Facing the fact that existing training and testing databases consist of almost Caucasian subjects, there are still no independent testing databases to evaluate racial bias and even no training databases and methods to reduce it. To facilitate the research towards conquering those unfair issues, this paper contributes a new dataset called Racial Faces in-the-Wild (RFW) database with two important uses, 1) racial bias testing: four testing subsets, namely Caucasian, Asian, Indian and African, are constructed, and each contains about 3000 individuals with 6000 image pairs for face verification, 2) racial bias reducing: one labeled training subset with Caucasians and three unlabeled training subsets with Asians, Indians and Africans are offered to encourage FR algorithms to transfer recognition knowledge from Caucasians to other races. For we all know, RFW is the first database for measuring racial bias in FR algorithms. After proving the existence of domain gap among different races and the existence of racial bias in FR algorithms, we further propose a deep information maximization adaptation network (IMAN) to bridge the domain gap, and comprehensive experiments show that the racial bias could be narrowed-down by our algorithm.


title: FoldingZero: Protein Folding from Scratch in Hydrophobic-Polar Model

abstract: De novo protein structure prediction from amino acid sequence is one of the most challenging problems in computational biology. As one of the extensively explored mathematical models for protein folding, Hydrophobic-Polar (HP) model enables thorough investigation of protein structure formation and evolution. Although HP model discretizes the conformational space and simplifies the folding energy function, it has been proven to be an NP-complete problem. In this paper, we propose a novel protein folding framework FoldingZero, self-folding a de novo protein 2D HP structure from scratch based on deep reinforcement learning. FoldingZero features the coupled approach of a two-head (policy and value heads) deep convolutional neural network (HPNet) and a regularized Upper Confidence Bounds for Trees (R-UCT). It is trained solely by a reinforcement learning algorithm, which improves HPNet and R-UCT iteratively through iterative policy optimization. Without any supervision and domain knowledge, FoldingZero not only achieves comparable results, but also learns the latent folding knowledge to stabilize the structure. Without exponential computation, FoldingZero shows promising potential to be adopted for real-world protein properties prediction.


title: Comparison of automatic summarisation methods for clinical free text notes

abstract: Abstract Objective A major source of information available in electronic health record (EHR) systems are the clinical free text notes documenting patient care. Managing this information is time-consuming for clinicians. Automatic text summarisation could assist clinicians in obtaining an overview of the free text information in ongoing care episodes, as well as in writing final discharge summaries. We present a study of automated text summarisation of clinical notes. It looks to identify which methods are best suited for this task and whether it is possible to automatically evaluate the quality differences of summaries produced by different methods in an efficient and reliable way. Methods and materials The study is based on material consisting of 66,884 care episodes from EHRs of heart patients admitted to a university hospital in Finland between 2005 and 2009. We present novel extractive text summarisation methods for summarising the free text content of care episodes. Most of these methods rely on word space models constructed using distributional semantic modelling. The summarisation effectiveness is evaluated using an experimental automatic evaluation approach incorporating well-known ROUGE measures. We also developed a manual evaluation scheme to perform a meta-evaluation on the ROUGE measures to see if they reflect the opinions of health care professionals. Results The agreement between the human evaluators is good (ICC=0.74, p 0.90 Spearman's rho). Three of the presented summarisation methods (‘Composite', ‘Case-Based' and ‘Translate') significantly outperform the other methods for all ROUGE measures ( p Conclusion The results indicate the feasibility of the automated summarisation of care episodes. Moreover, the high correlation between manual and automated evaluations suggests that the less labour-intensive automated evaluations can be used as a proxy for human evaluations when developing summarisation methods. This is of significant practical value for summarisation method development, because manual evaluation cannot be afforded for every variation of the summarisation methods. Instead, one can resort to automatic evaluation during the method development process.


title: Quantifying the dominant growth mechanisms of dimorphic yeast using a lattice-based model

abstract: A mathematical model is presented for the growth of yeast that incorporates both dimorphic behaviour and nutrient diffusion. The budding patterns observed in the standard and pseudohyphal growth modes are represented by a bias in the direction of cell proliferation. A set of spatial indices is developed to quantify the morphology and compare the relative importance of the directional bias to nutrient concentration and diffusivity on colony shape. It is found that there are three different growth modes: uniform growth, diffusion-limited growth (DLG) and an intermediate region in which the bias determines the morphology. The dimorphic transition due to nutrient limitation is investigated by relating the directional bias to the nutrient concentration, and this is shown to replicate the behaviour observed in vivo Comparisons are made with experimental data, from which it is found that the model captures many of the observed features. Both DLG and pseudohyphal growth are found to be capable of generating observed experimental morphologies.


title: A substrate-integrated-waveguide (SIW) quadrature hybrid-junction for low cost millimeter-wave planar antenna array

abstract: Couplers are critical elements in microwave and millimeter-wave frequency range systems and circuits [1]. Metallic rectangular waveguide couplers have been widely used in various millimeter-wave and microwave applications. However, metallic waveguide is not only a relatively high cost technology but also difficult to be integrated with planar circuits. The substrate integrated waveguide (SIW) is a planar technology that provides an attractive low- cost and low- loss alternative [2]. Planar circuits can be integrated into the same SIW substrate using planar transitions. In this paper, a SIW quadrature hybrid (−3 dB branch[3] line coupler) is presented and designed for millimeter- wave applications. Furthermore, a simple circuit model is proposed for fast design optimization of the presented junction. Simulated S-parameters results using both the proposed circuit model and HFSS package are provided for verification of the proposed model.


title: Disjunctive Programming for Multiobjective Discrete Optimisation

abstract: In this paper, I view and present the multiobjective discrete optimisation problem as a particular case of disjunctive programming where one seeks to identify efficient solutions from within a disjunction formed by a set of systems. The proposed approach lends itself to a simple yet effective iterative algorithm that is able to yield the set of all nondominated points, both supported and nonsupported, for a multiobjective discrete optimisation problem. Each iteration of the algorithm is a series of feasibility checks and requires only one formulation to be solved to optimality that has the same number of integer variables as that of the single objective formulation of the problem. The application of the algorithm show that it is particularly effective, and superior to the state-of-the-art, when solving constrained multiobjective discrete optimisation problem instances.


title: Design of a linear variable-stiffness mechanism using preloaded bistable beams

abstract: A machine with variable internal stiffness can meet the performance requirement in different working environments. Linear variable-stiffness mechanisms (LVSM) are rarely found compared with those producing rotary motion. This paper presents an approach to design a linear variable-stiffness mechanism. The idea is to parallel connect two lateral bistable beams with a vertical spring. Through the preload adjustment of the bistable beams, the output force to displacement curve can exhibit different stiffnesses. The merit of the proposed LVSM is that very large stiffness variation can be achieved in a compact space. The stiffness may even be tuned to zero by assigning a proper stiffness to the vertical spring. An optimization formulation is presented to design a LVSM with the largest stiffness variation. The effects of various parameters on the stiffness variation and linearity are discussed. The results are numerically verified with a prototype illustrated.


title: Pke: an Open Source Python-based Keyphrase Extraction Toolkit

abstract: We describe pke, an open source python-based keyphrase extraction toolkit. It provides an end-to-end keyphrase extraction pipeline in which each component can be easily modified or extented to develop new approaches. pke also allows for easy benchmarking of state-of-the-art keyphrase extraction approaches, and ships with supervised models trained on the SemEval-2010 dataset (Kim et al., 2010).


A Deep Learning Approach for Wi-Fi Based People Localization


Survey on Deep Learning Techniques for Wireless Communications *


title: Streaming End-to-end Speech Recognition for Mobile Devices

abstract: End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recog-nizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories.


title: Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model

abstract: With the goal of making high-resolution forecasts of regional rainfall, precipitation nowcasting has become an important and fundamental technology underlying various public services ranging from rainstorm warnings to flight safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to outperform traditional optical flow based methods for precipitation nowcasting, suggesting that deep learning models have a huge potential for solving the problem. However, the convolutional recurrence structure in ConvLSTM-based models is location-invariant while natural motion and transformation (e.g., rotation) are location-variant in general. Furthermore, since deep-learning-based precipitation nowcasting is a newly emerging area, clear evaluation protocols have not yet been established. To address these problems, we propose both a new model and a benchmark for precipitation nowcasting. Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU) model that can actively learn the location-variant structure for recurrent connections. Besides, we provide a benchmark that includes a real-world large-scale dataset from the Hong Kong Observatory, a new training loss, and a comprehensive evaluation protocol to facilitate future research and gauge the state of the art.


title: Uncovering the Face of Android Ransomware: Characterization and Real-Time Detection

abstract: In recent years, we witnessed a drastic increase of ransomware, especially on popular mobile platforms including Android. Ransomware extorts victims for a sum of money by taking control of their devices or files. In light of their rapid growth, there is a pressing need to develop effective countermeasure solutions. However, the research community is still constrained by the lack of a comprehensive data set, and there exists no insightful understanding of mobile ransomware in the wild. In this paper, we focus on the Android platform and aim to characterize existing Android ransomware. Specifically, we have managed to collect 2,721 ransomware samples that cover the majority of existing Android ransomware families. Based on these samples, we systematically characterize them from several aspects, including timeline and malicious features. In addition, the detection results of existing anti-virus tools are rather disappointing, which clearly calls for customized anti-mobile-ransomware solutions. To detect ransomware that extorts users by encrypting data, we propose a novel real-time detection system, called RansomProber. By analyzing the user interface widgets of related activities and the coordinates of users' finger movements, RansomProber can infer whether the file encryption operations are initiated by users. The experimental results show that RansomProber can effectively detect encrypting ransomware with high accuracy and acceptable runtime performance.


title: Sound event recognition in unstructured environments using spectrogram image processing

abstract: The objective of this research is to develop feature extraction and classification techniques for the task of sound event recognition (SER) in unstructured environments. Although this field is traditionally overshadowed by the popular field of automatic speech recognition (ASR), an SER system that can achieve human-like sound recognition performance opens up a range of novel application areas. These include acoustic surveillance, bio-acoustical monitoring, environmental context detection, healthcare applications and more generally the rich transcription of acoustic environments. The challenge in such environments are the adverse effects such as noise, distortion and multiple sources, which are more likely to occur with distant microphones compared to the close-talking microphones that are more common in ASR. In addition, the characteristics of acoustic events are less well defined than those of speech, and there is no sub-word dictionary available like the phonemes in speech. Therefore, the performance of ASR systems typically degrades dramatically in these challenging unstructured environments, and it is important to develop new methods that can perform well for this challenging task. In this thesis, the approach taken is to interpret the sound event as a two-dimensional spectrogram image, with the two axes as the time and frequency dimensions. This enables novel methods for SER to be developed based on spectrogram image processing, which are inspired by techniques from the field of image processing. The motivation for such an approach is based on finding an automatic approach to “spectrogram reading”, where it is possible for humans to visually recognise the different sound event signatures in the spectrogram. The advantages of such an approach are twofold. Firstly, the sound event image representation makes it possible to naturally capture the sound information in a two-dimensional feature. This has advantages over conventional onedimensional frame-based features, which capture only a slice of spectral information


title: Creating language and acoustic models using Kaldi to build an automatic speech recognition system for Kannada language

abstract: In this paper, creation of the Language Models (LMs) and Acoustic Models (AMs) using Kaldi speech recognition toolkit to build a robust Automatic Speech Recognition (ASR) system for Kannada language is demonstrated. The speech data is collected from the farmers of Karnataka under uncontrolled environment is used for the development of ASR models. The collected speech data needs to be translated to machine level language and hence the Indic Language Transliteration Tool (IT3 to UTF-8) is used for transcription. The dictionary for the collected speech data is created by using Indian Language Speech sound Label (ILSL12) set. The AMs are created by using Gaussian Mixture Model (GMM) and Subspace GMM (SGMM). The 80% and 20% of validated speech data is used for training and testing respectively. The accuracy and Word Error Rate (WER) of ASR models are highlighted and discussed in this work. The developed ASR models can be used in spoken query system which enables the farmers to access the on time agricultural commodity prices and weather information in Kannada language.


title: A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users' Reviews

abstract: In this paper we propose a multi-criteria recommender system based on collaborative filtering (CF) techniques, which exploits the information conveyed by users' reviews to provide a multi-faceted representation of users' interests. To this end, we exploited a framework for opinion mining and sentiment analysis, which automatically extracts relevant aspects and sentiment scores from users' reviews. As an example, in a restaurant recommendation scenario, the aspects may regard food quality, service, position, athmosphere of the place and so on. Such a multi-faceted representation of the user is used to feed a multi-criteria CF algorithm which predicts user interest in a particular item and provides her with recommendations. In the experimental session we evaluated the performance of the algorithm against several state-of-the-art baselines; Results confirmed the insight behind this work, since our approach was able to overcome both single-criteria recommendation algorithms as well as more sophisticated techniques based on matrix factorization.


title: A framework for designing a personalised web-based search assistant tool for eLearning

abstract: Search engine reveals significant contribution in learners' online and self-regulated learning experience. Learners currently rely on traditional search engines to retrieve relevant learning materials from the massive repository of materials in the Web. Unfortunately, these search engines do not consider differences in their learning aptitudes when delivering search results. As a consequence, a learner has to perform exhaustive search in order to obtain learning materials that better suit their individual needs. To address this problem, we propose a framework that augments the existing Google search engine with the ability to filter its search results according to learners' educational backgrounds, their learning behaviors when using the tool, as well as the behavioral patterns of other learners with similar profiles. The recommendation of the personalized search results is then implemented using dynamic learner profiling, and Groupization algorithm. Our preliminary evaluation of the prototype shows that the tool is able to personalize Google search results and produces recommendation of links according to three different learners' profiles.


title: Security Incident Response Criteria: A Practitioner's Perspective

abstract: Industrial reports indicate that security incidents continue to inflict large financial losses on organizations. Researchers and industrial analysts contend that there are fundamental problems with existing security incident response process solutions. This paper presents the Security Incident Response Criteria (SIRC) which can be applied to a variety of security incident response approaches. The criteria are derived from empirical data based on in-depth interviews conducted within a Global Fortune 500 organization and supporting literature. The research contribution of this paper is twofold. First, the criteria presented in this paper can be used to evaluate existing security incident response solutions and second, as a guide, to support future security incident response improvement initiatives.


title: AIF+: Dialogue in the Argument Interchange Format

abstract: This paper extends the Argument Interchange Format to enable it to represent dialogic argumentation. One of the challenges is to tie together the rules expressed in dialogue protocols with the inferential relations between premises and conclusions. The extensions are founded upon two important analogies which minimise the extra ontological machinery required. First, locutions in a dialogue are analogous to AIF I-nodes which capture propositional data. Second, steps between locutions are analogous to AIF S-nodes which capture inferential movement. This paper shows how these two analogies combine to allow both dialogue protocols and dialogue histories to be represented alongside monologic arguments in a single coherent system.


Exploring a continuum between main-memory and disk-oriented OLTP systems


Broadband nanophotonic wireless links and networks using on-chip integrated plasmonic antennas


title: Crowd vs. crowd: large-scale cooperative design through open team competition

abstract: Following the recent remarkable successes of crowdsourcing, there have been attempts to apply it to design. However a design problem is often too complex and difficult to break down into simpler, distributable tasks as required by the conventional crowdsourcing model. In this paper, we present Crowd vs. Crowd (CvC), a novel design crowdsourcing method, where several design teams made up of designers and crowd compete with each other. In each team, a designer coordinates effective communication between the crowd members and takes responsibility for the final design output, and the crowd contributes at different stages of design. We conducted an initial evaluation of CvC in comparison with other collaborative design methods, and found that: CvC can attract more people to participate; the crowd can make useful contribution in CvC; CvC can produce competent design outputs. We then applied CvC to two real-life design problems: first, designing a new logo for a university department; second, for a small tech company. With quantitative and qualitative analyses on these applications, we observed that the elements of competition and collaboration helped to sustain the crowd's motivation to participate, and to produce quality design outcomes with higher level of satisfaction for the stakeholders.


title: A Differential CMOS T/R Switch for Multistandard Applications

abstract: This brief presents a differential transmit-receive (T/R) switch integrated in a 0.18-mum standard CMOS technology for wireless applications up to 6 GHz. This switch design employs fully differential architecture to accommodate the design challenge of differential transceivers and improve the linearity performance. It exhibits less than 2-dB insertion loss, higher than 15-dB isolation, in a 60 mumtimes40 mum area. 15-dBm power at 1-dB compression point (P1dB) is achieved without using additional techniques to enhance the linearity. This switch is suitable for differential transceiver front-ends with a moderate power level. To the best of the authors' knowledge, this is the first reported differential T/R switch in CMOS for multistandard and wideband wireless applications


title: Anisotropic Kuwahara Filtering with Polynomial Weighting Functions*

abstract: Within the field of non-photorealistic rendering, a classical area of research is the stylization and abstraction of photographs using edge-preserving smoothing and enhancement filters. Prominent techniques in this area have in common to remove detail in low-contrast regions without filtering across discontinuities and thus leave the overall structure of the input image unaffected. Popular examples are the bilateral filter and mean shift. Another popular filter in this field is the Kuwahara filter [Kuwahara et al. 1976]. The general idea behind this filter is to divide the filter kernel into four rectangular subregions which overlap by one pixel. The filter response is then defined by the mean of a subregion with minimum variance. The Kuwahara filter produces clearly noticeable artifacts. These are due to the use of rectangular subregions. In addition, the subregion selection process is unstable if noise is present or subregions have the same variance. This results in randomly chosen subregions and corresponding artifacts. A more detailed discussion of limitations of the Kuwahara filter can be found in [Papari et al. 2007].


title: Use of GIS and agent-based modeling to simulate the spread of influenza

abstract: The propagation of influenza through a population is an inherent spatial-temporal process of great importance for modern society. So a spatial explicit epidemiologic model of flu is proposed for a greater understanding of the disease's spatial diffusion through a network of human contacts. The objective of this study is to develop an agent-based modeling (ABM) approach that integrates geographic information system (GIS) to simulate the spread of influenza in an urban environment, as a result of individuals' interactions in a geospatial context. The methodology for simulating spatiotemporal dynamics of influenza propagation is presented. Based on Repast Simphony platform, and applying Java and GIS tools, this paper designs and develops a prototype system to carry out agent-based simulation of influenza transmission and control in a geographic environment. The model is implemented using the H1N1 outbreak in Kunming, Yunnan province, southwest of china in 2009 as a case study. Individuals in a closed population are explicitly represented by agents associated to places where they interact with other agents. They are endowed with mobility, through a transportation network allowing them to move between places within the urban environment, in order to represent the spatial heterogeneity and the complexity involved in infectious diseases diffusion. In the model, influenza is described with the mathematic relation between transmission probability, the distance of two individuals, latent period, the time of infected to die, cure rate and other parameters. User can change the simulation results through adjusting the value of time to hospital and so on. The results show that the GIS-agent based model designed for this study can be easily customized to study the spread dynamics of any other influenza by simply adjusting the disease parameters. This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention and control of an influenza outbreak.


title: Standby power reduction using dynamic voltage scaling and canary flip-flop structures

abstract: Lowering V/sub DD/ during standby mode reduces power by decreasing both voltage and current. Analysis of flip-flop structures shows how low the voltage can scale before destroying the state information. Measurements of a 0.13-/spl mu/m, dual-V/sub T/ test chip show that reducing V/sub DD/ to near the point where state is lost gives the best power savings. We show that "canary" flip-flops provide a mechanism for observing the proximity to failure for the flip-flops. The canary flip-flops enable closed-loop standby voltage scaling for achieving savings near the optimum. This approach potentially provides over 2/spl times/ higher savings than an optimal open-loop approach without loss of state.


title: Hardware Acceleration for an Accurate Stereo Vision System Using Mini-Census Adaptive Support Region

abstract: Domain of stereo vision is highly important in the fields of autonomous cars, video tolling, robotics, and aerial surveys. The specific feature of this domain is that we should handle not only the pixel-by-pixel 2D processing in one image but also the 3D processing for depth estimation by comparing information about a scene from several images with different perspectives. This feature brings challenges to memory resource utilization, because an extra dimension of data has to be buffered. Due to the memory limitation, few of previous stereo vision implementations provide both accurate and high-speed processing for high-resolution images at the same time. To achieve domain-specific acceleration for stereo vision, the memory limitation has to be addressed. This article uses a Mini-Census ADaptive Support Region (MCADSR) stereo matching algorithm as a case study due to its high accuracy and representative operations in this domain. To relieve the memory limitation and achieve high-speed processing, the article proposes several efficient optimization methods including vertical-first cost aggregation, hybrid parallel processing, and hardware-friendly integral image. The article also presents a customizable system which provides both accurate and high-speed stereo matching for high-resolution images. The benefits of applying the optimization methods to the system are highlighted. With the aforesaid optimization and specific customization implemented on FPGA, the demonstrated system can process 47.6 fps (frames per second) and 129 fps for video size of 1920 × 1080 with a large disparity range of 256 and 1024 × 768 with a disparity range of 128, respectively. Our results are up to 1.64 times better than previous work in terms of Million Disparity Estimation per second (MDE/s). For accuracy, the 7.65p overall average error rate outperforms current work which can provide real-time processing with this high-resolution and large disparity range.


title: A Study on Vehicle Detection and Tracking Using Wireless Sensor Networks

abstract: Wireless Sensor network (WSN) is an emerging technology and has great potential to be employed in critical situations. The development of wireless sensor networks was originally motivated by military applications like battlefield surveillance. However, Wireless Sensor Networks are also used in many areas such as Industrial, Civilian, Health, Habitat Monitoring, Environmental, Military, Home and Office application areas. Detection and tracking of targets (eg. animal, vehicle) as it moves through a sensor network has become an increasingly important application for sensor networks. The key advantage of WSN is that the network can be deployed on the fly and can operate unattended, without the need for any pre-existing infrastructure and with little maintenance. The system will estimate and track the target based on the spatial differences of the target signal strength detected by the sensors at different locations. Magnetic and acoustic sensors and the signals captured by these sensors are of present interest in the study. The system is made up of three components for detecting and tracking the moving objects. The first component consists of inexpensive off-the shelf wireless sensor devices, such as MicaZ motes, capable of measuring acoustic and magnetic signals generated by vehicles. The second component is responsible for the data aggregation. The third component of the system is responsible for data fusion algorithms. This paper inspects the sensors available in the market and its strengths and weakness and also some of the vehicle detection and tracking algorithms and their classification. This work focuses the overview of each algorithm for detection and tracking and compares them based on evaluation parameters.


title: Rural-Urban Consumption Patterns In Bangladesh

abstract: The study attempts to analyze the consumption patterns in Bangladesh, over the years of 1973 to 2005 based on secondary data. It also attempts to show the patterns and trends of income, expenditure and consumption expenditure. Bangladesh achieved growth in consumption though some differences in the levels and patterns of consumption which have been observed between the rural and urban areas over the years. The paper therefore attempts to illustrate the distinctive nature of different consumption patterns among different socio-economic groups in Bangladesh. Consumption is the total expenditure by households on goods and services over a given period of time. The level of consumption depends upon some factors like the level of disposable income, marginal propensity to consume (MPC), marginal propensity to save (MPS), wealth, rate of inflation, rate of interest, availability of credit, expectation, composition of households, the determinants of saving, etc.. Therefore household consumption is a function of income of the household, the age and occupation of the household"s head, the member, age and sex of the family members, race, national origin, size of the community, section of the country, whether the household rents or owns its domicile, years of education, type of education and training, etc.. A change in the structure of population will also affect both consumption and savings. Keyensions hypothesize that consumption is a stable function of current disposable income in the short run. The life cycle hypothesis and the permanent income hypothesis both emphasize that consumption is a stable function of income only in the long run. In the short run, other factors such as the rate of the interest and wealth can have a significant impact upon consumption and savings. Although the subject "consumption pattern" has attracted a greater interest to a larger number of researchers in developed countries but in all under-developing countries research interest in consumer studies receive less attention. In developing countries like Bangladesh, the research in consumer studies has received least interest owing to the general stability of consumption patterns and the non-availability of relevant data on consumption patterns. An in-depth picture of consumption pattern of rural and urban households of Bangladesh is the main focus of the present study.


Speeding up Semantic Segmentation for Autonomous Driving


title: Arithmetic operations within memristor-based analog memory

abstract: This paper describes how memristors could be used as an analog memory and computing elements. The key idea is to apply comparison and programming phases cyclically so that the memristor can be programmed to a given conductance level at a fixed voltage. It is further described how the cyclical programming could be used in computing. A configuration needed to copy the sum of conductances of two memristors into a third one is described. It is further shown how the devices could be configured so that addition and subtraction of positive and negative analog conductances could be performed. The presented memory structure requires a memristor model with a nonlinear programming sensitivity (programming threshold) for proper programming selectivity. A model of such a memristor is shown and key simulations are presented.


title: A primal-dual augmented Lagrangian penalty-interior-point filter line search algorithm

abstract: Interior-point methods have been shown to be very efficient for large-scale nonlinear programming. The combination with penalty methods increases their robustness due to the regularization of the constraints caused by the penalty term. In this paper a primal–dual penalty-interior-point algorithm is proposed, that is based on an augmented Lagrangian approach with an \(\ell 2\)-exact penalty function. Global convergence is maintained by a combination of a merit function and a filter approach. Unlike the majority of filter methods, no separate feasibility restoration phase is required. The algorithm has been implemented within the solver WORHP to study different penalty and line search options and to compare its numerical performance to two other state-of-the-art nonlinear programming algorithms, the interior-point method IPOPT and the sequential quadratic programming method of WORHP.


RSL 17 BD at DBDC 3 : Computing Utterance Similarities based on Term Frequency and Word Embedding Vectors


title: Scalar implicatures: experiments at the semantics–pragmatics interface

abstract: In this article we present two sets of experiments designed to investigate the acquisition of scalar implicatures. Scalar implicatures arise in examples like Some professors are famous where the speaker’s use of some typically indicates that s/he had reasons not to use a more informative term, e.g. all. Some professors are famous therefore gives rise to the implicature that not all professors are famous. Recent studies on the development of pragmatics suggest that preschool children are often insensitive to such implicatures when they interpret scalar terms (Cognition 78 (2001) 165; Chierchia, G., Crain, S., Guasti, M.T., Gualmini, A., & Meroni, L. (2001). The acquisition of disjunction: evidence for a grammatical view of scalar implicatures. In A.H.-J. Do, L. Dominguez, & A. Johansen (Eds.), Proceedings of the 25th Boston University Conference on Language Development (pp. 157–168). Somerville, MA: Cascadilla Press; Musolino, J., & Lidz, J. (2002). Preschool logic: truth and felicity in the acquisition of quantification. In B. Skarabela, S. Fish, & A.H.-J. Do, Proceedings of the 26th Boston University Conference on Language Development (pp. 406–416). Somerville, MA: Cascadilla Press). This conclusion raises two important questions: (a) are all scalar terms treated in the same way by young children?, and (b) does the child’s difficulty reflect a genuine inability to derive scalar implicatures or is it due to demands imposed by the experimental task on an otherwise pragmatically savvy child? Experiment 1 addresses the first question by testing a group of 30 5-year-olds and 30 adults (all native speakers of Greek) on three different scales, koli, merikil (kall, somel), ktris, diol (kthree, twol) and kteliono, arxizol (kfinish, startl). In each case, subjects were presented with contexts which satisfied the semantic content of the stronger (i.e. more informative) terms on each scale (i.e. all, three and finish) but were described using the weaker terms of the scales (i.e. some, two, start). We found that, while adults overwhelmingly rejected these infelicitous descriptions, children almost never did so. Children also differed from adults in that their rejection rate on the numerical scale was reliably higher than on the two other scales. In order to address question (b), we trained a group of 30 5-yearolds to detect infelicitous statements. We then presented them with modified versions of the stories


title: Xen and the art of virtualization

abstract: Numerous systems have been designed which use virtualization to subdivide the ample resources of a modern computer. Some require specialized hardware, or cannot support commodity operating systems. Some target 100% binary compatibility at the expense of performance. Others sacrifice security or functionality for speed. Few offer resource isolation or performance guarantees; most provide only best-effort provisioning, risking denial of service.This paper presents Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality. This is achieved by providing an idealized virtual machine abstraction to which operating systems such as Linux, BSD and Windows XP, can be ported with minimal effort.Our design is targeted at hosting up to 100 virtual machine instances simultaneously on a modern server. The virtualization approach taken by Xen is extremely efficient: we allow operating systems such as Linux and Windows XP to be hosted simultaneously for a negligible performance overhead --- at most a few percent compared with the unvirtualized case. We considerably outperform competing commercial and freely available solutions in a range of microbenchmarks and system-wide tests.


title: The Consequences of Technostress for End Users in Organizations: Conceptual Development and Empirical Validation

abstract: The research reported in this paper studies the phenomenon of technostress, that is, stress experienced by end users of Information and Communication Technologies (ICTs), and examines its influence on their job satisfaction, commitment to the organization, and intention to stay. Drawing from the Transaction-Based Model of stress and prior research on the effects of ICTs on end users, we first conceptually build a nomological net for technostress to understand the influence of technostress on three variables relating to end users of ICTs: job satisfaction, and organizational and continuance commitment. Because there are no prior instruments to measure constructs related to technostress, we develop and empirically validate two second order constructs: technostress creators (i.e., factors that create stress from the use of ICTs) and technostress inhibitors (i.e., organizational mechanisms that reduce stress from the use of ICTs). We test our conceptual model using data from the responses of 608 end users of ICTs from multiple organizations to a survey questionnaire. Our results, based on structural equation modeling (SEM), show that technostress creators decrease job satisfaction, leading to decreased organizational and continuance commitment, while Technostress inhibitors increase job satisfaction and organizational and continuance commitment. We also find that age, gender, education, and computer confidence influence technostress. The implications of these results and future research directions are discussed.


title: A Comparison of Lipoatrophy and Aging: Volume Deficits in the Face

abstract: Background ::: Insight into the physical processes of aging can be gained by comparing the loss of facial volume that occurs during aging with the dramatic fat loss resulting from acquired lipoatrophy, including human immunodeficiency virus (HIV) treatment-associated lipoatrophy. The superficial effects of aging, such as rhytid formation, often are the focus of investigations into this phenomenon. However, age-related volume loss often is ignored.


title: An Introduction to fMRI

abstract: Functional magnetic resonance imaging (fMRI) provides an opportunity to indirectly observe neural activity noninvasively in the human brain as it changes in near real time. Most fMRI experiments measure the blood oxygen-level dependent (BOLD) signal, which rises to a peak several seconds after a brain area becomes active. Several experimental designs are common in fMRI research. Block designs alternate periods in which subjects perform some task with periods of rest, whereas event-related designs present the subject with a set of discrete trials. After the fMRI experiment is complete, pre-processing analyses prepare the data for task-related analyses. The most popular task-related analysis uses the General Linear Model to correlate a predicted BOLD response with the observed activity in each brain region. Regions where this correlation is high are identified as task related. Connectivity analysis then tries to identify active regions that belong to the same functional network. In contrast, multivariate methods, such as independent component analysis and multi-voxel pattern analysis identify networks of event-related regions, rather than single regions, so they simultaneously address questions of functional connectivity.


title: Magnet-Technology Development at the Dresden High Magnetic Field Laboratory

abstract: The Dresden High Magnetic Field Laboratory (HLD) is a user facility which provides scientists with the possibility to perform a broad range of experiments in pulsed magnetic fields. Recent progress in the magnet-technology development at the HLD has led to significant advances in achieving non-destructive pulsed magnetic fields close to the megagauss mark. Using 9.5 MJ dual-coil magnets with 16 mm bore, in 2011 a world-record field of 91.4 T has been achieved. Later 94.2 T have been reached. We report on the magnet design and performance of these magnets as well as on the design for the next generation of dual-coil magnets characterized by interchangeable inner sub-coils and improved control of the axial preload.


Text Document Clustering based on Phrase


title: Perceived learning environment and students' emotional experiences: A multilevel analysis of mathematics classrooms *

abstract: A multilevel approach was used to analyse relationships between perceived classroom environments and emotions in mathematics. Based on Pekrun’s (2000) [A social-cognitive, control-value theory of achievement emotions. In J. Heckhausen (Ed.), Motivational psychology of human development (pp. 143 163)] social-cognitive, control-value theory of achievement emotions, we hypothesized that environmental characteristics conveying control and value to the students would be related to their experience of enjoyment, anxiety, anger, and boredom in mathematics. Multilevel modelling of data from 1623 students from 69 classes (grades 5 10) confirmed close relationships between environmental variables and emotional experiences that functioned predominantly at the individual level. Compositional effects further revealed that classes’ aggregate environment perceptions as well as their compositions in terms of aggregate achievement and gender ratio were additionally linked to students’ emotions in mathematics. Methodological and practical implications of the findings are discussed.


title: Dual-band slotted waveguide antenna array for adaptive mm-wave 5G networks

abstract: This paper describes the design, simulation and measurement of a dual-band slotted waveguide antenna array for adaptive 5G networks, operating in the millimeter wave frequency range. Its structure is composed by two groups of slots milled onto the opposite faces of a rectangular waveguide, enabling antenna operation over two different frequency bands, namely 28 and 38 GHz. Measured and numerical results, obtained using ANSYS HFSS, demonstrate two bandwidths of approximately 26.36% and 9.78% for 28 GHz and 38 GHz, respectively. The antenna gain varies from 12.6 dBi for the lower frequency band to 15.6dBi for the higher one.


title: A general framework for object detection

abstract: This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of a subset of an overcomplete dictionary of wavelet basis functions, we derive a compact representation of an object class which is used as an input to a support vector machine classifier. This representation overcomes both the problem of in-class variability and provides a low false detection rate in unconstrained environments. We demonstrate the capabilities of the technique in two domains whose inherent information content differs significantly. The first system is face detection and the second is the domain of people which, in contrast to faces, vary greatly in color, texture, and patterns. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or motion-based segmentation. The paper also presents a motion-based extension to enhance the performance of the detection algorithm over video sequences. The results presented here suggest that this architecture may well be quite general.


Distributional Hypernym Generation by Jointly Learning Clusters and Projections


title: Multiple UAV area decomposition and coverage

abstract: Multiple UAVs can be used to cover a region effectively. Area coverage involves two stages - area decomposition into cells and path planning inside the cells. The area is decomposed using sweeping technique. For path planning inside the cells, a novel method is developed where optimal number of lanes are generated to minimize the number of UAV turns to accomplish the mission in minimum time. Also to allow operator interaction and precedence in coverage, a novel lawnmower/Zamboni planner is designed. Simulation results are presented for multiple UAVs are presented and flight test results for one UAV performing a persistent mission is presented.


title: Interaction of Mesoscopic Magnetic Textures with Superconductors.

abstract: In this thesis, we studied the problem of the interaction between a superconducting thin film and a two-dimensional magnetization distribution. We considered the magnetization such that it depends on r, the radius, only. We gave a general formalism for this problem. The magnetization generates a magnetic field, which penetrates into the superconductor; hence screening currents are induced in the superconductor. We also studied the possibility of the simultaneous creation of vortices in the superconductor, and established the required conditions for that. In particular, we studied two configurations for the magnetization distribution, namely the magnetic vortex and magnetic dot. We found that the dot was capable of simultaneously creating vortices in the superconductor. However, the magnetic vortex did not.


title: A New Parameter Estimation Method of Solar Photovoltaic

abstract: The accuracy in electrical model parameters of solar photovoltaic (PV), such as photon current, the diode dark saturation current, series resistance, shunt resistance, and diode ideality factor, are desirable to predict the real performance characteristics of solar PV under varying environment conditions. First, this paper derives mathematical model of solar PV, in terms of two unknown, namely, series resistance and ideality factor. Then, using combination of analytical method, simulated annealing method, and derived model, a new parameter estimation technique has been proposed. Finally, performance indices, such as PV characteristics curve, relative maximum power error, root mean square deviation, and normalized root mean square deviation are estimated for the various solar PV panels, using proposed and existing methods, to reveal the effectiveness of the proposed method. Also, experimental data have been considered for the validation. Finally, through the comparative analysis of the results, it is revealed that the proposed method offers solar PV characteristics more closer to the real characteristics than the other existing methods.


title: First Comparison of Sentinel-1 and TerraSAR-X Data in the Framework of Maritime Targets Detection: South Italy Case

abstract: The Sentinel-1A is the first of two satellites that composes the Sentinel-1 radar mission. Both satellites operate a C-band synthetic aperture radar (SAR) system to give continuity to the European SAR program. SAR is a flexible sensor able to fulfil users/applications requirements in terms of resolution and coverage thanks to different operational modes and polarizations. With the in-orbit availability of very-high-resolution X-band SAR sensors, the Sentinel-1 satellites have been designed to achieve wide coverage at medium to high resolution. The interferometric wide swath (IWS) mode implemented with the terrain observation with progressive scan (TOPS) technique is the standard acquisition mode over European waters and land masses. IWS in dual-polarization (VV/VH) combination offers 250-km swath at $\hbox{5 m} \times \hbox{20 m}$ (range $\times$ azimuth) spatial resolution. These specifications are in line with the needs of the European Maritime and Security Agency (EMSA) for oil spill and ship detection applications included in the CleanSeaNet program. The main goals of this paper are: assessment of medium-to-high-resolution C-band Sentinel-1 data with very-high-resolution X-band TerraSAR-X data for maritime targets detection; synergetic use of multiplatforms satellite SAR data for target features extraction; evaluation of polarimetric target detectors for the available co-polarization and cross-polarization Sentinel-1A IWS VV/VH products. The objectives are achieved by means of real, almost coincident C-band and X-band SAR data acquired by Sentinel-1A and TerraSAR-X satellites over Gulf of Naples and Catania (South Italy). Furthermore, the obtained results are supported by recorded ground truth vessel reports via terrestrial automatic identification system (AIS) stations located in the area.


title: Phrase-Level Class based Language Model for Mandarin Smart Speaker Query Recognition

abstract: The success of speech assistants requires precise recognition of a number of entities on particular contexts. A common solution is to train a class-based n-gram language model and then expand the classes into specific words or phrases. However, when the class has a huge list, e.g., more than 20 million songs, a fully expansion will cause memory explosion. Worse still, the list items in the class need to be updated frequently, which requires a dynamic model updating technique. In this work, we propose to train pruned language models for the word classes to replace the slots in the root n-gram. We further propose to use a novel technique, named Difference Language Model (DLM), to correct the bias from the pruned language models. Once the decoding graph is built, we only need to recalculate the DLM when the entities in word classes are updated. Results show that the proposed method consistently and significantly outperforms the conventional approaches on all datasets, esp. for large lists, which the conventional approaches cannot handle.


title: Complex spectral minutiae representation for fingerprint recognition

abstract: The spectral minutiae representation is designed for combining fingerprint recognition with template protection. This puts several constraints to the fingerprint recognition system: first, no relative alignment of two fingerprints is allowed due to the encrypted storage; second, a fixed-length feature vector is required as input of template protection schemes. The spectral minutiae representation represents a minutiae set as a fixed-length feature vector, which is invariant to translation, rotation and scaling. These characteristics enable the combination of fingerprint recognition systems with template protection schemes and allow for fast minutiae-based matching as well. In this paper, we introduce the complex spectral minutiae representation (SMC): a spectral representation of a minitiae set, as the location-based and the orientation-based spectral minutiae representations (SML and SMO), but it encodes minutiae orientations differently. SMC improves the recognition accuracy, expressed in term of the Equal Error Rate, about 2–4 times compared with SML and SMO. In addition, the paper presents two feature reduction algorithms: the Column-PCA and the Line-DFT feature reductions, which achieve a template size reduction around 90% and results in a 10–15 times higher matching speed (with 125,000 comparisons per second).


title: A PLANAR COMPACT TRIPLE-BAND MONOPOLE ANTENNA FOR WLAN/WiMAX APPLICATIONS

abstract: A planar triple-band monopole antenna with a U-shaped stripline and a L slot is presented. The antenna is very compact with a size of 20 £ 30 £ 1:5mm 3 and fed by a 50› microstrip line with a defected ground. The measured i10dB impedance bandwidth of the proposed antenna covers 2.33GHz{2.51GHz, 3.25GHz{3.82GHz, and 4.83GHz{8.4GHz, respectively, which meets the speciflcations of WLAN 2.4/5.2/5.8GHz and WiMAX 3.5/5.5GHz. The radiation characteristics shows a monopole-like pattern, and the measured results are in agreements with the simulated ones.


title: Implementing the "GrabCut" segmentation technique as a plugin for the GIMP

abstract: Image segmentation requires a segmentation tool that is fast and easy to use. The GIMP has built in segmentation tools, but under some circumstances these tools perform badly. "GrabCut" is an innovative segmentation technique that uses both region and boundary information in order to perform segmentation. Several variations on the "GrabCut" algorithm have been implemented as a plugin for the GIMP. The results obtained using "GrabCut" are comparable, and often better than the results of all the other built in segmentation tools.


title: Performance evaluation of NoSQL big-data applications using multi-formalism models

abstract: Abstract Starting with the birth of Web 2.0, the quantity of data managed by large-scale web services has grown exponentially, posing new challenges and infrastructure requirements. This has led to new programming paradigms and architectural choices, such as map-reduce and NoSQL databases, which constitute two of the main peculiarities of the specialized massively distributed systems referred to as Big Data architectures. The underlying computer infrastructures usually face complexity requirements, resulting from the need for efficiency and speed in computing over huge evolving data sets. This is achieved by taking advantage from the features of new technologies, such as the automatic scaling and replica provisioning of Cloud environments. Although performances are a key issue for the considered applications, few performance evaluation results are currently available in this field. In this work we focus on investigating how a Big Data application designer can evaluate the performances of applications exploiting the Apache Hive query language for NoSQL databases, built over a Apache Hadoop map-reduce infrastructure. This paper presents a dedicated modeling language and an application, showing first how it is possible to ease the modeling process and second how the semantic gap between modeling logic and the domain can be reduced, by means of vertical multiformalism modeling.


title: Code hunt: gamifying teaching and learning of computer science at scale

abstract: Code Hunt (http://www.codehunt.com/) is an educational coding game (that runs in a browser) for teaching and learning computer science at scale. The game consists of a series of worlds and levels, which get increasingly challenging. In each level, the player has to discover a secret code fragment and write code for it. The game has sounds and a leaderboard to keep the player engaged. Code Hunt targets teachers and students from introductory to advanced programming or software engineering courses. In addition, Code Hunt can be used by seasoned developers to hone their programming skills or by companies to evaluate job candidates. At the core of the game experience is an automated program analysis and grading engine based on dynamic symbolic execution. The engine detects any behavioral differences between the player's code and the secret code fragment. The game works in any modern browser, and currently supports C# or Java programs. Code Hunt is a dramatic evolution of our earlier Pex4Fun web platform, from which we have gathered considerable experience (including over 1.4 million programs submitted by users).


title: Predicting Knowledge Sharing Intention Based on Theory of Reasoned Action Framework: An Empirical Study on Higher Education Institution

abstract: Based on theory of reasoned action (TRA) theoretical framework, this study try to predict individual knowledge sharing intention on higher education institution context. This study also integrate some potential factors (channel richness, psychological forces and organizational climate factors) which supporting individuals' knowledge-sharing intentions Through a field survey of 242 lecturer on public and privateuniversity at Purwokerto and Yogyakarta city, this study confirm our hypothesis that attitudes toward knowledge sharing and subjective norms with regard to knowledge sharing as well as organizational climate affect individuals' intentions to share knowledge. This study showed thatPerceived Reciprocal Benefits, Perceived Enjoyment in Helping Others, Perceived Reputation Enhancement and channel richness affect individuals'attitudes toward knowledge sharing while organizational climate affect subjective norms. Overall, the results of the study advance prior research in the area of knowledge sharing by shedding light on the determinants of knowledge sharing behaviors of knowledge workers. These insights could be used by university in developing work environments that are conducive for knowledge worker to share their knowledge. Keyword: Knowledge sharing intention, TRA, Knowledge worker, attitude, university.


title: Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

abstract: The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.


Stress, Depression, Social Support, and Help-Seeking in College Student-Athletes


title: Effects of the Use of Social Network Sites on Task Performance: Toward a Sustainable Performance in a Distracting Work Environment

abstract: As the use of social network sites (SNS) has become increasingly prevalent, its effect on sustainable performance has received much attention. The existing literature has taken either a positive or negative view of SNS, arguing that it either decreases performance by taking time and effort away from work, or increases performance by providing social benefits for enhancing performance. In contrast, this experimental study, investigates how SNS use can disturb or enhance the performance of different types of tasks differently, thus influencing the sustainability of task performance. Based on distraction-conflict theory, this study distinguishes between simple and complex tasks, examines the role of SNS, and analyzes data including electroencephalography data captured by a brain-computer interface. The results show that task performance can be sustainable such that SNS use positively influences performance when participants are engaged in a simple task and influences performance neither positively nor negatively when participants are engaged in a complex task. The study finds the former result is attributable to the positive effect of the psychological arousal induced by SNS use and the latter result to the negative effect of the psychological arousal offsetting the positive effect of reduced stress resulting from SNS use.


title: A new approach to detect and segment overlapping cells in multi-layer cervical cell volume images

abstract: Detection and segmentation of overlapping cell nuclei and cytoplasm in multi-layer Pap smear volumes are critical prerequisites for automatic screening of cervical cancer. The novel approach proposed here is an improved version of our top ranked method for the Segmentation of Overlapping Cervical Cells from Multi-layer Cytology Preparation Volumes Challenge — ISBI 2015. From the images provided of multiple focal planes through liquid-based Pap smear samples, nuclei within Extended Depth of Field (EDF) images are first located with an iterative approach and then clumps are segmented using a learned Gaussian mixture model by Expectation-Maximization (EM) algorithm from pixel intensities. The overlapping boundaries of cytoplasm on the multifocal planes are approximated using a new distance metric and then refined in two post-processing steps. Promising results with Dice Coefficient (0.861), False Negative object rate (0.352) and True Positive pixel rate (0.874) indicate that nuclei and their corresponding cytoplasm in highly overlapping cytology multi-layer Pap smear volumes can be effectively detected and segmented using this approach.


title: Sensor fusion for robot control through deep reinforcement learning

abstract: Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy. In addition to sensors mounted on the robot, sensors might also be deployed in the environment, although these might need to be accessed via an unreliable wireless connection. In this paper, we demonstrate deep neural network architectures that are able to fuse information generated by multiple sensors and are robust to sensor failures at runtime. We evaluate our method on a search and pick task for a robot both in simulation and the real world.


title: Quranic Verse Extraction base on Concepts using OWL-DL Ontology

abstract: In recent years, there has been a global growing demand for Islamic knowledge by both Muslims and non-Muslims. This has brought about a number of automated applications that ease the retrieval of knowledge from the Holy Book, being the major source of Knowledge in Islam. However, the current retrieval methods in the Quranic domain lack adequate semantic search capabilities; they are mostly based on the keywords matching approach. There is a lack of adequate linked data to provide a better description of concepts found in the Holy Quran. In this study we propose an ontology assisted semantic search system in the Qur’an domain. The system makes use of Quran ontology and various relationships and restrictions. This will enable the user to semantically search for verses related to their query in Al-Quran. The system has improved the search capability of the Holy Quran knowledge to 95 percent accuracy level.


Forensic Analysis of WhatsApp on Android Smartphones


title: Design of Power and Area Efficient Approximate Multipliers

abstract: Approximate computing can decrease the design complexity with an increase in performance and power efficiency for error resilient applications. This brief deals with a new design approach for approximation of multipliers. The partial products of the multiplier are altered to introduce varying probability terms. Logic complexity of approximation is varied for the accumulation of altered partial products based on their probability. The proposed approximation is utilized in two variants of 16-bit multipliers. Synthesis results reveal that two proposed multipliers achieve power savings of 72% and 38%, respectively, compared to an exact multiplier. They have better precision when compared to existing approximate multipliers. Mean relative error figures are as low as 7.6% and 0.02% for the proposed approximate multipliers, which are better than the previous works. Performance of the proposed multipliers is evaluated with an image processing application, where one of the proposed models achieves the highest peak signal to noise ratio.


title: An Effective High Threating Alarm Mining Method for Cloud Security Management

abstract: Security equipment such as intrusion prevention system is an important supplementary for security management. They reduce the difficulty of network management by giving alarms corresponding to different attacks instead of raw traffic packet inspection. But there are many false alarms due to their running mechanism, which greatly reduces its usability. In this paper, we develop a hierarchical framework to mine high threating alarms from the massive alarm logs, and aim to provide fundamental and useful information for administrators to design efficient management policy. First, the alarms are divided into two parts based on their attributes, the first part mainly includes several kinds of famous attacks which are critical for security management, we proposed a similar alarm mining method based on Choquet integral to cluster and rank the frequently occurred attacks. The rest alarms constitute the second part, which are caused by the potential threats attacks, also include many false alarms. To reduce the effect of false alarms and rank the potential threats, we employ the frequent pattern mining algorithm to mine correlation rules and then filter false alarms. Following, we proposed a self-adapting threat degree calculation method to qualify the threat degree of these alarms after filtering. To verity the methods developed, an experimental platform is constructed in the campus network of Xi’an Jiaotong University. Experimental results based on the data collected verify the efficiency of the developed methods. For the first kind of alarms, the similar alarms mining accuracy is higher than 97% and the alarms are ranked with different processing urgencies. For the rest alarms, the proposed methods have filtering accuracy above 80% and can rank the potential threats. Based on the ranking results, administrators can deal with the high threats with their limited time and energy, in turn, keep the network under control.


title: Category Use and Category Learning

abstract: Categorization models based on laboratory research focus on a narrower range of explanatory constructs than appears necessary for explaining the structure of natural categories. This mismatch is caused by the reliance on classification as the basis of laboratory studies. Category representations are formed in the process of interacting with category members. Thus, laboratory studies must explore a range of category uses. The authors review the effects of a variety of category uses on category learning. First, there is an extensive discussion contrasting classification with a predictive inference task that is formally equivalent to classification but leads to a very different pattern of learning. Then, research on the effects of problem solving, communication, and combining inference and classification is reviewed. A central goal of cognitive psychology is to understand how categories are learned and used. Research on categorization has explored both the structure of people’s natural categories and their ability to acquire novel categories through laboratory studies. This work has generated a significant empirical record and a number of important insights into the nature of category representations. Despite this great success, there is a troubling gap between the observations derived from people’s natural categories and the models that have been developed on the basis of laboratory studies. In particular, theories of categorization based on laboratory studies do not provide a compelling explanation for the variety of category types, structures, and functions observed in natural categories. In this article, we suggest that this gap reflects important differences between laboratory methods and the way categories are normally acquired and that the way to bridge the gap is to develop laboratory methods that are analogous to the way natural categories are acquired. Specifically, people’s natural categories are acquired in the course of interacting with the categories and with category members. People classify novel items, make predictions about unknown properties, solve problems with categories, make explanations based on them, communicate about them, and form preferences. Each of these tasks leaves its mark on the representation of these categories. Often, category information is learned as a by-product of interactions with the category and is not the central goal of the interaction. In contrast to the variety of ways people interact with natural categories, participants in laboratory studies of category acquisition learn almost exclusively through classification. Furthermore, in most laboratory studies, the participants’ explicit goal is to learn to classify category members. If laboratory studies of acquisition were extended to include a broader range of tasks, the resulting theories might have greater applicability to natural category structures. To address this point, we begin with an overview of what we mean by categories. Then, we discuss existing research on a range of important uses of categories. Research on categorization must provide insight into how people represent their knowledge about categories in a way that allows these functions to be carried out. Next, we summarize the factors that have been incorporated into models of categorization derived from laboratory studies. What emerges from this comparison is that theories derived from laboratory studies fall short of explaining the range of functions for which natural categories are used.


Ensemble methods: a review


title: Weighted-to-Spherically-Uniform Quality Evaluation for Omnidirectional Video

abstract: Omnidirectional video records a scene in all directions around one central position. It allows users to select viewing content freely in all directions. Assuming that viewing directions are uniformly distributed, the isotropic observation space can be regarded as a sphere. Omnidirectional video is commonly represented by different projection formats with one or multiple planes. To measure objective quality of omnidirectional video in observation space more accurately, a weighted-to-spherically-uniform quality evaluation method is proposed in this letter. The error of each pixel on projection planes is multiplied by a weight to ensure the equivalent spherical area in observation space, in which pixels with equal mapped spherical area have the same influence on distortion measurement. Our method makes the quality evaluation results more accurate and reliable since it avoids error propagation caused by the conversion from resampling representation space to observation space. As an example of such quality evaluation method, weighted-to-spherically-uniform peak signal-to-noise ratio is described and its performance is experimentally analyzed.


title: Authenticating mobile phone users using keystroke analysis

abstract: Mobile handsets have found an important place in modern society, with hundreds of millions currently in use. The majority of these devices use inherently weak authentication mechanisms, based upon passwords and PINs. This paper presents a feasibility study into a biometric-based technique, known as keystroke analysis – which authenticates the user based upon their typing characteristic. In particular, this paper identifies two typical handset interactions, entering telephone numbers and typing text messages, and seeks to authenticate the user during their normal handset interaction. It was found that neural network classifiers were able to perform classification with average equal error rates of 12.8%. Based upon these results, the paper concludes by proposing a flexible and robust framework to permit the continuous and transparent authentication of the user, thereby maximising security and minimising user inconvenience, to service the needs of the insecure and evermore functional mobile handset.


title: Safety helmet with alcohol detection and theft control for bikers

abstract: In this paper, we've designed a Smart Helmet for the safety of bike riders which includes an alcohol sensor to ensure the one who is driving the bike is sober and a rider authentication using face recognition to control theft. The programmed microcontroller 8051 indicates whether the rider is wearing the helmet with the help of two IR sensors, the alcohol sensor MQ-3 which is used to check the alcohol consumption level and the ARM 7 board, which is integrated with the PC using COM port1. A face detection algorithm called Viola-Jones coupled with SVM (support vector machines) for classification is used to track down faces for additional safety and security for the bike rider. If all the parameters are ok, then the biker is all set to use the vehicle.


title: IoT based traffic light control system using Raspberry Pi

abstract: Congestion in traffic is a serious issue. In existing system signal timings are fixed and they are independent of traffic density. Large red light delays leads to traffic congestion. In this paper, IoT based traffic control system is implemented in which signal timings are updated based on the vehicle counting. This system consists of WI-FI transceiver module it transmits the vehicle count of the current system to the next traffic signal. Based on traffic density of previous signal it controls the signals of the next signal. The system is based on raspberry-pi and Arduino. Image processing of traffic video is done in MATLAB with simulink support. The whole vehicle counting is performed by raspberry pi.


title: Towards Distributed Clouds A review about the evolution of centralized cloud computing, distributed ledger technologies, and a foresight on unifying opportunities and security implications

abstract: This review focuses on the evolution of cloud computing and distributed ledger technologies (blockchains) over the last decade. Cloud computing relies mainly on a conceptually centralized service provisioning model, while blockchain technologies originate from a peer-to-peer and a completely distributed approach. Still, noteworthy commonalities between both approaches are often overlooked by researchers. Therefore, to the best of the authors knowledge, this paper reviews both domains in parallel for the first time. We conclude that both approaches have advantages and disadvantages. The advantages of centralized service provisioning approaches are often the disadvantages of distributed ledger approaches and vice versa. It is obviously an interesting question whether both approaches could be combined in a way that the advantages can be added while the disadvantages could be avoided. We derive a software stack that could build the foundation unifying the best of these two worlds and that would avoid existing shortcomings like vendor lock-in, some security problems, and inherent platform dependencies.


title: Accelerating and Compressing LSTM Based Model for Online Handwritten Chinese Character Recognition

abstract: With the development of deep learning tools, the online handwritten Chinese character recognition (HCCR) performance has been greatly improved by using deep neural networks (DNNs) especially for long short-term memory (LSTM). However, DNNs suffer from large consumption of computation and storage resources, which may cause problems for service providers, such as server pressure, longer service latency and higher energy consumption. To solve these problems, we propose a framework that combines singular value decomposition (SVD) and adaptive drop weight (ADW) to accelerate and compress LSTM based models. We first build an LSTM based model that achieves an accuracy of 97.83% on the ICDAR2013 online HCCR competition dataset. After restructuring the model with SVD and ADW, it can reduce the FLOPs (floating point operations per second) of the forward process by approximately 10 times and compress the model with 1/30 of the original size with only a 0.5% decrease in accuracy. Finally, integrated with our efficient forward implementation, the recognition of an online character requires only 2.7 ms in average on a CPU with a single thread, while requiring only 0.45 MB for model storage.


Unsupervised Event Coreference for Abstract Words


title: ARTIST: The Android Runtime Instrumentation Toolkit

abstract: Smartphones are becoming more and more ubiquitous in the modern world, entrusted with such sensitive information as the user's location and banking data. Since Android is the most widespread smartphone platform, reliable and versatile means for Android application analysis are of great importance. Most of the existing code instrumentation approaches for Android suffer from two important shortcomings: the need for root access and limited support for the new Android Runtime(ART). WeaimtofillthisgapbyproposingARTIST, the Android Runtime Instrumentation Toolkit1. ARTIST is a framework that allows analysts to easily monitor the execution of Java and native code using native instrumentation techniques. ARTIST, to the best of our knowledge, is the first tool allowing monitoring of both native and Java code with the same instrumentation technique. ARTIST provides two methods to locate instrumentation targets. First, it can parse OAT executable files in memory to find classes and methods of interest. This allows monitoring a specific set of Java methods. Second, ARTIST can locate internal structures of the Android Runtime in memory. Monitoring function pointers found in these allows the user to track specific interactions of Java code with the Android Runtime. We evaluate the applicability of native instrumentation for Java code using a set of the most popular Android apps. The results show that over 80% of the tested Java methods are targetable using this approach. The performance impact, estimated with the CaffeineMark benchmark suite, does not exceed 20% and therefore can be considered generally acceptable.


title: Data mining for customer service support

abstract: In traditional customer service support of a manufacturing environment, a customer service database usually stores two types of service information: (1) unstructured customer service reports record machine problems and its remedial actions and (2) structured data on sales, employees, and customers for day-to-day management operations. This paper investigates how to apply data mining techniques to extract knowledge from the database to support two kinds of customer service activities: decision support and machine fault diagnosis. A data mining process, based on the data mining tool DBMiner, was investigated to provide structured management data for decision support. In addition, a data mining technique that integrates neural network, case-based reasoning, and rule-based reasoning is proposed; it would search the unstructured customer service records for machine fault diagnosis. The proposed technique has been implemented to support intelligent fault diagnosis over the World Wide Web.


title: A robust feature extraction algorithm based on class-Modular Image Principal Component Analysis for face verification

abstract: Face verification systems reach good performance on ideal environmental conditions. Conversely, they are very sensitive to non-controlled environments. This work proposes the class-Modular Image Principal Component Analysis (cMIMPCA) algorithm for face verification. It extracts local and global information of the user faces aiming to reduce the effects caused by illumination, facial expression and head pose changes. Experimental results performed over three well-known face databases showed that cMIMPCA obtains promising results for the face verification task.


title: Coordinating Inventory Control and Pricing Strategies for Perishable Products

abstract: We analyze a joint pricing and inventory control problem for a perishable product with a fixed lifetime over a finite horizon. In each period, demand depends on the price of the current period plus an additive random term. Inventories can be intentionally disposed of, and those that reach their lifetime have to be disposed of. The objective is to find a joint pricing, ordering, and disposal policy to maximize the total expected discounted profit over the planning horizon taking into account linear ordering cost, inventory holding and backlogging or lost-sales penalty cost, and disposal cost. Employing the concept of L♮-concavity, we show some monotonicity properties of the optimal policies. Our results shed new light on perishable inventory management, and our approach provides a significantly simpler proof of a classical structural result in the literature. Moreover, we identify bounds on the optimal order-up-to levels and develop an effective heuristic policy. Numerical results show that our heuristic policy performs well in both stationary and nonstationary settings. Finally, we show that our approach also applies to models with random lifetimes and inventory rationing models with multiple demand classes.


title: AN ALTERNATIVE EXTENSION OF THE k-MEANS ALGORITHM FOR CLUSTERING CATEGORICAL DATA

abstract: Most of the earlier work on clustering has mainly been focused on numerical data whose inherent geometric properties can be exploited to naturally define distance functions between data points. Recently, the problem of clustering categorical data has started drawing interest. However, the computational cost makes most of the previous algorithms unacceptable for clustering very large databases. The k-means algorithm is well known for its efficiency in this respect. At the same time, working only on numerical data prohibits them from being used for clustering categorical data. The main contribution of this paper is to show how to apply the notion of “cluster centers” on a dataset of categorical objects and how to use this notion for formulating the clustering problem of categorical objects as a partitioning problem. Finally, a k-means-like algorithm for clustering categorical data is introduced. The clustering performance of the algorithm is demonstrated with two well-known data sets, namely, soybean diseaseand nursery databases.


title: Power systems fault diagnosis based on grid computing

abstract: A grid computing model is proposed in this paper and it will used in the power grid fault diagnosis. It includes the power grid computing system's structure, software service and hardware integration method. Though technology of grid computing is quite successful, its application to power system fault diagnosis is a deficiency. According to the five architecture layers of gird, a new power system fault diagnosis model is designed and it take the topology as the example to explain how to use its high performance computing method (or parallel computing method) and distributed environment to design a new intelligent algorithm with information fusion and multi-data resources in grid computing environment.


title: An Investigation into Mobile Application Development Processes: Challenges and Best Practices

abstract: Abstract —The mobile device market has witnessed swift industrial growth over the last decade. The quick expansion of this new computing platform has almost outpaced the software engineering processes customized to mobile application development. However, there is still lack of novel research initiatives around the mobile application development process. There remains a deficiency in development standards and best practices which expose the mobile device to potential attacks. This deficiency needs to be addressed promptly and requires further work. The objective of this research is to better understand the current methodologies adapted and to investigate challenges faced during the mobile application development processes that are different from traditional enterprise application. For this purpose, an online survey was conducted from the mobile research and development community. The survey questions covered the entire mobile application development lifecycle starting with requirements, and ending with bringing to life a complete mobile application. The study contributes towards a greater understanding of mobile application development process, examines real challenges confronted, and investigates the best practices that can be successfully implemented to enhance, evaluate, and improve the performance of the mobile application development process. These findings can also be considered as a possible research topic that indicates the breadth of research requirements and prospects in mobile computing.