title: A 10-bit Resistor-Floating-Resistor-String DAC (RFR-DAC) for High Color-Depth LCD Driver ICs

abstract: This work proposes a novel resistor-floating-resistor-string digital-to-analog converter (RFR-DAC) architecture with a 10-bit resolution for liquid crystal display (LCD) driver applications. The proposed architecture improves the linearity of DAC, unifies its channel performance, and achieves a 10-bit resolution with a compact die size smaller than those of the state-of-the-art 10-bit DACs. The proposed RFR-DAC combines a 6-bit RDAC and a 4-bit FR-DAC (floating-resistor-string DAC) to offer unique two-voltage-selection and one-voltage-selection schemes without the need of unity-gain buffers to isolate parallel-connected resistor strings. A stacked floating class-AB control is also devised to bias the last output buffer stage. The 10-bit RFR-DAC prototypes are implemented using 0.35-μm/0.5-μm CMOS technology with the worst DNL/INL = 0.11/0.92 LSB via a two-voltage-selection scheme; and 1.37/1.45 LSB via a one-voltage-selection scheme.


title: Speech and Language Technology in Education: the perspective from SLA research and practice

abstract: This paper weighs the implications of Second Language Acquisition (SLA) research and practice for Speech and Language Technology in Education (SLaTE). It describes the different psychological processes of implicit learning, explicit learning, and explicit instruction, and reviews educational research into the benefits and limitations of each. It considers how SLA differs from first language acquisition and, therefore, why implicit learning from usage does not suffice for SLA. It outlines the range of types of knowledge necessary for fluent nativelike proficiency, and how this requires a balanced learning curriculum that provides opportunities for implicit learning from meaning-based usage and explicit attention to form in use contexts. It then considers what SLaTE might offer in each of these domains.


title: Temporomandibular joint pain and dysfunction.

abstract: Pain caused by temporomandibular disorders originates from either muscular or articular conditions, or both. Distinguishing the precise source of the pain is a signi ficant diagnostic challenge to clinicians, and effective management hinges on establishing a correct diagnosis. This paper examines terminology and regional anatomy as it pertains to functional and dysfunctional states of the temporomandibular joint and muscles of mastication. A review of the pathophysiology of the most common disorders is provided. Trends in evaluation, diagnosis, treatment, and research are presented.


title: Conflict style differences between individualists and collectivists

abstract: This study investigated the fundamental beliefs regarding cross-cultural differences in conflict styles. The sample consisted of 188 graduate students from 31 different countries residing in the U.S. Findings indicated that assumptions regarding the relationship of culture to conflict style preferences may not be valid. Preference for using five conflict styles were measured: avoiding, obliging, integrating, compromising, and dominating. The integrating style is generally the most preferred; obliging and avoiding are next, followed by compromising and dominating. Avoiding is preferred by individualists rather than by collectivists. Individualists do not differ from collectivists in their preference for the dominating conflict style. We also found that collectivists prefer compromising and integrating more than individualists do, whereas individualism-collectivism had no significant linear effect on preference for the obliging style. Conflict styles are highly multidimensional for both individualists and c...


title: A Case Study for Blockchain in Manufacturing:"FabRec": A Prototype for Peer-to-Peer Network of Manufacturing Nodes

abstract: With product customization an emerging business opportunity, organizations must find ways to collaborate and enable sharing of information in an inherently trustless network. In this paper, we propose -"FabRec": a decentralized approach to handle manufacturing information generated by various organizations using blockchain technology. We propose a system in which a decentralized network of manufacturing machines and computing nodes can enable automated transparency of an organization's capability, third party verification of such capability through a trail of past historic events and automated mechanisms to drive paperless contracts between participants using 'smart contracts'. Our system decentralizes critical information about the manufacturer and makes it available on a peer-to-peer network composed of fiduciary nodes to ensure transparency and data provenance through a verifiable audit trail. We present a testbed platform through a combination of manufacturing machines, system-on-chip platforms and computing nodes to demonstrate mechanisms through which a consortium of disparate organizations can communicate through a decentralized network. Our prototype testbed demonstrates the value of computer code residing on a decentralized network for verification of information on the blockchain and ways in which actions can be autonomously initiated in the physical world. This paper intends to expose system elements in preparation for much larger field tests through the working prototype and discusses the future potential of blockchain for manufacturing IT.


title: Bibliometric Approximation of a Scientific Specialty by Combining Key Sources, Title Words, Authors and References

abstract: Bibliometric methods for the analysis of highly specialized subjects are increasingly investigated and debated. Information and assessments well-focused at the specialty level can help make important decisions in research and innovation policy. This paper presents a novel method to approximate the specialty to which a given publication record belongs. The method partially combines sets of key values for four publication data fields: source, title, authors and references. The approach is founded in concepts defining research disciplines and scholarly communication, and in empirically observed regularities in publication data. The resulting specialty approximation consists of publications associated to the investigated publication record via key values for at least three of the four data fields. This paper describes the method and illustrates it with an application to publication records of individual scientists. The illustration also successfully tests the focus of the specialty approximation in terms of its ability to connect and help identify peers. Potential tracks for further investigation include analyses involving other kinds of specialized publication records, studies for a broader range of specialties, and exploration of the potential for diverse applications in research and research policy context.


title: RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks

abstract: Neuromorphic computing using post-CMOS technologies is gaining immense popularity due to its promising abilities to address the memory and power bottlenecks in von-Neumann computing systems. In this paper, we propose RESPARC - a reconfigurable and energy efficient architecture built-on Memristive Crossbar Arrays (MCA) for deep Spiking Neural Networks (SNNs). Prior works were primarily focused on device and circuit implementations of SNNs on crossbars. RESPARC advances this by proposing a complete system for SNN acceleration and its subsequent analysis. RESPARC utilizes the energy-efficiency of MCAs for inner-product computation and realizes a hierarchical reconfigurable design to incorporate the data-flow patterns in an SNN in a scalable fashion. We evaluate the proposed architecture on different SNNs ranging in complexity from 2k–230k neurons and 1.2M–5.5M synapses. Simulation results on these networks show that compared to the baseline digital CMOS architecture, RESPARC achieves 500× (15×) efficiency in energy benefits at 300× (60×) higher throughput for multi-layer perceptrons (deep convolutional networks). Furthermore, RESPARC is a technology-aware architecture that maps a given SNN topology to the most optimized MCA size for the given crossbar technology.


title: A Multi-faceted OCR Framework for Artificial Urdu News Ticker Text Recognition

abstract: Content based information search and retrieval has allowed for easier access to data. While Latin based scripts have gained attention and support from academia and industry, there is limited support for cursive script languages, like Urdu. In this paper, we present the first instance of Urdu news ticker detection and recognition and take a micron sized step towards the goal of super intelligence. The presented solution allows for automating the transcription, indexing and captioning of Urdu news video content. We present the first comprehensive data set, to our knowledge, for Urdu news ticker recognition, collected from 41 different news channels. The data set covers both high and low quality channels, distorted and blurred news tickers, making the data set an ideal test case for any automatic Urdu News Recognition system in future. We identify and address the key challenges in Urdu News Ticker text recognition. We further propose an adjustment to the ground-truth labeling strategy focused on improving the readability of recognized output. Finally, we propose and present results from a Bi-Directional Long Short-Term Memory (BDLSTM) network architecture for news ticker text recognition. Our custom trained model outperforms Google's commercial OCR engine in two of the four experiments conducted.


title: Incremental Recurrent Neural Network Dependency Parser with Search-based Discriminative Training

abstract: We propose a discriminatively trained recurrent neural network (RNN) that predicts the actions for a fast and accurate shift-reduce dependency parser. The RNN uses its output-dependent model structure to compute hidden vectors that encode the preceding partial parse, and uses them to estimate probabilities of parser actions. Unlike a similar previous generative model (Henderson and Titov, 2010), the RNN is trained discriminatively to optimize a fast beam search. This beam search prunes after each shift action, so we add a correctness probability to each shift action and train this score to discriminate between correct and incorrect sequences of parser actions. We also speed up parsing time by caching computations for frequent feature combinations, including during training, giving us both faster training and a form of backoff smoothing. The resulting parser is over 35 times faster than its generative counterpart with nearly the same accuracy, producing state-of-art dependency parsing results while requiring minimal feature engineering.


A Syntactic Valency Lexicon for Persian Verbs : The First Steps towards Persian Dependency Treebank


title: A Replication and Extension of the PEERS Intervention: Examining Effects on Social Skills and Social Anxiety in Adolescents with Autism Spectrum Disorders

abstract: This study aimed to evaluate the Program for the Education and Enrichment of Relational Skills (PEERS: Laugeson et al. in J Autism Dev Disord 39(4):596–606, 2009). PEERS focuses on improving friendship quality and social skills among adolescents with higher-functioning ASD. 58 participants aged 11–16 years-old were randomly assigned to either an immediate treatment or waitlist comparison group. Results revealed, in comparison to the waitlist group, that the experimental treatment group significantly improved their knowledge of PEERS concepts and friendship skills, increased in their amount of get-togethers, and decreased in their levels of social anxiety, core autistic symptoms, and problem behaviors from pre-to post-PEERS. This study provides the first independent replication and extension of the empirically-supported PEERS social skills intervention for adolescents with ASD.


Social Networks , Gender and Friending : An Analysis of MySpace Member Profiles 1


title: Generalised Spatial Modulation System with Multiple Active Transmit Antennas and Low Complexity Detection Scheme

abstract: A generalised spatial modulation (SM) scheme with multiple active transmit antennas, named as multiple active-spatial modulation (MA-SM), is proposed in this paper. By allowing multiple transmitting antennas in the SM system to transmit different symbols at the same time instant, MA-SM takes advantages of the low complexity of SM and high multiplexing gain of Vertical-Bell Lab Layered Space-Time (V-BLAST) system. In the MA-SM system, the transmitted symbols are mapped into a high dimensional constellation space including the spatial dimension. The general principle for designing the efficient MA-SM for arbitrary number of transmit antennas and modulation scheme is presented. Moreover, a near-optimal detection scheme with low complexity for MA-SM is also proposed and analyzed. A closed form bound for the bit error probability (BEP) of the proposed detection scheme is also derived in this paper. Numerical results with the comparison among the existing multiple-input multiple-output (MIMO) systems such as space time block code (STBC) and V-BLAST demonstrate the efficiency of MA-SM.


Kobe University Repository : Kernel タイトル Tit le Within-individual associat ions among third-party intervent ion strategies : Third-party helpers , but not punishers , reward generosity 著者


title: Machines and mindlessness: Social responses to computers

abstract: Following Langer (1992), this article reviews a series of experimental studiesthat demonstrate that individuals mindlessly apply social rules and expecta-tions to computers. The first set of studies illustrates how individuals overusehuman social categories, applying gender stereotypes to computers and ethnicallyidentifying with computer agents. The second set demonstrates that people exhibitoverlearned social behaviors such as politeness and reciprocity toward comput-ers.Inthethirdsetofstudies,prematurecognitivecommitmentsaredemonstrated:Aspecialisttelevisionsetisperceivedasprovidingbettercontentthanageneralisttelevision set. A final series of studies demonstrates the depth of social responseswith respect to computer “personality.” Alternative explanations for these find -ings, such as anthropomorphism and intentional social responses, cannot explainthe results. We conclude with an agenda for future research.Computer users approach the personal computer in many different ways.Experienced word processors move smoothly from keyboard to mouse to menu,mixing prose and commands to the computer automatically; the distinctionbetween the hand and the tool blurs (Heidegger, 1977; Winograd & Flores, 1987).Novices cautiously strike each key, fearing that one false move will initiate anuncontrollable series of unwanted events. Game players view computers as


title: Fast Binary Feature Selection with Conditional Mutual Information

abstract: We propose in this paper a very fast feature selection technique based on conditional mutual information. By picking features which maximize their mutual information with the class to predict conditional to any feature already picked, it ensures the selection of features which are both individually informative and two-by-two weakly dependant. We show that this feature selection method outperforms other classical algorithms, and that a naive Bayesian classifier built with features selected that way achieves error rates similar to those of state-of-the-art methods such as boosting or SVMs. The implementation we propose selects 50 features among 40,000, based on a training set of 500 examples in a tenth of a second on a standard 1Ghz PC.


title: CORPORATE ECOLOGICAL RESPONSIVENESS, ENVIRONMENTAL AMBIDEXTERITY AND IT-ENABLED ENVIRONMENTAL SUSTAINABILITY STRATEGY

abstract: An increased focus on creating a sustainable society has thrust environmental sustainability issues to societal and governmental forefront. Organizations can seize this opportunity to use environmental sustainability initiatives to set themselves apart from competitors. Achieving sustainability requires organizations to incorporate sustainability as part of their corporate strategy. A review of extant Information Systems (IS) literature on environmental sustainability revealed that the strategic role of Information Technology (IT) in enabling environmental sustainability strategy is one perspective that has not been explored in depth. Our paper addresses this gap in research. In this research paper, we propose that firms that use IT strategically to enable their environmental sustainability strategies and are able to demonstrate environmental ambidexterity are set to achieve competitive advantage, legitimacy, and reputation from their corporate ecological responsiveness initiatives. We present preliminary results from interviews that were part of our in-depth case study approach.


title: Drowsiness detection for the perfection of brain computer interface using Viola-jones algorithm

abstract: Security and reconnaissance applications are prominent BCI paradigms which are less complex and sophisticated if there is no contamination in Electroencephalogram (EEG) signal. The better the quality of EEG signal ensures the better the performance (better Information Transfer Rate (ITR), high Signal to Noise Ratio (SNR), high Bandwidth (BW), and so on) of BCI paradigms. Drowsiness is one of the major contamination in EEG signal that hampers the operation of modern BCI paradigms. In this research, a non-intrusive machine vision based concept is used to determine the drowsiness from the patient which ensure the drowsy free EEG signal. In this proposed system, a camera which placed in a way that it records subjects (BCI Users) eye movement in every time as well as it can monitor the open and close state of eye. Viola-jones Algorithm is applicable for the detection of face as well as state of eye (Open, closed or semi-open) which is the key concern for the detection of drowsiness from the patient's EEG signal. After detecting this drowsiness, decision can be easily made for the perfect operation of BCI.


title: On Code Execution Tracking via Power Side-Channel

abstract: With the proliferation of Internet of Things, there is a growing interest in embedded system attacks, e.g., key extraction attacks and firmware modification attacks. Code execution tracking, as the first step to locate vulnerable instruction pieces for key extraction attacks and to conduct control-flow integrity checking against firmware modification attacks, is therefore of great value. Because embedded systems, especially legacy embedded systems, have limited resources and may not support software or hardware update, it is important to design low-cost code execution tracking methods that require as little system modification as possible. In this work, we propose a non-intrusive code execution tracking solution via power-side channel, wherein we represent the code execution and its power consumption with a revised hidden Markov model and recover the most likely executed instruction sequence with a revised Viterbi algorithm. By observing the power consumption of the microcontroller unit during execution, we are able to recover the program execution flow with a high accuracy and detect abnormal code execution behavior even when only a single instruction is modified.


title: Asymmetrical Doherty amplifier using GaN HEMTs for high-power applications

abstract: In this paper, we present an asymmetrical Doherty amplifier (DA) that can enhance the efficiency at a 9.5 dB backoff from its saturated output power (P SAT ). The asymmetrical DA consists of a peak amplifier and a carrier amplifier with two and one 210 W gallium nitride (GaN) high-electron-mobility transistors (HEMTs), respectively. A Wilkinson combiner at the output of the peak amplifier enables the stable operation of the GaN HEMTs for high-power applications. The asymmetrical DA is designed and fabricated for the ultrahigh-frequency (UHF) band. Measurement results using a continuous wave (CW) signal indicate a drain efficiency (η d ) of 60% at a P SAT of 57.5 dBm and a η d of 56% at a 9.5 dB backoff from P SAT . For a 6 MHz orthogonal frequency-division multiplexing (OFDM) signal with a high peak-to-average power ratio (PAPR), the measurement results indicate a η d of 52% and an adjacent-channel leakage power ratio (ACLR) of −41 dBc at an average output power (P AVE ) of 48 dBm using a digital predistorter. To the best of our knowledge, this is the highest η d and output power for an OFDM signal.


title: Beyond Dashboards: On the Many Facets of Metrics and Feedback in Agile Organizations

abstract: One of the key principles in agile development is that the team should use continuous feedback cycles to maintain awareness both about the product, the process and the involved people. In healthy agile environments, people see a lot of value in the collection of metrics and in their collaborative analysis to support a continuous improvement process. In pseudo agile environments, metrics are often perceived as a threat and they make people uncomfortable. In theses conditions, they can do more harm than good. In this paper, we argue that the acceptance of a data-driven continuous improvement process is heavily influenced by the nature of the channels through which the metrics are published. We argue for the need to go beyond typical dashboards and propose additional engagement channels. We discuss the notions of awareness and story telling and illustrate them with various experiments conducted with agile teams.


title: Detecting APT Malware Infections Based on Malicious DNS and Traffic Analysis

abstract: Advanced persistent threat (APT) is a serious threat to the Internet. With the aid of APT malware, attackers can remotely control infected machines and steal sensitive information. DNS is popular for malware to locate command and control (C&C) servers. In this paper, we propose a novel system placed at the network egress point that aims to efficiently and effectively detect APT malware infections based on malicious DNS and traffic analysis. The system uses malicious DNS analysis techniques to detect suspicious APT malware C&C domains, and then analyzes the traffic of the corresponding suspicious IP using the signature-based and anomaly based detection technology. We extracted 14 features based on big data to characterize different properties of malware-related DNS and the ways that they are queried, and we also defined network traffic features that can identify the traffic of compromised clients that have remotely been controlled. We built a reputation engine to compute a reputation score for an IP address using these features vector together. Our experiment was performed at a large local institute network for two months, and all the features were studied with big data, which includes $\sim 400$ million DNS queries. Our security approach cannot only substantially reduce the volume of network traffic that needs to be recorded and analyzed but also improve the sustainability of the system.


title: EFFECT ON COMPANY CHARACTERISTICS CORPORATE SOCIAL RESPONSIBILITY DISCLOSURES IN CORPORATE ANNUAL REPORT OF CONSUMPTION LISTED IN INDONESIA STOCK EXCHANGE

abstract: Keywords: Corporate, Stock, Exchange ABSTRACT Social impact of companies depending on the type or operating characteristics. Companies operating characteristics that generate high social impact will demand the fulfillment of social responsibility is higher. Implementation of social responsibility will be communicated to the public one of them through social disclosures in annual reports. The aim of this research is to determine how the image of disclosures of social responsibility and how the influence of corporate characteristics on disclosure of corporate social responsibility, consumer goods listed in the Stock Exchange from 2006 to 2008 period. The data used are secunder data like annual reports 2006-2008 and ICMD 2007-2009, from consumer goods companies the sample of 18 companies over three years is 2006-2008 with a total of 54 samples. From this research we can conclude that the description of disclosures of social responsibility is still low, amounting to 0.5554 or 55.54%. Simultaneously, the four independent variables (Ln Size, ROA, LEV and AGE) significantly influenced by the Sig .000 while partially only three independent variables that significantly affect the disclosure of corporate social responsibility, namely consumer goods Ln Size, ROA and AGE. While LEV did not have a significant.


title: MGAE: Marginalized Graph Autoencoder for Graph Clustering

abstract: Graph clustering aims to discovercommunity structures in networks, the task being fundamentally challenging mainly because the topology structure and the content of the graphs are difficult to represent for clustering analysis. Recently, graph clustering has moved from traditional shallow methods to deep learning approaches, thanks to the unique feature representation learning capability of deep learning. However, existing deep approaches for graph clustering can only exploit the structure information, while ignoring the content information associated with the nodes in a graph. In this paper, we propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering. The key innovation of MGAE is that it advances the autoencoder to the graph domain, so graph representation learning can be carried out not only in a purely unsupervised setting by leveraging structure and content information, it can also be stacked in a deep fashion to learn effective representation. From a technical viewpoint, we propose a marginalized graph convolutional network to corrupt network node content, allowing node content to interact with network features, and marginalizes the corrupted features in a graph autoencoder context to learn graph feature representations. The learned features are fed into the spectral clustering algorithm for graph clustering. Experimental results on benchmark datasets demonstrate the superior performance of MGAE, compared to numerous baselines.


title: Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks

abstract: We propose a novel spatiotemporal fusion method based on deep convolutional neural networks (CNNs) under the application background of massive remote sensing data. In the training stage, we build two five-layer CNNs to deal with the problems of complicated correspondence and large spatial resolution gaps between MODIS and Landsat images. Specifically, we first learn a nonlinear mapping CNN between MODIS and low-spatial-resolution (LSR) Landsat images and then learn a super-resolution CNN between LSR Landsat and original Landsat images. In the prediction stage, instead of directly taking the outputs of CNNs as the fusion result, we design a fusion model consisting of high-pass modulation and a weighting strategy to make full use of the information in prior images. Specifically, we first map the input MODIS images to transitional images via the learned nonlinear mapping CNN and further improve the transitional images to LSR Landsat images via the fusion model; then, via the learned SR CNN, the LSR Landsat images are supersolved to transitional images, which are further improved to Landsat images via the fusion model. Compared with the previous learning-based fusion methods, mainly referring to the sparse-representation-based methods, our CNNs-based spatiotemporal method has the following advantages: 1) automatically extracting effective image features; 2) learning an end-to-end mapping between MODIS and LSR Landsat images; and 3) generating more favorable fusion results. To examine the performance of the proposed fusion method, we conduct experiments on two representative Landsat–MODIS datasets by comparing with the sparse-representation-based spatiotemporal fusion model. The quantitative evaluations on all possible prediction dates and the comparison of fusion results on one key date in both visual effect and quantitative evaluations demonstrate that the proposed method can generate more accurate fusion results.


title: SPrank: Semantic Path-Based Ranking for Top-N Recommendations Using Linked Open Data

abstract: In most real-world scenarios, the ultimate goal of recommender system applications is to suggest a short ranked list of items, namely top-N recommendations, that will appeal to the end user. Often, the problem of computing top-N recommendations is mainly tackled with a two-step approach. The system focuses first on predicting the unknown ratings, which are eventually used to generate a ranked recommendation list. Actually, the top-N recommendation task can be directly seen as a ranking problem where the main goal is not to accurately predict ratings but to directly find the best-ranked list of items to recommend. In this article we present SPrank, a novel hybrid recommendation algorithm able to compute top-N recommendations exploiting freely available knowledge in the Web of Data. In particular, we employ DBpedia, a well-known encyclopedic knowledge base in the Linked Open Data cloud, to extract semantic path-based features and to eventually compute top-N recommendations in a learning-to-rank fashion. Experiments with three datasets related to different domains (books, music, and movies) prove the effectiveness of our approach compared to state-of-the-art recommendation algorithms.


title: Marketing meets Web 2.0, social media, and creative consumers: Implications for international marketing strategy

abstract: The 21st century has brought both opportunities and challenges in our global, boundaryless world. Importantly, managers face a dynamic and interconnected international environment. As such, 21st century managers need to consider the many opportunities and threats that Web 2.0, social media, and creative consumers present and the resulting respective shifts in loci of activity, power, and value. To help managers understand this new dispensation, we propose five axioms: (1) social media are always a function of the technology, culture, and government of a particular country or context; (2) local events rarely remain local; (3) global events are likely to be (re)interpreted locally; (4) creative consumers’ actions and creations are also dependent on technology, culture, and government; and (5) technology is historically dependent. At the heart of these axioms is the managerial recommendation to continually stay up to date on technology, customers, and social media. To implement this managerial recommendation, marketers must truly engage customers, embrace technology, limit the power of bureaucracy, train and invest in their employees, and inform senior management about the opportunities of social media.


Near Field Communication (NFC) Technology: A Survey


title: Adoption and effectiveness of electronic banking in Kenya

abstract: A global explosion in the use of electronic commerce has been witnessed in recent times with the monetary value of products and services exchanged electronically being estimated at above US$ 7 trillion in the year 2004. Research has followed the same pattern especially in investigating factors influencing the adoption and effectiveness of e-commerce in retail businesses. However, little has been done to establish these factors in electronic banking (e-banking) in developing countries. The aim of this research was to investigate the factors influencing the adoption and effectiveness of e-banking in retail banking. Initial and follow-up surveys were carried out in the years 2005 and 2009, respectively, which involved banks controlling approximately 90% of formal retail banking in Kenya. The follow-up survey was meant to monitor trends between the periods of the two surveys. Results varied across the period.


title: Towards a Context-based Citizen Participation Approach: a Literature Review of Citizen Participation Issues and a Conceptual Framework

abstract: Governments are more and more adopting Citizens' participation processes since they may help better understand needs and better reach communities' goals. With the emergence of ICTs, citizens' participation processes took new forms such as social media, blogs and participative platforms. Nevertheless, although citizens' participation outstands a great value for governments, its implementation raises several issues. Based on a review of the literature, this paper identifies a typology of issues regarding the implementation of CP processes. This paper is an attempt to open new research avenues on citizens' participation through the multidisciplinary typology of issues it proposes. As well, drawing on two categories of issues (citizens' and technology issues), a context-based citizen participation approach is proposed that is based on three main concepts: context-based reasoning, spatio-temporal representation, and visual analytics. Further empirical studies need to be established to assert the relevance of the proposed approach.


title: A Three-Axis CMOS-MEMS Accelerometer Structure With Vertically Integrated Fully Differential Sensing Electrodes

abstract: This study presents a novel CMOS-microelectromechanical systems (MEMS) three-axis accelerometer design using Taiwan Semiconductor Manufacturing Company 0.18-μm one-poly-Si six-metal/dielectric CMOS process. The multilayer metal and dielectric stacking features of the CMOS process were exploited to vertically integrate the in-plane and out-of-plane capacitive sensing electrodes. Thus, the three-axis sensing electrodes can be integrated on a single proof mass to reduce the footprint of the accelerometer. Moreover, the fully differential gap-closing sensing electrodes among all three axes are implemented to increase the sensitivities and decrease the noise. The in-plane and out-of-plane sensing gaps are respectively defined by the minimum metal line width and the thickness of one metal layer by means of the metal wet-etching post-CMOS process. Thus, the capacitive sensitivities are further improved. The fully differential gap-closing sensing electrodes also bring the advantage of reduced cross talks between all three axes. As a result, the footprint of the presented three-axis accelerometer structure is only 400 × 400 μm2. Compared with existing commercial or CMOS-MEMS studies, the size is significantly reduced. The measured sensitivities (nonlinearities) are 14.7 mV/G (3.2%) for the X-axis, 15.4 mV/G (1.4%) for the Y-axis, and 14.6 mV/G (2.8%) for the Z-axis.


title: Color and Texture Based Identification and Classification of food Grains using different Color Models and Haralick features

abstract: This paper presents the study on identification and classification of food grains using different color models such as L*a*b, HSV, HSI and YCbCr by combining color and texture features without performing preprocessing. The K-NN and minimum distance classifier are used to identify and classify the different types of food grains using local and global features. Texture and color features are the important features used in the classification of different objects. The local features like Haralick features are computed from co-occurrence matrix as texture features and global features from cumulative histogram are computed along with color features. The experiment was carried out on different food grains classes. The non-uniformity of RGB color space is eliminated by L*a*b, HSV, HSI and YCbCr color space. The correct classification result achieved for different color models is quite good.


title: Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition

abstract: Face images captured in heterogeneous environments, e.g., sketches generated by the artists or composite-generation software, photos taken by common cameras and infrared images captured by corresponding infrared imaging devices, usually subject to large texture (i.e., style) differences. This results in heavily degraded performance of conventional face recognition methods in comparison with the performance on images captured in homogeneous environments. In this paper, we propose a novel sparse graphical representation based discriminant analysis (SGR-DA) approach to address aforementioned face recognition in heterogeneous scenarios. An adaptive sparse graphical representation scheme is designed to represent heterogeneous face images, where a Markov networks model is constructed to generate adaptive sparse vectors. To handle the complex facial structure and further improve the discriminability, a spatial partition-based discriminant analysis framework is presented to refine the adaptive sparse vectors for face matching. We conducted experiments on six commonly used heterogeneous face datasets and experimental results illustrate that our proposed SGR-DA approach achieves superior performance in comparison with state-of-the-art methods.


title: Characteristics of Alternative Schools and Programs Serving At-Risk Students. Research Report No. 16. Enrollment Options for Students with Disabilities.

abstract: More and more students are being labeled at-risk in our educational system. These students are often behind academically, have dropped out of school, or have been expelled or suspended from conventional high schools. Some states have implemented school choice options that address the needs of these students giving them a choice of an alternative high school setting. These programs, commonly referred to as "second chance" programs, are designed to address the specific needs of at-risk students. The second chance option combines the pedagogy of alternative programs with the philosophical orientation of school choice offering a second chance to those who are failing in the traditional system. Alternative programs and schools are an integral part of the second chance option. Yet little is known about the characteristics of these programs and how they relate to school choice. While other types of school choice options have received the majority of attention over the past few years, second chance programs have been quietly addressing the needs of students most disenfranchised from the system. An evaluation of these programs and their effectiveness is important to consider as we contemplate the large numbers of at-risk youth and how to address their needs. "Alternative" has meant different things to different people over the past several years. As early as 1978 the controversy over the definition of alternative was acknowledged. Arnove and Strout (1978) noted that "the definition of `alternative' has been a matter of controversy since the early seventies, and much of the literature on the movement has been concerned with that issue alone" (p. 79). Nearly 20 years later there is still discussion about what alternative means. Raywid (1994) notes the variety of definitions surrounding alternative schools. She provides a summary of the alternatives that now appear to be available. These fall into three different categories: Type I alternatives are schools of choice and are usually popular. They sometimes resemble magnet schools and in some locales constitute some or all of the choice systems. They are likely to reflect programmatic themes or emphases pertaining to content or instructional strategy, or both. Type II alternatives are programs to which students are sentenced--usually as one last chance prior to expulsion. Typically, Type II programs focus on behavior modification, and little attention is paid to modifying curriculum or pedagogy. Type III alternatives are for students who are presumed to need remediation or rehabilitation--academic, social/emotional, or both. The assumption is that after successful treatment students can return to mainstream programs. Further, Raywid contends that," alternative schools are usually identifiable as one of these three types, but particular programs can be a mix" (p. 27). Sometimes this "mix" of definitions results in a school choice option such as second chance programs. In these programs school choice, remediation, and innovation combine to address the needs of at-risk students. Rather than these being "last chance" programs, these programs provide another chance at success within the educational system. Yet, several questions arise about these hybrid programs. What are the characteristics of these schools? Do these schools maintain the characteristics of alternative schools in their organization and structure? What are the characteristics of the students they serve? Can alternative schools be more than a holding tank for students but an actual first choice for those students who do not desire the conventional high school? Minnesota has been a leader in the implementation of school choice options including second chance options. In 1987 Minnesota established the High School Graduation Incentives (HSGI) Program that allows students who are at-risk of not completing school, two or more years behind academically, pregnant or a custodial parent, or expelled from school an opportunity to choose to attend any traditional high school in the state, any alternative school, or any Area Learning Center. …


title: Real time correlation-based stereo: Algorithm, implementations and applications

abstract: This paper describes some of the work on stereo that has been going on at INRIA in the last four years. The work has concentrated on obtaining dense, accurate and reliable range maps of the environment at rates compatible with the real-time constraints of such applications as the navigation of mobile vehicles in man-made or natural environments. The class of algorithms which has been selected among several is the class of algorithms which has been selected among several is the class of correlation-based stereo algorithms because they are the only ones that can produce sufficiently dense range maps with an algoritmic structure which lends itself nicely to fast implementations because of the simplicity of the underlying computation. We describe the various improvements that we have brought to the original idea, including validation and characterization of the quality of the matches, a recursive implementation of the score computation which makes the method independent of the size of the correlation window and a calibration method which does not require the use of a calibration pattern. We then describe two implementations of this algorithm on two very different pieces of hardware. The first implementation is on a board with four digital signal processors designed jointly with Matra MSII. This implementation can produce 64x64 range maps at rate varying between 200 and 400 ms, depending upon the range of disparities. The second implementation is on a board developed by DEC-PRL and can perform the cross-correlation of two 256X256 images in 140 ms. The first implementation has been integrated in the navigation system of the INRIA cart and used to correct for inertial and odometric errors in navigation experiments both indoors and outdoors on road. This is the first application of our correlation-based algorithm which is described in the paper. The second application has been done jointly with people from the french national space agence (CNES) to study the possibility of using stereo on a future planetary rover for the construction of digital elevation maps. We have shown that real time stereo is possible today at low-cost and can be applied in real applications. The algorithm that has been described is not the most sophisticated available but we have made it robust and reliable thanks to a number of improvements. Evan though each of these improvements is not earth-shattering from the pure research point of view, altogether they have allowed us to go beyond a very important threshold. This threshold measures the difference between a program that runs in the laboratory on a few images and one that works continuously for hours on a sequence of stereo pairs and produces results at such rates and of such quality that they can be used to guide a real vehicle or to produce discrete elevation maps. We believe that this threshold has only been reached in a very small number of cases.


title: Minimum description length synthetic aperture radar image segmentation

abstract: We present a new minimum description length (MDL) approach based on a deformable partition - a polygonal grid - for automatic segmentation of a speckled image composed of several homogeneous regions. The image segmentation thus consists in the estimation of the polygonal grid, or, more precisely, its number of regions, its number of nodes and the location of its nodes. These estimations are performed by minimizing a unique MDL criterion which takes into account the probabilistic properties of speckle fluctuations and a measure of the stochastic complexity of the polygonal grid. This approach then leads to a global MDL criterion without an undetermined parameter since no other regularization term than the stochastic complexity of the polygonal grid is necessary and noise parameters can be estimated with maximum likelihood-like approaches. The performance of this technique is illustrated on synthetic and real synthetic aperture radar images of agricultural regions and the influence of different terms of the model is analyzed.


title: Imitating human playing styles in Super Mario Bros

abstract: Abstract We describe and compare several methods for generating game character controllers that mimic the playing style of a particular human player, or of a population of human players, across video game levels. Similarity in playing style is measured through an evaluation framework, that compares the play trace of one or several human players with the punctuated play trace of an AI player. The methods that are compared are either hand-coded, direct (based on supervised learning) or indirect (based on maximising a similarity measure). We find that a method based on neuroevolution performs best both in terms of the instrumental similarity measure and in phenomenological evaluation by human spectators. A version of the classic platform game “Super Mario Bros” is used as the testbed game in this study but the methods are applicable to other games that are based on character movement in space.


title: SALES-FORCE ACTIVITIES AND CUSTOMER TRUST

abstract: In the "relational era" sales-force role and activities are rapidly changing. Relationship marketing contributions and personal selling literature emphasize that salesmen should become value creators and customer partners, and should actively contribute to the acquisition of customer trust. This paper reviews the literature on relationship marketing and personal selling and through descriptive statistics, factor analyses and cluster analyses, provides empirical evidence of the current state of sales-force activities in a sample of 113 sales managers of Italian firms, and explores sales-force role, as perceived by the respondents, in gaining customer trust. The findings show four typical roles performed by the sales force and three classes of customer trust drivers, as well as identifying clusters of companies characterized by the diverse activities carried out by the sales force and the different contributions made by the latter to gaining customer trust. Key Words: Sales-force activities, trust, relationship marketing, Italy, sales managers.


title: Detection of synthetic speech for the problem of imposture

abstract: In this paper, we present new results from our research into the vulnerability of a speaker verification (SV) system to synthetic speech. We use a HMM-based speech synthesizer, which creates synthetic speech for a targeted speaker through adaptation of a background model and both GMM-UBM and support vector machine (SVM) SV systems. Using 283 speakers from the Wall-Street Journal (WSJ) corpus, our SV systems have a 0.35% EER. When the systems are tested with synthetic speech generated from speaker models derived from the WSJ journal corpus, over 91% of the matched claims are accepted. We propose the use of relative phase shift (RPS) in order to detect synthetic speech and develop a GMM-based synthetic speech classifier (SSC). Using the SSC, we are able to correctly classify human speech in 95% of tests and synthetic speech in 88% of tests thus significantly reducing the vulnerability.


title: Compressing deep neural networks for efficient visual inference

abstract: The deployments of deep neural network models on mobile or embedded devices have been challenged due to two main reasons: 1) the large model size for storage, and 2) the large memory bandwidth for inference. To address these issues, this paper develops a deep neural network compression framework to reduce the resource usage for efficient visual inference. By reviewing the trained deep model, we propose a hybrid model compression algorithm via four major modules. Approximation module reduces the number of weights in each fully connected layer with low rank approximation. Then, quantization module analyzes weight distribution in each layer and represents them with low precision fixed point, which reduces the bits for storing each weight. After that, pruning module suppresses small weights to further reduce the number of parameters. Finally, coding module joint optimizes the representation and encoding of the sparse structure of the pruned weights with relative index by Huffman coding. Beyond the compression of model size, we propose an adaptive fixed point memory allocation algorithm to reduce memory footprint in inference. The proposed framework, along with the model compression and memory allocation algorithms, can provide 20–30x compression rate with negligible accuracy loss. We conduct an evaluation on two representative models, AlexNet and VGG-16, for object recognition and face verification tasks, which demonstrate the effectiveness of our proposed compression framework.


Singularities of piecewise linear saddle spheres on S^3


title: Fuzzy control of a two-wheel balancing robot using DSPIC

abstract: This paper is concerned with the design and implementation of a two-wheel balancing robot. The angle and angle change are used as inputs to the robot system in order to calculate the appropriate motor force to balance the robot. ADXL330 accelerometer and two DC motors are used as the sensor and actuators, respectively. More importantly, the robot system uses fuzzy control that is implemented on DSPIC30F2010. The robot under study represents a mechatronic system because it involves an integrated design among mechanics, electronics, and embedded smart controllers. The system model is first tested under Matlab/Simulink. Then, the Printed Circuit Boards are designed, the C-program is written, and the mechanical structure is built. Finally, the robot is built and tested in the laboratory.


title: Model Order Reduction for Linear and Nonlinear Systems: A System-Theoretic Perspective

abstract: In the past decades, Model Order Reduction (MOR) has demonstrated its robustness and wide applicability for simulating large-scale mathematical models in engineering and the sciences. Recently, MOR has been intensively further developed for increasingly complex dynamical systems. Wide applications of MOR have been found not only in simulation, but also in optimization and control. In this survey paper, we review some popular MOR methods for linear and nonlinear large-scale dynamical systems, mainly used in electrical and control engineering, in computational electromagnetics, as well as in micro- and nano-electro-mechanical systems design. This complements recent surveys on generating reduced-order models for parameter-dependent problems (Benner et al. in 2013; Boyaval et al. in Arch Comput Methods Eng 17(4):435–454, 2010; Rozza et al. Arch Comput Methods Eng 15(3):229–275, 2008) which we do not consider here. Besides reviewing existing methods and the computational techniques needed to implement them, open issues are discussed, and some new results are proposed.


title: Zernike Moments and Neural Networks for Recognition of Isolated Arabic Characters

abstract: The aim of this work is to present a system for recognizing isolated Arabic printed characters. This system goes through several stages: preprocessing, feature extraction and classification. Zernike moments, invariant moments and Walsh transformation are used to calculate the features. The classification is based on multilayer neural networks. A recognition rate of 98% is achieved by using Zernike moments.


title: Word of Mouth Propagation in Online Social Networks

abstract: Online social networks (OSNs) are becoming an important propagation platform for Word of mouth (WOM). Therefore, it is of great significance to study the propagation of WOM in OSNs. A WOM propagation model named N-P-N is proposed in this paper, and some simulation experiments are carried out to investigate the mechanism of WOM propagation. From the sensitivity analysis of degree of initial information source node, it can be seen that the degree of initial information source node determines the scope and speed of the propagation of WOM in OSNs in some extent. Then the sensitivity analysis of number of initial information source nodes shows that the initial source nodes are crucial for controlling the propagation of negative information in OSNs. Moreover, from the user behavior respect, it is found that different user behavior in OSNs causes different propagation results, the more users who are willing to diffuse WOM, the more scope WOM can propagate and the faster the information diffuses. Findings in this paper are helpful for enterprises to form an effective WOM.


title: Active learning with the cyberciege video game

abstract: Hands-on exercises promote active learning where student experience reinforces material presented in lectures or reading assignments [1]. Drawing the student into a meaningful context where student decisions have clear consequences strengthens the learning experience and thus improves the potential for internalization of knowledge. The CyberCIEGE video game was designed to confront students with computer security decision points within an environment that encourages experimentation, failure and reflection. The game includes over twenty scenarios that address a range of computer and network security concepts. CyberCIEGE is extensible through use of a scenario development language that allows instructors to create and customize game scenarios. The Naval Postgraduate School uses the game in our Introduction to Computer Security course, and it has been used by hundreds of educational institutions worldwide. The game's tools allow ongoing experimentation with the student's learning experience. Student assessment is facilitated by log generation, collection and analysis. These logs help the game's developers identify areas within scenarios that may be confusing or may require additional player feedback. Ongoing development is focused on ultimately adapting the game and its student assessment functions for deployment in a broader range of formal education environments.


title: The severity of liver fibrosis is associated with high leptin levels in chronic hepatitis C.

abstract: Recent attention has focused on the liver profibrogenic role of leptin in animal models. The purpose of this study was to evaluate the role of leptin and TNF-alpha in the severity of liver fibrosis in patients with chronic hepatitis C (CHC). We used a radioimmunoassay to determine serum leptin concentrations in 77 consecutive patients with CHC and 22 healthy controls. Leptin was correlated with liver histological (METAVIR) and metabolic indices. Sixty five patients had none to moderate liver fibrosis (F0-F2) and twelve severe fibrosis (F3-F4). Steatosis was observed in all but 27 patients. Leptin was significantly increased in patients compared with controls and was significantly more elevated in females both in patients and controls. The age, age at infection, prothrombin index, body mass index (BMI), triglycerides, glycaemia, ferritin, leptin and TNF-alpha, were associated with severe fibrosis. Steatosis was significantly more pronounced in patients with severe than those without or moderate fibrosis (P = 0.04). Only leptin was significantly and independently associated with severe fibrosis (OR = 1.2, CI 95%: 1.1-1.4, P = 0.03). Leptin was significantly associated with BMI (r = 0.64, P < 0.001) and glycaemia (r = 0.43, P < 0.001). Significant correlations were found between steatosis and BMI (r = 0.30, P < 0.01) and glycaemia (r = 0.30, P < 0.01). In patients with CHC and higher BMI and glycaemia levels, the severity of liver fibrosis is associated with serum leptin. TNF-alpha is a putative candidate involved in the mechanism.


title: A comprehensive dosimetric evaluation of using RapidArc volumetric‐modulated arc therapy for the treatment of early‐stage nasopharyngeal carcinoma

abstract: The purpose of this study was to investigate the potential benefits of using triple-arc volumetric-intensity modulated arc radiotherapy (RapidArc (RA)) for the treatment of early-stage nasopharyngeal carcinoma (NPC). A comprehensive evaluation was performed including plan quality, integral doses, and peripheral doses. Twenty cases of stage I or II NPC were selected for this study. Nine-field sliding window IMRT, double-arc, and triple-arc RA treatment plans were compared with respect to target coverage, dose conformity, critical organ sparing, and integral doses. Measurement of peripheral doses was performed using thermoluminescent dosimeters in an anthropomorphic phantom. While similar conformity and target coverage were achieved by the three types of plans, triple-arc RA produced better sparing of parotid glands and spinal cord than double-arc RA or IMRT. Double-arc RA plans produced slightly inferior parotid sparing and dose homogeneity than the other two delivery methods. The monitor units (MU) required for triple-arc were about 50% less than those of IMRT plans, while there was no significant difference in the required MUs between triple-arc and double-arc RA plans. The peripheral dose in triple-arc RA was found to be 50% less compared to IMRT near abdominal and pelvic region. Triple-arc RA improves both the plan quality and treatment efficiency compared with IMRT for the treatment of early stage NPC. It has become the preferred choice of treatment delivery method for early stage NPC at our center.


title: A logic of knowing why

abstract: When we say "I know why he was late", we know not only the fact that he was late, but also an explanation of this fact. We propose a logical framework of "knowing why" inspired by the existing formal studies on why-questions, scientific explanation, and justification logic. We introduce the Ky_i operator into the language of epistemic logic to express "agent i knows why phi" and propose a Kripke-style semantics of such expressions in terms of knowing an explanation of phi. We obtain two sound and complete axiomatizations w.r.t. two different model classes depending on different assumptions about introspection.


title: G-Prop-II: Global Optimization of Multilayer Perceptrons using GAs

abstract: Abstract A general problem in model selection is to obtain the right parameters that make a model fit observed data. For a multilayer perceptron (MLP) trained with back-propagation (BP), this means finding appropiate layer size and initial weights. This paper proposes a method (G-Prop, genetic backpropagation) that attempts to solve that problem by combining a genetic algorithm (GA) and BP to train MLPs with a single hidden layer. The GA selects the initial weights and changes the number of neurons in the hidden layer through the application of specific genetic operators. G-Prop combines the advantages of the global search performed by the GA over the MLP parameter space and the local search of the BP algorithm. The application of the G-Prop algorithm to several real-world and benchmark problems shows that MLPs evolved using G-Prop are smaller and achieve a higher level of generalization than other perceptron training algorithms, such as Quick-Propagation or RPROP, and other evolutive algorithms, such as G-LVQ.


title: Harvesting Robots for High-value Crops: State-of-the-art Review and Challenges Ahead

abstract: This review article analyzes state-of-the-art and future perspectives for harvesting robots in high-value crops. The objectives were to characterize the crop environment relevant for robotic harvesting, to perform a literature review on the state-of-the-art of harvesting robots using quantitative measures, and to reflect on the crop environment and literature review to formulate challenges and directions for future research and development. Harvesting robots were reviewed regarding the crop harvested in a production environment, performance indicators, design process techniques used, hardware design decisions, and algorithm characteristics. On average, localization success was 85%, detachment success was 75%, harvest success was 66%, fruit damage was 5%, peduncle damage was 45%, and cycle time was 33 s. A kiwi harvesting robot achieved the shortest cycle time of 1 s. Moreover, the performance of harvesting robots did not improve in the past three decades, and none of these 50 robots was commercialized. Four future challenges with R&D directions were identified to realize a positive trend in performance and to successfully implement harvesting robots in practice: 1 simplifying the task, 2 enhancing the robot, 3 defining requirements and measuring performance, and 4 considering additional requirements for successful implementation. This review article may provide new directions for future automation projects in high-value crops.


Object Detection using the Canny Edge Detector


title: Adaptive MCMC in Bayesian phylogenetics: an application to analyzing partitioned data in BEAST

abstract: Motivation: Advances in sequencing technology continue to deliver increasingly large molecular sequence datasets that are often heavily partitioned in order to accurately model the underlying evolutionary processes. In phylogenetic analyses, partitioning strategies involve estimating conditionally independent models of molecular evolution for different genes and different positions within those genes, requiring a large number of evolutionary parameters that have to be estimated, leading to an increased computational burden for such analyses. The past two decades have also seen the rise of multi-core processors, both in the central processing unit (CPU) and Graphics processing unit processor markets, enabling massively parallel computations that are not yet fully exploited by many software packages for multipartite analyses. Results: We here propose a Markov chain Monte Carlo (MCMC) approach using an adaptive multivariate transition kernel to estimate in parallel a large number of parameters, split across partitioned data, by exploiting multi-core processing. Across several real-world examples, we demonstrate that our approach enables the estimation of these multipartite parameters more efficiently than standard approaches that typically use a mixture of univariate transition kernels. In one case, when estimating the relative rate parameter of the non-coding partition in a heterochronous dataset, MCMC integration efficiency improves by > 14-fold. Availability and Implementation: Our implementation is part of the BEAST code base, a widely used open source software package to perform Bayesian phylogenetic inference. Contact: guy.baele@kuleuven.be Supplementary information: Supplementary data are available at Bioinformatics online.


title: Finding topic words for hierarchical summarization

abstract: Hierarchies have long been used for organization, summarization, and access to information. In this paper we define summarization in terms of a probabilistic language model and use the definition to explore a new technique for automatically generating topic hierarchies by applying a graph-theoretic algorithm, which is an approximation of the Dominating Set Problem. The algorithm efficiently chooses terms according to a language model. We compare the new technique to previous methods proposed for constructing topic hierarchies including subsumption and lexical hierarchies, as well as the top TF.IDF terms. Our results show that the new technique consistently performs as well as or better than these other techniques. They also show the usefulness of hierarchies compared with a list of terms.


title: Generalized droop characteristic-based Demand response and secondary frequency control coordination in an isolated microgrid

abstract: Electricity crisis, environmental issues and as well as economic considerations enters modern power system to use alternative sources instead of the conventional ones. One of the newest ones in which controllable active and reactive loads are continuously contributed in the dynamic regulation called Demand response (DR). In this paper, a new method to apply DR in the microgrid (MG) are presented. It is based on generalized droop characteristic (GDC) which change controllable loads using a series of specific equations. The proper performance of GDC-based DR is achieved by changing controllable active and reactive loads which it changed based on the system conditions. To test the control method several scenarios are simulated in which the system frequency and voltage are studied in two states. The simulation results are carried out in the presence of conventional controller and in the next mode, in addition to the conventional ones DR is contributed in regulation dynamics. The results show that the proposed control methodology have appropriate performance such that the frequency deviations and voltage fluctuations subjected by different faults are far fewer in comparison of pervious controllers.


title: A Survey of Data Mining and Deep Learning in Bioinformatics

abstract: The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.


title: AC Power Losses Model for Planar Windings With Rectangular Cross-Sectional Conductors

abstract: In this letter, a method to calculate the ac losses, including skin effect and proximity losses, in planar windings with rectangular cross-sectional conductors is proposed. The aim is proposing proper ac losses expressions similar to the formulas available for round cross-sectional wires, to be used for the calculation of the ac losses and the optimization of planar magnetic windings implemented in the printed circuit board. The proposed model is based on the decomposition into conduction and proximity losses. Conduction losses only depend on the properties of the conductor, whereas proximity losses are calculated by using the orthogonal decomposition of the magnetic fields in which the conductors are immersed. Functions including the frequency and geometrical dependences of the both types of losses are extracted by means of finite element method simulation. Finally, several prototypes are used to verify the proposed expressions and some design considerations are also outlined.


title: Canonization rules for detecting different URLs

abstract: A Uniform Resource Locator (URL) represents the address of a web page in World Wide Web (WWW). A URL grants access to a single web page on the WWW. Here in this paper the main focus is on the URLs addressing the same web page/same content. A web Page can have two or more URLs with which the web page can be accessed. These duplicate URLs can be a serious threat to the entire pipeline of internet searcher administration for indexing and creeping. I am presenting a novel algorithm for detecting canonization rules for normalizing URLs to the original single URL. Here a pattern recognition approach has been used for analyzing textual data. This approach benefits search engines from information about duplicate URLs to optimize the performance of search engine in terms of reduced cost and improved quality.


title: The uptake of macroplastic & microplastic by demersal & pelagic fish in the Northeast Atlantic around Scotland

abstract: Abstract This study reports plastic ingestion in various fish found from coastal and offshore sites in Scottish marine waters. Coastal samples consisted of three demersal flatfish species ( n = 128) collected from the East and West coasts of Scotland. Offshore samples consisted of 5 pelagic species and 4 demersal species ( n = 84) collected from the Northeast Atlantic. From the coastal fish sampled, 47.7% of the gastrointestinal tracts contained macroplastic and microplastic. Of the 84 pelagic and demersal offshore fish, only 2 (2.4%) individuals from different species had ingested plastic identified as a clear polystyrene fibre and a black polyamide fibre. The average number of plastic items found per fish from all locations that had ingested plastic was 1.8 (± 1.7) with polyamide (65.3%), polyethylene terephthalate (14.4%) and acrylic (14.4%) being the three most commonly found plastics. This study adds to the existing data on macroplastic and microplastic ingestion in fish species.


title: Magnet design based on transient behavior of an IPMSM in event of malfunction

abstract: This paper deals with the approach of analyzing the magnet design based on transient behavior of an IPMSM during switching processes in the event of malfunction for automotive applications. Depending on the maximal current increase during the transient process the needed percentage of Dysprosium for the magnet design is conducted. Switching off strategy is introduced for both Voltage-Source- and Current-Source-Inverter for automotive application. Both inverters are compared concerning the transient current increase and respectively the Dy-content for the magnet design.


title: An enhanced steering algorithm for redirected walking in virtual environments

abstract: Redirected walking techniques enable natural locomotion through immersive virtual environments that are considerably larger than the available real world walking space. However, the most effective strategy for steering the user remains an open question, as most previously presented algorithms simply redirect toward the center of the physical space. In this work, we present a theoretical framework that plans a walking path through a virtual environment and calculates the parameters for combining translation, rotation, and curvature gains such that the user can traverse a series of defined waypoints efficiently based on a utility function. This function minimizes the number of overt reorientations to avoid introducing potential breaks in presence. A notable advantage of this approach is that it leverages knowledge of the layout of both the physical and virtual environments to enhance the steering strategy.


title: A technique for computer detection and correction of spelling errors

abstract: The method described assumes that a word which cannot be found in a dictionary has at most one error, which might be a wrong, missing or extra letter or a single transposition. The unidentified input word is compared to the dictionary again, testing each time to see if the words match—assuming one of these errors occurred. During a test run on garbled text, correct identifications were made for over 95 percent of these error types.


title: A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance

abstract: A genetic algorithm (GA) is proposed that uses a variable population size and periodic partial reinitialization of the population in the form of a saw-tooth function. The aim is to enhance the overall performance of the algorithm relying on the dynamics of evolution of the GA and the synergy of the combined effects of population size variation and reinitialization. Preliminary parametric studies to test the validity of these assertions are performed for two categories of problems, a multimodal function and a unimodal function with different features. The proposed scheme is compared with the conventional GA and micro GA (/spl mu/GA) of equal computing cost and guidelines for the selection of effective values of the involved parameters are given, which facilitate the implementation of the algorithm. The proposed algorithm is tested for a variety of benchmark problems and a problem generator from which it becomes evident that the saw-tooth scheme enhances the overall performance of GAs.


title: On-the-fly multi-scale infinite texturing from example

abstract: In computer graphics, rendering visually detailed scenes is often achieved through texturing. We propose a method for on-the-fly non-periodic infinite texturing of surfaces based on a single image. Pattern repetition is avoided by defining patches within each texture whose content can be changed at runtime. In addition, we consistently manage multi-scale using one input image per represented scale. Undersampling artifacts are avoided by accounting for fine-scale features while colors are transferred between scales. Eventually, we allow for relief-enhanced rendering and provide a tool for intuitive creation of height maps. This is done using an ad-hoc local descriptor that measures feature self-similarity in order to propagate height values provided by the user for a few selected texels only. Thanks to the patch-based system, manipulated data are compact and our texturing approach is easy to implement on GPU. The multi-scale extension is capable of rendering finely detailed textures in real-time.


title: Variability discrimination in humans and animals: Implications for adaptive action

abstract: Both humans and animals live in a rich world of events. Some events repeat themselves, whereas others constantly change. The authors propose that discriminating this stability, sameness, and uniformity from change, differentness, and diversity is fundamental to adaptive action. Evidence from many areas of behavioral science indicates that the discrimination of and preference for stimulus variability affects both human and animal action. Recent comparative research with humans and animals illustrates a promising approach to the study of these issues. Discovering and understanding the behavioral and neural processes related to stimulus variability and its consequences for behavior offer distinctive challenges and important new opportunities for psychologists and neuroscientists.


title: Fluid animation with dynamic meshes

abstract: This paper presents a method for animating fluid using unstructured tetrahedral meshes that change at each time step. We show that meshes that conform well to changing boundaries and that focus computation in the visually important parts of the domain can be generated quickly and reliably using existing techniques. We also describe a new approach to two-way coupling of fluid and rigid bodies that, while general, benefits from remeshing. Overall, the method provides a flexible environment for creating complex scenes involving fluid animation.


PhotoshopQuiA: A Corpus of Non-Factoid Questions and Answers for Why-Question Answering.


title: TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams

abstract: Detecting local events (e.g., protest, disaster) at their onsets is an important task for a wide spectrum of applications, ranging from disaster control to crime monitoring and place recommendation. Recent years have witnessed growing interest in leveraging geo-tagged tweet streams for online local event detection. Nevertheless, the accuracies of existing methods still remain unsatisfactory for building reliable local event detection systems. We propose TrioVecEvent, a method that leverages multimodal embeddings to achieve accurate online local event detection. The effectiveness of TrioVecEvent is underpinned by its two-step detection scheme. First, it ensures a high coverage of the underlying local events by dividing the tweets in the query window into coherent geo-topic clusters. To generate quality geo-topic clusters, we capture short-text semantics by learning multimodal embeddings of the location, time, and text, and then perform online clustering with a novel Bayesian mixture model. Second, TrioVecEvent considers the geo-topic clusters as candidate events and extracts a set of features for classifying the candidates. Leveraging the multimodal embeddings as background knowledge, we introduce discriminative features that can well characterize local events, which enables pinpointing true local events from the candidate pool with a small amount of training data. We have used crowdsourcing to evaluate TrioVecEvent, and found that it improves the performance of the state-of-the-art method by a large margin.


title: Design and implementation of vehicle tracking system using GPS/GSM/GPRS technology and smartphone application

abstract: An efficient vehicle tracking system is designed and implemented for tracking the movement of any equipped vehicle from any location at any time. The proposed system made good use of a popular technology that combines a Smartphone application with a microcontroller. This will be easy to make and inexpensive compared to others. The designed in-vehicle device works using Global Positioning System (GPS) and Global system for mobile communication / General Packet Radio Service (GSM/GPRS) technology that is one of the most common ways for vehicle tracking. The device is embedded inside a vehicle whose position is to be determined and tracked in real-time. A microcontroller is used to control the GPS and GSM/GPRS modules. The vehicle tracking system uses the GPS module to get geographic coordinates at regular time intervals. The GSM/GPRS module is used to transmit and update the vehicle location to a database. A Smartphone application is also developed for continuously monitoring the vehicle location. The Google Maps API is used to display the vehicle on the map in the Smartphone application. Thus, users will be able to continuously monitor a moving vehicle on demand using the Smartphone application and determine the estimated distance and time for the vehicle to arrive at a given destination. In order to show the feasibility and effectiveness of the system, this paper presents experimental results of the vehicle tracking system and some experiences on practical implementations.


title: A Generic Framework for Three-Factor Authentication: Preserving Security and Privacy in Distributed Systems

abstract: As part of the security within distributed systems, various services and resources need protection from unauthorized use. Remote authentication is the most commonly used method to determine the identity of a remote client. This paper investigates a systematic approach for authenticating clients by three factors, namely password, smart card, and biometrics. A generic and secure framework is proposed to upgrade two-factor authentication to three-factor authentication. The conversion not only significantly improves the information assurance at low cost but also protects client privacy in distributed systems. In addition, our framework retains several practice-friendly properties of the underlying two-factor authentication, which we believe is of independent interest.


Quality Of sulfadoxine/pyrimethamine tablets marketed In Dar es Salaam, Tanzania


title: An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

abstract: Existing fuzzy neural networks (FNNs) are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep fuzzy neural network, namely deep evolving fuzzy neural networks (DEVFNN). Fuzzy rules can be automatically extracted from data streams or removed if they play little role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely Generic Classifier (gClass), drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of input space dimension due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four state-of-the art data stream methods and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.


title: Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images

abstract: Localization of chest pathologies in chest X-ray images is a challenging task because of their varying sizes and appearances. We propose a novel weakly supervised method to localize chest pathologies using class aware deep multiscale feature learning. Our method leverages intermediate feature maps from CNN layers at different stages of a deep network during the training of a classification model using image level annotations of pathologies. During the training phase, a set of \emph{layer relevance weights} are learned for each pathology class and the CNN is optimized to perform pathology classification by convex combination of feature maps from both shallow and deep layers using the learned weights. During the test phase, to localize the predicted pathology, the multiscale attention map is obtained by convex combination of class activation maps from each stage using the \emph{layer relevance weights} learned during the training phase. We have validated our method using 112000 X-ray images and compared with the state-of-the-art localization methods. We experimentally demonstrate that the proposed weakly supervised method can improve the localization performance of small pathologies such as nodule and mass while giving comparable performance for bigger pathologies e.g., Cardiomegaly


title: State-of-the-Art Performance in Text-Independent Speaker Verification Through Open-Source Software

abstract: This paper illustrates an evolution in state-of-the-art speaker verification by highlighting the contribution from newly developed techniques. Starting from a baseline system based on Gaussian mixture models that reached state-of-the-art performances during the NIST'04 SRE, final systems with new intersession compensation techniques show a relative gain of around 50%. This work highlights that a key element in recent improvements is still the classical maximum a posteriori (MAP) adaptation, while the latest compensation methods have a crucial impact on overall performances. Nuisance attribute projection (NAP) and factor analysis (FA) are examined and shown to provide significant improvements. For FA, a new symmetrical scoring (SFA) approach is proposed. We also show further improvement with an original combination between a support vector machine and SFA. This work is undertaken through the open-source ALIZE toolkit.


title: B-spline-based road model for 3d lane recognition

abstract: The assumption of a planar road is rarely fulfilled on rural roads and often violated in construction zones. Hills and dips are common and curves with small radius often have an inclination towards the center. In this paper a novel road model is presented, which uses B-Spline modeling to describe the 3D road course. The right and left vertical road courses are modeled separately to represent the roll angle of the road. The well-proven clothoid model used for lateral road course estimation is extended with a B-Spline curve resulting in a more flexible road model as needed for rural roads and construction sites. This road model is tracked over time using a Kalman filter. Stereo measurements provide height information of the road surface, which are used for the 3D lane recognition.


title: Centrality in valued graphs: A measure of betweenness based on network flow

abstract: A new measure of centrality, C,, is introduced. It is based on the concept of network flows. While conceptually similar to Freeman’s original measure, Ca, the new measure differs from the original in two important ways. First, C, is defined for both valued and non-valued graphs. This makes C, applicable to a wider variety of network datasets. Second, the computation of C, is not based on geodesic paths as is C, but on all the independent paths between all pairs of points in the network.


title: Single ended 6T SRAM with isolated read-port for low-power embedded systems

abstract: This paper presents a six-transistor (6T) single-ended static random access memory (SE-SRAM) bitcell with an isolated read-port, suitable for low-V DD and low-power embedded applications. The proposed bitcell has a better static noise margin (SNM) and write-ability compared to a standard 6T bitcell and equivalent to an 8T bitcell [1]. An 8Kbit SRAM module with the proposed and standard 6T bitcells is simulated, including full blown parasitics using BPTM, 65nm CMOS technology node to evaluate and compare different performance parameters. The active power dissipation in the proposed 6T design is 28% and 25% less, compared to standard 6T and 8T SRAM modules respectively.


title: Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction

abstract: Rating prediction is to predict the preference rating of a user to an item that she has not rated before. Using the business review data from Yelp, in this paper, we study business rating prediction. A business here can be a restaurant, a shopping mall or other kind of businesses. Different from most other types of items that have been studied in various recommender systems (e.g., movie, song, book), a business physically exists at a geographical location, and most businesses have geographical neighbors within walking distance. When a user visits a business, there is a good chance that she walks by its neighbors. Through data analysis, we observe that there exists weak positive correlation between a business's ratings and its neighbors' ratings, regardless of the categories of businesses. Based on this observation, we assume that a user's rating to a business is determined by both the intrinsic characteristics of the business and the extrinsic characteristics of its geographical neighbors. Using the widely adopted latent factor model for rating prediction, in our proposed solution, we use two kinds of latent factors to model a business: one for its intrinsic characteristics and the other for its extrinsic characteristics. The latter encodes the neighborhood influence of this business to its geographical neighbors. In our experiments, we show that by incorporating geographical neighborhood influences, much lower prediction error is achieved than the state-of-the-art models including Biased MF, SVD++, and Social MF. The prediction error is further reduced by incorporating influences from business category and review content.


title: Adopting Metacognitive Interpersonal Therapy to Treat Narcissistic Personality Disorder with Somatization

abstract: Narcissistic personality disorder (NPD) is characterized by an intellectualizing narrative style, poor metacognition, maladaptive interpersonal schemas, a restricted set of states of mind, impaired agency and perfectionism. Metacognitive Interpersonal therapy targets these dysfunctions with a series of formalized procedures aimed at first forming a shared formulation of functioning which patients and therapists can then use to plan change. We describe here the core NPD pathology and illustrate the therapy process of a patient with NPD and a somatization disorder. Implications for treatment of NPD are discussed together with a call for empirical testing of therapies for this severe condition.


title: Negative impact of CSMA/CD on WLAN IEEE 802.11

abstract: IEEE 802.11 standard operates on frequency 2.4 GHz that provides channel bandwidth for Wireless Local Area Network (WLAN) communication. These frequency bands are widely used for Access Point (AP) on computer networking. It works based on Direct-Sequence Spread-Spectrum (DSSS) and Orthogonal Frequency Division Multiplexing (OFDM) encode technologies. Internet use has been growing and includes users on laptops, smart phones, and tablets. Those users want to access network by using the Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) mechanism. In order to offer the increasing number of internet users, a new AP is replaced to support more users and coverage areas that overlap each other. However, because of this, channel interference issues arise on AP. In this paper, the negative impact of channel interference and performance of the CSMA/CA mechanism are investigated. The results show the impact on AP in terms of delay, throughput, and signal to noise ratio (SNR) of overlapping channels. For non-overlapping channels, retransmission attempts and collision status are investigated. Delay is increased approximately 50%, throughput is dropped to 200%, and SNR is dropped to 58% if the channels of AP overlap each other.


title: Ad Serving with Multiple KPIs

abstract: Ad-servers have to satisfy many different targeting criteria, and the combination can often result in no feasible solution. We hypothesize that advertisers may be defining these metrics to create a kind of "proxy target". We therefore reformulate the standard ad-serving problem to one where we attempt to get as close as possible to the advertiser's multi-dimensional target inclusive of delivery. We use a simple simulation to illustrate the behavior of this algorithm compared to Constraint and Pacing strategies. The system is then deployed in one of the largest video ad-servers in the United States and we show experimental results from live test ads, as well as 6 months of production performance across hundreds of ads. We find that the live ad-server tests match the simulation, and we report significant gains in multi-KPI performance from using the error minimization strategy.


A Dataset for Open Event Extraction in English.


title: Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research

abstract: Researchers in HCI and behavioral science are increasingly exploring the use of technology to support behavior change in domains such as health and sustainability. This work, however, remain largely siloed within the two communities. We begin to address this silo problem by attempting to build a bridge between the two disciplines at the level of behavioral theory. Specifically, we define core theoretical terms to create shared understanding about what theory is, discuss ways in which behavioral theory can be used to inform research on behavior change technologies, identify shortcomings in current behavioral theories, and outline ways in which HCI researchers can not only interpret and utilize behavioral science theories but also contribute to improving them.


title: Probing the limits of predictability: data assimilation of chaotic dynamics in complex food webs

abstract: The daunting complexity of ecosystems has led ecologists to use mathematical modelling to gain understanding of ecological relationships, processes and dynamics. In pursuit of mathematical tractability, these models use simplified descriptions of key patterns, processes and relationships observed in nature. In contrast, ecological data are often complex, scale-dependent, space-time correlated, and governed by nonlinear relations between organisms and their environment. This disparity in complexity between ecosystem models and data has created a large gap in ecology between model and data-driven approaches. Here, we explore data assimilation (DA) with the Ensemble Kalman filter to fuse a two-predator-two-prey model with abundance data from a 2600+ day experiment of a plankton community. We analyse how frequently we must assimilate measured abundances to predict accurately population dynamics, and benchmark our population model's forecast horizon against a simple null model. Results demonstrate that DA enhances the predictability and forecast horizon of complex community dynamics.


title: Automated Software Test Data Generation

abstract: An alternative approach to test-data generation based on actual execution of the program under test, function-minimization methods and dynamic data-flow analysis is presented. Test data are developed for the program using actual values of input variables. When the program is executed, the program execution flow is monitored. If during program execution an undesirable execution flow is observed then function-minimization search algorithms are used to automatically locate the values of input variables for which the selected path is traversed. In addition, dynamic data-flow analysis is used to determine those input variables responsible for the undesirable program behavior, significantly increasing the speed of the search process. The approach to generating test data is then extended to programs with dynamic data structures and a search method based on dynamic data-flow analysis and backtracking is presented. In the approach described, values of array indexes and pointers are known at each step of program execution; this information is used to overcome difficulties of array and pointer handling. >


title: A passive exoskeleton with artificial tendons: Design and experimental evaluation

abstract: We developed a passive exoskeleton that was designed to minimize joint work during walking. The exoskeleton makes use of passive structures, called artificial tendons, acting in parallel with the leg. Artificial tendons are elastic elements that are able to store and redistribute energy over the human leg joints. The elastic characteristics of the tendons have been optimized to minimize the mechanical work of the human leg joints. In simulation the maximal reduction was 40 percent. The performance of the exoskeleton was evaluated in an experiment in which nine subjects participated. Energy expenditure and muscle activation were measured during three conditions: Normal walking, walking with the exoskeleton without artificial tendons, and walking with the exoskeleton with the artificial tendons. Normal walking was the most energy efficient. While walking with the exoskeleton, the artificial tendons only resulted in a negligibly small decrease in energy expenditure.


title: Reverse Engineering of Design Patterns from Java Source Code

abstract: Recovering design patterns can enhance existing source code analysis tools by bringing program understanding to the design level. This paper presents a new, fully automated pattern detection approach. The new approach is based on our reclassification of the GoF patterns by their pattern intent. We argue that the GoF pattern catalog classifies design patterns in the forward-engineering sense; our reclassification is better suited for reverse engineering. Our approach uses lightweight static program analysis techniques to capture program intent. This paper also describes our tool, PINOT, that implements this new approach. PINOT detects all the GoF patterns that have concrete definitions driven by code structure or system behavior. Our tool is faster, more accurate, and targets more patterns than existing pattern detection tools. PINOT has been used successfully in detecting patterns in Java AWT, JHotDraw, Swing, Apache Ant, and many other programs and packages.


title: Observing the Effects of Mindfulness-Based Meditation on Anxiety and Depression in Chronic Pain Patients.

abstract: Abstract People whose chronic pain limits their independence are especially likely to become anxious and depressed. Mindfulness training has shown promise for stress-related disorders. Chronic pain patients who complained of anxiety and depression and who scored higher than moderate in Hamilton Depression Rating Scale (HDRS) and Hospital Anxiety and Depression Scale (HADS) as well as moderate in Quality of Life Scale (QOLS) were observed for eight weeks, three days a week for an hour of Mindfulness Meditation training with an hour daily home Mindfulness Meditation practice. Pain was evaluated on study entry and completion, and patients were given the Patients' Global Impression of Change (PGIC) to score at the end of the training program. Forty-seven patients (47) completed the Mindfulness Meditation Training program. Over the year-long observation, patients demonstrated noticeable improvement in depression, anxiety, pain, and global impression of change. Chronic pain patients who suffer with anxiety and depression may benefit from incorporating Mindfulness Meditation into their treatment plans.