title: Deep android malware detection and classification

abstract: Long short-term memory recurrent neural network (LSTM-RNN) have witnessed as a powerful approach for capturing long-range temporal dependencies in sequences of arbitrary length. This paper seeks to model a large set of Android permissions particularly the permissions from Normal, Dangerous, Signature and Signature Or System categories within a large number of Android application package (APK) files of Cyber Security Data Mining Competition (CDMC 2016), Android malware classification challenge. The sequences of Android permissions are transformed into features by using recurrent LSTM layer with bag-of-words embedding and the extracted features are fed into dense and activation layer with non-linear activation function such as sigmoid for classification. Furthermore, to selectively find out the optimal paramaters and network structure, we have done various experimens with different network parameters and network structures. All experiments are run up to 1000 epochs with a learning rate in the range [0.01-0.5]. All LSTM network configurations have substantially performed well in classification settings of 5-fold cross validation in comparison to the recurrent neural network (RNN). Most importantly, LSTM has achieved the highest accuracy as 0.897 on the real-world Android malware test data set, provided by CDMC2016. This is primarily due to fact that the LSTM houses a complex memory processing unit that facilitates to learn the temporal behaviors quickly with sparse representations of Android permissions sequences. Thus, we claim that applying LSTM network to permission based Android malware classification is more appropriate.


title: Gene flow in mongooses endemic to Namibia's granite inselbergs despite past climatic fluctuations and isolating landscape features

abstract: Past climatic fluctuations have had a significant impact on the patterns of genetic variation within taxa restricted to montane regions and forested biomes; however, little is known about whether equivalent processes have occurred in arid biomes. Northwestern Namibia's inselbergs provide a unique ecosystem in which to study the effects of major climatic events and geographical isolation on the genetic structuring of taxa in a subtropical arid biome. We investigated the phylogeographic structure of the black mongoose (Galerella nigrata), an inselberg habitat specialist endemic to this region, using mitochondrial DNA (mtDNA) haplotypes (cytochrome b, 1,089 base pairs) and nuclear microsatellite genotypes (15 loci) from 6 inselberg populations. Analyses of molecular variance and spatial analyses of molecular variance of the 14 mtDNA haplotypes identified 2 significant geographic barriers to the dispersal of black mongooses; these barriers occurred across the vast arid plains between Ruacana and Hobatere, and between Ohorongo and inselbergs to the south. The occurrence of mtDNA haplotypes that were restricted to specific populations also indicated some degree of isolation, likely resulting from limited gene flow and drift induced by the desertification of the landscape between inselbergs during the lead-up to the most recent glacial maxima. Despite this pattern of isolation, the widespread distribution of 1 mtDNA haplotype suggested that populations of black mongoose have, in general, been well connected in the past. Ongoing gene flow was further supported by Bayesian clustering of microsatellite genotypes, which showed 2 clusters, each spread across the mongoose's distribution, and a lack of significant differentiation between populations. In addition, a constant population size was indicated by a Bayesian skyline plot. These analyses suggested that inselbergs across the entire study area might have supported black mongoose populations during some or all of the most recent major climatic fluctuations, with periods of isolation during glacial maxima and reestablished connectivity during interglacial periods.


title: The Unreasonable Effectiveness of Word Representations for Twitter Named Entity Recognition

abstract: Named entity recognition (NER) systems trained on newswire perform very badly when tested on Twitter. Signals that were reliable in copy-edited text disappear almost entirely in Twitter’s informal chatter, requiring the construction of specialized models. Using wellunderstood techniques, we set out to improve Twitter NER performance when given a small set of annotated training tweets. To leverage unlabeled tweets, we build Brown clusters and word vectors, enabling generalizations across distributionally similar words. To leverage annotated newswire data, we employ an importance weighting scheme. Taken all together, we establish a new state-of-the-art on two common test sets. Though it is wellknown that word representations are useful for NER, supporting experiments have thus far focused on newswire data. We emphasize the effectiveness of representations on Twitter NER, and demonstrate that their inclusion can improve performance by up to 20 F1.


title: On preventing ARP poisoning attack utilizing Software Defined Network (SDN) paradigm

abstract: in this work, SDN has been utilized to alleviate and eliminate the problem of ARP poisoning attack. This attack is the underlying infrastructure for many other network attacks, such as, man in the middle, denial of service and session hijacking. In this paper we propose a new algorithm to resolve the problem of ARP spoofing. The algorithm can be applied in two different scenarios. The two scenarios are based on whether a network host will be assigned a dynamic or a static IP address. We call the first scenario SDN_DYN; the second scenario is called SDN_STA. For the evaluation process, a physical SDN-enabled switch has been utilized with Ryu controller. Our results show that the new algorithm can prevent ARP spoofing and other attacks exploiting it.


title: VHDL Implementation For a Fuzzy Logic Controller

abstract: This paper describes the implementation for a basic fuzzy logic controller in Very High speed integrated-circuit Hardware-Description Language (VHDL). It is not intended as an introduction to fuzzy logic control methodology; instead, we try to demonstrate the implementation of a fuzzy logic controller through the use of the VHDL code. Use of the hardware description language (HDL) in the application is suitable for being implemented into an Application Specific Integrated Circuit (ASIC) and Field Programmable Gate Array (FPGA). The main advantages of using the HDL approach are rapid prototyping, and allowing usage of powerful synthesis tools such as Xilinx ISE, Synosys, Mentor Graphic, or Cadence to be targeted easily and efficiently.


title: Life’s Simple 7 Approach to Atrial Fibrillation Prevention

abstract: Atrial fibrillation (AF) is the most commonly encountered arrhythmia in clinical practice. It constitutes a major public health problem, with total related annual expenses estimated at $6.65 billion. The American Heart Association developed Life's Simple 7 (LS7) to define and monitor ideal cardiovascular health (CVH). In this review, we examine the role of individual components of LS7 to provide further insight regarding potential influence of achieving AHA's strategic goal on AF prevention. While significant advances have been made in the secondary prevention of AF, little progress has been made to prevent the first occurrence of this arrhythmia in at-risk patients. Improvement of overall cardiovascular health as defined by LS7 may substantially reduce AF risk.


title: Efficient edge-aware surface mesh reconstruction for urban scenes

abstract: Meshing approach for both street-side SfM data and large-scale urban height maps.Our meshes preserve crease edges and discontinuities without staircasing artifacts.2D base mesh from superpixels or from piecewise-planar depth map partitioning.Fast linear vertex depth optimization including a curvature penalty term.Excellent trade-off between model compactness and approximation quality. We propose an efficient approach for building compact, edge-preserving, view-centric triangle meshes from either dense or sparse depth data, with a focus on modeling architecture in large-scale urban scenes. Our method constructs a 2D base mesh from a preliminary view partitioning, then lifts the base mesh into 3D in a fast vertex depth optimization. Different view partitioning schemes are proposed for imagery and dense depth maps. They guarantee that mesh edges are aligned with crease edges and discontinuities. In particular, we introduce an effective plane merging procedure with a global error guarantee in order to maximally compact the resulting models. Moreover, different strategies for detecting and handling discontinuities are presented. We demonstrate that our approach provides an excellent trade-off between quality and compactness, and is eligible for fast production of polyhedral building models from large-scale urban height maps, as well as, for direct meshing of sparse street-side Structure-from-Motion(SfM) data.


title: Epigenetic Basis of Mental Illness

abstract: Psychiatric disorders are complex multifactorial illnesses involving chronic alterations in neural circuit structure and function as well as likely abnormalities in glial cells. While genetic factors are important in the etiology of most mental disorders, the relatively high rates of discordance among identical twins, particularly for depression and other stress-related syndromes, clearly indicate the importance of additional mechanisms. Environmental factors such as stress are known to play a role in the onset of these illnesses. Exposure to such environmental insults induces stable changes in gene expression, neural circuit function, and ultimately behavior, and these maladaptations appear distinct between developmental versus adult exposures. Increasing evidence indicates that these sustained abnormalities are maintained by epigenetic modifications in specific brain regions. Indeed, transcriptional dysregulation and the aberrant epigenetic regulation that underlies this dysregulation is a unifying theme in psychiatric disorders. Here, we provide a progress report of epigenetic studies of the three major psychiatric syndromes, depression, schizophrenia, and bipolar disorder. We review the literature derived from animal models of these disorders as well as from studies of postmortem brain tissue from human patients. While epigenetic studies of mental illness remain at early stages, understanding how environmental factors recruit the epigenetic machinery within specific brain regions to cause lasting changes in disease susceptibility and pathophysiology is revealing new insight into the etiology and treatment of these conditions.


title: On the kinematic modeling and control of a mobile platform equipped with steering wheels and movable legs

abstract: Mobile platforms equipped with several steering wheels are known to be omnidirectional, i.e., able to independently translate and rotate on the plane. As an improvement to this design, the Justin mobile platform also possesses the ability to vary its footprint over time by extending/retracting the wheel legs during motion. In this paper, we discuss the kinematic modeling and control issues for such a platform. The goal is to obtain a tracking controller which is able to realize an arbitrary linear/angular platform motion while, at the same time, independently expanding/retracting each leg. Experimental results support the proposed approach.


Bilingual Lexicon Induction from Non-Parallel Data with Minimal Supervision.


SReN: Shape Regression Network for Comic Storyboard Extraction.


Protecting C++ Dynamic Dispatch Through VTable Interleaving


title: Economic load dispatch of wind thermal integrated system using dragonfly algorithm

abstract: Majority of electricity produced across world is from thermal power plants and they causes environmental pollution. With the increased awareness regarding environmental protection several countries has allotted share for electricity generation from wind power. As wind power is a clean energy, wind thermal integrated systems reduce emission. Wind integrated system are considered in this study along with valve point effect. The economic dispatch problem becomes highly nonlinear while considering valve point effect. The situation becomes more stringent when intermittent wind power is integrated with thermal system. In this paper Dragonfly Algorithm is implemented to solve the economic load dispatch problem with valve point effect. This dispatch model includes both Thermal and Wind farm. The stochastic nature of wind is modeled using weibull distribution function and over estimation/under estimation cost is analyzed using closed integral function. The model is tested on a modified IEEE-30 bus system comprising 6 thermal generating units and one wind farm. The performance of Dragonfly Algorithm is validated by comparing with other metaheuristic algorithm on the test systems. The proposed dispatch model can minimize the generating cost and enhance the renewable power consumption capacity. The MATLAB software 2010b has been used to simulate the model. The simulation demonstrates that Dragonfly Algorithm is capable of finding global optimum solution and is free from local optima problem as compare to other traditional and metaheuristic optimization techniques.


The DeLone and McLean Model of Information Systems Success – Original and Updated Models


title: Evaluation Of Machine Translation

abstract: This paper reports results of the 1992 Evaluation of machine translation (MT) systems in the DARPA MT initiative and results of a Pre-test to the 1993 Evaluation. The DARPA initiative is unique in that the evaluated systems differ radically in languages translated, theoretical approach to system design, and intended end-user application. In the 1992 suite, a Comprehension Test compared the accuracy and interpretability of system and control outputs; a Quality Panel for each language pair judged the fidelity of translations from each source version. The 1993 suite evaluated adequacy and fluency and investigated three scoring methods.


Morpheme-based automatic speech recognition for a morphologically rich language - Amharic.


Static Versus Dynamic Stretching Effect on Agility Performance


title: A 1.2–12 GHz Sliced Notch Antenna Array

abstract: Historically, Vivaldi arrays are known to suffer from high cross-polarization when scanning in the nonprincipal planes—a fault without a universal solution. In this paper, a solution to this issue is proposed in the form of a new Vivaldi-type array with low cross-polarization termed the Sliced Notch Antenna (SNA) array. For the first proof-of-concept demonstration, simulations and measurements are comparatively presented for two single-polarized $19 \times 19$ arrays—the proposed SNA and its Vivaldi counterpart—each operating over a 1.2–12 GHz (10:1) band. Both arrays are built using typical vertically integrated printed-circuit board cards, and are designed to exhibit VSWR $\theta \!=\!45 {^{\circ }}$ polarization purity improvement at the high frequency. Moreover, the SNA element also: 1) offers better suppression of classical Vivaldi E-plane scan blindnesses; 2) requires fewer plated through vias for stripline-based designs; and 3) allows relaxed adjacent element electrical contact requirements for dual-polarized arrangements.


title: Interactive Transfer Function Design Based on Editing Direct Volume Rendered Images

abstract: Direct volume rendered images (DVRIs) have been widely used to reveal structures in volumetric data. However, DVRIs generated by many volume visualization techniques can only partially satisfy users' demands. In this paper, we propose a framework for editing DVRIs, which can also be used for interactive transfer function (TF) design. Our approach allows users to fuse multiple features in distinct DVRIs into a comprehensive one, to blend two DVRIs, and/or to delete features in a DVRI. We further present how these editing operations can generate smooth animations for focus + context visualization. Experimental results on some real volumetric data demonstrate the effectiveness of our method.


title: Better bitmap performance with Roaring bitmaps

abstract: Bitmap indexes are commonly used in databases and search engines. By exploiting bit-level parallelism, they can significantly accelerate queries. However, they can use much memory, and thus, we might prefer compressed bitmap indexes. Following Oracle's lead, bitmaps are often compressed using run-length encoding RLE. Building on prior work, we introduce the Roaring compressed bitmap format: it uses packed arrays for compression instead of RLE. We compare it to two high-performance RLE-based bitmap encoding techniques: Word Aligned Hybrid compression scheme and Compressed 'n' Composable Integer Set. On synthetic and real data, we find that Roaring bitmaps 1 often compress significantly better e.g., 2× and 2 are faster than the compressed alternatives up to 900× faster for intersections. Our results challenge the view that RLE-based bitmap compression is best. Copyright © 2015 John Wiley & Sons, Ltd.


title: Combination of Neural Networks and Conditional Random Fields for Efficient Resume Parsing

abstract: Resume parsing is a technique to extract useful information from resumes for further processing such as resume ranking and selection. Different companies process thousands of resumes during their recruitment process using traditional methods like manual processing and by providing unique resume templates to applicants. The current job recruitment horizon demands better approaches for efficient resume parsing technologies and methods. Even though there are many elementary techniques for parsing the structured documents, they are not suitable for parsing unstructured documents like resumes. The ongoing approaches for resume parsing mainly use regular expressions, chunking, keyword based models and entity recognition models. Relevant to this context, this paper proposes a system for resume parsing using deep learning models such as the convolutional neural network (CNN), Bi-LSTM (Bidirectional Long Short-Term Memory) and Conditional Random Field (CRF). CNN Model is used for classifying different segments in a resume. CRF and Bi-LSTM-CNN models were used for sequence labeling inorder to tag different entities. Pre-trained Glove model is used for word embedding. The proposed system could classify a resume into three segments and extract 23 fields.


title: The Role of Social Media in Promoting Special Events: Acceptance of Facebook ‘Events’

abstract: This study examines Facebook ‘events’ as a medium for promoting special events to consumers. This study proposes a Social Technology Acceptance Model, an extension of the TAM model, to examine the influence of trust, expected relationships and perceived enjoyment in forming consumer attitudes towards Facebook and consumers intentions to attend an event. Data was collected through an online survey administered through special event organizer’s Facebook ‘Pages’. Findings of the study suggest that users’ trust and expected relationship through Facebook had a significant effect on users’ acceptance of Facebook and their intended offline behaviour to attend the event. Practical and theoretical implications are discussed.


title: Determination of essential and non-essential elements in herbal tea and camomile by polarised X rays fluorescence spectrometer (EDPXRF)

abstract: Essential and non-essential elements in the parts (leaves, fruits) of several kinds of herbal tea and camomile marked in Italy were determined by polarised X rays fluorescence spectrometer (EDPXRF). The quality of data was assured by calibrating the instrument with certified reference materials. The elements are found to be present in different plants in various proportions depending to soil composition and the climate in which the plant grows.


title: SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

abstract: A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circui...


title: Automatic solid decomposition and reduction for non-manifold geometric model generation

abstract: Abstract Oftentimes the level of complexity of a structure requires the analyst to idealize the finite element model, so that a solution may be obtained within a reasonable time scale. This paper describes some operations that have been defined and implemented which allow the user to reduce the dimensionality of geometric models automatically using a novel decomposition and reduction method. The model is first decomposed into simpler solids, and these solids undergo a reduction process to extract their midsurfaces. In some cases, not every part of the model can be reduced and thus, a non-manifold geometric model, suitable for mixed finite element modeling, is generated.


title: AEG: Automatic Exploit Generation

abstract: The automatic exploit generation challenge is given a program, automatically find vulnerabilities and generate exploits for them. In this paper we present AEG, the first end-to-end system for fully automatic exploit generation. We used AEG to analyze 14 open-source projects and successfully generated 16 control flow hijacking exploits. Two of the generated exploits (expect-5.43 and htget-0.93) are zero-day exploits against unknown vulnerabilities. Our contributions are: 1) we show how exploit generation for control flow hijack attacks can be modeled as a formal verification problem, 2) we propose preconditioned symbolic execution, a novel technique for targeting symbolic execution, 3) we present a general approach for generating working exploits once a bug is found, and 4) we build the first end-to-end system that automatically finds vulnerabilities and generates exploits that produce a shell.


title: Misrepresentation in Planning: Reference Class Forecasting in Practice

abstract: The American Planning Association recently endorsed a new forecasting method called reference class forecasting, which is based on theories of planning and decision-making that won the 2002 Nobel prize in economics. This paper details the method and describes the first instance of reference class forecasting in planning practice. First, the paper documents that inaccurate projections of costs, demand, and other impacts of plans are a major problem in planning. Second, the paper explains inaccuracy in terms of optimism bias and strategic misrepresentation. Third, the theoretical basis is presented for reference class forecasting, which achieves accuracy in projections by basing them on actual performance in a reference class of comparable actions and thereby bypassing both optimism bias and strategic misrepresentation. Fourth, the paper presents the first case of practical reference class forecasting, which concerns cost projections for planning of large transportation infrastructure investments in the UK, including the Edinburgh Tram and London's £15 billion Crossrail project. Finally, potentials for and barriers to reference class forecasting are assessed.


title: A survey on context awareness

abstract: We have reviewed the research of context awareness of recent years in this paper. First of all, we describe the definitions of context and context awareness which are made by different scholars in different fields, and propose our own definition. In addition, different classifications of context and context awareness are analyzed. We focus on context acquisition and sensing, context modeling and representation, context filtering and fusion, context storage and retrieval in context awareness computing. Finally, the context awareness application of domestic and foreign are discussed, and the development of the context awareness is prospected.


title: CLUSTERING POSTS IN ONLINE DISCUSSION FORUM THREADS

abstract: Online discussion forums are considered a challenging repository for data mining tasks. Forums usually contain hundreds of threads which in turn consist of hundreds, or even thousands, of posts. Clustering posts can be used to discover outlier and off-topic posts and would provide better visualization and exploration of online threads.In this paper, we propose the Leader-based Post Clustering (LPC), a modification to the Leader algorithm to be applied to the domain of clustering posts in threads of discussion boards. We also suggest using asymmetric pair-wise distances to measure the dissimilarity between posts. We further investigate the effect of indirect distance between posts, and how to calibrate it with the direct distance. In order to evaluate the proposed methods, we conduct experiments using artificial and real threads extracted from Slashdot and Ciao discussion forums. Experimental results demonstrate the effectiveness of the LPC algorithm when using the linear combination of direct and indirect distances, as well as using an averaging approach to evaluate a representative indirect distance. Furthermore, the results show the potential of the LPC algorithm for detecting off-topic or outlier posts compared with two state-of-the-art methods for off-topic post detection.


title: Fotiadis, “Artificial neural networks for solving ordinary and partial differential equations

abstract: We present a method to solve initial and boundary value problems using artificial neural networks. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the initial/boundary conditions and contains no adjustable parameters. The second part is constructed so as not to affect the initial/boundary conditions. This part involves a feedforward neural network containing adjustable parameters (the weights). Hence by construction the initial/boundary conditions are satisfied and the network is trained to satisfy the differential equation. The applicability of this approach ranges from single ordinary differential equations (ODE's), to systems of coupled ODE's and also to partial differential equations (PDE's). In this article, we illustrate the method by solving a variety of model problems and present comparisons with solutions obtained using the Galekrkin finite element method for several cases of partial differential equations. With the advent of neuroprocessors and digital signal processors the method becomes particularly interesting due to the expected essential gains in the execution speed.


title: Procedural Dungeon Generation Analysis and Adaptation

abstract: The objective of this article is to analyze several approaches that produce a particular form of procedural content in games - dungeon maps - and adapt the work to both 2D and 3D game projects. Procedural video game content provides unique levels for users to experience, reduced allocated memory, and more time for developers to focus on other aspects of a game. The work presented consists of two parts: the implementation and analysis of five different algorithmic approaches that generate a 2D dungeon map, consisting of traversable rooms and corridors; and the application of the original code to a 3D game environment in Epic Game's Unreal Engine 4.


title: Ultrasound Combined with Microbubbles Enhances the Effects of Methylprednisolone in Lipopolysaccharide-Induced Human Mesangial Cells.

abstract: A novel drug delivery system mediated by ultrasound (US) combined with microbubbles (MBs) (US+MB) could improve local drug concentration to enhance its efficacy. To investigate the influence of US+MB on methylprednisolone (MP), the effect of US+MB combined with MP (US+MB+MP) on lipopolysaccharide (LPS)-induced human mesangial cells (HMCs) and the underlying mechanism were explored in this study. The results revealed that HMCs treated with LPS underwent significant proliferation and exhibited an increase in nuclear transcription factor-κB (NF-κB) and transforming growth factor-β1 (TGF-β1) expression and a decrease in cellular apoptosis. This effect was significantly inhibited by MP (30-100 μg/ml), US combined with MBs (3.22 × 107 and 8.05 × 107 bubbles/ml), and US combined with both MBs (1.29 × 107 bubbles/ml) and MP (12 μg/ml) (US+MB1+MP12). The effect of US+MB1+MP12 was better than the effect of 12 μg/ml of MP alone and was similar to the effect of 100 μg/ml of MP. Additionally, the intracellular free MP content was significantly higher in the US+MB1+MP12 group than in the MP12 group. US combined with MBs not only inhibited LPS-induced HMC proliferation and NF-κB and TGF-β1 expression and increased cellular apoptosis but also synergized with the pharmacologic effect of MP. The mechanism is partially due to the US-assisted MB local drug delivery and the anti-inflammatory effect induced by US combined with MBs.


title: Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net

abstract: Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time. Therefore, top-performing deep models of high depth and complexity are not well suited for these applications with the limitations on the inference time. In order to further improve the neural networks' performance given the time and computational limitations, we propose an approach that exploits a cumbersome net to help train the lightweight net for prediction. We dub the whole process rocket launching, where the cumbersome booster net is used to guide the learning of the target light net throughout the whole training process. We analyze different loss functions aiming at pushing the light net to behave similarly to the booster net, and adopt the loss with best performance in our experiments. We use one technique called gradient block to improve the performance of the light net and booster net further. Experiments on benchmark datasets and real-life industrial advertisement data present that our light model can get performance only previously achievable with more complex models.


title: 5G-Enabled Cooperative Intelligent Vehicular (5GenCIV) Framework: When Benz Meets Marconi

abstract: As one of the most popular social media platforms today, Twitter provides people with an effective way to communicate and interact with each other. Through these interactions, influence among users gradually emerges and changes people's opinions. Although previous work has studied interpersonal influence as the probability of activating others during information diffusion, they ignore an important fact that information diffusion is the result of influence, while dynamic interactions among users produce influence. In this article, the authors propose a novel temporal influence model to learn users' opinion behaviors regarding a specific topic by exploring how influence emerges during communications. The experiments show that their model performs better than other influence models with different influence assumptions when predicting users' future opinions, especially for the users with high opinion diversity.


title: Nonsurgical correction of a severe anterior deep overbite accompanied by a gummy smile and posterior scissor bite using a miniscrew-assisted straight-wire technique in an adult high-angle case

abstract: In the present report, we describe the successful use of miniscrews to achieve vertical control in combination with the conventional sliding MBT™ straight-wire technique for the treatment of a 26-year-old Chinese woman with a very high mandibular plane angle, deep overbite, retrognathic mandible with backward rotation, prognathic maxilla, and gummy smile. The patient exhibited skeletal Class II malocclusion. Orthodontic miniscrews were placed in the maxillary anterior and posterior segments to provide rigid anchorage and vertical control through intrusion of the incisors and molars. Intrusion and torque control of the maxillary incisors relieved the deep overbite and corrected the gummy smile, while intrusion of the maxillary molars aided in counterclockwise rotation of the mandibular plane, which consequently resulted in an improved facial profile. After 3.5 years of retention, we observed a stable, well-aligned dentition with ideal intercuspation and more harmonious facial contours. Thus, we were able to achieve a satisfactory occlusion, a significantly improved facial profile, and an attractive smile for this patient. The findings from this case suggest that nonsurgical correction using miniscrew anchorage is an effective approach for camouflage treatment of high-angle cases with skeletal Class II malocclusion.


title: Integrated circuit with memristor emulator array and neuron circuits for neuromorphic pattern recognition

abstract: This paper details an array of switch resistor based memristor emulators with integrate & fire based neuron ASIC for the development of memristor based pattern recognition. The designed ASIC has 4 memristor emulators with a conductance range from 195 nS to 190 uS; processing has been planned to be off-chip to get the freedom of programmability of any function in ASIC. The ASIC has 2 integrate and fire (I & F) neuron circuits which are planned to be used in conjunction with memristors in a large multiple chip network for pattern recognition. This paper explains the memristor emulator, I & F neurons and an algorithm of pattern recognition simulated in Ltspice. The ASIC has been fabricated in AMS 350nm process.


title: 5G cellular network integration with SDN: Challenges, issues and beyond

abstract: The tremendous growth in communication technology is shaping a hyper-connected network where billions or connected devices are producing a huge volume of data. Cellular and mobile network is a major contributor towards this technology shift and require new architectural paradigm to provide low latency, high performance in a resource constrained environment. 5G technology deployment with fully IP-based connectivity is anticipated by 2020. However, there is no standard established for 5G technology and many efforts are being made to establish a unified 5G stander. In this context, variant technology such as Software Defined Network (SDN) and Network Function virtualization (NFV) are the best candidate. SDN dissociate control plane from data plane and network management is done on the centralized control plane. In this paper, a survey on state of the art on the 5G integration with the SDN is presented. A comprehensive review is presented for the different integrated architectures of 5G wireless network and the generalized solutions over the period 2010–2016. This comparative analysis of the existing solutions of SDN-based cellular network (5G) implementations provides an easy and concise view of the emerging trends by 2020.


title: Who is answering whom? Finding “Reply-To” relations in group chats with deep bidirectional LSTM networks

abstract: Social networks facilitate communication among Internet users while generating large volumes of online short-text conversations every day. This leads to a huge number of free-style asynchronous conversations where multiple users are involved and multiple topics are discussed at the same time in the same place, e.g., an instant group chat in WeChat. Here emerges an interesting problem: as a result of a large number of users and topics, the conversation structure may get into a mess, which often interferes with the acquisition of messages users are interested in. For example, when a user enters a conversation, (s)he usually does not want to read all the historical messages, but just hope to get the messages that are the most relevant to some messages (s)he cares about. Therefore, it is an essential task to understand the logical correlations among messages, which benefits text mining, natural language processing, and web intelligence techniques. In this paper, we focus on “reply-to” relations, such as QA The other two versions are end-to-end models, which are based on the word level and the sentence level, respectively. Finally, experimental results conducted on two real-world group chat data sets demonstrate the effectiveness of the proposed model.


title: Monotonic Chunkwise Attention

abstract: Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction. To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed. We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time. When applied to online speech recognition, we obtain state-of-the-art results and match the performance of a model using an offline soft attention mechanism. In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model.


The Mobile Connection : The Cell Phone ’ s Impact on Society


title: Measuring Mental Health Provider-Based Stigma: Development and Initial Psychometric Testing of a Self-Assessment Instrument

abstract: This article describes the development and initial psychometric testing of the Mental Health Provider Self-Assessment of Stigma Scale (MHPSASS), a 20-item instrument crafted in reflection of Charles’ (Social Work in Mental Health 11:360–375, 2013) empirically derived, experience-based, five-themed model of provider stigmatization. Following model and item review by construct experts, 220 mental health service providers in Virginia’s public mental health centers and in-patient facilities completed the survey package. Results indicate the refined MHPSASS is a reliable measure of provider-based stigma with promising face and content validity. However, rather than they hypothesized five-factors, analysis indicates a four-factor solution, a key finding signaling a discrepancy between what providers endorse and what clients’ experience. Notably absent from the MHPSASS’ were items related to blame and shame, in contrast to the experience of clients and families. Further refinement is indicated, particularly reconsideration of blame and shame items due to their practical and theoretical significance.


title: Learning Treatment Regimens from Electronic Medical Records

abstract: Appropriate treatment regimens play a vital role in improving patient health status. Although some achievements have been made, few of the recent studies of learning treatment regimens have exploited different kinds of patient information due to the difficulty in adopting heterogeneous data to many data mining methods. Moreover, current studies seem too rigid with fixed intervals of treatment periods corresponding to the varying lengths of hospital stay. To this end, this work proposes a generic data-driven framework which can derive group-treatment regimens from electronic medical records by utilizing a mixed-variate restricted Boltzmann machine and incorporating medical domain knowledge. We conducted experiments on coronary artery disease as a case study. The obtained results show that the framework is promising and capable of assisting physicians in making clinical decisions.


title: Wireless Software Defined Networking: A Survey and Taxonomy

abstract: One of the primary architectural principles behind the Internet is the use of distributed protocols, which facilitates fault tolerance and distributed management. Unfortunately, having nodes (i.e., switches and routers) perform control decisions independently makes it difficult to control the network or even understand or debug its overall emergent behavior. As a result, networks are often inefficient, unstable, and fragile. This Internet architecture also poses a significant, often insurmountable, challenge to the deployment of new protocols and evolution of existing ones. Software defined networking (SDN) is a recent networking architecture with promising properties relative to these weaknesses in traditional networks. SDN decouples the control plane, which makes the network forwarding decisions, from the data plane, which mainly forwards the data. This decoupling enables more centralized control where coordinated decisions directly guide the network to desired operating conditions. Moreover, decoupling the control enables graceful evolution of protocols, and the deployment of new protocols without having to replace the data plane switches. In this survey, we review recent work that leverages SDN in wireless network settings, where they are not currently widely adopted or well understood. More specifically, we evaluate the use of SDN in four classes of popular wireless networks: cellular, sensor, mesh, and home networks. We classify the different advantages that can be obtained by using SDN across this range of networks, and hope that this classification identifies unexplored opportunities for using SDN to improve the operation and performance of wireless networks.


title: Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation

abstract: This paper proposes a first attempt to build an end-to-end speech-to-text translation system, which does not use source language transcription during learning or decoding. We propose a model for direct speech-to-text translation, which gives promising results on a small French-English synthetic corpus. Relaxing the need for source language transcription would drastically change the data collection methodology in speech translation, especially in under-resourced scenarios. For instance, in the former project DARPA TRANSTAC (speech translation from spoken Arabic dialects), a large effort was devoted to the collection of speech transcripts (and a prerequisite to obtain transcripts was often a detailed transcription guide for languages with little standardized spelling). Now, if end-to-end approaches for speech-to-text translation are successful, one might consider collecting data by asking bilingual speakers to directly utter speech in the source language from target language text utterances. Such an approach has the advantage to be applicable to any unwritten (source) language.


title: A spectral algorithm for learning class-based n -gram models of natural language

abstract: The Brown clustering algorithm (Brown et al., 1992) is widely used in natural language processing (NLP) to derive lexical representations that are then used to improve performance on various NLP problems. The algorithm assumes an underlying model that is essentially an HMM, with the restriction that each word in the vocabulary is emitted from a single state. A greedy, bottom-up method is then used to find the clustering; this method does not have a guarantee of finding the correct underlying clustering. In this paper we describe a new algorithm for clustering under the Brown et al. model. The method relies on two steps: first, the use of canonical correlation analysis to derive a low-dimensional representation of words; second, a bottom-up hierarchical clustering over these representations. We show that given a sufficient number of training examples sampled from the Brown et al. model, the method is guaranteed to recover the correct clustering. Experiments show that the method recovers clusters of comparable quality to the algorithm of Brown et al. (1992), but is an order of magnitude more efficient.


title: Jena-HBase: A Distributed, Scalable and Effcient RDF Triple Store

abstract: Lack of scalability is one of the most significant problems faced by single machine RDF data stores. The advent of Cloud Computing has paved a way for a distributed ecosystem of RDF triple stores that can potentially allow up to a planet scale storage along with distributed query processing capabilities. Towards this end, we present Jena-HBase, a HBase backed triple store that can be used with the Jena framework. Jena-HBase provides end-users with a scalable storage and querying solution that supports all features from the RDF specification.


title: Molecular Assembly on Two-Dimensional Materials

abstract: Molecular self-assembly is a well-known technique to create highly functional nanostructures on surfaces. Self-assembly on two-dimensional materials is a developing field and has already resulted in the discovery of several rich and interesting phenomena. Here, we review this progress with an emphasis on the electronic properties of the adsorbates and the substrate in well-defined systems, as unveiled by scanning tunneling microscopy (STM). We cover three aspects of the self-assembly. The first one focuses on non-covalent self-assembly dealing with site-selectivity due to inherent moire pattern present on 2D materials deposited on substrates. Modification of intermolecular interactions and molecule-substrate interactions influences the assembly drastically and 2D materials can also be used as a platform to carry out covalent and metal-coordinated assembly. The second part deals with the electronic properties of molecules adsorbed on 2D materials. By virtue of being inert and possessing low density of states near the Fermi level, 2D materials decouple molecules electronically from the underlying metal substrate and allow high-resolution spectroscopy and imaging of molecular orbitals. The moire pattern on the 2D materials causes site-selective gating and charging of molecules in some cases. The last section covers the effects of self-assembled organic molecules on the electronic properties of graphene as revealed by spectroscopy and electrical transport measurements. Non-covalent functionalization of 2D materials has already been applied for their application as catalysts and sensors. With the current surge of activity on building van der Waals heterostructures from 2D materials, molecular self-assembly has the potential to add an extra level of flexibility and functionality for applications ranging from flexible electronics and OLEDs to novel electronic devices and spintronics.


title: A Modification of LambdaMART to Handle Noisy Crowdsourced Assessments

abstract: We consider noisy crowdsourced assessments and their impact on learning-to-rank algorithms. Starting with EM-weighted assessments, we modify LambdaMART in order to use smoothed probabilistic preferences over pairs of documents, directly as input to the ranking algorithm.


title: 3D object retrieval using salient views

abstract: This paper presents a method for selecting salient 2D views to describe 3D objects for the purpose of retrieval. The views are obtained by first identifying salient points via a learning approach that uses shape characteristics of the 3D points (Atmosukarto and Shapiro in International workshop on structural, syntactic, and statistical pattern recognition, 2008; Atmosukarto and Shapiro in ACM multimedia information retrieval, 2008). The salient views are selected by choosing views with multiple salient points on the silhouette of the object. Silhouette-based similarity measures from Chen et al. (Comput Graph Forum 22(3):223–232, 2003) are then used to calculate the similarity between two 3D objects. Retrieval experiments were performed on three datasets: the Heads dataset, the SHREC2008 dataset, and the Princeton dataset. Experimental results show that the retrieval results using the salient views are comparable to the existing light field descriptor method (Chen et al. in Comput Graph Forum 22(3):223–232, 2003), and our method achieves a 15-fold speedup in the feature extraction computation time.


title: Learning analytics for workplace and professional learning

abstract: Recognizing the need for addressing the rather fragmented character of research in this field, we have held a workshop on learning analytics for workplace and professional learning at the Learning Analytics and Knowledge (LAK) Conference. The workshop has taken a broad perspective, encompassing approaches from a number of previous traditions, such as adaptive learning, professional online communities, workplace learning and performance analytics. Being co-located with the LAK conference has provided an ideal venue for addressing common challenges and for benefiting from the strong research on learning analytics in other sectors that LAK has established. Learning Analytics for Workplace and Professional Learning is now on the research agenda of several ongoing EU projects, and therefore a number of follow-up activities are planned for strengthening integration in this emerging field.


title: RECONCEPTUALIZING USERS AS SOCIAL ACTORS IN INFORMATION SYSTEMS RESEARCH 1

abstract: A concept of the user is fundamental to much of the research and practice of information systems design, development, and evaluation. User-centered information studies have relied on individualistic cognitive models to carefully examine the criteria that influence the selection of information and communication technologies (ICTs) that people make. In many ways, these studies have improved our understanding of how a good information resource fits the people who use it. However, research approaches based on an individualistic user concept are limited. ::: ::: In this paper, we examine the theoretical constructs that shape this user concept and contrast these with alternative views that help to reconceptualize the user as a social actor. Despite pervasive ICT use, social actors are not primarily users of ICTs. Most people who use ICT applications utilize multiple applications, in various roles, and as part of their efforts to produce goods and services while interacting with a variety of other people, and often in multiple social contexts. Moreover, the socially thin user construct limits our understanding of information selection, manipulation, communication, and exchange within complex social contexts. Using analyses from a recent study of online information service use, we develop an institutionalist concept of a social actor whose everyday interactions are infused with ICT use. We then encourage a shift from the user concept to a concept of the social actor in IS research. We suggest that such a shift will sharpen perceptions of how organizational contexts shape ICT-related practices, and at the same time will help researchers more accurately portray the complex and multiple roles that people fulfill while adopting, adapting, and using information systems.


title: A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing

abstract: Accurate forecasting of sales/consumption is particularly important for marketing because this information can be used to adjust marketing budget allocations and overall marketing strategies. Recently, online social platforms have produced an unparalleled amount of data on consumer behavior. However, two challenges have limited the use of these data in obtaining meaningful business marketing insights. First, the data are typically in an unstructured format, such as texts, images, audio, and video. Second, the sheer volume of the data makes standard analysis procedures computationally unworkable. In this study, we combine methods from cloud computing, machine learning, and text mining to illustrate how online platform content, such as Twitter, can be effectively used for forecasting. We conduct our analysis on a significant volume of nearly two billion Tweets and 400 billion Wikipedia pages. Our main findings emphasize that, by contrast to basic surface-level measures such as the volume of or sentiments in Tweets, the information content of Tweets and their timeliness significantly improve forecasting accuracy. Our method endogenously summarizes the information in Tweets. The advantage of our method is that the classification of the Tweets is based on what is in the Tweets rather than preconceived topics that may not be relevant. We also find that, by contrast to Twitter, other online data (e.g., Google Trends, Wikipedia views, IMDB reviews, and Huffington Post news) are very weak predictors of TV show demand because users tweet about TV shows before, during, and after a TV show, whereas Google searches, Wikipedia views, IMDB reviews, and news posts typically lag behind the show.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0972 .


title: Design and implementation of interactive product manual system using chatbot and sensed data

abstract: Current electric appliances provide various functions to make users' lives more comfortable and fruitful. However, product instructions have become complicated with the increasing number of functions and the users take a long time to understand each electric appliance. To solve this problem, we propose an interactive product instruction system using a chatbot. A chatbot is a framework that performs interactive talks with the users through messaging applications, e.g., Slack, Facebook messenger, LINE, and others. Users can utilize their preferred interfaces to understand how to use the electric appliances by talking to the system. To test the applicability of the system, we conducted an experiment, and the results demonstrated its usefulness.


title: High frequency battery impedance measurements for EMI prediction

abstract: In embedded applications which include power converters, the battery that provides the energy is a part of the EMI path. As conducted emissions generated by power converters spread into a large frequency band, the battery characterization should be extended to cover this range of frequencies. In this paper, a method to measure the battery impedance using an impedance analyzer is explained and applied to a cell-phone battery from 1MHz to 100 MHz. In this frequency range, measurements show that the battery impedance is unrelated to the battery state of charge and that a model including just inductances and resistances matches very well with the measured behavior. The measured impedance is validated by predicting the frequency spectrum of the battery's voltage when feeding a switched-mode power converter. The converter switches at 1.2MHz and the related harmonics extend beyond 100 MHz. The measured and predicted voltages match up to 80 MHz.


title: Short-term effects of complex and contrast training in soccer players' vertical jump, sprint, and agility abilities.

abstract: The purpose of this study was to analyze the short-term effects of complex and contrast training (CCT) on vertical jump (squat and countermovement jump), sprint (5 and 15 m), and agility (505 Agility Test) abilities in soccer players. Twenty-three young elite Portuguese soccer players (age 17.4 ± 0.6 years) were divided into 2 experimental groups (G1, n = 9, and G2, n = 8) and 1 control group (G3, n = 6). Groups G1 and G2 have done their regular soccer training along with a 6-week strength training program of CCT, with 1 and 2 training sessions·wk-1, respectively. G3 has been kept to their regular soccer training program. Each training session from the CCT program was organized in 3 stations in which a general exercise, a multiform exercise, and a specific exercise were performed. The load was increased by 5% from 1 repetition maximum each 2 weeks. Obtained results allowed identifying (a) a reduction in sprint times over 5 and 15 m (9.2 and 6.2% for G1 and 7.0 and 3.1%, for G2; p < 0.05) and (2) an increase on squat and jump (12.6% for G1 and 9.6% for G2; p < 0.05). The results suggested that the CCT induced the performance increase in 5 and 15 m sprint and in squat jump. Vertical jump and sprint performances after CCT program were not influenced by the number of CCT sessions per week (1 or 2 sessions·wk-1). From the obtained results, it was suggested that the CCT is an adequate training strategy to develop soccer players' muscle power and speed.


title: Functional programming with structured graphs

abstract: This paper presents a new functional programming model for graph structures called structured graphs. Structured graphs extend conventional algebraic datatypes with explicit definition and manipulation of cycles and/or sharing, and offer a practical and convenient way to program graphs in functional programming languages like Haskell. The representation of sharing and cycles (edges) employs recursive binders and uses an encoding inspired by parametric higher-order abstract syntax. Unlike traditional approaches based on mutable references or node/edge lists, well-formedness of the graph structure is ensured statically and reasoning can be done with standard functional programming techniques. Since the binding structure is generic, we can define many useful generic combinators for manipulating structured graphs. We give applications and show how to reason about structured graphs.


abstract: This paper presents using of JSBSim library for flight dynamics modelling of a mini-UAV (Unmanned Aerial Vehicle). The first part of the paper is about general information of UAVs and about the fundamentals of airplane flight mechanics, forces, moments, and the main components of typical aircraft. The main section briefly describes a flight dynamics model and summarizes the information about JSBSim library. Then, a way of using the library for the modelling of a mini-UAV is shown. A basic script for lifting and stabilization of the UAV has been developed and described. Finally, the results of JSBSim test are discussed.


title: Testing Security Properties of Protocol Implementations - a Machine Learning Based Approach

abstract: Security and reliability of network protocol implementations are essential for communication services. Most of the approaches for verifying security and reliability, such as formal validation and black-box testing, are limited to checking the specification or conformance of implementation. However, in practice, a protocol implementation may contain engineering details, which are not included in the system specification but may result in security flaws. We propose a new learning-based approach to systematically and automatically test protocol implementation security properties. Protocols are specified using symbolic parameterized extended finite state machine (SP-EFSM) model, and an important security property - message confidentiality under the general Dolev-Yao attacker model - is investigated. The new testing approach applies black-box checking theory and a supervised learning algorithm to explore the structure of an implementation under test while simulating the teacher with a conformance test generation scheme. We present the testing procedure, analyze its complexity, and report experimental results.


title: Tracking Sentiment in Mail: How Genders Differ on Emotional Axes

abstract: With the widespread use of email, we now have access to unprecedented amounts of text that we ourselves have written. In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in many types of mail. We create a large word--emotion association lexicon by crowdsourcing, and use it to compare emotions in love letters, hate mail, and suicide notes. We show that there are marked differences across genders in how they use emotion words in work-place email. For example, women use many words from the joy--sadness axis, whereas men prefer terms from the fear--trust axis. Finally, we show visualizations that can help people track emotions in their emails.


title: Purification of the E. coli ogt gene product to homogeneity and its rate of action on O6-methylguanine, O6-ethylguanine and O4-methylthymine in dodecadeoxyribonucleotides.

abstract: Abstract ::: The E. coli gene ogt encodes the DNA repair protein O6-alkylguanine-DNA-alkyltransferase (O6-AlkG ATase). The protein coding region of the gene was cloned into a multicopy expression vector to obtain high yields of the enzyme (approximately 0.2% of total protein) which was purified to apparent homogeneity by affinity, molecular exclusion and reverse-phase chromatography. Good correlation was found between the determined and predicted amino acid compositions. The ability of the purified protein to act on O6-methylguanine (O6-MeG), O6-ethylguanine (O6-EtG) and O4-methylthymine (O4-MeT) in self-complementary dodecadeoxyribonucleotides was compared to that of 19 kDa fragment of the related ada-protein. With both proteins the rate order was O6-MeG greater than O6-EtG greater than O4-MeT, however, the ogt protein was found to repair O6-MeG, O6-EtG and O4-Met, 1.1, 173 and 84 times, respectively, faster than the ada protein.


title: An asymptotic theory for Cauchy-Euler differential equations with applications to the analysis of algorithms

abstract: Cauchy-Euler differential equations surfaced naturally in a number of sorting and searching problems, notably in quicksort and binary search trees and their variations. Asymptotics of coefficients of functions satisfying such equations has been studied for several special cases in the literature. We study in this paper a very general framework for Cauchy-Euler equations and propose an asymptotic theory that covers almost all applications where Cauchy-Euler equations appear. Our approach is very general and requires almost no background on differential equations. Indeed the whole theory can be stated in terms of recurrences instead of functions. Old and new applications of the theory are given. New phase changes of limit laws of new variations of quicksort are systematically derived. We apply our theory to about a dozen of diverse examples in quicksort, binary search trees, urn models, increasing trees, etc.


Empowering Students to Create Better Virtual Reality Applications: A Longitudinal Study of a VR Capstone Course


title: Simulation, control and construction of a four Mecanum-wheeled robot

abstract: This paper presents the process of simulation, control and construction of a four Mecanum-wheeled robot. Moreover, the kinematic model of the proposed robot is calculated to end of obtaining a better understanding about the robot's movement. In order to assess the effect of vibration on the motors' shafts of the robot and the hubs which are used as a connector between the shaft and the Mecanum wheel, the robot is simulated in MSC ADAMS. According to the obtained results from the proposed simulation, the material of the aforementioned hubs are considered as PTFE. Furthermore, in order to synchronize the robot's motors, a PID controller is designed for the robot. In order to estimate the performance of the designed PID controller for synchronizing the robot's motors, a co-simulation between MSC ADAMS and MATLAB software is done. The results which are obtained based on the proposed co-simulation prove the high performance of the designed controller in terms of the robot's motors synchronization. As well as the experimental results corroborate the acceptable performance of the designed PID controller in terms of robot's motors synchronization.


A Survey on Approaches to Text Mining using Information Extraction


title: Facial recognition with PCA and machine learning methods

abstract: Facial recognition is a challenging problem in image processing and machine learning areas. Since widespread applications of facial recognition make it a valuable research topic, this work tries to develop some new facial recognition systems that have both high recognition accuracy and fast running speed. Efforts are made to design facial recognition systems by combining different algorithms. Comparisons and evaluations of recognition accuracy and running speed show that PCA + SVM achieves the best recognition result, which is over 95% for certain training data and eigenface sizes. Also, PCA + KNN achieves the balance between recognition accuracy and running speed.


title: A surface-based Next-Best-View approach for automated 3D model completion of unknown objects

abstract: The procedure of manually generating a 3D model of an object is very time consuming for a human operator. Next-best- view (NBV) planning is an important aspect for automation of this procedure in a robotic environment. We propose a surface-based NBV approach, which creates a triangle surface from a real-time data stream and determines viewpoints similar to human intuition. Thereby, the boundaries in the surface are detected and a quadratic patch for each boundary is estimated. Then several viewpoint candidates are calculated, which look perpendicular to the surface and overlap with previous sensor data. A NBV is selected with the goal to fill areas which are occluded. This approach focuses on the completion of a 3D model of an unknown object. Thereby, the search space for the viewpoints is not restricted to a cylinder or sphere. Our NBV determination proves to be very fast, and is evaluated in an experiment on test objects, applying an industrial robot and a laser range scanner.


The Amount and Variation of Craniovertebral Angle Changes in College-aged Students Using One-strapped and Two-strapped Backpacks and Bags


title: Investigating the contribution of short wavelengths in the alerting effect of bright light

abstract: Abstract Introduction Short-wavelengths can have an acute impact on alertness, which is allegedly due to their action on intrinsically photosensitive retinal ganglion cells. Classical photoreceptors cannot, however, be excluded at this point in time as contributors to the alerting effect of light. The objective of this study was to compare the alerting effect at night of a white LED light source while wearing blue-blockers or not, in order to establish the contribution of short-wavelengths. Materials and methods 20 participants stayed awake under dim light ( 2 of unfiltered LED light, while group B (4M/6F) was required to wear blue-blocking glasses, while exposed to 1500 μW/cm 2 from the same light device in order to achieve 500 μW/cm 2 at eye level (as measured behind the glasses). Subjective alertness, energy, mood and anxiety were assessed for both nights at 23:30 h, 01:30 h and 03:30 h using a visual analog scale (VAS). Subjective sleepiness was assessed with the Stanford Sleepiness Scale (SSS). Subjects also performed the Conners' Continuous Performance Test II (CPT-II) in order to assess objective alertness. Mixed model analysis was used to compare VAS, SSS and CPT-II parameters. Results No difference between group A and group B was observed for subjective alertness, energy, mood, anxiety and sleepiness, as well as CPT-II parameters. Subjective alertness ( p p p Conclusions The current study shows that when sleepiness is high, the alerting effect of light can still be triggered at night in the absence of short-wavelengths with a 30 minute light pulse of 500 μW/cm 2 . This suggests that the underlying mechanism by which a brief polychromatic light exposure improves alertness is not solely due to short-wavelengths through intrinsically photosensitive retinal ganglion cells.


title: Extreme Network Compression via Filter Group Approximation

abstract: In this paper we propose a novel decomposition method based on filter group approximation, which can significantly reduce the redundancy of deep convolutional neural networks (CNNs) while maintaining the majority of feature representation. Unlike other low-rank decomposition algorithms which operate on spatial or channel dimension of filters, our proposed method mainly focuses on exploiting the filter group structure for each layer. For several commonly used CNN models, including VGG and ResNet, our method can reduce over 80% floating-point operations (FLOPs) with less accuracy drop than state-of-the-art methods on various image classification datasets. Besides, experiments demonstrate that our method is conducive to alleviating degeneracy of the compressed network, which hurts the convergence and performance of the network.


title: Zero Shot Transfer Learning for Robot Soccer

abstract: We present a method for doing zero-shot transfer of multi-agent policies as the number of teammates, opponents, and environment size varies. We apply our approach to RoboCup inspired test domains, where it is necessary for policies to adapt to changing numbers of robots due to in-game breakages. We introduce the concept of encoding not only the states as an image, but also the action space as a multi-channel image, which allows the state and action size to remain fixed across team size changes. We also introduce Fully Convolutional Q-Networks, which represent Q-functions in this space using Fully Convolutional Networks. We present results for zero-shot transfer of these policies across team sizes and field sizes, showing that performance remains consistent as both change.


title: Challenges in Exploiting Conversational Memory in Human-Agent Interaction

abstract: In human interactions, language is used to project and maintain a social identity over time. The way people speak with others and revisit language across repeated interactions helps to create rapport and develop a feeling of coordination between conversational partners. Memory of past conversations is the main mechanism that allows us to exploit and explore ways of speaking, given knowledge acquired in previous encounters. As such, we introduce an agent that uses its conversational memory to revisit shared history with users to maintain a coherent social relationship over time. In this paper, we describe the dialog management mechanisms to achieve these goals when applied to a robot that engages in social chit-chat. In a study lasting 14 days with 28 users, totaling 474 interactions, we find that it is difficult to leverage the shared history with individual users and to also accommodate to expected conversational coordination patterns. We discuss the implications of this finding for long-term human-agent interaction. In particular, we highlight the importance of topic modeling and signaling explicit recall of previous episodes. Moreover, the way that users contribute to interactions requires additional adaptation, indicating a significant challenge for language interaction designers.


title: Formation and suppression of acoustic memories during human sleep

abstract: Sleep and memory are deeply related, but the nature of the neuroplastic processes induced by sleep remains unclear. Here, we report that memory traces can be both formed or suppressed during sleep, depending on sleep phase. We played samples of acoustic noise to sleeping human listeners. Repeated exposure to a novel noise during Rapid Eye Movements (REM) or light non-REM (NREM) sleep leads to improvements in behavioral performance upon awakening. Strikingly, the same exposure during deep NREM sleep leads to impaired performance upon awakening. Electroencephalographic markers of learning extracted during sleep confirm a dissociation between sleep facilitating memory formation (light NREM and REM sleep) and sleep suppressing learning (deep NREM sleep). We can trace these neural changes back to transient sleep events, such as spindles for memory facilitation and slow waves for suppression. Thus, highly selective memory processes are active during human sleep, with intertwined episodes of facilitative and suppressive plasticity. Though memory and sleep are related, it is still unclear whether new memories can be formed during sleep. Here, authors show that people could learn new sounds during REM or light non-REM sleep, but that learning was suppressed when sounds were played during deep NREM sleep.


title: Emergence of scaling in random networks

abstract: Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.


title: The Canny Edge Detector Revisited

abstract: Canny (IEEE Trans. Pattern Anal. Image Proc. 8(6):679-698, 1986) suggested that an optimal edge detector should maximize both signal-to-noise ratio and localization, and he derived mathematical expressions for these criteria. Based on these criteria, he claimed that the optimal step edge detector was similar to a derivative of a gaussian. However, Canny's work suffers from two problems. First, his derivation of localization criterion is incorrect. Here we provide a more accurate localization criterion and derive the optimal detector from it. Second, and more seriously, the Canny criteria yield an infinitely wide optimal edge detector. The width of the optimal detector can however be limited by considering the effect of the neighbouring edges in the image. If we do so, we find that the optimal step edge detector, according to the Canny criteria, is the derivative of an ISEF filter, proposed by Shen and Castan (Graph. Models Image Proc. 54:112---133, 1992). ::: ::: In addition, if we also consider detecting blurred (or non-sharp) gaussian edges of different widths, we find that the optimal blurred-edge detector is the above optimal step edge detector convolved with a gaussian. This implies that edge detection must be performed at multiple scales to cover all the blur widths in the image. We derive a simple scale selection procedure for edge detection, and demonstrate it in one and two dimensions.


title: How To Login From an Internet Café Without Worrying About Keyloggers

abstract: Roaming users who use untrusted machines to access password protected accounts have few good options. An internet cafe machine can easily be running a keylogger. The roaming user has no reliable way of determining whether it is safe, and has no alternative to typing the password. We describe a simple trick the user can employ that is entirely effective in concealing the password. We verify its efficacy against the most popular keylogging programs.


title: Die-stacked DRAM caches for servers: hit ratio, latency, or bandwidth? have it all with footprint cache

abstract: Recent research advocates using large die-stacked DRAM caches to break the memory bandwidth wall. Existing DRAM cache designs fall into one of two categories --- block-based and page-based. The former organize data in conventional blocks (e.g., 64B), ensuring low off-chip bandwidth utilization, but co-locate tags and data in the stacked DRAM, incurring high lookup latency. Furthermore, such designs suffer from low hit ratios due to poor temporal locality. In contrast, page-based caches, which manage data at larger granularity (e.g., 4KB pages), allow for reduced tag array overhead and fast lookup, and leverage high spatial locality at the cost of moving large amounts of data on and off the chip. This paper introduces Footprint Cache, an efficient die-stacked DRAM cache design for server processors. Footprint Cache allocates data at the granularity of pages, but identifies and fetches only those blocks within a page that will be touched during the page's residency in the cache --- i.e., the page's footprint. In doing so, Footprint Cache eliminates the excessive off-chip traffic associated with page-based designs, while preserving their high hit ratio, small tag array overhead, and low lookup latency. Cycle-accurate simulation results of a 16-core server with up to 512MB Footprint Cache indicate a 57% performance improvement over a baseline chip without a die-stacked cache. Compared to a state-of-the-art block-based design, our design improves performance by 13% while reducing dynamic energy of stacked DRAM by 24%.


title: M-Flash: Fast Billion-scale Graph Computation Using a Bimodal Block Processing Model

abstract: Recent graph computation approaches have demonstrated that a single PC can perform efficiently on billion-scale graphs. While these approaches achieve scalability by optimizing I/O operations, they do not fully exploit the capabilities of modern hard drives and processors. To overcome their performance, in this work, we introduce the Bimodal Block Processing (BBP), an innovation that is able to boost the graph computation by minimizing the I/O cost even further. With this strategy, we achieved the following contributions: (1) M-Flash, the fastest graph computation framework to date; (2) a flexible and simple programming model to easily implement popular and essential graph algorithms, including the first single-machine billion-scale eigensolver; and (3) extensive experiments on real graphs with up to 6.6 billion edges, demonstrating M-Flash's consistent and significant speedup.


title: Screened poisson surface reconstruction

abstract: Poisson surface reconstruction creates watertight surfaces from oriented point sets. In this work we extend the technique to explicitly incorporate the points as interpolation constraints. The extension can be interpreted as a generalization of the underlying mathematical framework to a screened Poisson equation. In contrast to other image and geometry processing techniques, the screening term is defined over a sparse set of points rather than over the full domain. We show that these sparse constraints can nonetheless be integrated efficiently. Because the modified linear system retains the same finite-element discretization, the sparsity structure is unchanged, and the system can still be solved using a multigrid approach. Moreover we present several algorithmic improvements that together reduce the time complexity of the solver to linear in the number of points, thereby enabling faster, higher-quality surface reconstructions.


title: e-GovQual: A multiple-item scale for assessing e-government service quality

abstract: article i nfo Available online 25 September 2011 e-Government Service Quality Measurement Instrument A critical element in the evolution of governmental services through the internet is the development of sites that better serve the citizens' needs. To deliver superior service quality, we must first understand how citi- zens perceive and evaluate online. Citizen assessment is built on defining quality, identifying underlying di- mensions, and conceptualizing measurements of these e-government services. In this article an e-gov service quality model (e-GovQual) is conceptualized and then a multiple-item scale for measuring e-gov service quality of governmental sites where citizens seek either information or service, is developed, refined, validat- ed confirmed and tested.


title: Fabric defect detection using local homogeneity and morphological image processing

abstract: In this paper, a new fabric detect detection algorithm based on local homogeneity and mathematical morphology is presented. In a first step, the local homogeneity of each pixel is computed to construct a new homogeneity image denoted as (H-image). Then a classical histogram is computed for the H-image to choose an optimal thresholding value to produce a corresponding binary image, which will be used to extract the optimal size and the shape of the Structuring Element (SE) for mathematical morphology. In a second step, the image is subjected to a series of morphological operations with this SE to detect the possible existing fabric defect. Simulation results exhibit accurate defect detection with low false alarms.


title: Semantic Segmentation for High Spatial Resolution Remote Sensing Images Based on Convolution Neural Network and Pyramid Pooling Module

abstract: Semantic segmentation provides a practical way to segment remotely sensed images into multiple ground objects simultaneously, which can be potentially applied to multiple remote sensed related aspects. Current classification algorithms in remotely sensed images are mostly limited by different imaging conditions, the multiple ground objects are difficult to be separated from each other due to high intraclass spectral variances and interclass spectral similarities. In this study, we propose an end-to-end framework to semantically segment high-resolution aerial images without postprocessing to refine the segmentation results. The framework provides a pixel-wise segmentation result, comprising convolutional neural network structure and pyramid pooling module, which aims to extract feature maps at multiple scales. The proposed model is applied to the ISPRS Vaihingen benchmark dataset from the ISPRS 2D Semantic Labeling Challenge. Its segmentation results are compared with previous state-of-the-art method UZ_1, UPB and three other methods that segment images into objects of all the classes (including clutter/background) based on true orthophoto tiles, and achieve the highest overall accuracy of 87.8% over the published performances, to the best of our knowledge. The results validate the efficiency of the proposed model in segmenting multiple ground objects from remotely sensed images simultaneously.


Chimera: a unified framework for B/FV, TFHE and HEAAN fully homomorphic encryption and predictions for deep learning.


title: Implementation of deep learning algorithm for automatic detection of brain tumors using intraoperative IR-thermal mapping data

abstract: The efficiency of deep machine learning for automatic delineation of tumor areas has been demonstrated for intraoperative neuronavigation using active IR-mapping with the use of the cold test. The proposed approach employs a matrix IR-imager to remotely register the space-time distribution of surface temperature pattern, which is determined by the dynamics of local cerebral blood flow. The advantages of this technique are non-invasiveness, zero risks for the health of patients and medical staff, low implementation and operational costs, ease and speed of use. Traditional IR-diagnostic technique has a crucial limitation - it involves a diagnostician who determines the boundaries of tumor areas, which gives rise to considerable uncertainty, which can lead to diagnosis errors that are difficult to control. The current study demonstrates that implementing deep learning algorithms allows to eliminate the explained drawback.


title: Patterns of weight loss and supplement consumption of male wrestlers in Tehran

abstract: BackgroundTo evaluate the weight loss behavior of male wrestlers in TehranMethodsThis study was a population-based cross sectional survey. Subjects were 436 wrestlers randomly selected from the wrestling clubs in Tehran employing cluster sample setting method. Subjects were interviewed based on a designed questionnaire. Body fat levels were measured based on skin fold measurements.ResultsWeight loss methods practiced by 62% of all subjects during the previous year employing rapid (≤7 days before the matches) and gradual (>7 days before the matches) weight reduction methods (73% and 34% of wrestlers who reduced their weight respectively). In addition, opinions on weight reduction, the methods of weight loss used, and the side effects of the weight loss practices as well as consumption of supplements among the subjects were reported in this study. The mean percentage of body fat of subjects was 15.9%.ConclusionsRapid weight loss for matches and the use of unsafe methods of weight reduction such as fasting, and fluid reduction methods as well as acute side effects of weight loss were prevalent among wrestlers in Tehran. Some preventive measures including education and new rules such as scheduling weigh-ins immediately prior to the competitions and mat-side weigh-in are needed to prevent these unhealthy practices. The weight loss behaviors of these wrestlers should be changed from using dehydration methods to using gradual methods of weight loss.


title: Histological and immunohistochemical analysis of initial and early subepithelial connective tissue attachment at chemically modified and conventional SLA®titanium implants. A pilot study in dogs

abstract: The aim of the present pilot study was to histologically/immunohistochemically investigate initial and early subepithelial connective tissue attachment at transmucosal parts of modified (mod) and conventional sandblasted, large grit and acid-etched (SLA) titanium implants. Implantation of modSLA and SLA implants was performed bilaterally in both the mandible and maxilla of four beagle dogs. The implants were submerged to prevent bacterial contamination. The animals were killed after 1, 4, 7 and 14 days. Peri-implant tissue reactions were assessed histologically (Masson Goldner Trichrome stain-MG) and immunohistochemically (IH) using monoclonal antibodies to fibronectin (FN) and proliferating cell nuclear antigen (PCNA). The surgical procedure of implant submerging resulted in the formation of an artificial gap in the transmucosal area of both types of implants. After 14 days of healing, MG stain revealed the formation of well-organized collagen fibres and numerous blood vessels in a newly formed loose connective tissue zone adjacent to modSLA. While some fibres were oriented in a parallel direction, others have started to extend and attach partially perpendicular to the implant surface. In contrast, SLA implants appeared to be clearly separated by a dense connective tissue zone with parallel-running collagen fibres and rare blood vessel formation. First signs of a positive FN and PCNA staining adjacent to both implant surfaces were observed at day 4. Within the limits of a pilot study, it might be concluded that modSLA titanium surfaces might possess the potential to promote subepithelial connective tissue attachment at the transmucosal part of the implant.


title: Towards Health (Aware) Recommender Systems

abstract: People increasingly use the Internet for obtaining information regarding diseases, diagnoses and available treatments. Currently, many online health portals already provide non-personalized health information in the form of articles. However, it can be challenging to find information relevant to one's condition, interpret this in context, and understand the medical terms and relationships. Recommender Systems (RS) already help these systems perform precise information filtering. In this short paper, we look one step ahead and show the progress made towards RS helping users find personalized, complex medical interventions or support them with preventive healthcare measures. We identify key challenges that need to be addressed for RS to offer the kind of decision support needed in high-risk domains like healthcare.


title: The Singularity is Not Near: Slowing Growth of Wikipedia

abstract: Prior research on Wikipedia has characterized the growth in content and editors as being fundamentally exponential in nature, extrapolating current trends into the future. We show that recent editing activity suggests that Wikipedia growth has slowed, and perhaps plateaued, indicating that it may have come against its limits to growth. We measure growth, population shifts, and patterns of editor and administrator activities, contrasting these against past results where possible. Both the rate of page growth and editor growth has declined. As growth has declined, there are indicators of increased coordination and overhead costs, exclusion of newcomers, and resistance to new edits. We discuss some possible explanations for these new developments in Wikipedia including decreased opportunities for sharing existing knowledge and increased bureaucratic stress on the socio-technical system itself.


title: Women and women of color in leadership: complexity, identity, and intersectionality.

abstract: This article describes the challenges that women and women of color face in their quest to achieve and perform in leadership roles in work settings. We discuss the barriers that women encounter and specifically address the dimensions of gender and race and their impact on leadership. We identify the factors associated with gender evaluations of leaders and the stereotypes and other challenges faced by White women and women of color. We use ideas concerning identity and the intersection of multiple identities to understand the way in which gender mediates and shapes the experience of women in the workplace. We conclude with suggestions for research and theory development that may more fully capture the complex experience of women who serve as leaders.


title: Wireless Charging Technologies: Fundamentals, Standards, and Network Applications

abstract: Wireless charging is a technology of transmitting power through an air gap to electrical devices for the purpose of energy replenishment. The recent progress in wireless charging techniques and development of commercial products have provided a promising alternative way to address the energy bottleneck of conventionally portable battery-powered devices. However, the incorporation of wireless charging into the existing wireless communication systems also brings along a series of challenging issues with regard to implementation, scheduling, and power management. In this paper, we present a comprehensive overview of wireless charging techniques, the developments in technical standards, and their recent advances in network applications. In particular, with regard to network applications, we review the static charger scheduling strategies, mobile charger dispatch strategies and wireless charger deployment strategies. Additionally, we discuss open issues and challenges in implementing wireless charging technologies. Finally, we envision some practical future network applications of wireless charging.


DeepPointSet : A Point Set Generation Network for 3 D Object Reconstruction from a Single Image Supplementary Material


title: A Modular Machine Learning System for Flow-Level Traffic Classification in Large Networks

abstract: The ability to accurately and scalably classify network traffic is of critical importance to a wide range of management tasks of large networks, such as tier-1 ISP networks and global enterprise networks. Guided by the practical constraints and requirements of traffic classification in large networks, in this article, we explore the design of an accurate and scalable machine learning based flow-level traffic classification system, which is trained on a dataset of flow-level data that has been annotated with application protocol labels by a packet-level classifier. Our system employs a lightweight modular architecture, which combines a series of simple linear binary classifiers, each of which can be efficiently implemented and trained on vast amounts of flow data in parallel, and embraces three key innovative mechanisms, weighted threshold sampling, logistic calibration, and intelligent data partitioning, to achieve scalability while attaining high accuracy. Evaluations using real traffic data from multiple locations in a large ISP show that our system accurately reproduces the labels of the packet level classifier when runs on (unlabeled) flow records, while meeting the scalability and stability requirements of large ISP networks. Using training and test datasets that are two months apart and collected from two different locations, the flow error rates are only 3% for TCP flows and 0.4% for UDP flows. We further show that such error rates can be reduced by combining the information of spatial distributions of flows, or collective traffic statistics, during classification. We propose a novel two-step model, which seamlessly integrates these collective traffic statistics into the existing traffic classification system. Experimental results display performance improvement on all traffic classes and an overall error rate reduction by 15%. In addition to a high accuracy, at runtime, our implementation easily scales to classify traffic on 10Gbps links.


title: 5G Backhaul Challenges and Emerging Research Directions: A Survey

abstract: 5G is the next cellular generation and is expected to quench the growing thirst for taxing data rates and to enable the Internet of Things. Focused research and standardization work have been addressing the corresponding challenges from the radio perspective while employing advanced features, such as network densification, massive multiple-input-multiple-output antennae, coordinated multi-point processing, inter-cell interference mitigation techniques, carrier aggregation, and new spectrum exploration. Nevertheless, a new bottleneck has emerged: the backhaul. The ultra-dense and heavy traffic cells should be connected to the core network through the backhaul, often with extreme requirements in terms of capacity, latency, availability, energy, and cost efficiency. This pioneering survey explains the 5G backhaul paradigm, presents a critical analysis of legacy, cutting-edge solutions, and new trends in backhauling, and proposes a novel consolidated 5G backhaul framework. A new joint radio access and backhaul perspective is proposed for the evaluation of backhaul technologies which reinforces the belief that no single solution can solve the holistic 5G backhaul problem. This paper also reveals hidden advantages and shortcomings of backhaul solutions, which are not evident when backhaul technologies are inspected as an independent part of the 5G network. This survey is key in identifying essential catalysts that are believed to jointly pave the way to solving the beyond-2020 backhauling challenge. Lessons learned, unsolved challenges, and a new consolidated 5G backhaul vision are thus presented.


title: Quantifying Political Leaning from Tweets, Retweets, and Retweeters

abstract: The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media, and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 232,000 ordinary users who retweeted them, and the hashtags used by these sources, our quantitative study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold.


title: Event management - A special kind of project management

abstract: Event management is a strong and fast growing profession with a rather low level of standardization. Often we take event management as a part of project management, but we have to consider that event management has very specific concepts and issues, and needs further developed methods and tools. We classify events, we compare project management and event management, we reconsider standards in both areas, and discuss perspectives for a stronger standardization of event management in the future.


title: A 2.7 V 900 MHz/1.9 GHz dual-band transceiver IC for digital wireless communication

abstract: A 2.7 V 900 MHz/1.9 GHz dual-band transceiver IC consisting of receive, transmit and local oscillator sections is presented. The transmit section achieves an unwanted sideband-suppression of -43 dBc, LO leakage of -59 dBc, 3/sup rd/ order spurious rejection of -70 dBc. The transmit output noise level is -165 dBc/Hz at a 20 MHz offset from the carrier. The on-chip VHF oscillator has a phase noise level of -106 dBc/Hz at 100 kHz offset when operating at 800 MHz. The receive section has 36 dB of gain with 36 dB of gain range in 4 steps.


title: Design of Low-Voltage Low-Dropout Regulator with Wide-Band High-PSR Characteristic

abstract: Utilizing a novel unit-gain compensation cell (UGCC) to perform frequency compensation and power supply rejection (PSR) enhancement, a low-voltage high-PSR low-dropout regulator (LDO) is presented in this paper. The proposed UGCC, consisting of a 1 pF on-chip capacitor and six transistors, effectively overcomes the drawbacks that exist in the conventional compensation schemes by generating an internal low-frequency zero. A LDO that adopts this new structure is implemented and simulated using a standard 0.18-mum CMOS process. Simulation results show that the circuit can work properly when the supply voltage is as low as 1.2 V. The PSR over the frequency range of dc to 1 GHz is better than -40dB in all cases at 1.2 V supply voltage. The dropout voltage is 200 mV when the load current is 100mA. Therefore, it is well suited for low-voltage ripple-sensitive applications


How Jews Became Smart : Anti-" Natural History of Ashkenazi Intelligence " l


title: Comparative study of biological and artificial neural networks

abstract: In this research project, the features of biological and artificial neural networks were studied by reviewing the ::: existing works of authorities in print and electronics on biological and artificial neural networks. The features were ::: then assessed and evaluated and comparative analysis of the two networks was carried out. The metrics such as ::: structures, layers, size and functional capabilities of neurons, learning capabilities, style of computation, processing ::: elements, processing speed, connections, strength, information storage, information transmission, communication ::: media selection, signal transduction and fault tolerance were used as basis for comparison. A major finding in the ::: research showed that artificial neural networks served as the platform for neuro-computing technology and as such ::: a major driver of the development of neuron-like computing system. It was also discovered that Information ::: processing of the future computer systems will greatly be influenced by the adoption of artificial neural network ::: model.


title: The PHANToM haptic interface: A device for probing virtual objects

abstract: 1. Abstract This paper describes the PHANToM haptic interface - a device which measures a user’s finger tip position and exerts a precisely controlled force vector on the finger tip. The device has enabled users to interact with and feel a wide variety of virtual objects and will be used for control of remote manipulators. This paper discusses the design rationale, novel kinematics and mechanics of the PHANToM. A brief description of the programming of basic shape elements and contact interactions is also given.


A robust frontend for VAD: exploiting contextual, discriminative and spectral cues of human voice


Refining the conditions on the Fermat quotient