title: Rethinking Map Legends with Visualization

abstract: This design paper presents new guidance for creating map legends in a dynamic environment. Our contribution is a set ofguidelines for legend design in a visualization context and a series of illustrative themes through which they may be expressed. Theseare demonstrated in an applications context through interactive software prototypes. The guidelines are derived from cartographicliterature and in liaison with EDINA who provide digital mapping services for UK tertiary education. They enhance approaches tolegend design that have evolved for static media with visualization by considering: selection, layout, symbols, position, dynamismand design and process. Broad visualization legend themes include: The Ground Truth Legend, The Legend as Statistical Graphicand The Map is the Legend. Together, these concepts enable us to augment legends with dynamic properties that address specificneeds, rethink their nature and role and contribute to a wider re-evaluation of maps as artifacts of usage rather than statements offact. EDINA has acquired funding to enhance their clients with visualization legends that use these concepts as a consequence ofthis work. The guidance applies to the design of a wide range of legends and keys used in cartography and information visualization.


title: A 120 dB Dynamic Range 400 mW Class-D Speaker Driver With Fourth-Order PWM Modulator

abstract: High efficiency Class-D speaker drivers have become ubiquitous in battery driven mobile audio applications. Analog switching errors and timing jitter are known limitations to the dynamic range in these types of switching audio drivers. To overcome these limitations the Class-D architecture chosen in this paper provides a low 667 kHz pulse rate. This architecture is based on a 4th-order digital modulator with pulse-width modulation (PWM) feedback. The modulator loop processes the PWM signals at a high 24 MHz clock rate, and provides pulse edge timing resolution within a single clock period of the high-rate clock. Timing distortion errors due to uniform sampling of the low-rate PWM signals are reduced with the high forward loop gain of the digital modulator feedback loop. The driver receives a 22-bit digital audio input and combines the functionality of a high-fidelity digital-to-analog converter (DAC) with a speaker driver. It achieves 120 dB dynamic range over a 20 kHz bandwidth, provides 88% power efficiency while driving an 8 Ω speaker load, and consumes a total quiescent current of 2.4 mA.


title: A 2.0–5.5 GHz Wide Bandwidth Ring-Based Digital Fractional-N PLL With Extended Range Multi-Modulus Divider

abstract: Phase noise performance of ring oscillator based digital fractional-N phase-locked loops (FNPLLs) is severely compromised by conflicting bandwidth requirements to simultaneously suppress oscillator phase and quantization noise introduced by the time-to-digital converter (TDC), $\Delta \Sigma $ fractional divider, and digital-to-analog converter (DAC). As a consequence, their figure-of-merit (FoMJ) that quantifies the power–jitter tradeoff is at least 25 dB worse than their LC-oscillator-based FNPLL counterparts. This paper seeks to close this performance gap by extending PLL bandwidth (BW) using quantization noise cancellation techniques and by employing a dual-path digital loop filter to suppress the detrimental impact of DAC quantization noise. Fabricated in 65 nm CMOS process, the proposed FNPLL operates over a wide frequency range of 2.0–5.5 GHz using a modified extended range multi-modulus divider with seamless switching. The proposed digital FNPLL achieves 1.9 psrms integrated jitter while consuming only 4 mW at 5 GHz output. The measured in-band phase noise is better than −96 dBc/Hz at 1 MHz offset. The proposed FNPLL achieves wide BW up to 6 MHz using a 50 MHz reference and its FoMJ is −228.5 dB, which is the best among all reported ring-based FNPLLs.


title: Rule extraction for fatty liver detection using neural networks

abstract: Non-alcoholic fatty liver disease (NAFLD) is one of the most common diseases in the world. Recently the FibroScan device is used as a noninvasive, yet costly method to measure the liver’s elasticity as a NAFLD indicator. Other than the cost, the diagnosis is not widely accessible to all patients. On the other hand, early detection of the disease can prevent later risks. In this study, we aim to use learning methods to infer the NAFLD severity level, only based on clinical tests. A dataset was constructed from clinical and ultrasonography data of 726 patients who were diagnosed with different NAFLD severity levels. Artificial neural networks (ANN) were used to model the relationship between NAFLD and the clinical tests. Next, a method was used to analyze the ANN and extract compact and human understandable rules. The derived rules can detect the fatty liver disease with an accuracy above 80%.


title: First Comes Love, Then Comes Google: An Investigation of Uncertainty Reduction Strategies and Self-Disclosure in Online Dating:

abstract: This study investigates relationships between privacy concerns, uncertainty reduction behaviors, and self-disclosure among online dating participants, drawing on uncertainty reduction theory and the warranting principle. The authors propose a conceptual model integrating privacy concerns, self-efficacy, and Internet experience with uncertainty reduction strategies and amount of self-disclosure and then test this model on a nationwide sample of online dating participants ( N = 562). The study findings confirm that the frequency of use of uncertainty reduction strategies is predicted by three sets of online dating concerns—personal security, misrepresentation, and recognition—as well as self-efficacy in online dating. Furthermore, the frequency of uncertainty reduction strategies mediates the relationship between these variables and amount of self-disclosure with potential online dating partners. The authors explore the theoretical implications of these findings for our understanding of uncertainty reductio...


title: An Improved Perturb and Observe Maximum Power Point Tracking Algorithm for Photovoltaic Systems

abstract: The paper aims to give a comprehensive mathematical analysis of a photovoltaic array. The model representation was implemented using MATLAB/Simulink software. To track the maximum power point, an improved perturb and observe (P&O) algorithm method based was implemented. To demonstrate the performance of the proposed method, a comparative study between the conventional P&O and the proposed algorithms was discussed. The encouraging results indicate the opportunities for the utilization of the strategy covered by this work.


title: Optimization and performance modeling of stencil computations on modern microprocessors

abstract: Stencil-based kernels constitute the core of many important scientific applications on blockstructured grids. Unfortunately, these codes achieve a low fraction of peak performance, due primarily to the disparity between processor and main memory speeds. In this paper, we explore the impact of trends in memory subsystems on a variety of stencil optimization techniques and develop performance models to analytically guide our optimizations. Our work targets cache reuse methodologies across single and multiple stencil sweeps, examining cache-aware algorithms as well as cache-oblivious techniques on the Intel Itanium2, AMD Opteron, and IBM Power5. Additionally, we consider stencil computations on the heterogeneous multicore design of the Cell processor, a machine with an explicitly managed memory hierarchy. Overall our work represents one of the most extensive analyses of stencil optimizations and performance modeling to date. Results demonstrate that recent trends in memory system organization have reduced the efficacy of traditional cache-blocking optimizations. We also show that a cache-aware implementation is significantly faster than a cache-oblivious approach, while the explicitly managed memory on Cell enables the highest overall efficiency: Cell attains 88% of algorithmic peak while the best competing cache-based processor achieves only 54% of algorithmic peak performance.


title: On the interpretation of weight vectors of linear models in multivariate neuroimaging

abstract: Abstract The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain–computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models , as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses.


title: Adult hypospadias: urethral and penile reconstruction

abstract: This article provides a subjective, concise review of contemporary advances in reconstructive urology as it pertains to adult hypospadias repair. Herein, we highlight the most important and interesting articles among the many published within the past 12 months.The main themes in the recent literature covered herein, include management of postoperative complications, long-term follow-up, and penile cosmesis.Recent literature would suggest beauty is in the eye of the beholder when it comes to cosmesis after hypospadias repair. Long-term data are reassuring regarding uroflow improvement through puberty and into adulthood for common distal shaft hypospadias repairs.


title: From Entity Recognition to Entity Linking: A Survey of Advanced Entity Linking Techniques

abstract: Several research results have shown that specifying the information about certain entities is the most common information demand of information retrieval users. The needs should be answered by returning specific entities, their properties or related concepts instead of just any type of documents. While some search engines are capable of recognizing specific types of entities, true entity-oriented search still has a long way to go because of the high ambiguity in names across documents. Entity linking (EL) goes beyond the entity recognition task by linking a textual named entity mention to a knowledge base entry. It is a difficult task involving several challenges. This paper gives a survey of the EL tasks in the general and the biomedical domain. In addition, results of our latest EL work are provided for reference, which uncover new EL challenges found in biomedical text mining, along with discussions regarding their possible solutions.


title: Design, construction and measurements of a permanent magnet axial flux machine

abstract: The results of the design, the construction and first measurements of an axial flux permanent magnet (AFPM) machine are summarized. The goal of this project was to build an axial flux machine with surface mounted magnets in the simplest possible way to use the machine as a demonstrator for small wheel hub drives with a reduced torque demand. Therefore the machine was designed as double-sided motor with internal stator and tooth-coil winding to enable a modular structure and two rotor discs. For the ease of a simple integration of the machine, the outer diameter was limited to a certain value. Therefore the design yield to a machine with 12 poles and 18 stator teeth (number of slots per pole and phase q = ½). 3D-electromagnetic and 3D-thermal studies with the finite-element-method (FEM) software JMAG were carried out. After the design of the active elements, the construction of the prototype was done. The machine was build up on a test bench and coupled with a load machine to compare the simulation results with the measurements. The measurements show that the electromagnetic properties (torque, efficiency) fit with the simulation results, but due to the open ventilation motor design the thermal behavior of the machine is better than assumed for the simulations. With the results it is possible to estimate also the behavior of bigger wheel hub drives.


title: Semantics-preserving bag-of-words models for efficient image annotation

abstract: The Bag-of-Words (BoW) model is a promising image representation for annotation. One critical limitation of existing BoW models is the semantic loss during the codebook generation process, in which BoW simply clusters visual words in Euclidian space. However, distance between two visual words in Euclidean space does not necessarily reflect the semantic distance between the two concepts, due to the semantic gap between low-level features and high-level semantics. In this paper, we propose a novel scheme for learning a codebook such that semantically related features will be mapped to the same visual word. In particular, we consider the distance between semantically identical features as a measurement of the semantic gap, and attempt to learn an optimized codebook by minimizing this gap. We refer to such a new codebook method as Semantics-Preserving Codebook (SPC) and the corresponding model as Semantics-Preserving Bag-of-Words model (SPBoW). This novel model generates codebook for each object category and only needs to update the codebook for a specific category when incomes an object, which makes it convenient to scale up with the increasing number of objects. Experiments on image annotation tasks with a public testbed from MIT's Labelme project, which contains 11,281 objects of 495 categories, show that the SPC learning scheme is efficient in handling large number of objects and is able to greatly improve the performance of the existing BoW model.


title: Towards a benchmark for RGB-D SLAM evaluation

abstract: We provide a large dataset containing RGB-D image sequences and the ground-truth camera trajectories with the goal to establish a benchmark for the evaluation of visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor and the ground-truth trajectory of camera poses. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). Further, we provide the accelerometer data from the Kinect. Finally, we propose an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems.


title: A Generative Modeling Approach to Limited Channel ECG Classification

abstract: Processing temporal sequences is central to a variety of applications in health care, and in particular multi-channel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling. While Recurrent Neural Networks (RNNs) have led to significant advances in automated diagnosis with time-series data, they perform poorly when models are trained using a limited set of channels. A crucial limitation of existing solutions is that they rely solely on discriminative models, which tend to generalize poorly in such scenarios. In order to combat this limitation, we develop a generative modeling approach to limited channel ECG classification. This approach first uses a Seq2Seq model to implicitly generate the missing channel information, and then uses the latent representation to perform the actual supervisory task. This decoupling enables the use of unsupervised data and also provides highly robust metric spaces for subsequent discriminative learning. Our experiments with the Physionet dataset clearly evidence the effectiveness of our approach over standard RNNs in disease prediction.


title: A Generalized Approach to the Load Flow Analysis of AC–DC Hybrid Distribution Systems

abstract: This paper proposes a unified load flow (LF) model for AC–DC hybrid distribution systems (DSs). The proposed model can be applied in hybrid DSs with mixed configurations for AC/DC buses and AC/DC lines. A new classification of DS buses is also introduced for LF analysis. A set of generic LF equations has been derived based on comprehensive analysis of the possible AC–DC hybrid system configurations. Three binary matrices, which are used as a means of describing the configuration of the AC and DC buses and lines, have been employed in the construction of the unified power equations. These matrices enable a single configuration at a time to be activated in the power equations. The proposed LF model is generic and can be used for both grid-connected and isolated hybrid DSs. The new model has been tested using several case studies of hybrid DSs that include different operational modes for the AC and DC distributed generators. As a means of evaluating the effectiveness and accuracy of the proposed model, the LF solution was compared to the solution produced by PSCAD/EMTDC. A comparison of the results reveals the efficacy of the proposed model.


title: Raising Awareness of Career Goals of Insurance Agents : A Case Study of Choomthong 24K26, AIA Company.

abstract: This article is a summary of key study done on a group of AIA insurance agents (Choomthong 24K26). It focused on raising awareness of career goals of insurance agents. The study was based on three main factors; performance, excellence and self-development. The respondents of this study were 33 AIA agents who were selected according to their overall performance. Two main methods were used to collect data: interview and questionnaire. Data gathered through questionnaires were collected twice during the study period, firstly before ODI and secondly, after ODI while interview was only done after the ODI phase. There were various theories which aided in understanding the key factors behind the study. These were; whole brain, AIDA model, Ice-berg, Self-mastery model, Kurt Lewin’s theory of change, Kotter’s eight stages of change and lastly which was selected to guide in the ODI process was AI (Appreciative Inquiry). These theories gave an understanding on how the performance, self-development and excellence of an individual can be improved. Data analysis was done through the use of mean, standard deviation and the Wilcoxon matched-pair signed rank test. The alternative hypothesis for the study was that there is an impact of ODI on the performance, excellence and self-development of insurance agents. It was evident from the comparison of Pre and Post-ODI result of the data analysis that there was an improvement in the career goals of insurance thus the alternative hypothesis was accepted. Keywords: performance, excellence, self-development, individual development plan, career path setting, competency, recruitment.


title: Analysis and Comparison of a Fast Turn-On Series IGBT Stack and High-Voltage-Rated Commercial IGBTS

abstract: High-voltage-rated solid-state switches such as insulated-gate bipolar transistors (IGBTs) are commercially available up to 6.5 kV. Such voltage ratings are attractive for pulsed power and high-voltage switch-mode converter applications. However, as the IGBT voltage ratings increase, the rate of current rise and fall are generally reduced. This tradeoff is difficult to avoid as IGBTs must maintain a low resistance in the epitaxial or drift region layer. For high-voltage-rated IGBTs with thick drift regions to support the reverse voltage, the required high carrier concentrations are injected at turn on and removed at turn off, which slows the switching speed. An option for faster switching is to series multiple, lower voltage-rated IGBTs. An IGBT-stack prototype with six, 1200 V rated IGBTs in series has been experimentally tested. The six-series IGBT stack consists of individual, optically isolated, gate drivers and aluminum cooling plates for forced air cooling which results in a compact package. Each IGBT is overvoltage protected by transient voltage suppressors. The turn-on current rise time of the six-series IGBT stack and a single 6.5 kV rated IGBT has been experimentally measured in a pulsed resistive-load, capacitor discharge circuit. The IGBT stack has also been compared to two IGBT modules in series, each rated at 3.3 kV, in a boost circuit application switching at 9 kHz and producing an output of 5 kV. The six-series IGBT stack results in improved turn-on switching speed, and significantly higher power boost converter efficiency due to a reduced current tail during turn off. The experimental test parameters and the results of the comparison tests are discussed in the following paper


title: Postural orthostatic tachycardia syndrome: Dental treatment considerations.

abstract: BACKGROUND ::: Postural orthostatic tachycardia syndrome (POTS) is a chronic, relatively common autonomic disorder typically affecting younger females. It is distinguished by a dramatic increase in heart rate on the assumption of an upright posture from the supine position. ::: ::: ::: METHODS ::: The authors provide an overview of the demographics, clinical assessment, diagnostic features, differential diagnoses, pathogeneses and medical treatment of patients with POTS, with an emphasis on the clinical treatment of the dental patient affected by the syndrome. ::: ::: ::: CONCLUSION ::: Patients frequently exhibit symptoms of lightheadedness, fatigue, palpitations and syncope. Patients with POTS may have Ehlers-Danlos syndrome, mitral valve prolapse, chronic fatigue syndrome or, rarely, the Brugada syndrome. Despite widespread dissemination of information regarding POTS in the medical literature, scant information on it has appeared in dental publications. ::: ::: ::: PRACTICE IMPLICATIONS ::: Dentists need to be familiar with the clinical features of POTS and be prepared to treat patients at risk of developing syncope.


Twitter Geolocation Prediction Shared Task of the 2016 Workshop on Noisy User-generated Text


title: A General Greedy Approximation Algorithm with Applications

abstract: Greedy approximation algorithms have been frequently used to obtain sparse solutions to learning problems. In this paper, we present a general greedy algorithm for solving a class of convex optimization problems. We derive a bound on the rate of approximation for this algorithm, and show that our algorithm includes a number of earlier studies as special cases.


title: Vehicle detection fusing 2D visual features

abstract: This paper presents a method for detection and tracking of vehicles by finding various characteristic features in the images of a monochrome camera. The detection process uses shadow and symmetry features to generate vehicle hypotheses. These are fused and tracked over time using an Interacting Multiple Model method (IMM). Results for natural traffic scenes demonstrate high reliability of the proposed method.


title: Brain Activation and Sexual Arousal in Healthy Heterosexual Males

abstract: Summary Despite the brain’s central role in sexual function, little is known about relationships between brain activation and sexual response. In this study, we employed functional MRI (fMRI) to examine relationships between brain activation and sexual arousal in a group of young, healthy, heterosexual males. Each subject was exposed to two sequences of video material consisting of explicitly erotic (E), relaxing (R) and sports (S) segments in an unpredictable order. Data on penile turgidity was collected using a custom-built pneumatic pressure cuff. Both traditional block analyses using contrasts between sexually arousing and non-arousing video clips and a regression using penile turgidity as the covariate of interest were performed. In both types of analyses, contrast images were computed for each subject and these images were subsequently used in a random effects analysis. Strong activations specifically associated with penile turgidity were observed in the right subinsular region including the claustrum, left caudate and putamen, right middle occipital/ middle temporal gyri, bilateral cingulate gyrus and right sensorimotor and pre-motor regions. Smaller, but significant activation was observed in the right hypothalamus. Few significant activations were found in the block analyses. Implications of the findings are discussed. Our study demonstrates the feasibility of examining brain activation/sexual response relationships in an fMRI environment and reveals a number of brain structures whose activation is time-locked to sexual arousal.


Exploring Vector Spaces for Semantic Relations


title: resMBS: Constructing a Financial Supply Chain from Prospectus

abstract: Understanding the behavior of complex financial supply chains is usually difficult due to a lack of data capturing the interactions between financial institutions (FIs) and the roles that they play in financial contracts (FCs). resMBS is an example supply chain corresponding to the US residential mortgage backed securities that were critical in the 2008 US financial crisis. In this paper, we describe the process of creating the resMBS graph dataset from financial prospectus. We use the SystemT rule-based text extraction platform to develop two tools, ORG NER and Dict NER, for named entity recognition of financial institution (FI) names. The resMBS graph comprises a set of FC nodes (each prospectus) and the corresponding FI nodes that are extracted from the prospectus. A Role-FI extractor matches a role keyword such as originator, sponsor or servicer, with FI names. We study the performance of the Role-FI extractor, and ORG NER and Dict NER, in constructing the resMBS dataset. We also present preliminary results of a clustering based analysis to identify financial communities and their evolution in the resMBS financial supply chain.


title: A Two-Stage Deanonymization Attack against Anonymized Social Networks

abstract: Digital traces left by users of online social networking services, even after anonymization, are susceptible to privacy breaches. This is exacerbated by the increasing overlap in user-bases among various services. To alert fellow researchers in both the academia and the industry to the feasibility of such an attack, we propose an algorithm, Seed-and-Grow, to identify users from an anonymized social graph, based solely on graph structure. The algorithm first identifies a seed subgraph, either planted by an attacker or divulged by a collusion of a small group of users, and then grows the seed larger based on the attacker's existing knowledge of the users' social relations. Our work identifies and relaxes implicit assumptions taken by previous works, eliminates arbitrary parameters, and improves identification effectiveness and accuracy. Simulations on real-world collected data sets verify our claim.


title: Emoticons vs. Emojis on Twitter: A Causal Inference Approach

abstract: Online writing lacks the non-verbal cues present in face-to-face communication, which provide additional contextual information about the utterance, such as the speaker's intention or affective state. To fill this void, a number of orthographic features, such as emoticons, expressive lengthening, and non-standard punctuation, have become popular in social media services including Twitter and Instagram. Recently, emojis have been introduced to social media, and are increasingly popular. This raises the question of whether these predefined pictographic characters will come to replace earlier orthographic methods of paralinguistic communication. In this abstract, we attempt to shed light on this question, using a matching approach from causal inference to test whether the adoption of emojis causes individual users to employ fewer emoticons in their text on Twitter.


The Effect of Ageing Treatment of Aluminum Alloys for Fuselage Structure-Light Aircraft


title: Models of object recognition

abstract: Understanding how biological visual systems recognize objects is one of the ultimate goals in computational neuroscience. From the computational viewpoint of learning, different recognition tasks, such as categorization and identification, are similar, representing different trade-offs between specificity and invariance. Thus, the different tasks do not require different classes of models. We briefly review some recent trends in computational vision and then focus on feedforward, view-based models that are supported by psychophysical and physiological data.


title: Measuring and simulating EMI on very small components at high frequencies

abstract: A new test method has been implemented for testing the EMC performance of small components like small connectors and IC's, mainly used in mobile applications. The test method is based on the EMC-stripline method. Both emission and immunity can be tested up to 6GHz, based on good RF matching conditions and with high field strengths.


title: Automatic lens distortion correction using single images by finding reliable lines for rectification

abstract: It is advantageous but difficult to automatically and accurately estimate parameters of lens models from single images, particularly in internet image processing and thermal image processing. As not all objects have straight edges, it is natural that some edges should be straight and some should be curved in undistorted images. Therefore, the key problem is how to find curved lines in original images that should be straight in undistorted images. In this paper, a novel algorithm is proposed to solve this key problem by automatically selecting reliable line segments for the estimation of lens model parameters. Experimental results show that the proposed algorithm is able to detect reliable lines and produce quality results for both indoor and outdoor images taken by different cameras using wide-angle and fisheye lenses as well as zoom lenses.


title: The "index technique" in worn dentition: a new and conservative approach.

abstract: The development and reliability of adhesive resin composite systems have offered clinicians a further option for the management of tooth-surface loss. Patients with minimum, moderate, and severe hard tissue wear can be treated based on the application of minimally invasive adhesive composite restorations for posterior and anterior worn dentition. This article presents the "index technique", a new and very conservative approach to the management of worn dentition. The technique allows for a purely additive treatment without sacrificing healthy hard tooth tissue. It follows the principles of bioeconomics (maximum conservation of healthy tissue) and the reinforcing of residual dental structure. Depending on the severity of enamel and dentin wear, the number of caries, and the size of existing restorations, different treatment options can be applied to each tooth: direct and indirect partial restorations or full crowns. It is essential to diagnose and treat tooth-surface loss in order to properly restore biomechanics, function, and esthetics by means of adhesive restorations. This article proposes that the index technique is a fast and conservative approach for the planning and management of a fullmouth adhesive treatment in all cases of worn dentition. The technique is based on stamping composite directly on the tooth surface by means of a transparent index created from the full-mouth wax-up following an initially planned increase in occlusal vertical dimension (OVD).


title: Effect of Physical Contact (Hand-Holding) on Heart Rate Variability

abstract: Heart’s electric field can be measured anywhere on the surface of the body (ECG). When individuals touch, one person’s ECG signal can be registered in other person’s EEG and elsewhere on his body. Now, the aim of this study was to test the hypothesis that physical contact (hand-holding) of two persons changes their heart rate variability. Subjects were sixteen healthy female (age: 20- 26) which divided into eight sets. In each sets, we had two friends that they passed intimacy test of J.sternberg. ECG of two subjects (each set) acquired for 5 minutes before hand-holding (as control group) and 5 minutes during they held their hands (as experimental group). Then heart rate variability signals were extracted from subjects' ECG and analyzed in linear feature space (time and frequency domain) and nonlinear feature space. Considering the results, we conclude that physical contact (hand-holding of two friends) increases parasympathetic activity, as indicate by increase SD1, SD1/SD2, HF and MF power (p<0.05) and decreases sympathetic activity, as indicate by decrease LF power (p<0.01) and LF/HF ratio (p<0.05).


Model-Driven Game Development Addressing Architectural Diversity and Game Engine-Integration


Understanding Benefit and Risk Framework of Fintech Adoption: Comparison of Early Adopters and Late Adopters


title: Knowledge-based Word Sense Disambiguation using Topic Models

abstract: Word Sense Disambiguation is an open problem in Natural Language Processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. Typically WSD systems use the sentence or a small window of words around the target word as the context for disambiguation because their computational complexity scales exponentially with the size of the context. In this paper, we leverage the formalism of topic model to design a WSD system that scales linearly with the number of words in the context. As a result, our system is able to utilize the whole document as the context for a word to be disambiguated. The proposed method is a variant of Latent Dirichlet Allocation in which the topic proportions for a document are replaced by synset proportions. We further utilize the information in the WordNet by assigning a non-uniform prior to synset distribution over words and a logistic-normal prior for document distribution over synsets. We evaluate the proposed method on Senseval-2, Senseval-3, SemEval-2007, SemEval-2013 and SemEval-2015 English All-Word WSD datasets and show that it outperforms the state-of-the-art unsupervised knowledge-based WSD system by a significant margin.


title: Effective Self-Training For Parsing

abstract: We present a simple, but surprisingly effective, method of self-training a two-phase parser-reranker system using readily available unlabeled data. We show that this type of bootstrapping is possible for parsing when the bootstrapped parses are processed by a discriminative reranker. Our improved model achieves an f-score of 92.1%, an absolute 1.1% improvement (12% error reduction) over the previous best result for Wall Street Journal parsing. Finally, we provide some analysis to better understand the phenomenon.


title: A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture

abstract: Support vector machine (SVM) is a well-regarded machine learning algorithm widely applied to classification tasks and regression problems. SVM was founded based on the statistical learning theory and structural risk minimization. Despite the high prediction rate of this technique in a wide range of real applications, the efficiency of SVM and its classification accuracy highly depends on the parameter setting as well as the subset feature selection. This work proposes a robust approach based on a recent nature-inspired metaheuristic called multi-verse optimizer (MVO) for selecting optimal features and optimizing the parameters of SVM simultaneously. In fact, the MVO algorithm is employed as a tuner to manipulate the main parameters of SVM and find the optimal set of features for this classifier. The proposed approach is implemented and tested on two different system architectures. MVO is benchmarked and compared with four classic and recent metaheuristic algorithms using ten binary and multi-class labeled datasets. Experimental results demonstrate that MVO can effectively reduce the number of features while maintaining a high prediction accuracy.


title: A multidisciplinary approach to 3D survey and reconstruction of historical buildings

abstract: The aim of this research is to suggest a methodology based on 3D survey and reconstructive modeling, suitable to increase the actual knowledge of an historical building and supporting its historical interpretation. The case study used for testing the proposed methodology is the huge Chartreuse of Pavia, with a special focus on a relatively unexplored portion of the monument. The survey, based on 3D laser scanning and orthoimages, integrated by historical studies and other complementary information (thermoluminescence dating, IR imaging, hystorical analysis), allowed to read all the architectural aspects hidden in this highly architecturally stratified monument, improving in this way the comprehension of the building's transformations in time. A 3D reconstruction approach was then suggested, merging several information of different nature, from the actual geometry of the building to the interpretation of historical documents, suggesting a sequence of diachronic models as virtual narration of the historical evolution. On other hand the 3D models were used to obtain a cross-validation of the historical evolution hypotheses developed by experts in the various disciplines involved in the project. The data collected were exploited through a web portal in order to enhance the readability of tangible and intangible heritage associated to that Chartreuse portion, nowadays not accessible to common public.


title: Modern human origins: progress and prospects

abstract: The question of the mode of origin of modern humans (Homo sapiens) has dominated palaeoanthropological debate over the last decade. This review discusses the main models proposed to explain modern human origins, and examines relevant fossil evidence from Eurasia, Africa and Australasia. Archaeological and genetic data are also discussed, as well as problems with the concept of 'modernity' itself. It is concluded that a recent African origin can be supported for H. sapiens, morphologically, behaviourally and genetically, but that more evidence will be needed, both from Africa and elsewhere, before an absolute African origin for our species and its behavioural characteristics can be established and explained.


title: Cultivating Trust and Harvesting Value in Virtual Communities

abstract: Although previous scholars have examined the value of virtual communities to customers, in this study we investigate the role of a firm's efforts in cultivating trust and harvesting value for themselves via the virtual communities that they sponsor. We hypothesize that the perceptions of a firm's efforts to provide quality content, to foster member embeddedness, and to encourage interaction foster favorable customer beliefs about and trust in a virtual community sponsor. Further, we hypothesize that trust motivates customers to behave relationally toward the sponsoring firm by sharing information with, coproducing new products with, and granting loyalty to, the sponsoring firm. Data from 663 customers are analyzed using structural equation modeling techniques. We find that efforts to provide quality content and foster member embeddedness have positive effects on customer beliefs about the sponsor. In fact, fostering member embeddedness has a stronger explanatory effect on customer beliefs than does providing quality content. However, despite the fact that previous studies show that customers value interaction in virtual communities, our findings suggest that firms must do more than encourage interaction among their community members if they hope to create value from their virtual communities.


title: Game-Theory-Based Active Defense for Intrusion Detection in Cyber-Physical Embedded Systems

abstract: Cyber-Physical Embedded Systems (CPESs) are distributed embedded systems integrated with various actuators and sensors. When it comes to the issue of CPES security, the most significant problem is the security of Embedded Sensor Networks (ESNs). With the continuous growth of ESNs, the security of transferring data from sensors to their destinations has become an important research area. Due to the limitations in power, storage, and processing capabilities, existing security mechanisms for wired or wireless networks cannot apply directly to ESNs. Meanwhile, ESNs are likely to be attacked by different kinds of attacks in industrial scenarios. Therefore, there is a need to develop new techniques or modify the current security mechanisms to overcome these problems. In this article, we focus on Intrusion Detection (ID) techniques and propose a new attack-defense game model to detect malicious nodes using a repeated game approach. As a direct consequence of the game model, attackers and defenders make different strategies to achieve optimal payoffs. Importantly, error detection and missing detection are taken into consideration in Intrusion Detection Systems (IDSs), where a game tree model is introduced to solve this problem. In addition, we analyze and prove the existence of pure Nash equilibrium and mixed Nash equilibrium. Simulations show that the proposed model can both reduce energy consumption by up to 50% compared with the existing All Monitor (AM) model and improve the detection rate by up to 10% to 15% compared with the existing Cluster Head (CH) monitor model.


title: Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey

abstract: In this paper, we provide a survey of the traffic sign detection literature, detailing detection systems for traffic sign recognition (TSR) for driver assistance. We separately describe the contributions of recent works to the various stages inherent in traffic sign detection: segmentation, feature extraction, and final sign detection. While TSR is a well-established research area, we highlight open research issues in the literature, including a dearth of use of publicly available image databases and the over-representation of European traffic signs. Furthermore, we discuss future directions of TSR research, including the integration of context and localization. We also introduce a new public database containing U.S. traffic signs.


title: Query Expansion Techniques for Information Retrieval: a Survey

abstract: Abstract With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user’s initial query is reformulated by adding additional meaningful terms with similar significance. QE – as part of information retrieval (IR) – has long attracted researchers’ attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Research in QE has gained further prominence because of IR dedicated conferences such as TREC (Text Information Retrieval Conference) and CLEF (Conference and Labs of the Evaluation Forum). This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications – bringing out similarities and differences.


title: Minimap: a web page visualization method for mobile phones

abstract: The Web has become available even on mobile phones, but the current methods to view large pages on small screens have not been highly usable. Current mobile phone browsers reformat Web pages to a single column that fits the screen width. Because not all content is comprehensible in this format, browsers provide a second mode for viewing pages in the same layout as on a PC. We have developed a modeless Web page visualization method called Minimap that shows pages in a modified Original layout. We conducted a long-term usability study with 20 participants to compare the state-of-the-art mobile phone browser with this new method. 18 participants preferred the new method, and it also scored better in more detailed usability ratings.


title: Testing Untestable Neural Machine Translation: An Industrial Case

abstract: Neural Machine Translation (NMT) has shown great advantages and is becoming increasingly popular. However, in practice, NMT often produces unexpected translation failures in its translations. While reference-based black-box system testing has been a common practice for NMT quality assurance during development, an increasingly critical industrial practice, named in-vivo testing, exposes unseen types or instances of translation failures when real users are using a deployed industrial NMT system. To fill the gap of lacking test oracles for in-vivo testing of NMT systems, we propose a new methodology for automatically identifying translation failures without reference translations. Our evaluation conducted on real-world datasets shows that our methodology effectively detects several targeted types of translation failures. Our experiences on deploying our methodology in both production and development environments of WeChat (a messenger app with over one billion monthly active users) demonstrate high effectiveness of our methodology along with high industry impact.


title: SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation

abstract: Recent techniques in self-supervised monocular depth estimation are approaching the performance of supervised methods, but operate in low resolution only. We show that high resolution is key towards high-fidelity self-supervised monocular depth prediction. Inspired by recent deep learning methods for Single-Image Super-Resolution, we propose a subpixel convolutional layer extension for depth super-resolution that accurately synthesizes high-resolution disparities from their corresponding low-resolution convolutional features. In addition, we introduce a differentiable flip-augmentation layer that accurately fuses predictions from the image and its horizontally flipped version, reducing the effect of left and right shadow regions generated in the disparity map due to occlusions. Both contributions provide significant performance gains over the state-of-the-art in self-supervised depth and pose estimation on the public KITTI benchmark. A video of our approach can be found at https://youtu.be/jKNgBeBMx0I.


title: Automatic verification of probabilistic concurrent finite state programs

abstract: The verification problem for probabilistic concurrent finite-state program is to decide whether such a program satisfies its linear temporal logic specification. We describe an automata-theoretic approach, whereby probabilistic quantification over sets of computations is reduced to standard quantification over individual computations. Using new determinization construction for ω-automata, we manage to improve the time complexity of the algorithm by two exponentials. The time complexity of the final algorithm is polynomial in the size of the program and doubly exponential in the size of the specification.


title: Designing Business Models for the Internet of Things

abstract: This article investigates challenges pertaining to business model design in the emerging context of the Internet of Things (IOT). The evolution of business perspectives to the IOT is driven by two underlying trends: i) the change of focus from viewing the IOT primarily as a technology platform to viewing it as a business ecosystem; and ii) the shift from focusing on the business model of a firm to designing ecosystem business models. An ecosystem business model is a business model composed of value pillars anchored in ecosystems and focuses on both the firm's method of creating and capturing value as well as any part of the ecosystem's method of creating and capturing value. The article highlights three major challenges of designing ecosystem business models for the IOT, including the diversity of objects, the immaturity of innovation, and the unstructured ecosystems. Diversity refers to the difficulty of designing business models for the IOT due to a multitude of different types of connected objects combined with only modest standardization of interfaces. Immaturity suggests that quintessential IOT technologies and innovations are not yet products and services but a "mess that runs deep". The unstructured ecosystems mean that it is too early to tell who the participants will be and which roles they will have in the evolving ecosystems. The study argues that managers can overcome these challenges by using a business model design tool that takes into account the ecosystemic nature of the IOT. The study concludes by proposing the grounds for a new design tool for ecosystem business models and suggesting that "value design" might be a more appropriate term when talking about business models in ecosystems. New web-based business models being hatched for the Internet of Things are bringing together market players who previously had no business dealings with each other. Through partnerships and acquisitions, […] they have to sort out how they will coordinate their business development efforts with customers and interfaces with other stakeholders.


title: A Survey of Procedural Methods for Terrain Modelling ∗

abstract: Procedural methods are a promising but underused alternative to manual content creation. Commonly heard drawbacks are the randomness of and the lack of control over the output and the absence of integrated solutions, although more recent publications increasingly address these issues. This paper surveys procedural methods applied to terrain modelling, evalating realism of their output, performance and control users can exert over the procedure


title: Development of Ambient Cured Polyesteramide Coatings from Linseed Oil: a Sustainable Resource

abstract: The resourceful employment of vegetable oil based polymers in coating applications that yield novel properties, faces challenges usually in their processing. We have developed polyesteramide coatings from linseed (Linnum ussitatissium seeds) oil with improved coating properties. Linseed oil was first converted into N, N-bis 2-hydroxy ethyl linseed oil fatty amide diol (HELA). The resin was synthesized by the reaction of HELA with ethylenediaminetetraacetic acid (EDTA) to develop ethylenediamine polyesteramide (Ed-PEA). The latter was further treated with poly (styrene co-maleic anhydride) (SMA) in different (35–50) phr (part per hundred part of resin) to obtain ambient cured polyesteramide (AC-PEA). The structural elucidation of polymeric resin (AC-PEA) was carried out by FT-IR, 1H-NMR, and 13C-NMR spectroscopic techniques. Thermal behavior of AC-PEA was studied by thermogravimetric analyses (TGA) and differential scanning calorimetry (DSC). The coatings of AC-PEA were prepared on mild steel strips to investigate their physico-mechanical and anticorrosive behavior (in acid, alkali, water and xylene). It was found that among all the samples, the one having 45 phr of SMA showed the best physico-mechanical and corrosion resistance performance. The thermal stability performance suggests that AC-PEA45 system could be safely used up to 150 °C.


title: An $X$ -Band Lumped-Element Wilkinson Combiner With Embedded Impedance Transformation

abstract: An integrated equal-split Wilkinson power combiner/divider tailored for operation in the X-band is reported in this letter. The combiner features differential input/output ports with different characteristic impedances, thus embedding an impedance transformation feature. Over the frequency range from 8 to 14 GHz it shows insertion loss of 1.4dB, return loss greater than 12 dB and isolation greater than 10 dB. It is implemented in a SiGe bipolar technology, and it occupies an area of 0.12 mm ::: 2 ::: .


title: Unraveling the Mechanisms of Manual Therapy: Modeling an Approach

abstract: Synopsis Manual therapy interventions are popular among individual health care providers and their patients; however, systematic reviews do not strongly support their effectiveness. Small treatment effect sizes of manual therapy interventions may result from a “one-size-fits-all” approach to treatment. Mechanistic-based treatment approaches to manual therapy offer an intriguing alternative for identifying patients likely to respond to manual therapy. However, the current lack of knowledge of the mechanisms through which manual therapy interventions inhibit pain limits such an approach. The nature of manual therapy interventions further confounds such an approach, as the related mechanisms are likely a complex interaction of factors related to the patient, the provider, and the environment in which the intervention occurs. Therefore, a model to guide both study design and the interpretation of findings is necessary. We have previously proposed a model suggesting that the mechanical force from a manual ther...


title: SGDR: Stochastic Gradient Descent with Warm Restarts

abstract: Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at https://github.com/loshchil/SGDR


title: Blind Detection in Coexistence of Human-Type and Machine-Type Communications

abstract: In this paper, we study joint device activity identification, channel estimation, and signal detection for the uplink transmission of a human-type communication (HTC) and machine-type communication (MTC) coexisted massive MIMO system. We first establish a probability model to characterize the crucial system features including channel sparsity of massive MIMO, signal sparsity of MTC packets, and sporadic access of MTC. With the probability model, we formulate a blind detection problem and establish a factor graph representation of the problem. Based on that, we develop a turbo message passing (TMP) algorithm involving affine sparse matrix factorization and service type identification. We show that our proposed blind detection algorithm significantly outperform their counterpart algorithms including the training-based algorithm.


title: Comparison Between Analog Radio-Over-Fiber and Sigma Delta Modulated Radio-Over-Fiber

abstract: With the continuously increasing demand of cost effective, broadband wireless access, radio-over-fiber (RoF) starts to gain more and more momentum. Various techniques already exist, using analog (ARoF) or digitized (DRoF) radio signals over fiber; each with their own advantages and disadvantages. By transmitting a sigma delta modulated signal over fiber (SDoF), a similar immunity to impairments as DRoF can be obtained while maintaining the low complexity of ARoF. This letter describes a detailed experimental comparison between ARoF and SDoF that quantifies the improvement in linearity and error vector magnitude (EVM) of SDoF over ARoF. The experiments were carried out using a 16-QAM constellation with a baudrate from 20 to 125 MBd modulated on a central carrier frequency of 1 GHz. The sigma delta modulator runs at 8 or 13.5 Gbps. A high-speed vertical-cavity surface-emitting laser (VCSEL) operating at 850 nm is used to transmit the signal over 200-m multimode fiber. The receiver amplifies the electrical signals and subsequently filters to recover the original RF signal. Compared with ARoF, improvements exceeding 40 dB were measured on the third order intermodulation products when SDoF was employed, the EVM improves between 2.4 and 7.1 dB.


title: Learning Semantic Script Knowledge with Event Embeddings

abstract: Induction of common sense knowledge about prototypical sequences of events has recently received much attention. Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the compositional process for computing the event representations and the ranking component of the model are jointly estimated from texts. We show that this approach results in a substantial boost in ordering performance with respect to previous methods.


title: A Novel Lip Descriptor for Audio-Visual Keyword Spotting Based on Adaptive Decision Fusion

abstract: Keyword spotting remains a challenge when applied to real-world environments with dramatically changing noise. In recent studies, audio-visual integration methods have demonstrated superiorities since visual speech is not influenced by acoustic noise. However, for visual speech recognition, individual utterance mannerisms can lead to confusion and false recognition. To solve this problem, a novel lip descriptor is presented involving both geometry-based and appearance-based features in this paper. Specifically, a set of geometry-based features is proposed based on an advanced facial landmark localization method. In order to obtain robust and discriminative representation, a spatiotemporal lip feature is put forward concerning similarities among textons and mapping the feature to intra-class subspace. Moreover, a parallel two-step keyword spotting strategy based on decision fusion is proposed in order to make the best use of audio-visual speech and adapt to diverse noise conditions. Weights generated using a neural network combine acoustic and visual contributions. Experimental results on the OuluVS dataset and PKU-AV dataset demonstrate that the proposed lip descriptor shows competitive performance compared to the state of the art. Additionally, the proposed audio-visual keyword spotting (AV-KWS) method based on decision-level fusion significantly improves the noise robustness and attains better performance than feature-level fusion, which is also capable of adapting to various noisy conditions.


title: Cerebellar infarction presenting as inner ear decompression sickness following scuba diving: a case report.

abstract: INTRODUCTION ::: Inner ear decompression sickness following scuba diving is not uncommon and the characteristic features of this disorder are acute peripheral vestibular syndrome, sometimes associated with cochlear signs, requiring urgent hyperbaric oxygen therapy. Cerebellar infarction can also mimic isolated peripheral vestibulopathy. ::: ::: ::: CASE REPORT ::: The authors report the case of a 47-year-old man in good general health admitted with acute left vestibular dysfunction suggestive of inner ear decompression sickness 6 hours after scuba diving. Normal videonystagmography and delayed onset of occipital headache finally led to brain MRI that confirmed the presence of recent ischaemic infarction in the territory of the medial branch of the posterior inferior cerebellar artery. Complementary investigations revealed the presence of a patent foramen ovale with atrial septal aneurysm. No underlying atherosclerotic disease or clotting abnormalities were observed. ::: ::: ::: DISCUSSION/CONCLUSION ::: Cerebellar infarction can present clinically with features of inner ear decompression sickness following scuba diving. An underlying air embolism mechanism cannot be excluded, particularly in patients with a large right-to-left circulatory shunt and no other cardiovascular risk factors.


title: Analysis and Implementation of Half-Bridge Series–Parallel Resonant Converter for Battery Chargers

abstract: This paper presents a novel application of half-bridge series-parallel resonant converter (HBSPRC) for dc source and secondary battery interface. The converter practically eliminates both low- and high-frequency current ripple on the battery, thus maximizing battery life without penalizing the volume of the charger circuit. Moreover, operation above resonance is preferred, because the power switches turn on at zero current and zero voltage; thus, the freewheeling diodes do not need to have very fast reverse-recovery characteristics. The proposed battery charger circuit has few components and low-energy conversion loss, which enhance the system's overall efficiency. Circuit operation modes are determined from the conduction profiles. Operating equations and an operating theory are also developed. Experimental results based on a 12-V 48-Ah battery charger are proposed to validate the analysis and to demonstrate the performance of the proposed approach. The maximum charging efficiency of the proposed topology during the overall charging period is 93.98%. Satisfactory performance is obtained from the measured results.


title: Investigation on Hot-Carrier-Induced degradation of STI-nLDMOS with two-step-oxide process for high side application

abstract: In this work, the Hot-Carrier-Induced (HCI) degradation mechanism of the 30 V shallow trench isolation (STI) n-type lateral DMOS (nLDMOS) with two-step-oxide process applied in high side application (called as HS-nLDMOS) is investigated. It implies that the Ron, sp (on-resistance measured under high side condition) suffers from more damage impact than Ron, ls (on-resistance measured under low side condition). Moreover, a shift of the worst stress condition from the maximum substrate current stress condition (Isubmax) to the maximum gate voltage stress condition (Vgmax) is observed when the HS-nLDMOS is evaluated with Ron, hs instead of Ron, ls. Hence our investigation suggests estimating the degradation of HS-nLDMOS by Ron, hs under Vgmax stress condition in order to evaluate the exact life of the device.


title: "Who are these people?" Evaluating the demographic characteristics and political preferences of MTurk survey respondents

abstract: As Amazon’s Mechanical Turk (MTurk) has surged in popularity throughout political science, scholars have increasingly challenged the external validity of inferences made drawing upon MTurk samples. At workshops and conferences experimental and survey-based researchers hear questions about the demographic characteristics, political preferences, occupation, and geographic location of MTurk respondents. In this paper we answer these questions and present a number of novel results. By introducing a new benchmark comparison for MTurk surveys, the Cooperative Congressional Election Survey, we compare the joint distributions of age, gender, and race among MTurk respondents within the United States. In addition, we compare political, occupational, and geographical information about respondents from MTurk and CCES. Throughout the paper we show several ways that political scientists can use the strengths of MTurk to attract respondents with specific characteristics of interest to best answer their substantive research questions.


Passive Realtime Datacenter Fault Detection and Localization


title: Book Reviews Strategies in Learning and Using a Second

abstract: Author's Acknowledgements 1. Introduction 2. Second language learning and language use strategies: defining terms 3. Methods for investigating language learning and language use strategies 4. Strategy training for learners and the role of the teacher 5. The impact of strategies-based instruction on speaking a foreign language 6. Strategies for choosing the language of thought 7. Strategy use in testing situations 8. Discussion and conclusions References


title: Fighting Sample Degeneracy and Impoverishment in Particle Filters: A Review of Intelligent Approaches

abstract: During the last two decades there has been a growing interest in Particle Filtering (PF). However, PF suffers from two long-standing problems that are referred to as sample degeneracy and impoverishment. We are investigating methods that are particularly efficient at Particle Distribution Optimization (PDO) to fight sample degeneracy and impoverishment, with an emphasis on intelligence choices. These methods benefit from such methods as Markov Chain Monte Carlo methods, Mean-shift algorithms, artificial intelligence algorithms (e.g., Particle Swarm Optimization, Genetic Algorithm and Ant Colony Optimization), machine learning approaches (e.g., clustering, splitting and merging) and their hybrids, forming a coherent standpoint to enhance the particle filter. The working mechanism, interrelationship, pros and cons of these approaches are provided. In addition, approaches that are effective for dealing with high-dimensionality are reviewed. While improving the filter performance in terms of accuracy, robustness and convergence, it is noted that advanced techniques employed in PF often causes additional computational requirement that will in turn sacrifice improvement obtained in real life filtering. This fact, hidden in pure simulations, deserves the attention of the users and designers of new filters.


title: Bioengineered cardiac grafts: A new approach to repair the infarcted myocardium?

abstract: BACKGROUND ::: The myocardium is unable to regenerate because cardiomyocytes cannot replicate after injury. The heart is therefore an attractive target for tissue engineering to replace infarcted myocardium and enhance cardiac function. We tested the feasibility of bioengineering cardiac tissue within novel 3-dimensional (3D) scaffolds. ::: ::: ::: METHODS AND RESULTS ::: We isolated and grew fetal cardiac cells within 3D porous alginate scaffolds. The cell constructs were cultured for 4 days to evaluate viability and morphology before implantation. Light microscopy revealed that within 2 to 3 days in culture, the dissociated cardiac cells form distinctive, multicellular contracting aggregates within the scaffold pores. Seven days after myocardial infarction, rats were randomized to biograft implantation (n=6) or sham-operation (n=6) into the myocardial scar. Echocardiography study was performed before and 65+/-5 days after implantation to assess left ventricular (LV) remodeling and function. Hearts were harvested 9 weeks after implantation. Visual examination of the biograft revealed intensive neovascularization from the neighboring coronary network. Histological examination revealed the presence of myofibers embedded in collagen fibers and a large number of blood vessels. The specimens showed almost complete disappearance of the scaffold and good integration into the host. Although control animals developed significant LV dilatation accompanied by progressive deterioration in LV contractility, in the biograft-treated rats, attenuation of LV dilatation and no change in LV contractility were observed. ::: ::: ::: CONCLUSIONS ::: Alginate scaffolds provide a conducive environment to facilitate the 3D culturing of cardiac cells. After implantation into the infarcted myocardium, the biografts stimulated intense neovascularization and attenuated LV dilatation and failure in experimental rats compared with controls. This strategy can be used for regeneration and healing of the infarcted myocardium.


title: A case study of constructivist instructional strategies for adult online learning

abstract: This case study explores how a constructivist-based instructional design helped adult learners learn in an online learning environment. Two classes of adult learners pursuing professional development and registered in a web-based course were studied. The data consisted of course documents, submitted artefacts, surveys, interviews, in-class observations, and online observations. The study found that the majority of the learners were engaged in two facets of learning. On the one hand, the instructional activities requiring collaboration and interaction helped the learners support one another's learning, from which most claimed to have benefited. On the other hand, the constructivist-based course assisted many learners to develop a sense of becoming more responsible, self-directed learners. Overall, the social constructivist style of instructional strategy seems promising to facilitate adult learning, which not only helps change learners' perceptions of the online learning, but also assists them to learn in a more collaborative, authentic and responsible way. The study, however, also disclosed that in order to maintain high-quality learning, appropriate assessment plans and adequate facilitation must be particularly reinforced. A facilitation model is thus suggested.


title: Real-time drowsiness detection system for an intelligent vehicle

abstract: In the last years, the traffic accidents study is become important because they produce several died and hurt around the world. To help in reducing this fatality, in this paper, a new advanced driver assistance system (ADAS) for automatic driver's drowsiness detection based on visual information and artificial intelligent is presented. This system works on several stages to be fully automatic. In addition, the aim of this algorithm is to locate and to track the face and the eyes to compute a drowsiness index. Examples of different driver's images taken over real vehicle are shown to validate the algorithm that works in real time.


An Analysis of BitTorrent’s Two Kademlia-Based DHTs


title: Complexity Theoretic Lower Bounds for Sparse Principal Component Detection

abstract: In the context of sparse principal component detection, we bring evidence towards the existence of a statistical price to pay for computational efficiency. We measure the performance of a test by the smallest signal strength that it can detect and we propose a computationally efficient method based on semidefinite programming.We also prove that the statistical performance of this test cannot be strictly improved by any computationally efficient method. Our results can be viewed as complexity theoretic lower bounds conditionally on the assumptions that some instances of the planted clique problem cannot be solved in randomized polynomial time.


title: A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems

abstract: This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.


title: A survey of sparse representation: algorithms and applications

abstract: Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision, and pattern recognition. Sparse representation also has a good reputation in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. The main purpose of this paper is to provide a comprehensive study and an updated review on sparse representation and to supply guidance for researchers. The taxonomy of sparse representation methods can be studied from various viewpoints. For example, in terms of different norm minimizations used in sparsity constraints, the methods can be roughly categorized into five groups: 1) sparse representation with $l_{0}$ -norm minimization; 2) sparse representation with $l_{p}$ -norm ( $0 ) minimization; 3) sparse representation with $l_{1}$ -norm minimization; 4) sparse representation with $l_{2,1}$ -norm minimization; and 5) sparse representation with $l_{2}$ -norm minimization. In this paper, a comprehensive overview of sparse representation is provided. The available sparse representation algorithms can also be empirically categorized into four groups: 1) greedy strategy approximation; 2) constrained optimization; 3) proximity algorithm-based optimization; and 4) homotopy algorithm-based sparse representation. The rationales of different algorithms in each category are analyzed and a wide range of sparse representation applications are summarized, which could sufficiently reveal the potential nature of the sparse representation theory. In particular, an experimentally comparative study of these sparse representation algorithms was presented.


title: Image Inpainting : Overview and Recent Advances

abstract: Image inpainting refers to the process of restoring missing or damaged areas in an image. This field of research has been very active over recent years, boosted by numerous applications: restoring images from scratches or text overlays, loss concealment in a context of impaired image transmission, object removal in a context of editing, or disocclusion in image-based rendering (IBR) of viewpoints different from those captured by the cameras. Although earlier work dealing with disocclusion has been published in [1], the term inpainting first appeared in [2] by analogy with a process used in art restoration.


title: The future of computer forensics: A needs analysis survey (Tech Report

abstract: The current study was a pilot study and attempted to add to the growing body of knowledge regarding inherent issues in computer forensics. The study consisted of an Internet-based survey that asked respondents to identify the top five issues in computer forensics. Sixty respondents answered the survey using a free form text field. The results indicated that education/training and certification were the most reported issue (18%) and lack of funding was the least reported (4%). These findings are consistent with a similar law enforcement community study (Stambaugh, Beaupre, Icove, Cassaday, Williams. State and local law enforcement needs to combat electronic crime. National Institute of Justice Research in Brief (2001)). The findings emphasize the fragmented nature of the computer forensics discipline. Currently there is a lack of a national framework for curricula and training development, and no gold standard for professional certification. The findings further support the criticism that there is a disproportional focus on the applied aspects of computer forensics, at the expense of the development of fundamental theories. Further implications of the findings are discussed and suggestions for future research in the area are presented.


title: A Coarse-to-Fine Face Hallucination Method by Exploiting Facial Prior Knowledge

abstract: Face hallucination technique generates high-resolution (HR) face images from low-resolution (LR) ones. In this paper, we propose to use a coarse-to-fine method for face hallucination by constructing a two-branch network, which makes full use of the specific prior knowledge of face images and the advantages of generic image super-resolution (SR) methods. Specifically, we jointly build a deep neural network (DNN) with a face image SR branch and a semantic face parsing branch. The former branch implements the image upsampling and feature extraction using a cascade of convolutional layers. The latter branch extracts facial semantic parsing as prior knowledge. Then, we combine the image features and the prior know ledge to reconstruct HR face images. Finally, we optimize the DNN, by using adversarial training and a perceptual loss, in order to obtain high realism. Extensive experiments show that the proposed method outperforms the state-of-the-art alternatives in terms of accuracy and realism.


title: Successful failure: what Foucault can teach us about privacy self-management in a world of Facebook and big data

abstract: The "privacy paradox" refers to the discrepancy between the concern individuals express for their privacy and the apparently low value they actually assign to it when they readily trade personal information for low-value goods online. In this paper, I argue that the privacy paradox masks a more important paradox: the self-management model of privacy embedded in notice-and-consent pages on websites and other, analogous practices can be readily shown to underprotect privacy, even in the economic terms favored by its advocates. The real question, then, is why privacy self-management occupies such a prominent position in privacy law and regulation. Borrowing from Foucault's late writings, I argue that this failure to protect privacy is also a success in ethical subject formation, as it actively pushes privacy norms and practices in a neoliberal direction. In other words, privacy self-management isn't about protecting people's privacy; it's about inculcating the idea that privacy is an individual, commodified good that can be traded for other market goods. Along the way, the self-management regime forces privacy into the market, obstructs the functioning of other, more social, understandings of privacy, and occludes the various ways that individuals attempt to resist adopting the market-based view of themselves and their privacy. Throughout, I use the analytics practices of Facebook and social networking sites as a sustained case study of the point.


title: An updated evolutionary classification of CRISPR–Cas systems

abstract: The evolution of CRISPR-cas loci, which encode adaptive immune systems in archaea and bacteria, involves rapid changes, in particular numerous rearrangements of the locus architecture and horizontal transfer of complete loci or individual modules. These dynamics complicate straightforward phylogenetic classification, but here we present an approach combining the analysis of signature protein families and features of the architecture of cas loci that unambiguously partitions most CRISPR-cas loci into distinct classes, types and subtypes. The new classification retains the overall structure of the previous version but is expanded to now encompass two classes, five types and 16 subtypes. The relative stability of the classification suggests that the most prevalent variants of CRISPR-Cas systems are already known. However, the existence of rare, currently unclassifiable variants implies that additional types and subtypes remain to be characterized.


title: A partially supervised cross-collection topic model for cross-domain text classification

abstract: Cross-domain text classification aims to automatically train a precise text classifier for a target domain by using labelled text data from a related source domain. To this end, one of the most promising ideas is to induce a new feature representation so that the distributional difference between domains can be reduced and a more accurate classifier can be learned in this new feature space. However, most existing methods do not explore the duality of the marginal distribution of examples and the conditional distribution of class labels given labeled training examples in the source domain. Besides, few previous works attempt to explicitly distinguish the domain-independent and domain-specific latent features and align the domain-specific features to further improve the cross-domain learning. In this paper, we propose a model called Partially Supervised Cross-Collection LDA topic model (PSCCLDA) for cross-domain learning with the purpose of addressing these two issues in a unified way. Experimental results on nine datasets show that our model outperforms two standard classifiers and four state-of-the-art methods, which demonstrates the effectiveness of our proposed model.


title: Developing a unified framework of the business model concept

abstract: Recent rapid advances in Information and Communication Technologies (ICTs) have highlighted the rising importance of the Business Model (BM) concept in the field of Information Systems (IS). Despite agreement on its importance to an organization's success, the concept is still fuzzy and vague, and there is little consensus regarding its compositional facets. Identifying the fundamental concepts, modeling principles, practical functions, and reach of the BM relevant to IS and other business concepts is by no means complete. This paper, following a comprehensive review of the literature, principally employs the content analysis method and utilizes a deductive reasoning approach to provide a hierarchical taxonomy of the BM concepts from which to develop a more comprehensive framework. This framework comprises four fundamental aspects. First, it identifies four primary BM dimensions along with their constituent elements forming a complete ontological structure of the concept. Second, it cohesively organizes the BM modeling principles, that is, guidelines and features. Third, it explains the reach of the concept showing its interactions and intersections with strategy, business processes, and IS so as to place the BM within the world of digital business. Finally, the framework explores three major functions of BMs within digital organizations to shed light on the practical significance of the concept. Hence, this paper links the BM facets in a novel manner offering an intact definition. In doing so, this paper provides a unified conceptual framework for the BM concept that we argue is comprehensive and appropriate to the complex nature of businesses today. This leads to fruitful implications for theory and practice and also enables us to suggest a research agenda using our conceptual framework.


title: Semantic matching: Algorithms and implementation

abstract: We view match as an operator that takes two graph-like structures (e.g., classifications, XML schemas) and produces a mapping between the nodes of these graphs that correspond semantically to each other. Semantic matching is based on two ideas: (i) we discover mappings by computing semantic relations (e.g., equivalence, more general); (ii) we determine semantic relations by analyzing the meaning (concepts, not labels) which is codified in the elements and the structures of schemas. In this paper we present basic and optimized algorithms for semantic matching, and we discuss their implementation within the S-Match system. We evaluate S-Match against three state of the art matching systems, thereby justifying empirically the strength of our approach.


Developing Emotional, Social, and Cognitive Intelligence Competencies in Managers and Leaders


title: Zero-Shot Super-Resolution Using Deep Internal Learning

abstract: Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance in the past few years. However, being supervised, these SR methods are restricted to specific training data, where the acquisition of the low-resolution (LR) images from their high-resolution (HR) counterparts is predetermined (e.g., bicubic downscaling), without any distracting artifacts (e.g., sensor noise, image compression, non-ideal PSF, etc). Real LR images, however, rarely obey these restrictions, resulting in poor SR results by SotA (State of the Art) methods. In this paper we introduce "Zero-Shot" SR, which exploits the power of Deep Learning, but does not rely on prior training. We exploit the internal recurrence of information inside a single image, and train a small image-specific CNN at test time, on examples extracted solely from the input image itself. As such, it can adapt itself to different settings per image. This allows to perform SR of real old photos, noisy images, biological data, and other images where the acquisition process is unknown or non-ideal. On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods. To the best of our knowledge, this is the first unsupervised CNN-based SR method.


title: Finite element analysis of composite overwrapped pressure vessel for hydrogen storage

abstract: This paper investigates the effect of winding angle on composite overwrapped pressure vessel (COPV) manufactured by filament winding process where continuous fibers impregnated in resin and wound over a liner. Three dimensional shell model is considered for the structural analysis of pressure vessel. The study on COPV is carried out by considering carbon T300/epoxy material. The thickness of composite vessel is calculated by using netting analysis. The study focused on optimum winding angle, total deformation, stress generation and failure analysis of composite pressure vessel. The failure of COPV is predicted by using Tsai-Wu failure criteria. The classical laminate theory (CLT) and failure criteria is considered for analytical method and obtained results are compared with numerical results which are obtained from ANSYS workbench (ACP) for validation. This comparison further helps in predicting behavior of COPV for change in winding angle and operating internal pressure.


title: Normal values of heart rate variability at rest in a young, healthy and active Mexican population

abstract: This study analyzed Heart Rate Variability in a large sample of active young subjects within a narrow age range (18 to 25), using time and frequency domain methods and a Poincare plot. Heart rate was recorded (beat to beat) for 30 minutes at rest in 200 healthy subjects divided into 4 groups: 50 sportsmen (20.54 ± 1.52 years); 50 active men (21.22 ± 1.31 years); 50 sportswomen (20.10 ± 1.87 years) and 50 active women (20.92 ± 1.87 years). Significant differences were found for most parameters between athletes and active subjects (male and female) but not between genders. Percentile distributions were provided for all parameters (according to gender and physical activity level) to be used as references in future researches.


title: Extending the Interaction Flow Modeling Language (IFML) for Model Driven Development of Mobile Applications Front End

abstract: Front-end design of mobile applications is a complex and multidisciplinary task, where many perspectives intersect and the user experience must be perfectly tailored to the application objectives. However, development of mobile user interactions is still largely a manual task, which yields to high risks of errors, inconsistencies and inefficiencies. In this paper we propose a model-driven approach to mobile application development based on the IFML standard. We propose an extension of the Interaction Flow Modeling Language tailored to mobile applications and we describe our implementation experience that comprises the development of automatic code generators for cross-platform mobile applications based on HTML5, CSS and JavaScript optimized for the Apache Cordova framework. We show the approach at work on a popular mobile application, we report on the application of the approach on an industrial application development project and we provide a productivity comparison with traditional approaches.


title: An Analysis of Public Clouds Elasticity in the Execution of Scientific Applications: a Survey

abstract: Elasticity can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. It is an important feature provided by cloud computing, that has been widely used in web applications and is also gaining attention in the scientific community. Considering the possibilities of using elasticity in this context, a question arises: "Are the available public cloud solutions suitable to provide elasticity to scientific applications?" To answer the question, in a first moment we present a survey on the use of cloud computing in scientific scenarios, providing an overview of the subject. Next, we describe the elasticity mechanisms offered by major public cloud providers and analyzes the limitations of the solutions in providing elasticity for scientific applications. As the main contribution of the article, we also present an analysis over some initiatives that are being developed to overcome the current challenges. In our opinion, current computational clouds are developing rapidly but have not yet reached the necessary maturity level to meet all scientific applications elasticity requirements. We expect that in the coming years the efforts being taken by numerous researchers in this area identify and address these challenges and lead to better and more mature technologies that will improve cloud computing practices.


title: Outcome Studies on the Efficacy of Art Therapy: A Review of Findings

abstract: In response to a review by Reynolds, Nabors, and Quinlan (2000) of the art therapy literature prior to 1999, this review article identifies studies from 1999–2007 that measured outcomes of art therapy effectiveness with all ages of clinical and nonclinical populations. Although numerous studies blend art therapy with other modalities, this review is limited to studies that isolate art therapy as the specific intervention. The results of this review suggest that there is a small body of quantifiable data to support the claim that art therapy is effective in treating a variety of symptoms, age groups, and disorders.


title: Nuclear feature extraction for breast tumor diagnosis

abstract: Interactive image processing techniques, along with a linear-programming-based inductive classifier, have been used to create a highly accurate system for diagnosis of breast tumors. A small fraction of a fine needle aspirate slide is selected and digitized. With an interactive interface, the user initializes active contour models, known as snakes, near the boundaries of a set of cell nuclei. The customized snakes are deformed to the exact shape of the nuclei. This allows for precise, automated analysis of nuclear size, shape and texture. Ten such features are computed for each nucleus, and the mean value, largest (or 'worst') value and standard error of each feature are found over the range of isolated cells. After 569 images were analyzed in this fashion, different combinations of features were tested to find those which best separate benign from malignant samples. Ten-fold cross-validation accuracy of 97% was achieved using a single separating plane on three of the thirty features: mean texture, worst area and worst smoothness. This represents an improvement over the best diagnostic results in the medical literature. The system is currently in use at the University of Wisconsin Hospitals. The same feature set has also been utilized in the much more difficult task of predicting distant recurrence of malignancy in patients, resulting in an accuracy of 86%.


title: Social influence effects on automatic racial prejudice.

abstract: Although most research on the control of automatic prejudice has focused on the efficacy of deliberate attempts to suppress or correct for stereotyping, the reported experiments tested the hypothesis that automatic racial prejudice is subject to common social influence. In experiments involving actual interethnic contact, both tacit and expressed social influence reduced the expression of automatic prejudice, as assessed by two different measures of automatic attitudes. Moreover, the automatic social tuning effect depended on participant ethnicity. European Americans (but not Asian Americans) exhibited less automatic prejudice in the presence of a Black experimenter than a White experimenter (Experiments 2 and 4), although both groups exhibited reduced automatic prejudice when instructed to avoid prejudice (Experiment 3). Results are consistent with shared reality theory, which postulates that social regulation is central to social cognition.


A digital-signal-processor-based optical tomographic system for dynamic imaging of joint diseases


title: An Out-of-band Authentication Scheme for Internet of Things Using Blockchain Technology

abstract: While the rapid development of IoT devices is changing our daily lives, some particular issues hinder the massive deployment of IoT devices. For example, current network ID management system cannot handle so many new terminals; there is no agreed security standards for IoT manufacturers to follow when designing their products. The whole IoT industry is expecting the breakthrough in network infrastructure and the development of novel security mechanisms that can enable the flexible, secure and reliable access and management of IoT devices. Bitcoin, first released in 2009, breeds the decentralized Blockchain technology. The decentralization, anonymity and proof of security characteristics of Blockchain can prevent collusion and single point failure of a centralized server. We believe that the application of Blockchain into the IoT system can clear the obstacles facing the development of IoT architecture and security. To this end, we propose an out-of-band two-factor authentication scheme for IoT devices based on Blockchain infrastructure. We implemented the IoT and Blockchain integrated system with Eris Blockchain and equivalent computing devices to emulate IoT devices. The overheads to run Blockchain and smart contract services on the emulator devices are measured. The BeagleBone Black and Raspberry Pi 3 nodes have an average memory usage of 29.5M, and the CPU usage of 29.55% and 13.35%, respectively.


title: Selective Experience Replay for Lifelong Learning

abstract: Deep reinforcement learning has emerged as a powerful tool for a variety of learning tasks, however deep nets typically exhibit forgetting when learning multiple tasks in sequence. To mitigate forgetting, we propose an experience replay process that augments the standard FIFO buffer and selectively stores experiences in a long-term memory. We explore four strategies for selecting which experiences will be stored: favoring surprise, favoring reward, matching the global training distribution, and maximizing coverage of the state space. We show that distribution matching successfully prevents catastrophic forgetting, and is consistently the best approach on all domains tested. While distribution matching has better and more consistent performance, we identify one case in which coverage maximization is beneficial - when tasks that receive less trained are more important. Overall, our results show that selective experience replay, when suitable selection algorithms are employed, can prevent catastrophic forgetting.


title: Linked Data Contracts to Support Data Protection and Data Ethics in the Sharing of Scientific Data

abstract: Supported by the ADAPT Centre for Digital Content Technology which is funded under ::: the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under ::: the European Regional Development Fund.


title: Degeneration in VAE: in the Light of Fisher Information Loss

abstract: While enormous progress has been made to Variational Autoencoder (VAE) in recent years, similar to other deep networks, VAE with deep networks suffers from the problem of degeneration, which seriously weakens the correlation between the input and the corresponding latent codes, deviating from the goal of the representation learning. To investigate how degeneration affects VAE from a theoretical perspective, we illustrate the information transmission in VAE and analyze the intermediate layers of the encoders/decoders. Specifically, we propose a Fisher Information measure for the layer-wise analysis. With such measure, we demonstrate that information loss is ineluctable in feed-forward networks and causes the degeneration in VAE. We show that skip connections in VAE enable the preservation of information without changing the model architecture. We call this class of VAE equipped with skip connections as SCVAE and perform a range of experiments to show its advantages in information preservation and degeneration mitigation.


title: Efficient Optimal Algorithm of Task Scheduling in Cloud Computing Environment

abstract: Cloud computing is an emerging technology in distributed computing which facilitates pay per model as per user demand and requirement.Cloud consist of a collection of virtual machine which includes both computational and storage facility. The primary aim of cloud computing is to provide efficient access to remote and geographically distributed resources. Cloud is developing day by day and faces many challenges, one of them is scheduling. Scheduling refers to a set of policies to control the order of work to be performed by a computer system. A good scheduler adapts its scheduling strategy according to the changing environment and the type of task. In this research paper we presented a Generalized Priority algorithm for efficient execution of task and comparison with FCFS and Round Robin Scheduling. Algorithm should be tested in cloud Sim toolkit and result shows that it gives better performance compared to other traditional scheduling algorithm.