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8th ACM Conference on Security and Privacy in Wireless and Mobile Networks, WiSec 2015

作者： You, Wanqing Qian, Kai Guo, Minzhe Bhattacharya, Prabir Qian, Ying Tao, Lixin Department of Computer Science and Software Engineering Southern Polytechnic State University MariettaGA United States Department of Electrical Engineering and Computing Systems University of Cincinnati United States Department of Computer Science East China Normal University Shanghai China Dept. of Computer Science Pace University New York United States

ISBN: (纸本)9781450336239

Research on effective and efficient mobile threat analysis becomes an emerging and important topic in cybersecurity research area. Static analysis and dynamic analysis constitute two of the most popular types of techniques for security analysis and evaluation;nevertheless, each of them has its strengths and weaknesses. To leverage the benefits of both approaches, we propose a hybrid approach that integrates the static and dynamic analysis for detecting security threats in mobile applications. The key of this approach is the unification of data states and software execution on critical test paths. The approach consists of two phases. In the first phase, a pilot static analysis is conducted to identify potential critical attack paths based on Android APIs and existing attack patterns. In the second phase, a dynamic analysis follows the identified critical paths to execute the program in a limited and focused manner. Attacks shall be detected by checking the conformance of the detected paths with existing attack patterns. The method will report the types of detected attack scenarios based on types of sensitive data that may be compromised, such as web browser cookie.

关键词： Android application analysis Data path tracing Dynamic analysis Static analysis Symbolic execution

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Evaluating findability of open educational resources from the perspective of users with disabilities: a preliminary approach

Evaluating findability of open educational resources from th...

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International Conference on eDemocracy & eGovernment (ICEDEG)

作者： Rosa Navarrete Sergio Lujan-Mora Faculty of System Engineering National Polytechnic School Ecuador Department of Software and Computing Systems University of Alicante Spain

e-Education is an umbrella term that refers to educational collaborative environments supported by technology that related not only formal academic institutions programs but also self-learners and teachers around the world. This educational paradigm is of paramount importance for society nowadays, because expands access opportunities to education at all levels. These learning environments can take advantage of Open Educational Resources. Using OER is a growing trend and it is turning into a recommendation by some governments and international education forums. Nevertheless, in spite of the availability of OER websites, quite a few of them do not consider accessibility design principles, therefore these websites present access barriers for users with disabilities. Besides, only a reduce number of these websites offer accessible resources, i.e. digital contents suitable for users with disabilities. On the other hand, in web environment, findability is a design consideration that improves the user's searchable experience by simplifying the location and acquisition of elements in websites. In this restricted scenario, findability of educational resources is a key issue for users with disabilities. In this research, we address the issue of findability of Open Educational Resources (OER), from the perspective of inclusive participation of users with disabilities, by considering three aspects: web accessibility, some usability best practices oriented to users with disabilities and Information Architecture. The evaluation is performed in some important and well known OER websites.

关键词： Usability Navigation Guidelines Education Information architecture Standards Organizations

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Development of intelligent system to support management decision-making in education

Development of intelligent system to support management deci...

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International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)

作者： Indira Uvalieva Zhadyra Garifullina Anar Utegenova Shara Toibayeva Bakhtiyar Issin Information Systems Department Technical University Oskemen Kazakhstan Computing and software subdepartment Department of Computer and program engineering “Turan” university

The article takes a look at an algorithm of decision making in educational management data system based on the Analytic Hierarchy Process. This technique allows to choose the best of the proposed alternatives, the characteristics of which are vectors with heterogeneous individual components including those that are not clearly defined. The architecture of a decision-making support system that implements the proposed algorithm is suggested.

关键词： Education Analytic hierarchy process Decision support systems Computers Information technology software

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Active Learning for Efficient Sampling of Control Models of Collectives

Active Learning for Efficient Sampling of Control Models of ...

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International Conference on Self-Adaptive and Self-Organizing systems (SASO)

作者： Alexander Schiendorfer Christoph Lassner Gerrit Anders Wolfgang Reif Rainer Lienhart Institute for Software & Systems Engineering University of Augsburg Germany Perceiving Systems Department Max Planck Institute for Intelligent Systems Germany Multimedia Computing and Computer Vision Lab University of Augsburg Germany

Many large-scale systems benefit from an organizational structure to provide for problem decomposition. A pivotal problem solving setting is given by hierarchical control systems familiar from hierarchical task networks. If these structures can be modified autonomously by, e.g., Coalition formation and reconfiguration, adequate decisions on higher levels require a faithful abstracted model of a collective of agents. An illustrative example is found in calculating schedules for a set of power plants organized in a hierarchy of Autonomous Virtual Power Plants. Functional dependencies over the combinatorial domain, such as the joint costs or rates of change of power production, are approximated by repeatedly sampling input-output pairs and substituting the actual functions by piecewise linear functions. However, if the sampled data points are weakly informative, the resulting abstracted high-level optimization introduces severe errors. Furthermore, obtaining additional point labels amounts to solving computationally hard optimization problems. Building on prior work, we propose to apply techniques from active learning to maximize the information gained by each additional point. Our results show that significantly better allocations in terms of cost-efficiency (up to 33.7 % reduction in costs in our case study) can be found with fewer but carefully selected sampling points using Decision Forests.

关键词： Power generation Production Cost function Schedules Robots Switches

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A fully implicit method for lattice boltzmann equations

A fully implicit method for lattice boltzmann equations

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作者： Huang, Jizu Yang, Chao Cai, Xiao-Chuan Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Institute of Software Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Computer Science Beijing100190 China Department of Computer Science University of Colorado Boulder BoulderCO80309 United States

Existing approaches for solving the lattice Boltzmann equations with finite difference methods are explicit and semi-implicit;both have certain stability constraints on the time step size. In this work, a fully implicit second-order finite difference scheme is developed. We focus on a parallel, highly scalable, Newton-Krylov-RAS algorithm for the solution of a large sparse nonlinear system of equations arising at each time step. Here, RAS is a restricted additive Schwarz preconditioner based on a first-order spatial discretization. We show numerically that by using the fully implicit method the time step size is no longer constrained by the CFL condition, and the Newton-Krylov- RAS algorithm is scalable on a supercomputer with more than ten thousand processors. Moreover, to calculate the steady state solution we investigate an adaptive time stepping strategy. The total compute time required by the implicit method with adaptive time stepping is much smaller than that of an explicit method for several test cases. © 2015 Society for Industrial and Applied Mathematics.

关键词： Finite difference method

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Foreword

Proceedings of the 2016 International Conference on Image Pr...

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Proceedings of the 2016 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

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Deep Learning with MCA-based Instance Selection and Bootstrapping for Imbalanced Data Classification

Deep Learning with MCA-based Instance Selection and Bootstra...

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International Conference on Collaborative computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Sheng Guan Min Chen Hsin-Yu Ha Shu-Ching Chen Mei-Ling Shyu Chengde Zhang School of Computing and Information Sciences Florida International University Miami FL USA Computing and Software Systems University of Washington Bothell Bothell WA USA Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA School of Information and Safety Engineering Zhongnan University of Economics and Law Hubei China

In this paper, we propose an extended deep learning approach that incorporates instance selection and bootstrapping techniques for imbalanced data classification. In supervised learning, classification performance often deteriorates when the training set is imbalanced where at least one of the classes has a substantially fewer number of instances than the others. We propose to use adaptive synthetic sampling approach (ADASYN) to generate synthetic instances for the minority class. A data pruning process based on multiple correspondence analysis (MCA) is then performed to identify a sub-set of synthetic instances that are most suitable to supplement the existing minority instances. This results in a relatively more balanced training dataset which is then bootstrapped and fed into the convolutional neural networks (CNNs) for classification. Furthermore, we propose to use low-level features pre-processed by principal component analysis (PCA), instead of the commonly used raw signal data, as the input to CNNs to reduce the computational time. The experimental results show the effectiveness of our framework in classifying 54 TRECVID concepts with different imbalanced levels by comparing with other state-of-the-art methods.

关键词： Training Feature extraction Machine learning Neurons Multimedia communication Biological neural networks Principal component analysis

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Cross-project build co-change prediction

Cross-project build co-change prediction

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Xin Xia David Lo Shane McIntosh Emad Shihab Ahmed E. Hassan College of Computer Science and Technology Zhejiang University Hangzhou China School of Information Systems Singapore Management University Singapore School of Computing Queen's University Kingston Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

Build systems orchestrate how human-readable source code is translated into executable programs. In a software project, source code changes can induce changes in the build system (aka. build co-changes). It is difficult for developers to identify when build co-changes are necessary due to the complexity of build systems. Prediction of build co-changes works well if there is a sufficient amount of training data to build a model. However, in practice, for new projects, there exists a limited number of changes. Using training data from other projects to predict the build co-changes in a new project can help improve the performance of the build co-change prediction. We refer to this problem as cross-project build co-change prediction. In this paper, we propose CroBuild, a novel cross-project build co-change prediction approach that iteratively learns new classifiers. CroBuild constructs an ensemble of classifiers by iteratively building classifiers and assigning them weights according to its prediction error rate. Given that only a small proportion of code changes are build co-changing, we also propose an imbalance-aware approach that learns a threshold boundary between those code changes that are build co-changing and those that are not in order to construct classifiers in each iteration. To examine the benefits of CroBuild, we perform experiments on 4 large datasets including Mozilla, Eclipse-core, Lucene, and Jazz, comprising a total of 50,884 changes. On average, across the 4 datasets, CroBuild achieves a F1-score of up to 0.408. We also compare CroBuild with other approaches such as a basic model, AdaBoost proposed by Freund et al., and TrAdaBoost proposed by Dai et al.. On average, across the 4 datasets, the CroBuild approach yields an improvement in F1-scores of 41.54%, 36.63%, and 36.97% over the basic model, AdaBoost, and TrAdaBoost, respectively.

关键词： Predictive models Training Data models Measurement Error analysis Buildings Training data

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Virtual Machine Image Content Aware I/O Optimization for Mobile Virtualization

Virtual Machine Image Content Aware I/O Optimization for Mob...

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IEEE International Symposium on Cyberspace Safety and Security

作者： Renhai Chen Yi Wang Jingtong Hu Duo Liu Zili Shao Yong Guan Embedded Systems and CPS Laboratory Department of Computing The Hong Kong Polytechnic University College of Computer Science and Software Engineering Shenzhen University School of Electrical and Computer Engineering Oklahoma State University College of Computer Science Chongqing University College of Computer and Information Management Capital Normal University

ISBN: (纸本)9781479989386

Mobile virtualization introduces extra layers in software stacks, which leads to performance degradation. Especially, each I/O operation has to pass through several software layers to reach the NAND-flash-based storage systems. This paper targets at optimizing I/O for mobile virtualization, since I/O becomes one of major performance bottlenecks that seriously affects the performance of mobile devices. Among all the I/O operations, a large percentage is updating metadata. Frequent updating metadata not only degrades overall I/O performance but also severely reduces flash memory lifetime. In this paper, we propose a novel I/O optimization technique to identify the metadata of a guest file system which is stored in a VM (Virtual Machine) image file and frequently updated. Then, these metadata are stored in a small additional NVM (Non-Volatile Memory) which is faster and more endurable to greatly improve flash memory's performance and lifetime. To the best of our knowledge, this is the first work to identify the file system metadata from regular data in a guest OS VM image file under mobile virtualization. The proposed scheme is evaluated on a real hardware embedded platform. The experimental results show that the proposed techniques can improve write performance to 45.21% in mobile devices with virtualization.

关键词： IO Virtualization Metadata Mobile devices virtual machines Cache Memory System degradation of performance Mobile Conference documents

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