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2014 International Conference on IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2014

作者： Orakwue, Stella I. Ngah, Razali Rahman, T.A. Mohd Hashim, Siti Zaiton Skudai Johor81310 Malaysia Department of Software Engineering Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia Skudai81310 Malaysia

ISBN: (纸本)9781479937110

A neural network based switched beam smart antenna has been proposed. A feed-forward back propagation artificial neural network (FFBPANN) was applied in antenna beam switching. The network takes samples of the calibration of the footprint of the base station, in terms of azimuth and elevation angle, and maps it to the radiation pattern of different base station antenna arrays covering the area. It was treated as a classification problem. The developed methodology was tested for a 4×4 Butler matrix and the result obtained shows an improvement in the performance of beam switching. The developed network can be used to switch different antennas of the base stations depending on the position of the target. © 2014 IEEE.

关键词： Butler matrix

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Distributed stochastic ADMM for matrix factorization 14

Distributed stochastic ADMM for matrix factorization

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23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

作者： Yu, Zhi-Qin Shi, Xing-Jian Yan, Ling Li, Wu-Jun Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450325981

Matrix factorization (MF) has become the most popular technique for recommender systems due to its promising performance. Recently, distributed (parallel) MF models have received much attention from researchers of big data community. In this paper, we propose a novel model, called distributed stochastic alternating direction methods of multipliers (DS-ADMM), for large-scale MF problems. DS-ADMM is a distributed stochastic variant of ADMM. In particular, we first devise a new data split strategy to make the distributed MF problem fit for the ADMM framework. Then, a stochastic ADMM scheme is designed for learning. Finally, we implement DS-ADMM based on message passing interface (MPI), which can run on clusters with multiple machines (nodes). Experiments on several data sets from recommendation applications show that our DS-ADMM model can outperform other state-of-the-art distributed MF models in terms of both efficiency and accuracy. Copyright 2014 ACM.

关键词： Recommender systems

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Large-scale supervised multimodal hashing with semantic correlation maximization 28

Large-scale supervised multimodal hashing with semantic corr...

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28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014

作者： Zhang, Dongqing Li, Wu-Jun Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781577356790

Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing (SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability. Copyright © 2014, Association for the Advancement of Artificial Intelligence.

关键词： Semantics

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Supervised hashing with latent factor models 14

Supervised hashing with latent factor models

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37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014

作者： Zhang, Peichao Zhang, Wei Li, Wu-Jun Guo, Minyi Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450322591

Due to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean) structure of the training data is preserved. Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods. In this paper, we propose a novel supervised hashing method, called latent factor hashing (LFH), to learn similarity-preserving binary codes based on latent factor models. An algorithm with convergence guarantee is proposed to learn the parameters of LFH. Furthermore, a linear-time variant with stochastic learning optimization is proposed for training LFH on large-scale datasets. Experimental results on two large datasets with semantic labels show that LFH can achieve superior accuracy than state-of-the-art methods with comparable training time. Copyright 2014 ACM.

关键词： Big data

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When to automate software testing? decision support based on system dynamics: An industrial case study

When to automate software testing? decision support based on...

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2014 International Conference on software and systems Process, ICSSP 2014

作者： Sahaf, Zahra Garousi, Vahid Pfahl, Dietmar Irving, Rob Amannejad, Yasaman Department of Electrical and Computer Engineering University of Calgary Calgary AB Canada Department of Software Engineering Atilim University Ankara Turkey Institute of Computer Science University of Tartu Tartu Estonia Pason Systems Corporation Calgary AB Canada

ISBN: (纸本)9781450327541

software test processes are complex and costly. To reduce testing effort without compromising effectiveness and product quality, automation of test activities has been adopted as a popular approach in software industry. However, since test automation usually requires substantial upfront investments, automation is not always more cost-effective than manual testing. To support decision-makers in finding the optimal degree of test automation in a given project, we propose in this paper a simulation model using the System Dynamics (SD) modeling technique. With the help of the simulation model, we can evaluate the performance of test processes with varying degrees of automation of test activities and help testers choose the most optimal cases. As the case study, we describe how we used our simulation model in the context of an Action Research (AR) study conducted in collaboration with a software company in Calgary, Canada. The goal of the study was to investigate how the simulation model can help decision-makers decide whether and to what degree the company should automate their test processes. As a first step, we compared the performances of the current fully manual testing with several cases of partly automated testing as anticipated for implementation in the partner company. The development of the simulation model as well as the analysis of simulation results helped the partner company to get a deeper understanding of the strengths and weaknesses of their current test process and supported decision-makers in the cost effective planning of improvements of selected test activities. © 2014 ACM.

关键词： software testing

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Using social media to perform local influenza surveillance in an inner-city hospital: A retrospective observational study

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JMIR Public Health and Surveillance 2015年第1期1卷 e5页

作者： Broniatowski, David Andre Dredze, Mark Paul, Michael J. Dugas, Andrea Department of Engineering Management and Systems Engineering The George Washington University Washington DC United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD United States Department of Computer Science Johns Hopkins University Baltimore MD United States Department of Emergency Medicine Johns Hopkins University Baltimore MD United States

Background: Public health officials and policy makers in the United States expend significant resources at the national, state, county, and city levels to measure the rate of influenza infection. These individuals rely on influenza infection rate information to make important decisions during the course of an influenza season driving vaccination campaigns, clinical guidelines, and medical staffing. Web and social media data sources have emerged as attractive alternatives to supplement existing practices. While traditional surveillance methods take 1-2 weeks, and significant labor, to produce an infection estimate in each locale, web and social media data are available in near real-time for a broad range of locations. Objective: The objective of this study was to analyze the efficacy of flu surveillance from combining data from the websites Google Flu Trends and HealthTweets at the local level. We considered both emergency department influenza-like illness cases and laboratory-confirmed influenza cases for a single hospital in the City of Baltimore. Methods: This was a retrospective observational study comparing estimates of influenza activity of Google Flu Trends and Twitter to actual counts of individuals with laboratory-confirmed influenza, and counts of individuals presenting to the emergency department with influenza-like illness cases. Data were collected from November 20, 2011 through March 16, 2014. Each parameter was evaluated on the municipal, regional, and national scale. We examined the utility of social media data for tracking actual influenza infection at the municipal, state, and national levels. Specifically, we compared the efficacy of Twitter and Google Flu Trends data. Results: We found that municipal-level Twitter data was more effective than regional and national data when tracking actual influenza infection rates in a Baltimore inner-city hospital. When combined, national-level Twitter and Google Flu Trends data outperformed each data source ind

关键词： Social computing Time series analysis Web mining

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A modelica coordination pattern library for cyber-physical systems 14

A modelica coordination pattern library for cyber-physical s...

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7th International Conference on Simulation Tools and Techniques, SIMUTools 2014

作者： Pohlmann, Uwe Dziwok, Stefan Meyer, Matthias Tichy, Matthias Thiele, Sebastian Fraunhofer IPT Project Group Mechatronic Systems Design Software Engineering Zukunftsmeile 1 Paderborn33102 Germany Software Engineering Group Heinz Nixdorf Institute University of Paderborn Zukunftsmeile 1 Paderborn33102 Germany Software Engineering Division Department of Computer Science and Engineering Chalmers University of Gothenburg Sweden

ISBN: (纸本)9781631900075

Today's embedded systems often do not operate individually anymore. Instead, they form so called cyber-physical systems, where the overall functionality is provided by the collaboration of systems. Consequently, the design of this collaboration is an important activity during development and strongly affects system quality. In previous work, we presented a catalog of reusable messagebased real-time coordination patterns to avoid manual creation of new and, thus, error-prone designs. In this paper, we present an implementation of this catalog by a library in the Modelica language and an appropriate development process. The library stores ready to reuse solutions for common coordination activities and, thus, increases efficiency and effectiveness for use. Furthermore, the use of Modelica enables early holistic simulation of cyber-physical systems including feedback controllers and message-based coordination. We illustrate the library with examples from an autonomous railway vehicle and present an early evaluation. Copyright © 2014 ICST.

关键词： Cyber Physical System

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Toward model-driven engineering principles and practices to support model replicability

Toward model-driven engineering principles and practices to ...

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Summer computer Simulation Conference, SCSC 2014, Part of the 2014 Summer Simulation Multiconference, SummerSim 2014

作者： Ledet, Joseph Teran-Somohano, Alejandro Butcher, Zachary Yilmaz, Levent Smith, Alice E. Oʇuztüzün, Halit Dayibaş, Orçun Görür, Bilge Kaan Computer Science and Software Engineering Samuel Ginn College of Engineering Auburn University United States Industrial and Systems Engineering Samuel Ginn College of Engineering Auburn University United States Department of Computer Engineering School of Engineering Middle East Technical University United States

ISBN: (纸本)9781632667335

Recent years have seen a proliferation of the use of simulation models in computational science. Most of these models have never been independently replicated by anyone but the original developer. Furthermore, there is a growing credibility gap due to widespread, relaxed attitudes in communication of experiments, models, and validation of simulations used in computational research. We examine various issues and challenges involved in model replication and simulation experiment reproducibility. Model-driven simulation engineering principles and model transformation concepts are adopted as solution strategies to improve replicability of models and reproducibility of experiments. A process model, an architectural framework, and an implementation strategy is introduced to address identified issues in simulation experiment management and model replication.

关键词： Experiment Modeling Model Replicability Model-driven Reproducibility Transformation-driven Replication

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Coupled group lasso for web-scale CTR prediction in display advertising 31

Coupled group lasso for web-scale CTR prediction in display ...

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31st International Conference on Machine Learning, ICML 2014

作者： Yan, Ling Li, Wu-Jun Xue, Gui-Rong Han, Dingyi Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University China Alibaba Group China

ISBN: (纸本)9781634393973

In display advertising, click through rate (CTR) prediction is the problem of estimating the probability that an advertisement (ad) is clicked when displayed to a user in a specific context. Due to its easy implementation and promising performance, logistic regression (LR) model has been widely used for CTR prediction, especially in industrial systems. However, it is not easy for LR to capture the nonlinear information, such as the conjunction information, from user features and ad features. In this paper, we propose a novel model, called coupled group lasso (CGL), for CTR prediction in display advertising. CGL can seamlessly integrate the conjunction information from user features and ad features for modeling. Furthermore, CGL can automatically eliminate useless features for both users and ads, which may facilitate fast online prediction. Scalability of CGL is ensured through feature hashing and distributed implementation. Experimental results on real-world data sets show that our CGL model can achieve state-of-the-art performance on web- scale CTR prediction tasks. Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved.

关键词： Forecasting

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Advanced information systems engineering workshops: CAiSE 2014 InternationalWorkshops Thessaloniki, Greece, June 16-20, 2014 Proceedings

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26th International Conference on Advanced Information systems engineering, CAiSE 2014

作者： Iliadis, Lazaros Papazoglou, Michael Pohl, Klaus Democritus University of Thrace Department of Forestry and Management of the Environment Orestiada Greece University of Tilburg Department of Computer Science Tilburg Netherlands University of Duisburg-Essen Department of Software Systems Engineering Essen Germany

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