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IEEE International Conference on Multimedia and Expo (ICME)

作者： Fei Tao Junbiao Pang Chunjie Zhang Liang Li Li Su Weigang Zhang Qingming Huang Guiping Su School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China College of Metropolitan Transportation Beijing University of Technology China Key Lab on Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9781467372596

In web topic detection, detecting “hot” topics from enormous User-Generated Content (UGC) on web data poses two main difficulties that conventional approaches can barely handle: 1) poor feature representations from noisy images and short texts; and 2) uncertain roles of modalities where visual content is either highly or weakly relevant to textual cues due to less-constrained data. In this paper, following the detection by ranking approach, we address the problem by learning a robust shared representation from multiple, noisy and complementary features, and integrating both textual and visual graphs into a k-Nearest Neighbor Similarity Graph (k-N 2 SG). Then Non-negative Matrix Factorization using Random walk (NMFR) is introduced to generate topic candidates. An efficient fusion of multiple graphs is then done by a Latent Poisson Deconvolution (LPD) which consists of a poisson deconvolution with sparse basis similarities for each edge. Experiments show significantly improved accuracy of the proposed approach in comparison with the state-of-the-art methods on two public data sets.

关键词： Visualization Deconvolution Robustness Feature extraction Optimization Correlation Noise measurement

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The Beauty of Geometry: A touch-operation-based DGS for mathematics education

The Beauty of Geometry: A touch-operation-based DGS for math...

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International Conference on computer science & Education (ICCSE)

作者： Baoling Chen Yong Huang Yongsheng Rao Ying Hu Ying Wang Chuanjun Zhang School of Computer Science and Educational Software Guangzhou University Guangzhou China Guizhou Provincial Academician Workstation of Educational Big Data Technology and Educational Mathematics Guiyang China GuangDong Provincial Engineering Technology Research Center for Mathematical Educational Software Guangzhou China Network and Modern Education Technology Center Guangzhou University Guangzhou China South China Institute of Software Engineering Guangzhou University Guangzhou China Guizhou Provincial Educational Science Academy Guiyang China

ISBN: (纸本)9781509022199

The Beauty of Geometry, a touch-operation-based Dynamic Geometry software (DGS) we develop for mathematics education by Objective-C, is introduced. The paper starts with the main contents of the Beauty of Geometry, including Intelligent drawing, Dynamic geometry, Geometric transformations, Animation, Measurement and Sharing, then we talk about the features of the system from three aspects, and consider a concrete example to show it how to make geometry learning be more simple and intuitive, finally we make a conclusion that the exploring of the touch-operation-based DGS is still in the initial stage, and propose what to do next to make the Beauty of Geometry smart and friendly.

关键词： Geometry software Education Thumb Mobile handsets

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Modeling parameter interactions in ranking SVM 15

Modeling parameter interactions in ranking SVM

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Zhang, Yaogong Xu, Jun Lan, Yanyan Guo, Jiafeng Xie, Maoqiang Huang, Yalou Cheng, Xueqi College of Computer and Control Engineering Nankai University China CAS Key Lab. of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China College of Software Nankai University China

ISBN: (纸本)9781450337946

Ranking SVM, which formalizes the problem of learning a ranking model as that of learning a binary SVM on preference pairs of documents, is a state-of-the-art ranking model in information retrieval. The dual form solution of Ranking SVM model can be written as a linear combination of the preference pairs, i.e., w = Σ(i,j)αij(xi - xj), where αij denotes the Lagrange parameters associated with each pair (i,j). It is obvious that there exist significant interactions over the document pairs because two preference pairs could share a same document as their items. Thus it is natural to ask if there also exist interactions over the model parameters αij, which we may leverage to propose better ranking model. This paper aims to answer the question. Firstly, we found that there exists a low-rank structure over the Ranking SVM model parameters αij, which indicates that the interactions do exist. Then, based on the discovery, we made a modification on the original Ranking SVM model by explicitly applying a low-rank constraint to the parameters. Specifically, each parameter αij is decomposed as a product of two low-dimensional vectors, i.e., αij = (υi,υj), where vectors vi and vj correspond to document i and j, respectively. The learning process, thus, becomes to optimize the modified dual form objective function with respect to the low-dimensional vectors. Experimental results on three LETOR datasets show that our method, referred to as Factorized Ranking SVM, can outperform state-of-the-art baselines including the conventional Ranking SVM. © 2015 ACM.

关键词： Information retrieval

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Privacy preserving for network coding in smart grid 15th

Privacy preserving for network coding in smart grid

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： He, Shiming Zeng, Weini Xie, Kun Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Hunan Province Engineering Research Center of Electric Transportation and Smart Distributed Network Changsha University of Science and Technology Changsha410114 China The 716th Research Institute China Shipbuilding Industry Corporation Lianyungang222061 China College of Computer Science and Electronics Engineering Hunan University Changsha410082 China Department of Electrical and Computer Engineering State University of New York at Stony Brook New York United States

ISBN: (纸本)9783319271361

In smart grid, privacy implications to individuals and their family is an important issue, due to the fine-grained usage data collection. Wireless communications are considered by many utility companies to obtain information. Network coding is exploited in smart grids, to enhance network performance in terms of throughput, delay, robustness, and energy consumption. However, Network Coding introduces new challenge for privacy preserving due to the encoding of packet in forwarder nodes. We propose a distributed privacy preserving scheme for network coding in smart grid, which considers the converged flows character of smart grid and exploits a homomorphic encryption function to decrease the complex in forwarder node. The message content of packet is encrypted and the tag of packet is encrypted by homomorphic encryption function. Then the forwarder node linear random codes the encrypted message contents and directly processes the tags cryptotext based on the homomorphism feature. It offers message content confidentiality privacy preserving feature, which can efficiently thwart traffic analysis. Extensive security analysis and performance evaluations demonstrate the validity and efficiency of the proposed scheme. © Springer International Publishing Switzerland 2015.

关键词： Smart power grids

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Cover Image, Volume 84, Issue 7

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Proteins: Structure, Function, and Bioinformatics 2016年第7期84卷

作者： Wei Wang Juan Liu Lin Sun Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University Xinxiang 453007 China Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining Henan Province China Institute of Computer Software School of Computer Wuhan University Wuhan 430072 China

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A mixture language model for the classification of Chinese online reviews

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International Journal of Information and Communication Technology 2015年第1期7卷 109-122页

作者： Jiang, Ming Wang, Jian Wang, Xingqi Tang, Jingfan Wu, Chunming Institute of Software and Intelligent Technology Hangzhou Dianzi University Hangzhou310018 China Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis Hangzhou Dianzi University Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

In this essay, we propose an unsupervised topic and sentiment mixture model (LSS model) mainly on the basis of LDA model and combining with the sentiment factor, as an approach to sentiment classification of online reviews. There is an extra constraint that all words in a sentence are generated from one topic and one sentiment. Which means the hypothesis of the model is a word in a sentence is of one meaning only. LSS model is totally unsupervised and it needs no labelled corpora or any other sentiment seed words. Experiments show that LSS model performs better than JST model and ASUM model. The F-1 value of sentiment classification is 8% higher than ASUM model and 12.5% than JST model.

关键词： Mixtures

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Rail Inspection Meets big data: Methods and Trends

Rail Inspection Meets Big Data: Methods and Trends

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International Conference on Network-Based Information Systems (NBIS)

作者： Qingyong Li Zhangdui Zhong Zhengping Liang Yong Liang Beijing Key Lab of Transportation Data Analysis and Mining Beijing Jiaotong University Beijing China College of Computer Science and Software Engineering Shenzhen University Communication & Power Utilization Technology Subcompany State Grid Electric Power Research Institute State Grid Beijing China

Rail inspection is one of the most important tasks for rail industry, in order to guarantee the safety of railway systems and control their cost. Rail are systematically inspected for defects using various non-destructive evaluation (NDE) techniques, which include ultrasonic inspection, visual detection, magnetic flux leakage method, acoustic emission inspection etc. The data obtained by these NDE devices are going to increase in both quality and quantity, therefore big data has emerged as a potential challenge for rail inspection. This paper reviews the advanced NDE techniques for rail inspection, and brings forward a new framework of rail inspection based on big data, according to the characteristics of inspection data.

关键词： Rails Inspection Acoustics Surface cracks Visualization Surface treatment big data

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Towards continuous and passive authentication via touch biometrics: an experimental study on smartphones 14

Towards continuous and passive authentication via touch biom...

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Proceedings of the Tenth USENIX Conference on Usable Privacy and Security

作者： Hui Xu Yangfan Zhou Michael R. Lyu Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute The Chinese University of Hong Kong and MoE Key Laboratory of High Confidence Software Technologies (CUHK Sub-Lab) Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute The Chinese University of Hong Kong and Dept. of Computer Science & Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781931971133

Current smartphones generally cannot continuously authenticate users during runtime. This poses severe security and privacy threats: A malicious user can manipulate the phone if bypassing the screen lock. To solve this problem, our work adopts a continuous and passive authentication mechanism based on a user's touch operations on the touchscreen. Such a mechanism is suitable for smartphones, as it requires no extra hardware or intrusive user interface. We study how to model multiple types of touch data and perform continuous authentication accordingly. As a first attempt, we also investigate the fundamentals of touch operations as biometrics by justifying their distinctiveness and permanence. A one-month experiment is conducted involving over 30 users. Our experiment results verify that touch biometrics can serve as a promising method for continuous and passive authentication.

关键词： smartphone touch biometrics continuous authentication

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GPU accelerated item-based collaborative filtering for big-data applications

GPU accelerated item-based collaborative filtering for big-d...

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IEEE International Conference on big data

作者： Chandima Hewa Nadungodage Yuni Xia John Jaehwan Lee Myungcheol Lee Choon Seo Park Department of Computer & Information Science Purdue School of Science Indianapolis USA Department of Electrical & Computer Engineering Purdue School of Engineering & Technology Indianapolis USA Big-Data Software Platform Research Department Software Research Laboratory Electronics & Telecommunications Research Institute Korea

Recommendation systems are a popular marketing strategy for online service providers. These systems predict a customer's future preferences from the past behaviors of that customer and the other customers. Most of the popular online stores process millions of transactions per day; therefore, providing quick and quality recommendations using the large amount of data collected from past transactions can be challenging. Parallel processing power of GPUs can be used to accelerate the recommendation process. However, the amount of memory available on a GPU card is limited; thus, a number of passes may be required to completely process a large-scale dataset. This paper proposes two parallel, item-based recommendation algorithms implemented using the CUDA platform. Considering the high sparsity of the user-item data, we utilize two compression techniques to reduce the required number of passes and increase the speedup. The experimental results on synthetic and real-world datasets show that our algorithms outperform the respective CPU implementations and also the naïve GPU implementation which does not use compression.

关键词： Graphics processing units Runtime Kernel Instruction sets Indexes Memory management Acceleration

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