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Joint 2nd International Workshop on Semantic Web Enterprise Adoption and Best Practice, WaSABi 2014 and 2nd International Workshop on Finance and Economics on the Semantic Web, FEOSW 2014 - Co-located with 11th European Semantic Web Conference, ESWC 2014

作者： Coppens, Sam Hammar, Karl Knuth, Magnus Neumann, Marco Ritze, Dominique Sande, Miel Vander Ireland Information Engineering Group Jönköping University Sweden Hasso Plattner Institute University of Potsdam Germany KONA LLC NY United States Research Group Data and Web Science University of Mannheim Germany IMinds -Multimedia Lab Ghent University Belgium

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Time-correlation analysis of GSM telephone traffic in dense population district

Time-correlation analysis of GSM telephone traffic in dense ...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Dandan Yin Sihai Zhang Wuyang Zhou Yi Zheng Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei Anhui China Beijing Cloud Vally Big Data Lab Beijing China

Time-correlation of GSM telephone traffic is important for wireless network performance, but lack of in-depth investigation, especially in large-scale networks. In this paper, we investigate this topic using the data of call detail records (CDR) collected in June 2013 from thousands of GSM base stations. Based on results obtained by the Modified Allan Variance (MAVAR), we confirm the long-range dependency for all base stations, which is consistent with existing results. But through comprehensive chi-square test we believe that the call arrivals can NOT always be modeled as Poisson distribution in short-term in most cases, which disagree with previous conclusions. Our work may be beneficial to energy saving, realistic traffic modelling and performance evaluation of cellular networks.

关键词： Base stations Mobile communication GSM Analytical models data models Correlation

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NextGen VOICES: Research resolutions

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science (New York, N.Y.) 2018年第6371期359卷 26-28页

作者： Felicia Olmeta-Schult Lauren Massa Segal Sam Tyner Twila Alexandra Moon Ryan Dz-Wei Chow Prosanta Chakrabarty Martin Pacesa Anna Igorevna Podgornaia Jennifer Chen Bipin Singh Bo Cao Rishi Raj Singh Sidhu Bryce W Q Tan Prashant Sood Stuart Parker Matthew A Scult Desiree Van Haute Nikos Konstantinides Beat A Schwendimann Sudhakar Srivastava Raffaele Fiorenza Ken Dutton-Regester Rachel Hale Emre Ozan Polat Edward Lau Audrey L Mayer Easton R White Environmental and Natural Resource Sciences Washington State University Vancouver WA 98686 USA. folmeta@wsu.edu lsegal3@*** sctyner@iastate.edu twila.science@*** ryan.chow@yale.edu prosanta@lsu.edu m.pacesa@bioc.uzh.ch anna.podgornaia@*** jennifer.s.chen@yale.edu bipin.singh@bmu.edu.in bocao@*** rishirajsidhu@*** brycetan03@*** drprashantsood@*** parkstua@isu.edu matthew.scult@duke.edu desireevanhaute@*** nk1845@nyu.edu beat.schwendimann@*** sudhakar.srivastava@*** ralphie.fiorenza@*** ken.dutton-regester@qimrberghofer.edu.au r.hale@soton.ac.uk emre-ozan.polat@icfo.es lau1@stanford.edu almayer@mtu.edu eawhite@ucdavis.edu. Department of Plant Pathology University of Nebraska-Lincoln Lincoln NE 68583 USA. folmeta@wsu.edu lsegal3@*** sctyner@iastate.edu twila.science@*** ryan.chow@yale.edu prosanta@lsu.edu m.pacesa@bioc.uzh.ch anna.podgornaia@*** jennifer.s.chen@yale.edu bipin.singh@bmu.edu.in bocao@*** rishirajsidhu@*** brycetan03@*** drprashantsood@*** parkstua@isu.edu matthew.scult@duke.edu desireevanhaute@*** nk1845@nyu.edu beat.schwendimann@*** sudhakar.srivastava@*** ralphie.fiorenza@*** ken.dutton-regester@qimrberghofer.edu.au r.hale@soton.ac.uk emre-ozan.polat@icfo.es lau1@stanford.edu almayer@mtu.edu eawhite@ucdavis.edu. Center for Statistics and Applications in Forensic Evidence Iowa State University Ames IA 50014 USA. folmeta@wsu.edu lsegal3@*** sctyner@iastate.edu twila.science@*** ryan.chow@yale.edu prosanta@lsu.edu m.pacesa@bioc.uzh.ch anna.podgornaia@*** jennifer.s.chen@yale.edu bipin.singh@bmu.edu.in bocao@*** rishirajsidhu@*** brycetan03@*** drprashantsood@*** parkstua@isu.edu matthew.scult@duke.edu desireevanhaute@*** nk1845@nyu.edu beat.schwendimann@*** sudhakar.srivastava@*** ralphie.fiorenza@*** ken.dutton-regester@qimrberghofer.edu.au r.hale@soton.ac.uk emre-ozan.polat@icfo.es lau1@stanford.edu almayer@mtu.edu eawhite@ucdavis.edu. National Snow and Ice Data Center University of Colorado Boulder CO 80309 USA. folmeta@wsu.edu lsegal3@gmail.com sctyner@iastate.edu twila.science@gma

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Incorporating higher dimensionality in joint decomposition of EEG and fMRI 22

Incorporating higher dimensionality in joint decomposition o...

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22nd European Signal Processing Conference, EUSIPCO 2014

作者： Swinnen, Wout Hunyadi, Borbála Acar, Evrim Huffe, Sabine Van De Vos, Maarten STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Department of Electrical Engineering KU Leuven Leuven Belgium IMinds Medical IT Department Belgium Faculty of Science University of Copenhagen Denmark Methods in Neurocognitive Psychology Lab Department of Psychology Carl von Ossietzky University Oldenburg Germany Research Center Neurosensory Science Carl von Ossietzky University Oldenburg Germany

ISBN: (纸本)9780992862619

EEG-fMRI research to study brain function became popular because of the complementarity of the modalities. Through the use of data-driven approaches such as jointICA, sources extracted from EEG can be linked to regions in fMRI. Joint-ICA in its standard formulation however does not allow for the inclusion of multiple EEG electrodes, so it is a rather arbitrary choice which electrode is used in the analysis. In this study, we explore several ways to include the higher dimensionality of the EEG during a joint decomposition of EEG and fMRI. Our results show that incorporation of multiple channels in the jointICA can reveal new relations between fMRI activation maps and ERP features. © 2014 EURASIP.

关键词： Electrodes

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Preventing Client Side XSS with Rewrite Based Dynamic Information Flow

Preventing Client Side XSS with Rewrite Based Dynamic Inform...

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Wenmin Xiao Jianhua Sun Hao Chen Xianghua Xu College of Information Science and Engineering Hunan University Changsha China Zhejiang Provincial Key Lab of Data Storage and Transmission Technology Hangzhou Dianzi University

This paper presents the design and implementation of an information flow tracking framework based on code rewrite to prevent sensitive information leaks in browsers, combining the ideas of taint and information flow analysis. Our system has two main processes. First, it abstracts the semantic of JavaScript code and converts it to a general form of intermediate representation on the basis of JavaScript abstract syntax tree. Second, the abstract intermediate representation is implemented as a special taint engine to analyze tainted information flow. Our approach can ensure fine-grained isolation for both confidentiality and integrity of information. We have implemented a proof-of-concept prototype, named JSTFlow, and have deployed it as a browser proxy to rewrite web applications at runtime. The experiment results show that JSTFlow can guarantee the security of sensitive data and detect XSS attacks with about 3x performance overhead. Because it does not involve any modifications to the target system, our system is readily deployable in practice.

关键词： Security Semantics Engines Browsers Abstracts data models Syntactics

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Abstracts from Hydrocephalus 2016 Abstracts

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FLUIDS AND BARRIERS OF THE CNS 2017年第SUPPL 1期14卷 1-21页

作者： [Anonymous] Johns Hopkins University Baltimore MD USA. Department of Neurosurgery Johns Hopkins University Baltimore MD USA. Johns Hopkins Biostatistics Center Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. Department of Neurosurgery Johns Hopkins University School of Medicine Baltimore MD USA. Johns Hopkins Aramco Healthcare Dhahran Saudi Arabia. Johns Hopkins Hopkins Aramco Healthcare Dhahran Saudi Arabia. Johns Hopkins Aramco Healthcare Ras Tanura Saudi Arabia. Primary Care Johns Hopkins Aramco Healthcare Ras Tanura Saudi Arabia. Bloomberg School of Public Health Johns Hopkins University Baltimore MD USA. Department of Neurosurgery Rhode Island Hospital Providence RI USA. Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. The Interface Group Institute of PhysiologyUniversity of Zurich Zurich Switzerland. Institute of Physiology University of Zurich Zurich Switzerland. Neuroscience Center Zurich and the Zurich Center for Integrative Human Physiology University of Zurich Zurich Switzerland. BioFlowImage Laboratory University of Picardie Jules Verne Amiens France. Reims Mathematics Laboratory University of Reims Champagne-Ardenne Reims France. Image Processing Laboratory University Hospital of Amiens-Picardie Amiens France. BioFlowImage Laboratory Department of Medical Image Processing University Hospital of Picardie Jules Verne Amiens France. Department of Geriatric University Hospital of Toulouse Toulouse France. Departments of Geriatric University Hospital of Toulouse Toulouse France. Department of Geriatry University Hospital Toulouse Toulouse France. Neurosciences department University of Cambridge Cambridge UK. Brain Physics Lab Academic Neurosurgery University of Cambridge Cambridge UK. Department of Nuclear Medicine University Hospital of Toulouse Toulouse France. Department of Nuclear Medicine University Hospit

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Extremal optimization-based semi-supervised algorithm with conflict pairwise constraints for community detection

Extremal optimization-based semi-supervised algorithm with c...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Lei Li Mei Du Guanfeng Liu Xuegang Hu Gongqing Wu School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Soochow Advanced Data Analytics Lab Soochow University Suzhou Jiangsu China

The research on community structure is a key to analyze the network functionality and topology, and thus it is significant to detect and analysis the community structure. During the abstract process from an actual system to a network, especially for a large-scale network, it is inevitable to have mistaken connections between nodes or have connection missing. In addition, in real applications, from time to time we can obtain prior information in the form of pairwise constraints between nodes besides topology information, although they may be inaccurate or conflicted. These noises in the network-related information will dramatically reduce the accuracy of community detection. Hence, in this paper, we introduce a dissimilarity index to determine the trustworthiness of pairwise constraints and settle the conflict of pairwise constraints. Then, focusing on the community detection with false connections or conflicted connections, we propose a pairwise constrained structure-enhanced extremal optimization-based semi-supervised algorithm (PCSEO-SS algorithm). Compared with existing semi-supervised community detection approaches, the experimental results executed on real networks and synthetic networks, show that PCSEO-SS can solve the problem of false connections or conflicted connections to some extent and detect the community structure more precisely.

关键词： Communities Optimization Detection algorithms Indexes Accuracy Algorithm design and analysis Noise

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Angle historgam model of directional relationship in 3D based on Double-projection and Rounded-subdivision

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Journal of Information and Computational science 2014年第18期11卷 6471-6478页

作者： Kong, Dehan Liu, Yongshan Ma, Junlei Department of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China Data Industry Research Lab Qinhuangdao China

The formal model of spatial directional relations is one of the most important parts in spatial relation research. The most of models are based on Minimum Bounding Rectangle (MBR), and they are not compliant with the regular pattern of human cognition. In order to get a closer conclusion to human cognition on directional relationship, Angle Histogram model based on Double-projection and Rounded-subdivision (AHDPRS) is proposed in this paper. The model uses the maximum inscribed circles to find out the maximum parts of the object, and calculates the directional relationship between the centers of the circles. This model ignores the inessential details to ensure the result which will be closer to human cognition. The experiments show that this model is feasible.

关键词： Graphic methods

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Point cloud coarse registration based on 3DSIFT keypoint detection

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Journal of Computational Information Systems 2014年第23期10卷 10121-10128页

作者： Gu, Xiaoying Liu, Yongshan Department of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Yanshan University Qinhuangdao China Data Industry Research Lab Qinhuangdao China

Since the SIFT feature point extraction algorithm with scale changes, rotation transformation invariance, is widely used in image registration. In this paper, the SIFT algorithm is applied to three-dimensional point cloud coarse registration, the proposed 3DSIFT extraction algorithm is suitable for three-dimensional point cloud data, then point coordinates, curvature, the nearest neighbor distance mean and other information compose fourteen-dimensional vector to conduct correspondence match, use the interior point rate of Ransac to obtain optimal transformation, and finally transform the coordinates for source point clouds using the optimal transform, complete the point cloud data coarse registration. Experimental results show that our coarse registration algorithm can effectively extract feature points, and it is robust for the point cloud with noisy point, it can provide accurate and effective initial value for the precise registration such as ICP.

关键词： Metadata

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An algorithm of anisotropy and self-adaptive point cloud denoising

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Journal of Information and Computational science 2014年第8期11卷 2827-2834页

作者： Cao, Feifei Liu, Yongshan Gu, Xiaoying College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Lab. for Computer Virtual Tech. and Syst. Integration of Hebei Province Yanshan University Qinhuangdao 066004 China Data Industry Research Lab Qinhuangdao 066004 China

Since the method of isotropic filtering will result in blurring sharp features, a new approach of anisotropic filtering algorithm is proposed, direction and scale of such filter vary according to changes of sharp features' direction and scale. According to the local neighborhood of the point to be processed, the direction and scale of the filter were adaptively computed by covariance analysis. And because the results of covariance analysis are determined by the selection of neighborhood points, order to adaptively select neighborhood points, a method about the selection of optimal neighborhood radius was also proposed which is based on projection direction. This method adopts projection direction to restrict the selection of neighboring points, thus adaptively get best neighborhood radius related with surface features. Experimental results show that: the algorithm combined with bilateral filtering not only can make point cloud smooth while retaining sharp features but also can adaptively select parameters. Copyright © 2014 Binary Information Press.

关键词： Covariance matrix

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