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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hantao Yao Shiliang Zhang Dongming Zhang Yongdong Zhang Jintao Li Yu Wang Qi Tian University of Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Electronic Engineering and Computer Science Peking University Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Computer Science University of Texas at San Antonio San Antonio USA

ISBN: (纸本)9781509060689

This paper targets to bring together the research efforts on two fields that are growing actively in the past few years: multicamera person Re-Identification (ReID) and large-scale image retrieval. We demonstrate that the essentials of image retrieval and person ReID are the same, i.e., measuring the similarity between images. However, person ReID requires more discriminative and robust features to identify the subtle differences of different persons and overcome the large variance among images of the same person. Specifically, we propose a coarse-to-fine (C2F) framework and a Convolutional Neural Network structure named as Conv-Net to tackle the large-scale person ReID as an image retrieval task. Given a query person image, the C2F firstly employ Conv-Net to extract a compact descriptor and perform the coarse-level search. A robust descriptor conveying more spatial cues is hence extracted to perform the fine-level search. Extensive experimental results show that the proposed method outperforms existing methods on two public datasets. Further, the evaluation on a large-scale Person-520K dataset demonstrates that our work is significantly more efficient than existing works, e.g., only needs 180ms to identify a query person from 520K images.

关键词： Training Feature extraction Cameras Robustness Image retrieval Linear programming

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Computational analysis of the structural properties of economic and financial networks

arXiv

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arXiv 2017年

作者： Emmert-Streib, Frank Musa, Aliyu Baltakys, Kestutis Kanniainen, Juho Tripathi, Shailesh Yli-Harja, Olli Jodlbauer, Herbert Dehmer, Matthias Predictive Medicine and Data Analytics Lab Department of Signal Processing Tampere University of Technology Finland Institute of Biosciences and Medical Technology Tampere Finland Industrial and Information Management Tampere University of Technology Tampere Finland Institute for Systems Biology Seattle United States Institute for Intelligent Production Faculty for Management University of Applied Sciences Upper Austria Steyr Campus Austria Department for Biomedical Computer Science and Mechatronics UMIT - The Health and Lifesciences University Hall in Tyrol Austria College of Computer and Control Engineering Nankai University Tianjin300350 China

In recent years, methods from network science are gaining rapidly interest in economics and finance. A reason for this is that in a globalized world the interconnectedness among economic and financial entities are crucial to understand and networks provide a natural framework for representing and studying such systems. In this paper, we are surveying the use of networks and network-based methods for studying economy related questions. We start with a brief overview of graph theory and basic definitions. Then we discuss descriptive network measures and network complexity measures for quantifying structural properties of economic networks. Finally, we discuss different network and tree structures as relevant for applications. Copyright © 2017, The Authors. All rights reserved.

关键词： Finance

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Biogeography-based optimization for cluster analysis

Biogeography-based optimization for cluster analysis

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International Conference on computer, Communication and Computational sciences, ICCCCS 2016

作者： Wu, Xueyan Wang, Hainan Chen, Zhimin Lu, Zhihai Phillips, Preetha Wang, Shuihua Zhang, Yudong School of Computer Science and Technology Nanjing Normal University NanjingJiangsu210023 China Key Laboratory of Statistical Information Technology & Data Mining State Statistics Bureau ChengduSichuan610225 China School of Computer and Information Engineering Henan Normal University XinxiangHenan453000 China School of Electronic Information Shanghai Dianji University Shanghai200240 China Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology GuilinGuangxi541004 China School of Natural Sciences and Mathematics Shepherd University ShepherdstownWV25443 United States West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Department of Electrical Engineering The City College of New York CUNY New YorkNY10031 United States State Key Lab of CAD & CG Zhejiang University HangzhouZhejiang310027 China Manchester Metropolitan University ManchesterM156BH United Kingdom

ISBN: (纸本)9789811037696

With the aim of resolving the issue of cluster analysis more precisely and validly, a new approach was proposed based on biogeography-based optimization (abbreviated as BBO) algorithm. (Method) First, we reformulated the problem with an optimization model based on the variance ratio criterion (VARAC). Then, BBO was presented to search the optimal solution of the VARAC. There are 400 data of four groups in the experimental dataset, which have the degrees of overlapping of three distinct scales. The first one is nonoverlapping, the second one is partial overlapping, and the last is severely overlapping. BBO algorithm was compared with three different state-of-the-art approaches. We ran every algorithm 20 times. In this experiment, our results demonstrate the maximum VARAC values that can be found by BBO. The conclusion is that BBO is predominant which is extremely quick for the issue of clustering analysis. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Cluster analysis

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Connecting phrase based statistical machine translation adaptation 26

Connecting phrase based statistical machine translation adap...

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26th International Conference on Computational Linguistics, COLING 2016

作者： Wang, Rui Zhao, Hai Lu, Bao-Liang Utiyama, Masao Sumita, Eiichro Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China National Institute of Information and Communications Technology Kyoto Japan

ISBN: (纸本)9784879747020

Although more additional corpora are now available for Statistical Machine Translation (SMT), only the ones which belong to the same or similar domains of the original corpus can indeed enhance SMT performance directly. A series of SMT adaptation methods have been proposed to select these similar-domain data, and most of them focus on sentence selection. In comparison, phrase is a smaller and more fine grained unit for data selection, therefore we propose a straightforward and efficient connecting phrase based adaptation method, which is applied to both bilingual phrase pair and monolingual n-gram adaptation. The proposed method is evaluated on IWSLT/NIST data sets, and the results show that phrase based SMT performances are significantly improved (up to +1.6 in comparison with phrase based SMT baseline system and +0.9 in comparison with existing methods). © 1963-2018 ACL.

关键词： computer aided language translation

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A Decode-and-Forward Scheme for LDPC Coded Three-Way Relay Fading Channels

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China Communications 2015年第8期12卷 46-54页

作者： YE Xia GAO Feifei Department of Computer Science & Information Management Wenzhou Medical University Department of Automation Tsinghua University State Key Lab of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology (TNList)

In this paper a low-density pairwise check(LDPC) coded three-way relay system is considered, where three user nodes desire to exchange messages with the help of one relay node. Since physical-layer network coding is applied, two time slots are sufficient for one round information exchange. In this paper, we present a decode-and-forward(DF) scheme based on joint LDPC decoding for three-way relay channels, where relay decoder partially decodes the network code rather than fully decodes all the user messages. Simulation results show that the new DF scheme considerably outperforms other common schemes in three-way relay fading channels.

关键词： three-way relay channel physicallayer network coding LDPC codes decode-and forward

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York University at CLEF eHealth 2015: Medical document retrieval 16

York University at CLEF eHealth 2015: Medical document retri...

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16th Conference and labs of the Evaluation Forum, CLEF 2015

作者： Ghoddousi, Andia Huang, Jimmy Xiangji Information Retrieval and Knowledge Management Research Lab Department of Computer Science and Engineering School of Information Technology York University Canada

This paper presents the results for task 2 of ShAre/CLEF 2015 eHealth Evaluation lab. We use BM25 as our base normalization method and Pseudo Relevance Feedback to retrieve information regarding patients' health and find the best results to expand the efficiency of information retrieval system. Participants in task 2 are provided with a collection of datasets focused on health web pages. We used queries and submitted 10 runs in TREC style. The runs include the top1000 documents returned for each query. The objective of task 2 is to evaluate the effectiveness of information retrieval system and develop search engines for searching on the web looking for health related documents[1].

关键词： information retrieval

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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OMEG: Oulu multi-pose eye gaze dataset 19th

OMEG: Oulu multi-pose eye gaze dataset

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19th Scandinavian Conference on Image Analysis, SCIA 2015

作者： He, Qiuhai Hong, Xiaopeng Chai, Xiujuan Holappa, Jukka Zhao, Guoying Chen, Xilin Pietikäinen, Matti Department of Computer Science and Engineering University of Oulu Oulu Finland Key Lab of Intelligent Information Processing of the Chinese Academy of Sciences The Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319196640

Data is in a very important position for pattern recognition tasks including eye gaze estimation. In the literature, most researchers used normal face datasets, which are not specifically designed for eye gaze estimation. As a result, it is difficult to obtain fine labeled eye gaze direction. Therefore large datasets with well-defined gaze directions are desired. To facilitate related researches, we collect and establish the Oulu Multi-pose Eye Gaze Dataset. Inspired by the psychological observation that gaze direction is intrinsically linked with the head orientation, we are devoted to a new data set of eye gaze images captured under multiple head poses. It finally results in a dataset containing over 40K images from 50 subjects, who were asked to fixate on 10 special points on screen under different poses respectively. We investigate a new eye gaze estimation approach by using the IGO based description, and compare it with other popular eye gaze estimation approaches to provide the baseline results on our dataset. © Springer International Publishing Switzerland 2015.

关键词： Pattern recognition

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An Automated Finger-Knuckle-Print Identification System Using Jointly RBF & RFT Classifiers

An Automated Finger-Knuckle-Print Identification System Usin...

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International Conference on Ubiquitous Computing and Communications and International Symposium on Cyberspace and Security (IUCC-CSS)

作者： Abdallah Meraoumia Salim Chitroub Hakim Bendjenna Ahmed Bouridane Fac. des nouvelles technologies de l'information et de la communication Lab. de Génie Electrique Ouargla Algeria Informatics and Systems (LAMIS) University of Tebessa Tebessa Algeria Electronics and Computer Science Faculty Laboratory of Intelligent and Communication Systems Engineering (LISIC) Algiers Algeria Department of Computer Science and Digital Technologies Northumbria University Newcastle Newcastle upon Tyne UK

ISBN: (纸本)9781509055678

Ensuring the security of a wide variety of applications such as public security, access control and banking, is becoming an increasingly important problem as modern technology is integrated into the majority of these applications. Thus, establishing identity of a person is the significant method used to ensure a high security level. As a security method, biometric identification has a long tradition and is a synonym for the uniqueness of person. In this paper, based on Finger-Knuckle-Print (FKP), we present a multimodal personal identification system using two feature extraction methods with their fusion applied at the matching score level. In this study, the segmented FKP is firstly represented by the Histogram of Oriented Gradients(HOG) descriptors. Subsequently, the Radial Basis Function(RBF) and Random Forest Transform (RFT) models are used to design two sub-systems. The proposed method is validated for their efficacy on the available PolyU FKP Database of 165 users. Our experimental results show the effectiveness and reliability of the proposed approach, which brings both high identification and accuracy rate.

关键词： Feature extraction Security Databases Histograms Transforms Reliability Computational modeling

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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