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PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation 2009年 2卷 733-740页

作者： Liang, Weiming Huang, Chang-Ning Li, Mu Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China Microsoft Research Asia 49 Zhichun Road Haidian District Beijing 100080 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China

ISBN: (纸本)9789624423198

Keyphrases extracted from articles are beneficial in helping people boost browsing speed, but unfortunately keyphrases are rarely available for news articles due to the high expense of labor and time for manual annotation. This paper proposes a practical approach to extracting keyphrases for Chinese news articles using the Text Rank and query log knowledge. Previous work is word based, while our approach uses phrase as its basic element. We generate phrases by employing several statistical criteria with the huge amount of queries as a training corpus. We use Text Rank, a graph-based learning algorithm, for extracting keyphrases from Chinese news articles. In addition, two instructive features, lengths and positions of phrases, are incorporated into the Text Rank model. Experimental results demonstrate that our methods improve the performance significantly. © 2009 by Weiming Liang, Chang-Ning Huang, Mu Li, and Bao-Liang Lu.

关键词： Graphic methods

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WCE 2010 - World Congress on Engineering 2010: Preface

WCE 2010 - World Congress on Engineering 2010

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WCE 2010 - World Congress on Engineering 2010 2010年 1卷 l页

作者： Korsunsky, Alexander M. Hunter, Andrew Hukins, David W.L. Gelman, Len Hogger, Christopher John Ceglarek, Darek J. Payne, Stephen Ao, S.I. Ahmad, Munir Alexandrou, Ioannis Al-Nuaimy, Waleed Amavasai, Bala P. An, Yoon-Young Ariwa, Ezendu Arteche, Josu Audrino, Francesco Ayesh, Aladdin Baber, Chris Bailey, Colin Balkan, Naci Barria, Javier Bartosova, Jitka Benkrid, Khaled Bleijs, Hans Bluck, Michael Bose, Indranil Bouzas, Paula R. Braiden, Paul M. Brdys, Mietek Burriesci, Gaetano Cannataro, Mario Carvalho, Alexandre Chang, Chin-Chen Chen, Dechang Chen, Guanghui George Chen, Yuh-Shyan Chiclana, Francisco Cooke, Alastair Das, Diganta Bhusan Davis, Darryl N. Dayoub, Iyad Raman, Suash Deb C. V. Demetriou, Ioannis C. Devai, Frank Dilmaghani, Reza S. Dini, Daniele Drikakis, Dimitris Durkan, Colm Durodola, John Etebar, Kamran Fenn, Peter Figueiredo, Adelaide Florou, Giannoula Freear, Steven Gabrys, Bogdan Galbraith, Graham H. Gaskell, Philip H. Gaura, Elena Ge, Zhao-Qiang Ghafouri-Shiraz, H. Ghavami, Mohammad Giannopoulos, Kostas Pruneda Gonzalez, Rosa Eva Gracia, Angel Marin Grecos, Christos Guan, Lin Gulpinar, Nalan Guo, Renkuan Guo, Yakun Hardalupas, Y. He, Li Herrero, Josep R. Hicks, Ben James Hines, Evor L. Hodgson, Simon Horsfall, Alton Hosein, Patrick Hu, Fun Hu, Osheng Ijomah, Winifred Ming, Ji James, Anne Jancovic, Peter Jhumka, Arshad Kamareddine, Fairouz Kannan, Rajgopal Karsligil, M. Elif Katircioglu, Salih Turan Khalid, Allali Kokossis, Antonis Kontis, Konstantinos Kulekci, M. Oguzhan Laukaitis, Algirdas Leeson, Mark Limbachiya, Mukesh C. Li, Layuan Li, Lin Lin, Phone Ling, Wing-Kuen Macias Lopez, Elsa M. Lovas, Terese Luglio, Michele Mainardi, Stefano Mahanti, Prabhat K. Marinos, Ioannides Maropoulos, Paul Mativenga, Paul Mavrommatis, George Meintanis, S.G. Meisel, Yezid Donoso Mekid, Samir Mias, Christos Morin, Annie Musliu, Nysret Nabhani, Farhad Naguib, Raouf Olatunbosun, Oluremi Olsen, Sarah Ozalp, A. Alper Pang, Ai-Chun Pao, William K.-S. Piera, Miquel Angel Pinciroli, Francesco Popescu, Theodor D. Power Trinity College Department of Engineering Science University of Oxford United Kingdom Vision and AI Research Group Lincoln University United Kingdom Department of Computing and Informatics Lincoln University United Kingdom Department of Bio-medical Engineering University of Birmingham United Kingdom Department of Mechanical and Manufacturing Engineering University of Birmingham United Kingdom Department of Vibro-Acoustic Monitoring United Kingdom Condition Monitoring Technical Committee British Institute of NDT United Kingdom Centre of Vibro-Acoustics and Fatigue United Kingdom Cranfield University United Kingdom Department of Computing Imperial College London United Kingdom International Manufacturing Centre University of Warwick United Kingdom Department of Industrial and Systems Engineering University of Wisconsin-Madison United States University Lecturer in BioMedical Engineering United Kingdom Degrees Keble College Department of Engineering Science University of Oxford United Kingdom Oxford University Computing Laboratory University of Oxford United Kingdom Harvard School of Engineering and Applied Sciences Harvard University United States University of Teesside United Kingdom University of Liverpool United Kingdom Sheffield Hallam University United Kingdom Electronics and Telecommunications Research Institute Korea Republic of London Metropolitan University United Kingdom Universidad del Pais Vasco Euskal Herriko Unibertsitatea Spain University of St. Gallen Switzerland De Montfort University United Kingdom University of Birmingham United Kingdom University of Manchester United Kingdom University of Essex United Kingdom Imperial College London United Kingdom University of Economics Czech Republic University of Edinburgh United Kingdom University of Leicester United Kingdom University of Hong Kong Hong Kong Hong Kong University of Granada Spain Newcastle University United Kingdom University College London United Kingdom University Magna Graecia of Catanza

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Analysis and Design of Mobile Model for DTN

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电子学报(英文版) 2009年第4期

作者： LIN Zhiting LIN Zhiting QU Yugui QU Yugui JI Yusheng JI Yusheng ZHAO Baohua ZHAO Baohua Department of Computer Science University of Science and Technology of China Hefei 230027 China Department of Computer Science University of Science and Technology of China Hefei 230027 China Province Key Laboratory of Software in Computing and Communication Hcfei 230027 China Province Key Laboratory of Software in Computing and Communication Hcfei 230027 China Information Systems Architecture Science Research Division National Institute of Info~natics Tokyo Japan) Information Systems Architecture Science Research Division National Institute of Info~natics Tokyo Japan)

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Performance analysis of multiple site resource provisioning: effects of the precision of availability information 08

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Proceedings of the 15th international conference on High performance computing

作者： Marcos Dias De Assunção Rajkumar Buyya Grid Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering The University of Melbourne Australia and NICTA Victoria Research Laboratory The University of Melbourne Australia Grid Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering The University of Melbourne Australia

ISBN: (纸本)9783540898931

Emerging deadline-driven Grid applications require a numberof computing resources to be available over a time frame, startingat a specific time in the future. To enable these applications, it is importantto predict the resource availability and utilise this informationduring provisioning because it affects their performance. It is impracticalto request the availability information upon the scheduling of everyjob due to communication overhead. However, existing work has notconsidered how the precision of availability information influences theprovisioning. As a result, limitations exist in developing advanced resourceprovisioning and scheduling mechanisms. This work investigateshow the precision of availability information affects resource provisioningin multiple site environments. Performance evaluation is conductedconsidering both multiple scheduling policies in resource providers andmultiple provisioning policies in brokers, while varying the precision ofavailability information. Experimental results show that it is possible toavoid requesting availability information for every Grid job scheduledthus reducing the communication overhead. They also demonstrate thatmultiple resource partition policies improve the slowdown of Grid jobs.

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Figure Classification in Biomedical Literature towards Figure Mining

Figure Classification in Biomedical Literature towards Figur...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Natsu Ishii Asako Koike Yasunori Yamamoto Toshihisa Takagi Department of Computational Biology Graduate school of Frontier Science University of Tokyo Japan Central Research Laboratory Hitachi and Limited Japan Database Center for Life Science Research Organization of Information and Systems Japan

Biomedical papers contain large amounts of figures. Since they provide important information about research outcomes, mining techniques targeting them have attracted a great deal of attention. Our final goal is to develop a figure finding system, FigFinder, to retrieve figures relevant to a userpsilas query by mining information contained in figures, their legends, and the main text in an integrative manner. In this study, we worked on figure classification to choose those representing signaling or metabolic pathways, based on textual information contained in biomedical papers, as the first step to develop FigFinder. We took several supervised machine learning methods, and could confirm that the use of main text combined with figure legends was quite effective. Although many groups have considered figure legends, this is the first attempt to address figure classification task by utilizing figure legends together with main text to our knowledge.

关键词： databases Information retrieval Data mining Computational biology Educational institutions Proteins Machine learning Abstracts Bioinformatics Laboratories

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IEEE Transactions on Haptics: Editorial

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IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

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Enhanced Biologically Inspired Model

Enhanced Biologically Inspired Model

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26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), vol.7

作者： Yongzhen Huang Kaiqi Huang Liangsheng Wang Dacheng Tao Tieniu Tan Xuelong Li National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China Biometrics Research Centre Department of Computing Hong Kong Polytechnic University Hong Kong China School of Computer Science and Information Systems Birkbeck University of London London UK

It has been demonstrated by Serre et al. that the biologically inspired model (BIM) is effective for object recognition. It outperforms many state-of-the-art methods in challenging databases. However, BIM has the following three problems: a very heavy computational cost due to dense input, a disputable pooling operation in modeling relations of the visual cortex, and blind feature selection in a feedforward framework. To solve these problems, we develop an enhanced BIM (EBIM), which removes uninformative input by imposing sparsity constraints, utilizes a novel local weighted pooling operation with stronger physiological motivations, and applies a feedback procedure that selects effective features for combination. Empirical studies on the CalTech5 database and CalTech101 database show that EBIM is more effective and efficient than BIM. We also apply EBIM to the MIT-CBCL street scene database to show it achieves comparable performance in comparison with the current best performance. Moreover, the new system can process images with resolution 128 × 128 at a rate of 50 frames per second and enhances the speed 20 times at least in comparison with BIM in common applications.

关键词： Biological system modeling Spatial databases Image databases Brain modeling Object recognition Visual databases Computational efficiency Feedforward systems Feedback Layout

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Resource-aware video multicasting via access gateways in wireless mesh networks

Resource-aware video multicasting via access gateways in wir...

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International Conference on Network Protocols

作者： Wanqing Tu Cormac J. Sreenan Chun Tung Chou Archan Misra Sanjay Jha School of Computing and Communication Technology Glyndŵr University Wrexham UK The Mobile & Internet Systems Laboratory Department of Computer University College Cork Cork Ireland School of Computer Science and Engineering University of New South Wales Sydney Australia IBM Thomas J. Watson Research Center Hawthorne NY USA

This paper studies video multicasting in large scale areas using wireless mesh networks. The focus is on the use of Internet access gateways that allow a choice of alternative routes to avoid potentially lengthy multi-hop wireless paths with low capacity. A set of heuristic-based algorithms are described that together aim to maximize network capacity: the two-tier integrated architecture algorithm, the weighted gateway uploading algorithm, the link-controlled routing tree algorithm, and the alternative channel assignment algorithm. These algorithms use different approaches to arrange multicast group members into a clustered and two-tier integrated architecture in which network protocols can make use of multiple gateways to improve system throughput. Simulation results are used to determine the performance of the different approaches.

关键词： Wireless mesh networks Internet Routing Clustering algorithms Multicast algorithms Telecommunication traffic Videoconference Large-scale systems Multicast protocols Throughput

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DejaVu: Transparent user-level checkpointing, migration, and recovery for distributed systems

DejaVu: Transparent user-level checkpointing, migration, and...

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21st International Parallel and Distributed Processing Symposium, IPDPS 2007

作者： Ruscio, Joseph F. Heffner, Michael A. Varadarajan, Srinidhi Computing Systems Research Laboratory Department of Computer Science Virginia Tech VA 24061 United States

ISBN: (纸本)1424409101

In this paper, we present a new fault tolerance system called DejaVu for transparent and automatic checkpointing, migration, and recovery of parallel and distributed applications. DejaVu provides a transparent parallel checkpointing and recovery mechanism that recovers from any combination of systems failures without any modification to parallel applications or the OS. It uses a new runtime mechanism for transparent incremental checkpointing that captures the least amount of state needed to maintain global consistency and provides a novel communication architecture that enables transparent migration of existing MPI codes, without source-code modifications. Performance results from the production-ready implementation show less than 5% overhead in real-world parallel applications with large memory footprints. © 2007 IEEE.

关键词： Fault tolerant computer systems

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Explorative data mining on stock data –experimental results and findings

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作者： Soon, Lay-Ki Lee, Sang Ho Database Systems Laboratory Department of Computing School of Information Science Soongsil University 1-1 Sangdo-dong Dongjak-gu Seoul156-743 Korea Republic of

ISBN: (纸本)9783540738701

This paper presents our preliminary explorative data mining experiments on stock data. The experiments are performed using information gain evaluation, chi-square test and decision tree, which aim to explore the underlying patterns of the stock dataset. The explorative patterns obtained show some unexpected outliers in deriving the influential stocks with regard to Kuala Lumpur Composite Index. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Data mining

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