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作者： Anand, Sarabjot Singh Bunescu, Razvan Carvcdho, Vitor Chomicki, Jan Conitzer, Vincent Cox, Michael T. Dignum, Virginia Dodds, Zachary Dredze, Mark Furcy, David Gabrilovich, Evgeniy Göker, Mehmet H. Guesgen, Hans Hirsh, Haym Jannach, Dietmar Junker, Ulrich Ketter, Wolfgang Kobsa, Alfred Koenig, Sven Lau, Tessa Lewis, Lundy Matson, Eric Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Pineau, Joelle Poupart, Pascal Raja, Anita Ruml, Wheeler Sadeh, Norman Shani, Guy Shapiro, Daniel Smith, Trey Taylor, Matthew E. Wagstaff, Kiri Walsh, William Zhou, Rong Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering and Computer Science Ohio University United States Microsoft Live Labs United States computer science and engineering University at Buffalo United States Computer science and economics Duke University United States Intelligent Computing group of BBN Technologies Utrecht University Netherlands Department of Computer science Harvey Mudd College United States University of Pennsylvania United States University of Wisconsin United States Yahoo Research PricewaterhouseCoopers Center for Advanced Research United States Department of Computer science School of Engineering and Advanced Technology Massey University New Zealand Departent of Computer science Rutgers University United States Department of Computer Science Dortmund University of Technology. Germany ILOG Rotterdam School of Management Erasmus University Netherlands Donald Bren School of Information and Computer Sciences University of California Irvine United States Depatrment of Computer science University of Southern California United States IBM Almaden Research Center United States Department of Computer Information Technology Southern New Hampshire University United States Computer and Information Technology Program College of Technology Purdue University United States Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computer Science and Engineering University of North Texas United States School of Computing DePaul University United States Department of Computer Science McGill University Canada School of Computer Science University of Waterloo Canada Department of Software and Information Systems University of North Carolina Charlotte United States University of New Hampshire United States Microsoft Research Institute for the Study of Learning and Expertise Applied Reactivity Inc. United States NASA Ames Research Center Carnegie Mellon University West

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers;AI Education Workshop Colloquium;Coordination, Organizations, Institutions, and Norms in Agent systems, Enhanced Messaging;Human Implications of Human-Robot Interaction;Intelligent Techniques for Web Personalization and Recommender systems;Metareasoning: Thinking about Thinking;Multidisciplinary Workshop on Advances in Preference Handling;Search in Artificial Intelligence and Robotics;Spatial and Temporal Reasoning;Trading Agent Design and Analysis;Transfer Learning for Complex Tasks;What Went Wrong and Why: Lessons from AI Research and Applications;and Wikipedia and Artificial Intelligence: An Evolving Synergy. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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LogisticLDA: Regularizing latent dirichlet allocation by logistic regression

PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference ...

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PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation 2009年 1卷 160-169页

作者： Guo, Jia-Cheng Lu, Bao-Liang Li, Zhiwei Zhang, Lei Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200040 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200040 China Microsoft Research Asia 49 Zhichun Road Haidian District Beijing 100080 China

ISBN: (纸本)9789624423198

We present in this paper a supervised topic model for multi-class classification problems. To incorporate supervisory information, we jointly model documents and their labels in a graphical model called LogisticLDA, which mathematically integrates a generative model and a discriminative model in a principled way. By maximizing the posterior of document labels using logistic normal distributions, the model effectively incorporates the supervisory information to maximize inter-class distance in the topic space, while documents still enjoy the interchangeability characteristic for ease of inference. Experimental results on three benchmark datasets demonstrate that the model outperforms state-of-the-art supervised topic models. Compared with support vector machine, the model also achieves comparable performance, but meanwhile it discovers a topic space, which is valuable for dimension reduction, topic mining and document retrieval. © 2009 by Jia-Cheng Guo, Bao-Liang Lu, Zhiwei Li, and Lei Zhang.

关键词： Normal distribution

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Extracting keyphrases from Chinese news articles using Text Rank and query log knowledge

PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference ...

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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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Green supercomputing comes of age

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IT Professional 2008年第1期10卷 17-23页

作者： Feng, Wu-Chun Feng, Xizhou Ge, Rong Department of Computer Science and Electrical and Computer Engineering Virginia Tech. Network Dynamics and Simulation Science Laboratory Virginia Tech. Center for High-End Computing Systems Virginia Tech.

Energy-efficient (green) supercomputing has evolved to improve system reliability and provide better availability and productivity. Green Destiny supercomputing system is a Linux-based cluster that uses low-power components whose performance could be optimized for supercomputing. Low-power supercomputing focuses on energy efficiency at system integration time, selects low-power chips as the building blocks for power reduction and system reliability, and achieves better performance by scaling up to a larger number of processors. Researchers have implemented various power-aware software prototypes for commodity supercomputing starting with cluster of high-performance, high-power processors that support mechanism called dynamic voltage and frequency scaling (DVFS). Various approaches for parallel supercomputing nodes are studied by aiming execution phases that are not on the critical execution path.

关键词： Supercomputers

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A projection-based optimization framework for abstractions with application to the unstructured mesh domain

A projection-based optimization framework for abstractions w...

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22nd ACM International Conference on Supercomputing, ICS'08

作者： White, Brian S. McKee, Sally A. Quinlan, Daniel Computer Systems Lab. Cornell University Ithaca NY 14853 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA 94551 United States

ISBN: (纸本)9781605581583

Computational scientists often must choose between the greater programming productivity of high-level abstractions, such as matrices and mesh entities, and the greater execution efficiency of low-level constructs. Performance is degraded when abstraction indirection introduces overhead and hinders compiler analysis. This can be overcome by targeting the semantics, rather than the implementation, of abstractions. Raising operators specified by a domain expert project an application from an implementation space to an abstraction space, where optimizations leverage domain semantics to complement conservative analyses. Raising operators define a domain-specific intermediate representation, which optimizations target for improved portability. Following optimization, transformed code is reified as a concrete implementation via lowering operators. We have developed a framework to implement this optimization strategy, which we use to introduce two domainspecific unstructured mesh optimizations. The first uses an inspector/executor approach to avoid costly traversals over a static mesh by memoizing the relatively few references required for mathematical computations. The executor phase accesses stored entities without incurring the indirections. The second optimization lowers object-based mesh access and iteration to a low-level implementation, which uses integer-based access and iteration. Copyright 2008 ACM.

关键词： Semantics

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Editorial: Dependability and security

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IEEE Transactions on Dependable and Secure computing 2008年第1期5卷 1-3页

作者： Iyer, Ravishankar K. Gligor, Virgil D. Kalbarczyk, Zbigniew T. Kanoun, Karama Katzenbeisser, Stefan Mukherjee, Shubu Shieh, Shiuhpyng Zorian, Yervant Department of Electrical and Computer Engineering Carnegie Mellon University Microsoft's Trusted Computing Academic Advisory Board Center for Reliable and High-Performance Computing Coordinated Science Laboratory University of Illinois Urbana-Champaign United States IEEE IEEE Computer Society LAAS CNRS IFIP Working Group 10.4 on Dependable Computing and Fault Tolerance Special Interest Group on Dependability Benchmarking IFIP Working Group 10.4 French SEE Technical Committee on Trustworthy Computer Systems SEE Working Group Design and Validation for Dependability Technical University Munich Germany Darmstadt University of Technology ACM IACR Intel's SER Tech Council Intel's Architecture Patent Committee Taiwan Information Security Center NCTU IEEE Reliability Society Taipei and Tainan IACR United States Virage Logic Corp. AT and T Bell Laboratories Logic Vision Inc. University of British Columbia IEEE Test Technology Technical Council

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Untitled

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE computing 2008年第1期5卷 4-5页

作者： Gligor, Virgil D. Formal Methods Section Center for High Assurance Computer Systems Naval Research Laboratory NRL Protocol Analyzer Internet Engineering Task Force IFIP Working Group 1.7 on Foundations of Security Analysis and Design University of Texas Austin United States Boston University IFIP WG 10.4 on Dependability Microsoft's Trustworthy Computing Academic Advisory Board

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Program Phase Detection based on Critical Basic Block Transitions

Program Phase Detection based on Critical Basic Block Transi...

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IEEE International Symposium on Performance Analysis of systems and Software

作者： Paruj Ratanaworabhan Martin Burtscher Computer Systems Laboratory Cornell University USA Center for Distributed and Grid Computing University of Texas Austin USA

ISBN: (纸本)9781424422326

Many programs go through phases as they execute. Knowing where these phases begin and end can be beneficial. For example, adaptive architectures can exploit such information to lower their power consumption without much loss in performance. Architectural simulations can benefit from phase information by simulating only a small interval of each program phase, which significantly reduces the simulation time while still yielding results that are representative of complete simulations. This paper presents a lightweight profile-based phase detection technique that marks each phase change boundary in the program's binary at the basic block level with a critical basic block transition (CBBT). It is independent of execution windows and does not explicitly employ the notion of threshold to make a phase change decision. We evaluate the effectiveness of CBBTs for reconfiguring the LI data cache size and for guiding architectural simulations. Our CBBT method is as effective at dynamically reducing the L1 data cache size as idealized cache reconfiguration schemes are. Using CBBTs to statically determine simulation intervals yields as low a CPI error as the well-known SimPoint method does. In addition, experimental results indicate the CBBTs' effectiveness in both the self-trained and cross-trained inputs, demonstrating the CBBTs' stability across different program inputs.

关键词： Phase detection Grid computing Distributed computing Laboratories computer architecture Energy consumption Performance loss Computational modeling Stability Intrusion detection

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MemoryIO: An Extended I/O Technology in Embedded systems

MemoryIO: An Extended I/O Technology in Embedded Systems

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Xiaojun Yang Tao Liu Fei Chen Hailiang Cheng National Research Center for Intelligent Computing Systems Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy and Sciences China Graduate University of Advanced Studies (SOKENDAI) Beijing China Key Laboratory of Computer System and Architecture Institute of Computing TechnologyNational Research Center for Intelligent Computing Systems Chinese Academy and Sciences China

MemoryIO, a sort of extended I/O in embedded systems, is presented in this paper. MemoryIO makes it powerful for embedded systems to achieve the high-performance interconnect. In view of the facts that the main memory system is absolutely necessary in any embedded system, and not all embedded systems integrate HyperTransport (HT), PCI Express or RapidIO interface, the MemoryIO based interconnect in embedded systems has more universalities compared with that based on HT, PCI Express or RapidIO. MemoryIO can not only thoroughly compensates for the lack of high performance data transfer channel, but also efficiently utilizes the memory bus bandwidth and the direct memory access (DMA) engine to reduce the latency for data transfer in embedded systems. This paper discusses some key technologies of MemoryIO, and presents its application in DCNet and the implementation of MemoryIO IP core. The MemoryIO technology can be used in various systems, but not limited to embedded systems.

关键词： Embedded system Payloads Switches Clocks Storage area networks Physical layer Protocols

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In-network data estimation for sensor-driven scientific applications 08

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Proceedings of the 15th international conference on High performance computing

作者： Nanyan Jiang Manish Parashar Center for Autonomic Computing The Applied Software Systems Laboratory Department of Electrical and Computer Engineering Rutgers University Piscataway NJ

ISBN: (纸本)9783540898931

Sensor networks employed by scientific applications oftenneed to support localized collaboration of sensor nodes to perform in-network data processing. This includes new quantitative synthesis andhypothesis testing in near real time, as data streaming from distributedinstruments, to transform raw data into high level domain-dependent information. This paper investigates in-network data processing mechanismswith dynamic data requirements in resource constrained heterogeneoussensor networks. Particularly, we explore how the temporaland spatial correlation of sensor measurements can be used to trade offbetween the complexity of coordination among sensor clusters and thesavings that result from having fewer sensors involved in in-network processing,while maintaining an acceptable error threshold. Experimentalresults show that the proposed in-network mechanisms can facilitate theefficient usage of resources and satisfy data requirement in the presenceof dynamics and uncertainty.

关键词： in-network data estimation sensor system programming sensor-driven scientific applications

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