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International Conference on Advanced computing, ICAC

作者： C. Chandra Sekhar Sasi Kumar Madhan Subhas Raj Kumar Buyya Department of Computer Science and Engineering Indian Institute of Technology Madras CDAC Pune Chronus Corporation Computer Science and Software Engineering and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne Australia

Support vector machine (SVM) is a pattern classification model suitable for classification and annotation of images using non-vectorial type representations of images. Varying length patterns extracted from image data correspond to sets of local feature vectors. Kernels designed for varying length patterns are called as dynamic kernels. The talk presents the issues in designing the dynamic kernel based SVMS for classification and annotation of images. Different methods for designing the dynamic kernels are presented. An intermediate matching kernel (IMK) for a pair of varying length patterns is constructed by matching the pairs of local feature vectors selected using a set of virtual feature vectors. For patterns corresponding to sets of feature vectors, a Gaussian mixture model (GMM) is used as the set of virtual feature vectors. The GMM-based IMK is considered for image processing tasks such as image classification, matching and annotation in content-based image retrieval. The talk presents results of experimental studies on image classification, annotation and retrieval of images using the kernel methods.

关键词： Cloud computing Kernel Technological innovation Support vector machine classification Learning systems Hidden Markov models Computational modeling

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Faculty development in the EU ERAMIS project

Faculty development in the EU ERAMIS project

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2012 IEEE Global engineering Education Conference, EDUCON 2012

作者： Merceron, Agathe Adam, Jean-Michel Lujan-Mora, Sergio Milosz, Marek Toppinen, Arto Media Informatics Department Beuth University of Applied Sciences Berlin Germany Laboratory of Informatics of Grenoble Pierre Mendes France University Grenoble France Department of Software and Computing Systems University of Alicante Alicante Spain Institute of Computer Science Lublin University of Technology Lublin Poland School of Engineering and Technology Savonia University of Applied Sciences Savonia Finland

The aim of the ERAMIS project is to create a Master degree computer as a Second Competence in 9 beneficiary universities of Kazakhstan, Kyrgyzstan and Russia. This contribution presents how faculty development is orga... 详细信息

关键词： Teaching

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High Availability through Output Continuity

High Availability through Output Continuity

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IEEE International Conference on Cluster computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Wei Ye Yaozu Dong Ruhui Ma Alei Liang Haibing Guan Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China Intel China Software Center Shanghai China

Virtual machine (VM) based state machine approaches, i.e. VM replication, provide high availability without source code modifications, unfortunately, existing VM replication approaches suffer from excessive replication overheads. On the other hand, machine state replication is an overly strong (and therefore less efficient) requirement for high availability in the networked client-server system. In this paper, we propose a generic and highly efficient high availability solution through output continuity. Output continuity considers a backup server as a valid replica as long as the responses generated so far are the same between the backup and primary servers, the server state is propagated from the primary server to the backup server if and only if the previous outputs from the backup and primary servers are different.

关键词： Servers Availability Mathematical model Semantics Equations Hardware

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International Conference on computer Communications and Networks (ICCCN)

作者： Yuehua Wang Zhong Zhou Ling Liu Liang Cheng Wei Wu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Georgia Institute of Technology College of Computing Georgia Institute of Technology USA Department of Computer Science and Engineering Lehigh University USA

Group communication is essential for multi-user applications. However, due to unpredictable node departures and non-deterministic network partitions, providing reliable and scalable group communication services is challenging when the applications are utilized by the users with heterogeneous capacities on a large scale. To address this challenge, we propose a novel replication scheme to achieve high reliability and low-cost scalability in group communication with following three features. First, it introduces a new concept of replication based on topological similarity, which empowers each node with an ability of measuring similarity between the nodes in topology. By eliminating the topological similarity between the replicas, it intelligently mitigates service interruptions caused by node failures and network partitions. Second, instead of specifying the number of replicas, it provides a technique for nodes to dynamically adapt the replication placement schemes by exploiting functionality importance of the nodes in the group- communication session. It eliminates the bottleneck problem and improves the network resource utilization. Third, the scheme is self-converging and it can stabilize within a few adaptations even facing a high churn rate. Extensive simulations show that it yields significant improvements in reduction of replication overhead and service interruption when comparing to existing approaches.

关键词： Random access memory Topology Maintenance engineering Reliability theory computer network reliability

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Chinese Coreference Resolution via Ordered Filtering 12

Chinese Coreference Resolution via Ordered Filtering

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Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

作者： Xiaotian Zhang Chunyang Wu Hai Zhao Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong UniversityMOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University

ISBN: (纸本)9781627484046

We in this paper present the model for our participation (BCMI) in the CoNLL-2012 Shared Task. This paper describes a pure rule-based method, which assembles different filters in a proper order. Different filters handle different situations and the filtering strategies are designed manually. These filters are assigned to different ordered tiers from general to special cases. We participated in the Chinese and English closed tracks, scored 51.83 and 59.24 respectively.

关键词： Coreference Resolution Filtration Filters Ordered Chinese philosophy Chinese Chinese art proper order paper

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On efficient neighbor sensing in vehicular networks

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computer Communications 2012年第13期35卷 1639-1648页

作者： Zuoxiang Deng Yanmin Zhu Minglu Li Department of Computer Science and Engineering Shanghai Jiao Tong University Dongchuan Road No. 800 Minhang District Shanghai 200240 PR China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Dongchuan Road No.800 Minhang District Shanghai 200240 PR China

Efficient neighbor sensing in vehicular wireless networks is crucial to a number of applications such as driving safety and data delivery. For neighbor sensing, a vehicle has to send probe messages. The characteristics of vehicular networks raise several great challenges for real-time neighbor sensing. First, simultaneous wireless transmissions lead to packet collision. The aggressiveness of probe message transmission has a great impact on sensing latency, and it is difficult to determine the optimal aggressive degree of probe message transmission. Second, the number of neighbors of a vehicle in an urban environment may change over time and a static control method for probe message transmission results in poor performance. We design a protocol ENS for efficient neighbor sensing, in which each vehicle performs a randomized broadcast of probe messages in fix-length frames. To approach optimal neighbor sensing, ENS adopts an adaptive probe message transmission strategy. Based on an analytical framework, we theoretically determine the optimal configurations for number of probe messages and frame length. We have conducted trace driven simulation experiments, and performance results demonstrate that ENS outperforms two other alternative algorithms. In addition, more than 90% of association latencies are less than 600 ms, and more than 90% of disassociation latencies are less than 200 ms under a typical urban setting.

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Hybrid Rule-based Algorithm for Coreference Resolution 12

Hybrid Rule-based Algorithm for Coreference Resolution

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Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

作者： Heming Shou Hai Zhao Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong UniversityMOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University

ISBN: (纸本)9781627484046

This paper describes our coreference resolution system for the CoNLL-2012 shared task. Our system is based on the Stanford's dcore-f deterministic system which applies multiple sieves with the order from high precision to low precision to generate coreference chains. We introduce the newly added constraints and sieves and discuss the improvement on the o-riginal system. We evaluate the system using OntoNotes data set and report our results of average F-score 58.25 in the closed track.

关键词： Coreference Resolution Sieves sieving

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Inertial estimator learning automata

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IEICE Transactions on Fundamentals of Electronics, Communications and computer sciences 2012年第6期E95-A卷 1041-1048页

作者： Zhang, Junqi Ni, Lina Xie, Chen Gao, Shangce Tang, Zheng Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 200092 China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China Department of Intellectual Information Systems Engineering University of Toyama Toyama-shi 930-8555 Japan

This paper presents an inertial estimator learning automata scheme by which both the short-term and long-term perspectives of the environment can be incorporated in the stochastic estimator-the long term information crystallized in terms of the running reward-probability estimates, and the short term information used by considering whether the most recent response was a reward or a penalty. Thus, when the shortterm perspective is considered, the stochastic estimator becomes pertinent in the context of the estimator algorithms. The proposed automata employ an inertial weight estimator as the short-term perspective to achieve a rapid and accurate convergence when operating in stationary random environments. According to the proposed inertial estimator scheme, the estimates of the reward probabilities of actions are affected by the last response from environment. In this way, actions that have gotten the positive response from environment in the short time, have the opportunity to be estimated as "optimal", to increase their choice probability and consequently, to be selected. The estimates become more reliable and consequently, the automaton rapidly and accurately converges to the optimal action. The asymptotic behavior of the proposed scheme is analyzed and it is proved to be Ε-optimal in every stationary random environment. Extensive simulation results indicate that the proposed algorithm converges faster than the traditional stochastic-estimator-based SERI scheme, and the deterministicestimator- based DGPA and DPRI schemes when operating in stationary random environments. Copyright © 2012 The Institute of Electronics, Information and Communication Engineers.

关键词： Probability

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Partitioning the Conventional DBT System for Multiprocessors

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Journal of computer science & Technology 2011年第3期26卷 474-490页

作者：马汝辉管海兵朱二周杨洪波杨吟冬梁阿磊 Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University

Noticeable performance improvement via ever-increasing transistors is gradually trapped into a predicament since software cannot logically and efficiently utilize hardware resource, such as multi-core resource. This is an inevitable problem in dynamic binary translation （DBT） system as well. Though special purpose hardware as aide tool, through some interfaces, provided by DBT enables the system to achieve higher performance, the limitation of it is significant, that is, it is impossible to be used widely by another one. To overcome this drawback, we focus on building compatible software architecture to acquire higher performance without platform dependence. In this paper, we propose a novel multithreaded architecture for DBT system through partitioning distinct function module, which is to adequately utilize multiprocessors resource. This new architecture devides couples the common DBT system （DBTs） working routine into dynamic translation, optimization, and translated code execution phases, and then ramifies them into different threads to enable them concurrently executed. In this new architecture, several efficient novel methods are presented to cope with intractable work that puzzles most researchers, such as communication mechanism, cache layout, and mutual exclusion between threads. Experimental results using SPECint 2000 indicate that this new architecture for DBT system can achieve higher performance -- speed up the traditional DBT system by about average 10.75%, with better CPU utilization.

关键词： DBT optimization microprocessor multi-core SPECint 2000

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