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作者： Li, Mu Lian, Xiao-Chen Kwok, James T. Lu, Bao-Liang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong MOE Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781457703942

Spectral clustering is an elegant and powerful approach for clustering. However, the underlying eigen-decomposition takes cubic time and quadratic space w.r.t. the data set size. These can be reduced by the Nystrm method which samples only a subset of columns from the matrix. However, the manipulation and storage of these sampled columns can still be expensive when the data set is large. In this paper, we propose a time- and space-efficient spectral clustering algorithm which can scale to very large data sets. A general procedure to orthogonalize the approximated eigenvectors is also proposed. Extensive spectral clustering experiments on a number of data sets, ranging in size from a few thousands to several millions, demonstrate the accuracy and scalability of the proposed approach. We further apply it to the task of image segmentation. For images with more than 10 millions pixels, this algorithm can obtain the eigenvectors in 1 minute on a single machine. © 2011 IEEE.

关键词： Eigenvalues and eigenfunctions

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Wavelet neural networks optimized by QPSO for network anomaly detection

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Journal of Computational Information systems 2011年第7期7卷 2452-2460页

作者： Liu, Yuan Ma, Ruhui School of Digital Media Jiangnan University Wuxi 214122 China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer science and Engineering Shanghai Jiaotong University Shanghai 200240 China

In this paper, a novel hybrid algorithm based wavelet neural network (WNN) is proposed for network anomaly detection. This new evolutionary algorithm, which is based on a hybrid of Quantum-Behaved Particle Swarm Optimization (QPSO) and conjugate gradient algorithm (CG), is employed to train WNN. The Quantum-Behaved Particle Swarm Optimization, which outperforms other optimization algorithm considerably on its simple architecture and fast convergence, has previously applied to solve optimum problem. Due to the particles in the multi-dimensional space seeking the best position so quickly, it would result in the dangerous of stagnation, which would make the QPSO impossible to arrive at the global optimum. In order to overcome defects of QPSO, the improved hybrid algorithm was proposed. Experimental result on KDD 99 intrusion detection datasets shows that this WNN using the novel hybrid algorithm has high detection rate while maintaining a low false positive rate. Copyright © 2011 Binary Information Press.

关键词： Intrusion detection

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GPU-accelerated DNA distance matrix computation

Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGri...

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Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGrid 2011 2011年 42-47页

作者： Ying, Zhi Lin, Xinhua See, Simon Chong-Wee Li, Minglu Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of High Performance Computing AStar Singapore Singapore NVIDIA Corporation United States Shanghai Key Laboratory of Scalable Computing and Systems China

ISBN: (纸本)9780769544724

Distance matrix calculation used in phylogeny analysis is computational intensive. The growing sequences data sets necessitate fast computation method. This paper accelerate Felsenstein's DNADIST program by using OpenCL to exploit the great computation capability of graphic card. The GPU-accelerated DNADIST program achieves more than 12-fold speedup over the serial CPU program on a personal workstation with a 2.66GHz quad-core Intel CPU and an AMD HD5850 graphics card. And dual HD5850 cards on the same platform perform linear improvement of 24-fold speedup. The program also shows good performance portability by achieving 16-fold speedup with a NVIDIA Tesla C2050 card. © 2011 IEEE.

关键词： Graphics processing unit

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GPU-accelerated DNA Distance Matrix Computation

GPU-accelerated DNA Distance Matrix Computation

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ChinaGrid Annual Conference (ChinaGrid)

作者： Zhi Ying Xinhua Lin Simon Chong-Wee See Minglu Li Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Jiao Tong University Shanghai CN Shanghai Key Laboratory of Scalable Computing and Systems

关键词： Graphics processing unit Phylogeny Central Processing Unit Acceleration Newton method computer architecture Computational modeling

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Reader activation scheduling in multi-reader RFID systems: A study of general case

Reader activation scheduling in multi-reader RFID systems: A...

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25th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2011

作者： Tang, Shaojie Wang, Cheng Li, Xiang-Yang Jiang, Changjun Department of Computer Science Illinois Institute of Technology Chicago IL 60616 United States Department of Computer Science Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (纸本)9780769543857

Radio frequency identification (RFID) is a technology where a reader device can "sense" the presence of a close by object by reading a tag device attached to the object. To guarantee the coverage quality, multiple RFID readers can be deployed in the given region. In this paper, we consider the problem of activation schedule for readers in a multi-reader environment. In particular, we try to design a schedule for readers to maximize the number of served tags per time-slot while avoiding various interferences. We first develop a centralized algorithm under the assumption that different readers may have different interference and interrogation radius. Next, we propose a novel algorithm which does not need any location information of the readers. Finally, we extend the previous algorithm in distributed manner in order to suit the case where no central entity exists. We conduct extensive simulations to study the performances of our proposed algorithm. And our evaluation results corroborate our theoretical analysis. © 2011 IEEE.

关键词： Graph theory

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Rank-SIFT: Learning to rank repeatable local interest points

Rank-SIFT: Learning to rank repeatable local interest points

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作者： Li, Bing Xiao, Rong Li, Zhiwei Cai, Rui Lu, Bao-Liang Zhang, Lei Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Microsoft Research Asia Beijing 100190 China MOE Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781457703942

Scale-invariant feature transform (SIFT) has been well studied in recent years. Most related research efforts focused on designing and learning effective descriptors to characterize a local interest point. However, how to identify stable local interest points is still a very challenging problem. In this paper, we propose a set of differential features, and based on them we adopt a data-driven approach to learn a ranking function to sort local interest points according to their stabilities across images containing the same visual objects. Compared with the handcrafted rule-based method used by the standard SIFT algorithm, our algorithm substantially improves the stability of detected local interest point on a very challenging benchmark dataset, in which images were generated under very different imaging conditions. Experimental results on the Oxford and PASCAL databases further demonstrate the superior performance of the proposed algorithm on both object image retrieval and category recognition. © 2011 IEEE.

关键词： Image retrieval

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A remote debugging system for OpenMP parallel program

A remote debugging system for OpenMP parallel program

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2011 International Conference on Transportation, Mechanical, and Electrical Engineering, TMEE 2011

作者： Chai, Hua Shi, Qinqin Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai 201418 China

ISBN: (纸本)9781457717017

A remote debugging system for OpenMP parallel program is presented in this paper. The system consists of two parts, namely, an integrated debugging environment running on the clent-side and a background daemon running on the server-side. Information exchange between the two sides is accomplished through the socket-based network communication technology. The remote debugging function is realized by the automatic instrumentation technology at code level based on syntax tree. Instance tests show that the system can provide remote users with correctness checking, performance analysis and other debugging functions for OpenMP parallel programs. The user interface is simple and very easy to use. © 2011 IEEE.

关键词： User interfaces

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Enhance Top-down method with Meta-Classification for Very Large-scale Hierarchical Classification 5

Enhance Top-down method with Meta-Classification for Very La...

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5th International Joint Conference on Natural Language Processing, IJCNLP 2011

作者： Wang, Xiao-Lin Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China

ISBN: (纸本)9789744665645

Recent large-scale hierarchical classification tasks typically have tens of thousands of classes as well as a large number of samples, for which the dominant solution is the top-down method due to computational complexity. However, the top-down method suffers from accuracy deficiency, that is, its accuracy is generally lower than that of the flat approach of 1-vs-Rest. In this paper, we employ meta-classification technique to enhance the classifying procedure of the top-down method. We analyze the proposed method on the aspect of accuracy, and then test it with two real-world large-scale data sets. Our method both maintains the efficiency of the conventional top-down method and provides competitive classification accuracies. © 2011 AFNLP

关键词： Natural language processing systems

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Automatic Web service composition based on Horn clauses and Petri nets

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Expert systems with Applications 2011年第10期38卷 13024-13031页

作者： Tang, Xianfei Jiang, Changjun Zhou, Mengchu Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States

This paper introduces an automatic Web service composition method based on logical inference of Horn clauses and Petri nets. The Web service composition problem is transformed into the logical inference problem of Horn clauses by exploring the dependency relations among services. An algorithm is presented, to determine whether the composite service exists in time that is linear in the size of the knowledge base. It can also return the rule set necessary for the composition. Petri nets are chosen to model the rule set and its structural analysis techniques are used to obtain the composite service. © 2010 Elsevier Ltd. All rights reserved.

关键词： Automatic reasoning Knowledge base Petri nets Service composition Web service

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SelectCast: scalable data aggregation scheme in wireless sensor networks

SelectCast: Scalable data aggregation scheme in wireless sen...

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IEEE INFOCOM 2011

作者： Wang, Cheng Tang, Shaojie Li, Xiang-Yang Jiang, Changjun Department of Computer Science Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL 60616 United States TNLIST School of Software Tsinghua University China

ISBN: (纸本)9781424499212

In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density λ ∈ [1, n], we study the tradeoffs between the aggregation throughput and gathering efficiency. The gathering efficiency refers to the ratio of the number of the sensors whose data have been gathered to the total number of sensors. Specifically, we design two efficient aggregation schemes, called single-hop-length (SLH) scheme and multiple-hop-length (MLH) scheme. By novelly integrating these two schemes, we theoretically prove that our protocol achieves the optimal tradeoffs, and derive the optimal aggregation throughput depending on a given threshold value (lower bound) on gathering efficiency. Particularly, we show that under the MLH scheme, for a practically important set of symmetric functions called perfectly compressible functions, including the mean, max, or various kinds of indicator functions, etc., the data from Θ(n) sensors can be aggregated to the sink at the throughput of a constant order Θ(1), implying that our MLH scheme is indeed scalable. © 2011 IEEE.

关键词： Efficiency

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