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9th International Conference on Swarm Intelligence, ICSI 2018

作者： Zhang, JunQi Chen, JianQing Zhu, XiXun Wang, ChunHui Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (纸本)9783319938141

Particle swarm optimization (PSO) is a widely-adopted optimization algorithm which is based on particles’ fitness evaluations and their swarm intelligence. However, it is difficult to obtain the exact fitness evaluation value and is only able to compare particles in a pairwise manner in many real applications such as dose selection, tournament, crowdsourcing and recommendation. Such ordinal preferences from pairwise comparisons instead of exact fitness evaluations lead the traditional PSO to fail. This paper proposes a particle swarm optimization based on pairwise comparisons. Experiments show that the proposed method enables the traditional PSO to work well by using only ordinal preferences from pairwise comparisons. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Swarm intelligence

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A hybrid differential evolution algorithm for meta-task scheduling in grids

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High Technology Letters 2009年第3期15卷 261-266页

作者：康钦马 Jiang Changiun He Hong Huang Qiangsheng The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 P.R. China School of Infomlation Engineering Shandong University at Weihai Weihai 264209 P.R. China

Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the *** paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable *** encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress *** performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the *** results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.

关键词： Hybrid differential evolution grid computing task scheduling genetic algorithm

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Description and verification of dynamic software architectures for distributed systems

Journal of Software

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Journal of Software 2010年第7期5卷 721-728页

作者： Xu, Hongzhen Zeng, Guosun Chen, Bo Department of computer Science and Technology Tongji University Shanghai 201804 China Department of Computer Science and Technology East China Institute of Technology Fuzhou Jiangxi Province 344000 China Embedded System and Service Computing Key Lab of Ministry of Education Shanghai 201804 China

Continuing growth and increasing complexity of distributed software systems make them be more flexible, adaptive and easily extensible. Dynamic evolution or reconfiguration of distributed software systems is one possible solution to meet these demands. However, there are some challenges for building dynamically evolving distributed software systems at runtime, where dynamic software architectures for them is one of the most crucial problems. In this paper, we proposed a formal method of describing and verifying dynamic software architectures for distributed systems using hypergraph grammars. We firstly gave out reconfiguration production rules and operations for software architectures based on hypergraph grammars, and then described dynamic reconfiguration of software architectures for distributed systems according to those rules. At last we verified the invariant property of dynamic software architectures for those systems using model checking, and gave out corresponding verification algorithms. © 2010 ACADEMY PUBLISHER.

关键词： Dynamic models

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Multi-sensor based perception network for vehicle driving assistance

Multi-sensor based perception network for vehicle driving as...

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2009 2nd International Congress on Image and Signal Processing, CISP'09

作者： Jun, Han Fuqiang, Liu Zhipeng, Li Shanshan, Zhang Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China Tongji University China College of Electronics and Information Engineering Tongji China

ISBN: (纸本)9781424441310

In this paper, a multi-sensor based perception network for vehicle driving assistance is described. The network could reconstruct the 3D real world from the data obtained by the sensors, recognize dangerous occasions in advance and give the driver a suggestion or warning to prevent from the accident. Vehicle driving assistance is a research area which requires high accuracy and precision. The purpose of this perception network is to make up for the weakness of one sensor by means of the advantages of other sensors in the network. We will discuss the details of our adopted solutions, and present some real experimental results. ©2009 IEEE.

关键词： Vehicles

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Incremental sensor node deployment for low cost and highly available WSNs

Incremental sensor node deployment for low cost and highly a...

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International Conference on Mobile Ad-hoc and Sensor Networks

作者： Al-Omari, Safwan Shi, Weisong Jordan University of Science and Technology Jordan Key Laboratory of Embedded System and Service Computing Tongji University China Wayne State University United States

ISBN: (纸本)9780769543154

We attack the sensor network deployment problem. We define the deployment problem as the problem of deciding how many sensor nodes should be deployed in the sensor field over how many phases during its lifetime. We target the optimal deployment strategy that meets user-defined availability requirement with minimum total cost taking into consideration node failures and changing field trip to sensor node cost ratio. We model WSN availability and total cost as functions of the deployment plan, then, we formalize the deployment problem as a 2D optimization problem. Our modeling enables us to explore cost-benefit tradeoffs, we believe, this is a solid step toward bringing cost as an explicit dimension in the design space of WSN protocols. We compare the performance of the optimized solution (denoted as pro-active) to more ad-hoc solutions: on-demand) and at-front. The former strategy schedules future deployments only on demand. The latter strategy deploys all nodes at front with no later field trips. Using extensive simulations, we show that proactive outperforms at-front and on-demand in terms of total cost per availability unit in all application scenarios. For example, using pro-active costs $7 compared to $40 and $280 per total uptime in case of on-demand and at-front, respectively. © 2010 IEEE.

关键词： Sensor nodes

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The preference relation and its application in the information system

The preference relation and its application in the informati...

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2011 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2011

作者： Mingqing, Zhao Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China College of Information Science and Engineering SUST Qingdao China

ISBN: (纸本)9781424487257

In order to strengthen the traditional information system's capacity of describing practical problems, preference relation is introduced into information system and the concepts of preference information system, preference decision table, and information utility function are offered in this paper. In addition, the existence theorem of information utility function and several applications of information utility function in knowledge acquisition are provided. © 2011 IEEE.

关键词： Knowledge acquisition

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Covariance free incremental principal component analysis with exact mean update

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Journal of Computational Information systems 2013年第16期9卷 6703-6710页

作者： Zeng, Xueqiang Li, Guozheng The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Computer Center Nanchang University Nanchang 330029 China Department of Control Science and Engineering Tongji University Shanghai 201804 China

Incremental feature extraction is an essential data preprocessing technique for large-scale and streaming data mining. Among various covariance matrix-free Incremental Principal Component Analysis (IPCA) methods, Candid Covariance-free Incremental Principal Component Analysis (CCIPCA) is a state-of-the art algorithm. Since the training samples are required to be centred, CCIPCA applies an approximate centric alignment on the input data, where only the current sample is correctly centred and all historical data are not updated properly. In this paper, we propose a novel centred incremental principal component analysis algorithm with exact historical mean update, where not only the current sample is centred, but also all historical data are updated by the current mean correctly. Compared to CCIPCA, the proposed method converges more quickly, and the performance improvement is especially obvious when the data's inherent covariance is not stable. The experiments on real streaming dataset show that the proposed method is much superior to CCIPCA in convergence speed. © 2013 Binary Information Press.

关键词： Principal component analysis

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DenseNet+inception and its application for electronic transaction fraud detection 21

DenseNet+inception and its application for electronic transa...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Chen, Zhuo Liu, Guanjun Department of Computer Science Key Laboratory of the Ministry of Education for Embedded System and Service Computing Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China

ISBN: (纸本)9781728120584

With the development of online payment, electronic transaction fraud events also take place often and result in huge financial losses. Recently, DenseNet as one of the most prominent Convolutional Neural Networks (CNNs) has achieved a remarkable success in many applications such as image recognition. However, when we apply DenseNet to the electronic transaction fraud detection in which the original data is directly as the input of DenseNet, a perfect performance cannot be obtained since the transaction data is different from the image data and the features of the original transaction data are not rich. The Inception module used in GoogLeNet (a kind of CNN) has different convolution kernels of different sizes and thus can enhance the feature mining ability. In this paper, we improve DenseNet (iDenseNet for short) in order to suit electronic transaction data and then propose two integration patterns of iDenseNet and Inception module. Experiments are done on two large datasets with millions of transaction records and compare our methods with CNN, DenseNet and Random Forest that are the state-of-the-art methods used in electronic transaction fraud detection. Results illustrate better performances of our methods. © 2019 IEEE.

关键词： Large dataset

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Accelerating large-scale human action recognition with GPU-based spark 17th

Accelerating large-scale human action recognition with GPU-b...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Wang, Hanli Zheng, Xiaobin Xiao, Bo Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9783319488950

In this paper, a large-scale human action recognition system is proposed which is built upon the combination of the rising big data processing technology Spark and the powerful Graphics Processing Unit (GPU) in order to fully utilize the efficient in-memory computing ability of Spark and the fine-grained parallel computing capacity of GPU for visual data processing. A number of key algorithms for human action recognition including trajectory based feature extraction, Gaussian Mixture Model (GMM) generation and Fisher Vector (FV) encoding are performed with the proposed GPU-based Spark framework. The experimental results on the benchmark human action dataset Hollywood-2 demonstrate that the proposed GPU-based Spark framework is able to dramatically accelerate the process of human action recognition. © Springer International Publishing AG 2016.

关键词： MapReduce

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An Effective Principal Curves Extraction Algorithm for Complex Distribution Dataset 1

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5th International Conference on Rough Set and Knowledge Technology (RSKT)

作者： Zhang, Hongyun Miao, Duoqian Sun, Lijun Ye, Ying The Key Laboratory of Embedded System and Service Computing Ministry of Education China Tongji University Shanghai 201804 China School of Electronic and Information Engineering Tongji University Shanghai 201804 China

ISBN: (数字)9783642162480

ISBN: (纸本)9783642162473

This paper proposes a new method for finding principal curves from complex distribution dataset. Motivated by solving the problem, which is that existing methods did not perform well on finding principal curve in complex distribution dataset with high curvature, high dispersion and self-intersecting, such as spiral-shaped curves, Firstly, rudimentary principal graph of data set is created based on the thinning algorithm, and then the contiguous vertices are merged. Finally the fitting-and-smoothing step introduced by Kegl is improved to optimize the principal graph, and Kegl's restructuring step is used to rectify imperfections of principal graph. Experimental results indicate the effectiveness of the proposed method on finding principal curves in complex distribution dataset.

关键词： Principal curves Complex distribution dataset Thinning algorithm Fitting-smoothing step Image skeletonization

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