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International Conference on Systems and Informatics (ICSAI)

作者： Yiyun Zhang Guolong Chen College of Mathematics and Computer Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou Fujian China

ISBN: (纸本)9781479954599

With the development of network, web forensics is becoming more and more important due to the rampant cybercrime. In this paper, a forensics method of web browsing behavior based on association rule mining is presented. The method aims at providing the necessary data support to build the behavior pattern library for investigation. The records of the user's browsing history are collected to be analyzed. The obtained original data are pretreated to transactional data which are suitable for association rule mining. Frequent browsing time and frequent web browsing sequences are obtained from the transactional data by Apriori algorithm. The mining results are helpful for identification and recognition of anonymous or suspicious web browsing behavior patterns.

关键词： History Browsers Forensics Association rules Libraries Uniform resource locators

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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DRMST: A novel dynamic replica management strategy based on TOPSIS in cloud storage

Journal of Software Engineering

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Journal of Software Engineering 2014年第4期8卷 278-286页

作者： Zhiyong, Yang Chunlin, Li Yanpei, Liu Malek, Al-Gabri Department of Computer Science Wuhan University of Technology China State Key Laboratory for Novel Software Technology Nanjing University China Key Laboratory of Fiber Optic Sensing Technology and Information Processing Ministry of Education China

The present study proposes a dynamic replica management strategy based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in the cloud storage system. The replica management strategy includes the replica placement algorithm and the replica number strategy. The replica placement algorithm sorts the nodes by TOPSIS according to the node performance and load, the replicas are placed in the nodes with high performance and low load. The replica number strategy designs the reHability model and the availability model to compute the number of replicas, the initial number of the replicas is decided according to the reliability model and the number of the replicas is adjusted dynamically according to the availability model. Experimental results show that the strategy in the study has better performance in response delay and load-balance than the Default Replica Management Strategy in Hadoop (DRMSH) distributed file system. © 2014 Academic Journals Inc.

关键词： Cloud storage

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Adaptive weighted label propagation for local matching based face recognition

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8833卷 78-85页

作者： Guo, Yan Li, Xiaohui Yi, Yugen Wei, Yunyan Wang, Jianzhong Changchun Vocational Institute Of Technology Changchun China College of Computer Science and Information Technology Key Laboratory of Intelligent Information Processing of Jilin Universities Northeast Normal University Changchun China

In this paper, a new algorithm named Adaptive Weighted Label Propagation (AWLP) which explores the complementary property among subpatterns from the same face image is proposed for local matching based face recognition. The proposed AWLP first partitions the face images into several smaller sub-images. Then, multiple similarity graphs are constructed for different sub-pattern sets. At last, in order to take correlation among different subpatterns into account, the graphs obtained by various sub-pattern sets are combined and the procedures of label prediction and graph weight learning are integrated into a unified framework to propagate the class information of the labeled samples to unlabeled ones. Moreover, a simple yet efficient iterative update algorithm is also proposed to solve our AWLP. Extensive experiments on three face benchmark databases show that AWLP has very competitive performance with the state-of-the-art algorithms. © Springer International Publishing Switzerland 2014.

关键词： Face recognition

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A new dynamic probabilistic Particle Swarm Optimization with dynamic random population topology

A new dynamic probabilistic Particle Swarm Optimization with...

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Congress on Evolutionary Computation

作者： Qingjian Ni Cen Cao Xushan Yin College of Software Engineering Southeast University Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781479914869

Population topologies of Particle Swarm Optimization algorithm (PSO) have direct impacts on the information sharing amony particles during the evolution, and will influence the PSO algorithms' performance obviously. The canonical PSO algorithms usually use static population topologies, and the majority are the classic population topologies (such as fully connected topology and ring topology). In this paper, we present the strategies of dynamic random topology based on the random generation of population topologies. The basic idea is as follows: various random topologies are used at different stages of evolution in the population, and the solving performance of PSO algorithms is enhanced by improving the information exchange of population in different evolutionary stages. This provides a new way of thinking for the improvement of the PSO algorithm. Experimental results on a relatively new variant of dynamic probabilistic particle swarm optimization show that our strategies can achieve better performance compared with traditional static population topologies. Experimental data are analyzed and discussed in the paper, and the useful conclusions will provide a basis for further research.

关键词： Topology Sociology Statistics Heuristic algorithms Equations Mathematical model Particle swarm optimization

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An adaptive cultural algorithm based on dynamic particle swarm optimization

An adaptive cultural algorithm based on dynamic particle swa...

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5th International Symposium on IT in Medicine and Education, ITME 2013

作者： Peiyu, Liu Yuanyuan, Ren Suzhi, Xue Zhenfang, Zhu School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Shandong Normal University Jinan 250014 China School of Information Science and Electric Engineering Shandong Jiao Tong University Jinan 250357 China

ISBN: (纸本)9789400776173

To avoid the local optimum problems and to improve convergent speed when particle swarm optimization algorithm in solving complex problems, an adaptive cultural algorithm based on dynamic particle swarm optimization algorithm was proposed. Particle swarm algorithm introduced evaluation premature convergence degree of index to judge the population space condition to determine the role of the influence function time. The inertia weight of the particle was adjusted adaptively based on the premature convergence degree of the swarm. The diversity of inertia weight makes a compromise between the global convergence and the speed of convergence. The proposed algorithm was tested with four well-known benchmark functions. The experimental results show that the new algorithm has great global search ability convergence accuracy and convergence velocity is also increased and avoid the premature convergence problem effectively. © Springer Science+Business Media Dordrecht 2014.

关键词： Method of moments

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Integrating herb effect similarity for network-based herb target prediction

Integrating herb effect similarity for network-based herb ta...

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Kuo Yang Xuezhong Zhou Runshun Zhang Baoyan Liu Lei Lei Xiaoping Zhang Hongwei Chu Changkai Sun Zhuye Gao Hao Xu School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China China Academy of Chinese Medical Sciences Data Center of Traditional Chinese Medicine Beijing China Guanganmen Hospital China Academy of Chinese Medical Sciences Beijing China China Academy of Chinese Medical Sciences Institute of Information on Traditional Chinese Medicine Beijing China Liaoning Provincial Key Laboratory of Cerebral Diseases Institute for Brain Disorders Dalian Medical University Dalian China Xiyuan Hospital China Academy of China Academy of Chinese Medical Sciences Beijing China

ISBN: (纸本)9781509000234

Network pharmacology has become the new approach for drug mechanism research and novel drug design. Drug target prediction based on computational approach became one of the primary approaches. However, due to the diversity and complexity of herbal chemical structures, the performance of herb target prediction based on chemical structure similarity is limited by the quality and the data availability of herb chemical ingredients and their structural properties. To gain insights into the molecular mechanism of herbs by using clinical herb efficacies, we develop a computational approach to predict the potential targets of herbs by integrating the herb effect properties. We found that herbs with high effect similarities have high degree of shared targets. Meanwhile, an algorithm integrating propagation on protein-protein interaction network and effect-based herb similarity was proposed and obtained better accuracy than the chemical structure similarity. Furthermore, we manually evaluated some novel predictions like the target SP1 for herb turmeric, which is not recorded in the benchmark set, but has been confirmed by recent published paper.

关键词： Proteins Chemicals Drugs Prediction algorithms Correlation Diseases Databases

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Comparison of Particle Swarm Optimization algorithms in Wireless Sensor Network node localization

Comparison of Particle Swarm Optimization algorithms in Wire...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Cen Cao Qingjian Ni Xushan Yin School of Computer Science and Engineering Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

The node localization in Wireless Sensor Network (WSN) presently plays an important role in the field of applications. Particle Swarm Optimization (PSO) algorithm is a typical swarm intelligence method. Researchers propose many PSO variants and try to apply PSO algorithm to the related problems in WSN. This paper focuses on the WSN node localization using PSO algorithm. This paper conducts the experiment simulation, comparison and evaluation work in the node localization using PSO algorithm. The performance of different PSO variants with different population topologies is analyzed. Experiment simulations show that WSN node localization using PSO algorithm can get good performance in ring topology and square topology. In particular, the two newly proposed PSO variants (GDPSO and LDPSO) have good performance on this problem. This paper proposes some useful conclusions, which will provide a valuable reference to WSN engineering field.

关键词： Topology Wireless sensor networks Sociology Statistics Heuristic algorithms Particle swarm optimization Standards

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Fuzzy Sentiment Membership Determining for Sentiment Classification

Fuzzy Sentiment Membership Determining for Sentiment Classif...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Chuanjun Zhao Suge Wang Deyu Li School of Computer and Information Technology Key Laboratory of Computational Intelligence Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China

ISBN: (纸本)9781479942732

Traditional support vector machine treats all samples using the same weight. Therefore it is very sensitive to noisy data. While the fuzzy support vector machine assigns lower weights to the samples which make small contributions to classification, thus it is beneficial to reduce the effects of noisy and unimportant data on the classification accuracy rate. In this paper, we propose a novel fuzzy sentiment membership determining method for solving sentiment classification task. We assume that strong intensity texts make more contributions to sentiment classification, while weak intensity texts are unimportant for the classification. In order to get the fuzzy sentiment membership of review texts, this paper proposes a three-layer sentiment propagation model. Firstly, we calculate the sentiment score of texts by the interrelations of the texts, topics and words, and ensure that the absolute value of sentiment score as the fuzzy sentiment membership degree of texts. Then, we train a fuzzy support vector machine to classify the samples from the test data sets. Finally, we conduct some experiments on four English reviews data sets from Amazon shopping websites. The experimental results show that the proposed method can improve the accuracy of sentiment classification effectively.

关键词： Support vector machines Noise measurement Accuracy Data mining Training Semantics Analytical models

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A self-adaptive group search optimizer with Elitist strategy

A self-adaptive group search optimizer with Elitist strategy

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Congress on Evolutionary Computation

作者： Xiang-wei Zheng Dian-jie Lu Zhen-hua Chen School of Information Science and Engineering Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

To deal with the disadvantages of Group Search Optimizer (GSO) as slow convergence, easy entrapment in local optima and failure to use history information, a Self-adaptive Group Search Optimizer with Elitist strategy (SEGSO) is proposed in this paper. To maintain the group diversity, SEGSO employs a self-adaptive role assignment strategy, which determines whether a member is a scrounger or a ranger based on ConK consecutive iterations of the producer. On the other hand, scroungers are updated with elitist strategy based on simulated annealing by using history information to improve convergence and guarantee SEGSO to remain global search. Experimental results demonstrate that SEGSO outperform particle swarm optimizer and original GSO in convergence rate and escaping from local optima.

关键词： Convergence Benchmark testing Optimization History Algorithm design and analysis Sociology Statistics

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