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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Wei, Jin-Mao Yi, Wei-Guo Wang, Ming-Yang Institute of Computational Intelligence Northeast Normal University ChangChun Jilin 130024 China Open Symbol Computation and Knowledge Engineering Laboratory State Education Department Jilin University

ISBN: (纸本)078039092X

In this paper, we analyze the method of support-confidence framework when mining association rules. In order to avoid the limitation in the criterion, we propose a new method of match as the substitution of confidence. We analyze in detail the property of the proposed measurement. Experimental results show that there is higher correlation between the antecedent and the consequent of the rules produced by the improved method compared with the rules produced by the support-confidence framework. Furthermore, the improved method decreases the generation of redundancy rules. © 2005 IEEE.

关键词： Data mining

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SIoTFog:Byzantine-resilient IoT fog networking

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Frontiers of Information Technology & Electronic engineering 2018年第12期19卷 1546-1557页

作者： Jian-wen XU Kaoru OTA Mian-xiong DONG An-feng LIU Qiang LI Department of Information and Electronic Engineering Muroran Institute of Technology School of Information Science and Engineering Central South University MOE Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University

The current boom in the Internet of Things(IoT) is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution to transmit and process large-scale geo-distributed big data. We propose a Byzantine fault-tolerant networking method and two resource allocation strategies for IoT fog computing. We aim to build a secure fog network, called "SIoTFog," to tolerate the Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases, with a single Byzantine fault and with multiple faults, to compare the performances when facing different degrees of risk. We choose latency, number of forwarding hops in the transmission, and device use rates as the metrics. The simulation results show that our methods help achieve an efficient and reliable fog network.

关键词： Byzantine fault tolerance Fog computing Resource allocation Internet of Things(IoT)

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The BBC news hunter: A novel crawler for BBC news 2nd

The BBC news hunter: A novel crawler for BBC news

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2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016

作者： Wang, Mingxin Wang, Ning Wang, Boran Tian, Can Liang, Yanchun Zhao, Guozhong Han, Xiaosong College of Software Jilin University Changchun130012 China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory for Symbol Computation Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai519041 China Daqing Oilfield Personnel Development Institute CNPC Daqing163000 China

ISBN: (纸本)9789811020971

In order to distinguish and extract the topic information from other interferential information on the BBC news website for the study in social computing, the BBC News Hunter was proposed in this paper. The whole system consists of 6 subsystems, respectively named: UI, Control, Download, Analysis, Storage and Log. Numerical experiments show that satisfactory results can be obtained from the BBC news website, whose average accuracy as well as efficiency are acceptable. © Springer Science+Business Media Singapore 2016.

关键词： Data mining

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PSO based partitional clustering algorithm

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2008年第6期38卷 1371-1377页

作者： Zhang, Chang-Sheng Sun, Ji-Gui Cui, Yan Yang, Feng-Qin Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

To obtain the optimal partition of a data set, a hybrid clustering algorithm, PKPSO, based on PSO is proposed. In the proposed PKPSO the PSO algorithm is effectively integrated with the K means algorithm. Among the population, selected candidate solutions are further optimized to improve the accuracy by the K-means algorithm. By analyzing the algorithm, the criterions for control parameters selection are determined. Partional clustering result by the proposed PKPSO is compared with that by PSO or by K-means algorithm, and results show that the global convergent property of PKPSO is better than that of the other algorithms. The PKPSO can not only overcome the shortcoming of local minimum trapping of the K-means, but also the solution precision and algorithm stability are better than that of the other two algorithm.

关键词： Clustering algorithms

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A multi-heuristic cooperative ant colony system for optimizing elimination ordering of Bayesian networks

A multi-heuristic cooperative ant colony system for optimizi...

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2010 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2010

作者： Dong, Xuchu Ouyang, Dantong Ye, Yuxin Yu, Haihong Zhang, Yonggang Department of Computer Science and Technology Jilin University Changchun Jilin 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun Jilin 130012 China

ISBN: (纸本)9780769541914

To solve the problem of searching for an optimal elimination ordering of Bayesian networks, a novel effective heuristic, MinSumWeight, and an ACS approach incorporated with multi-heuristic mechanism are proposed. The ACS approach named MHC-ACS utilizes a set of heuristics to direct the ants moving in the search space. The cooperation of multiple heuristics helps ants explore more regions. Moreover, the most appropriate heuristic will be identified and be reinforced with the evolution of the whole system. Experiments demonstrate that MHC-ACS has a better performance than other swarm intelligence methods. © 2010 IEEE.

关键词： Bayesian networks

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A spectrum-based support vector algorithm for relational data semi-supervised classification

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13th International Conference on Neural Information Processing, ICONIP 2006

作者： Ping, Ling Zhe, Wang Chunguang, Zhou Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China School of Computer Science Xuzhou Normal University Xuzhou 221116 China

ISBN: (纸本)3540464794

A Spectrum-based Support Vector Algorithm (SSVA) to resolve semi-supervised classification for relational data is presented in this paper. SSVA extracts data representatives and groups them with spectral analysis. Label assignment is done according to affinities between data and data representatives. The Kernel function encoded in SSVA is defined to rear to relational version and parameterized by supervisory information. Another point is the selftuning of penalty coefficient and Kernel scale parameter to eliminate the need of searching parameter spaces. Experiments on real datasets demonstrate the performance and efficiency of SSVA. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Learning algorithms

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Adaptive support vector clustering for multi-relational data mining

Adaptive support vector clustering for multi-relational data...

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Ling, Ping Zhou, Chun-Guang Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China School of Computer Science Xuzhou Normal University Xuzhou 221116 China

ISBN: (纸本)354034439X

A novel self Adaptive Support Vector Clustering algorithm (ASVC) is proposed in this paper to cluster dataset with diverse dispersions. And a Kernel function is defined to measure affinity between multi-relational data. Task of clustering multi-relational data is addressed by integrating the designed Kernel into ASVC. Experimental results indicate that the designed Kernel can capture structured features well and ASVC is of fine performance. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Data mining

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Semi-automatic hardware design using ontologies

Semi-automatic hardware design using ontologies

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8th International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Hu, He Liu, Da-You Du, Xiao-Yong School of Information Renmin University of China Beijin 100872 China Open Symbol Computation and Knowledge Engineering Laboratory College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (纸本)0780386531

Hardware description language (HDL) is widely used to model the structure and behavior of digital systems ranging from simple hardware building blocks to complete systems in today's hardware design. To tackle with ever-increasing complexity of process models, higher level of abstraction needs to be exploited. We propose an ontology-based architecture in the process of hardware design using HDLs. The architecture combines the traditional HDL design processes with ontology models and offers a semi-automatic process utilizing the ontology reasoning abilities of OWL (Web Ontology Language) Based on such ontology assistant architecture, more flexible and automatic processes can be devised, which are of crucial importance to meet current market demands. © 2004 IEEE.

关键词： Computer hardware description languages

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Fuzzy neural network optimization by a particle swarm optimization algorithm

Fuzzy neural network optimization by a particle swarm optimi...

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Ma, Ming Zhang, Li-Biao Ma, Jie Zhou, Chun-Guang Key Laboratory for Symbol Computation and Knowledge Engineering Jilin University National Education Ministry of China Changchun 130012 China Information Manage Center Beihua University Jilin 132013 China

ISBN: (纸本)354034439X

Designing a set of fuzzy neural networks can be considered as solving a multi-objective optimization problem. An algorithm for solving the multi-objective optimization problem is presented based on particle swarm optimization through the improvement of the selection manner for global and individual extremum. The search for the Pareto Optimal Set of fuzzy neural networks optimization problems is performed, Numerical simulations for taste identification of tea show the effectiveness of the proposed algorithm. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Neural networks

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Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems

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Journal of Bionic engineering 2008年第2期5卷 111-119页

作者： Jin-hui Yang Liang Sun Heow Pueh Lee Yun Qian Yan-chun Liang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun 130012 P. R. China Institute of High Performance Computing Singapore 117528 Singapore Department of Mechanical Engineering National University of Singapore Singapore 117576 Singapore Institute of Electric and Information Engineering Beihua University Jilin 13202 l P. R. China

A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.

关键词： job shop scheduling problem clonal selection algorithm simulated annealing global search local search

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