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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Wang, Ruiwei Xia, Wei Yap, Roland H.C. Li, Zhanshan School of Software Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Education Ministry China School of Computing National University of Singapore Singapore

Maintaining Generalized Arc Consistency (GAC) during search is considered an efficient way to solve non-binary constraint satisfaction problems. Bit-based representations have been used effectively in Arc Consistency algorithms. We propose STRbit, a GAC algorithm, based on simple tabular reduction (STR) using an efficient bit vector support data structure. STRbit is extended to deal with compression of the underlying constraint with c-tuples. Experimental evaluation show our algorithms are faster than many algorithms (STR2, STR2-C, STR3, STR3-C and MDDc) across a variety of benchmarks except for problems with small tables where complex data structures do not payoff.

关键词： Data structures

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Hilbert-huang transform-based local regions descriptors

Hilbert-huang transform-based local regions descriptors

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2007 18th British Machine Vision Conference, BMVC 2007

作者： Han, Dongfeng Li, Wenhui Guo, Wu Li, Zongcheng Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China School of Engineering Technology Shandong University of Technology Zibo China

ISBN: (纸本)1901725340

This paper presents a new interest local regions descriptors method based on Hilbert-Huang Transform. The neighborhood of the interest local region is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs). Then the Hilbert transform is applied to each component and get the phase and amplitude information. The proposed descriptors samples the phase angles information and amalgamates them into 10 overlap squares with 8-bin orientation histograms. The experiments show that the proposed descriptors are better than SIFT and other standard descriptors. Essentially, the Hilbert-Huang Transform based descriptors can belong to the class of phase-based descriptors. So it can provides a better way to overcome the illumination changes. Additionally, the Hilbert-Huang transform is a new tool for analyzing signals and the proposed descriptors is a new attempt to the Hilbert-Huang transform.

关键词： Hilbert-Huang transform

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Research of mobile IPv6 application based on diameter protocol

Research of mobile IPv6 application based on diameter protoc...

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First International Multi- Symposiums on Computer and computational Sciences, IMSCCS'06

作者： Da, Wei Yanheng, Liu Xuegang, Yu Xiaodong, Li Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Ministry of Education China

ISBN: (纸本)0769525814

The Diameter protocol is recommended by IETF as AAA (Authentication, Authorization and Accounting) protocol criterion for the next generation network, Because the IPv6 protocol will be widely applied in the intending all-IP network, mobile IPv6 application based on Diameter protocol will play more important role in authentication, authorization and accounting. In this paper, the implementation of mobile node's authentication and authorization is presented with PANA (Protocol for carrying Authentication for Network Access) protocol. It is based on Diameter protocol for the application expansion of mobile IPv6, which provides the supports to the basic AAA process of mobile IPv6 nodes and dynamic Home Agent distribution in the visited network and the secret key distribution. Finally, the correctness of this application expansion is testified with developing the design of protocol based on OpenDiameter. © 2006 IEEE.

关键词： Mobile telecommunication systems

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Traffic optimization for distributed network model base on Petri Nets

Traffic optimization for distributed network model base on P...

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Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST)

作者： Chang, Xin Pang, Huanli Hu, Liang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun 130012 China Collage of Computer Science and Engineering Changchun University of Technology Changchun 130012 China

ISBN: (纸本)9780769541396

Petri Nets is a powerful mathematical modeling tool for system description and analysis, with which we can describe the relationship among entities efficiently. The present thesis puts forward the modeling of the distributed computer network DNMPN and DNMPNTO based on Petri Nets. DNMPN implements the basic function of distributed computer network, and DNMPNTO modifies DNMPN's repetitive forward problem. Thus, it is efficiently helpful to optimize the unit traffic of the network, save the net work resource and improve the efficiency of the network system. © 2010 IEEE.

关键词： Petri nets

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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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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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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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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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Combining GCN and Bi-LSTM for Protein Secondary Structure Prediction

Combining GCN and Bi-LSTM for Protein Secondary Structure Pr...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Jin, Hailong Du, Wei Gu, Jiawei Zhang, Tianhao Shi, Xiaohu College of Computer Science and Technology of Jilin University Changchun China Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

ISBN: (纸本)9781665401265

Protein secondary structure prediction is still a challenging task in bioinformatics, especially for 8-state (Q8) classification. To address this problem, we have proposed a deep learning based model by integrating graph convolutional network(GCN) and bidirectional long short-term memory (Bi-LSTM) network in this paper. In the model, GCN is utilized to synthesize the information of amino acids and their interactions, while Bi-LSTM has strong ability to capture the long-range dependencies of amino acids. For sequence representation, a new protein embedding derived by ProtTrans is used instead of the traditional amino acid one-hot encoding, together with evolutionary features of PSSM and HHM profiles. Amino acid contact potential derived from SPOTContact-Helical is used to construct amino acid graph. To verify the effectiveness of our proposed model, it is applied to several benchmark datasets, and obtained 78.05%, 76.81% 72.84%, 74.46% and 76.04% Q8 accuracy on CASP10, CASP11, CASP12, CB513 and TS115 datasets, respectively. Compared with 8 state-of-the-art competitions, our model obtained the best performance in most of datasets. © 2021 IEEE.

关键词： Amino acids

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A Layered Co-evolution Based Rough Feature Selection Using Adaptive Neighborhood Radius Hierarchy and Its Application in 3D-MRI

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Chinese Journal of Electronics 2017年第6期26卷 1168-1176页

作者： DING Weiping GUAN Zhijin WANG Jiehua TIAN Di School of Computer Science and Technology Nantong University State Key Laboratory for Novel Software Technology Nanjing University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology

As the conventional feature selection algorithms are prone to the poor running efficiency in largescale datasets with interacting features, this paper aims at proposing a novel rough feature selection algorithm whose innovation centers on the layered co-evolutionary strategy with neighborhood radius hierarchy. This hierarchy can adapt the rough feature scales among different layers as well as produce the reasonable decompositions through exploiting any correlation and interdependency among feature subsets. Both neighborhood interaction within layer and neighborhood cascade between layers are adopted to implement the interactive optimization of neighborhood radius matrix, so that both the optimal rough feature selection subsets and their global optimal set are obtained efficiently. Our experimental results substantiate the proposed algorithm can achieve better effectiveness, accuracy and applicability than some traditional feature selection algorithms.

关键词： Layered co-evolution Rough feature se lection Adaptive neighborhood radius hierarchy Longitudinal cortical surface labeling

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