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In-rank: An entropy-based informative nodes mining method in complex software network

ICIC Express Letters 2016年第7期10卷 1683-1689页

作者： Ren, Jiadong Li, Yanling Wu, Hongfei Liu, Yang Zhang, Peng College of Information Science and Engineering Yanshan University No. 438 Hebei Avenue Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 Hebei Avenue Qinhuangdao066004 China

It is a fundamental issue to find a small subset of influential individuals in a complex network such that they can spread information to the largest scope of nodes in the network. Informative functions in complex software network can lead maximum information propagation scope, and mining such function nodes is important to understand the system’s topology structure and information transfer flows. In this paper, a novel topk informative nodes mining approach based on global information in directed-weighted complex software network is proposed. Firstly, we map functions and call relationships between them as a Directed-Weighted Function Call Network (DWFCN). Secondly, an algorithm path-compute based on Software Executing Path Sequence (SEPS) generated by Depth-First-Search strategy is used to compute the software executing paths and get nodes’ information Accessible Set (AS). Thirdly, algorithm Entropy-Compute is proposed to calculate each source node’s information entropy in each information transfer flow process. Finally, an Informative Node Rank (IN-Rank) algorithm is put forward to mine most informative nodes. Experimental results show that our new method is accurate and effective. © 2016, ICIC Express Letters Office. All rights reserved.

关键词： Mining

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ICRA: An improved community detection algorithm on signed social networks

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Journal of Computational Information systems 2015年第11期11卷 4091-4099页

作者： Hu, Xinzhuan Guo, Jingfeng Zhao, Yue Liu, Yuanying School of Economics and Management Yanshan University Qinhuangdao China College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

The study on signed social networks community detection has been paid more and more attention. Research shows that two-phase signed social networks community detection algorithm can not correctly divide the network. To solve the problem, we propose a new algorithm: ICRA (Improved Clustering Re-clustering Algorithm). In the process of community division, the assignment of vertices with negative edges shall be determined by comparing the value of network positive density with community positive density. Our algorithm solves the problems existing in CRA, including network division failure and wrong division starting from different vertices. ©, 2015, Binary Information Press. All right reserved.

关键词： Clustering algorithms

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A novel hierarchical clustering method in signed social network

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Journal of Computational Information systems 2015年第21期11卷 7865-7872页

作者： Guo, Jingfeng Chen, Xiao Fan, Chaozhi Hu, Xinzhuan College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Qian'an College North China University of Science and Technology Qian'an China

Based on a new feature that macro network structure and micro game have mutual inuence on the formation of the community, to mining communities in signed network, a novel algorithm SNCGHC is proposed. Firstly, according to the signed attributes and the node degree, the Shapley Value is improved, to avoid ignoring the effect of external environment on the Shapley Value and reduce the calculation complexity. Secondly, considering the inuence of network density on the formation of the coalition (community), we find the calculation method of average coalition payoff and create the signed network coalitional game model. Thirdly, SNCGHC depends on average payoff maximization of coalition to achieve hierarchical clustering of signed network. Finally, experiments show that our method can detect better community structures as compared to other existing methods. Copyright © 2015 Binary Information Press.

关键词： Game theory

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A novel approach to identify influential functions in complex software network based on complex network

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ICIC Express Letters 2016年第2期10卷 485-492页

作者： Huang, Guoyan Chen, Xiaojuan Wu, Hongfei Zhang, Peng Ren, Jiadong College of Information Science and Engineering No. 438 West Hebei Ave Qinhuangdao China Computer Virtual Technology and System Integration Laboratory of Hebei Province Yanshan University No. 438 West Hebei Ave Qinhuangdao China

Identifying influential nodes is an important issue in understanding the process of information diffusion in complex software networks. Researchers generally define functions as nodes, and relationship of function calls as edges to map software into complex network. This paper proposes a novel approach to identify the influential nodes in complex software network based on complex network. Firstly, we map the function and relationship of function calling or dependency to a directed weighted call network (DWCN). Secondly, we define NodeScore (NS) to evaluate the importance of nodes in network. It has higher influence when the node has lager value of NS. The NS of each node is calculated by two parts in our opinion, Direct Dependency Nodes (DDN) and Indirect Dependency Nodes (IDN). Thirdly, algorithm ComputeNodeScore is proposed to calculate NS of each node. Finally, we mine top-k nodes based on the NS. Experimental results of various versions about software Tar and cflow show the approach is effective for identifying the influential nodes. © 2016, ICIC Express Letters ICIC International.

关键词： Complex networks

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The analysis to gamma oscillations induced in high dose DHPG and spike-gamma oscillation relationship

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International Journal of Advancements in Computing technology 2012年第20期4卷 553-560页

作者： Wen, Dong Lu, Chengbiao Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Yanshan University QinhuangdaoHebei 066004 China School of Information Science and Engineering Yanshan University QinhuangdaoHebei 066004 China Institue of Electrical Engineering Yanshan University Qinhuangdao Hebei 066004 China

Neuronal oscillations in the gamma frequency range and spikes have been reported in many cortical areas in the information processing, but the role of spikes play in cortical processing remains unclear. The aim of this study was to examine the role of high dose DHPG in the generation of oscillatory field activity at gamma frequency in the medical septal diagonal band of Broca (MSDB) slice prepared from rat brain, and to determine the phase relationship between spikes and gamma oscillations. The high dose DHPG induced large amplitude field population spiking activity, which is correlated linearly with the field gamma oscillations. The relationship between timing of spikes and the phase of gamma oscillation was determined by an analysis of circular statistics. Further analysis with Rayleigh test revealed that the relationship of phase-locking between spikes and gamma rhythm is of statistical significance. These data demonstrate that DHPG-sensitive neuronal networks within medial septum are able to generate gamma oscillations which are modulated by large amplitude population spikes and phase-locking of spikes to the gamma rhythm suggest the role of memory enhancement in the presence of mGluR1 activation.

关键词： Locks (fasteners)

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Improved BP classifier via distance for sample reduction

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Journal of computers (Taiwan) 2019年第2期30卷 1-13页

作者： Wen, Jia Deng, Jia Liu, Peng-Fei Wang, Hong-Jun Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China School of Information Science and Engineering Yanshan University Qinhuangdao China State Key Laboratory of Software Engineering of Hebei Province Yanshan University Qinhuangdao China

The BP neural network algorithm is a commonly used technique in the classification field of pattern recognition, which iterates training samples continuously to obtain the decision boundary, until the error reaches the convergence precision. However, due to the extensive redundancy of training samples, this carpet-like training method can easily lead to slow network convergence and low classification performance. In this paper, from the view of sample, a novel BP training method called improved BP Classifier algorithm using Distance for Sample Reduction (DRBP) is proposed. Through the sample weight to get the new training sample, which are the near of the classification interface like support vector, and then iterative training BP classifier to solve the classification issue. The main function of this method is to keep the original sample information, realize the sample reduction, and improve the generalization ability of the BP neural network. First, point sets is used to visualize the training process. And then use 10 sets of the University of California Irvine (UCI) to verify the effectiveness and superiority of our experiments. Finally through Cross-validation, the classification error rate of 10 sets of UCI datasets tested by DRBP training method is lower than that of standard BP neural network (STBP). So the result of the experiment showed that the method of the combined distance sample reduce with BP network can significantly enhance the generalization ability of BP network. © 2019 computer Society of the Republic of China. All rights reserved.

关键词： Classification (of information)

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An efficient collision detection algorithm of convex polygons based on Minkowski sum

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ICIC Express Letters 2013年第2期7卷 461-464页

作者： Geng, Qingjia Guo, Xijuan Zhang, Jianfei Zhang, Buying The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province College of Information Science and Engineering Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China College of Computer Hebei Normal University of Science and Technology No. 360 West Hebei St. Qinhuangdao 066004 China

Collision detection is a hot research topic in the field of path planning of mobile robot, virtual assembly simulation, and so on. Fast and accurate collision detection has become one of the most key technologies of restricting their development. The study method based on Minkowski sum is an effective method for accurate collision detection through accurate numerical calculations. This paper presents a new efficient collision detection algorithm of convex polygons based on Minkowski sum, which uses the minimum separation distance to detect whether two convex polygons collide or not. The algorithm's performance is also analyzed and verified by experiment in the paper. Compared with the traditional collision detection algorithms based on the Minkowski sum, the algorithm's calculation is simpler and execution performance is higher. © 2013 ISSN 1881-803X.

关键词： Motion planning

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Reinforced Heterogeneous Graphlet Design for Knowledge Graph Representation Learning

SSRN

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SSRN 2023年

作者： Gong, Jibing Lin, Yuting Zhao, Yi Lin, Tianyu Fang, Xiaohan Feng, Xinchao Peng, Jiquan School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology System Integration of Hebei Province Yanshan University Qinhuangdao066004 China

Knowledge Graphs have been practical tools to represent and integrate plentiful structural and semantic information in mainstream industrial scenarios. While promising, the heterogeneity and complexity of KGs pose a formidable obstacle, especially for graph representation learning. Most existing KG embedding models omit the dynamic high-order connectivity patterns, and heavily rely on the handcrafted patterns to handle the complex semantic relationships, thereby limiting their ability to adaptively capture nuanced and intricate relationships of knowledge graphs in different tasks. To fill this gap, we present ReHGD — a model designed for KGs that focuses on adaptively designing typed graphlets through a cooperative Multi-agent Reinforcement Learning algorithm. This approach can learn more discriminative graph representations tailored to specific downstream tasks. Specifically, ReHGD engages in the creation of typed graphlets through a two-stage process: it (1) establishes a reinforced chain design module to generate chains without predefined rules, and (2) employs a buffer-aware sampling technique to derive episodic chains from prior experiences. Subsequently, motifs are deduced by applying commute count and Hadamard product operations to the episodic chain-based subgraphs. Finally, ReHGD utilize chain and motif aggregations to learn graph representations. Experimental results on three real-world KGs show that ReHGD outperforms baselines on entity classification. © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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SYVR virtual Equipment Manufacturing Simulation

SYVR Virtual Equipment Manufacturing Simulation

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International Conference on virtual Reality and Visualization (ICVRV)

作者： Yuhan Liu Yong Tang Qian Sun Shiny VR Yanshan University College of Information Science and Engineering Qinhuangdao China Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9781538684986;9781538684979

SYVR is a real-time simulation graphic engine focusing on equipment manufacturing. It is based on decades of research works of physics simulations, is able to provide services like real-time simulation for key parts of devices, large-scale com-plicated situation simulation, virtual experiment, operational training, visualization of concept design and etc., which con-sist of the whole process of virtual manufacturing and simulation. SYVR can provide comprehensive technology support for industrial 3-dimensional data, accelerate applications of VR to all aspects of industry, cut down development costs of equipment manufacturing significantly, highly increase effi-ciency of equipment design, and promote development of equipment manufacturing.

关键词： Conferences virtual reality Visualization

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Mining important nodes in directed-weighted software network based on semi-global structure information

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ICIC Express Letters 2015年第10期9卷 2859-2866页

作者： Ren, Jiadong Wu, Hongfei Yin, Tengteng Liu, Yang He, Haitao College of Information Science and Engineering Yanshan University No. 438 West Hebei Ave. Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 West Hebei Ave. Qinhuangdao China

Software’s control flow graph can be treated as a complex software network, and a tiny fraction of key function nodes has considerable influence on the stability, reliability and robustness of the network. Identifying the top-k key nodes accurately and effectively has become an important topic in software engineering. In this paper, directedweighted function dependency network (DWFDN) is adopted to represent the function and relationship of function calling or dependency. The weight on a direct edge indicates the frequency of function calling. Weighted semi-global structure information (the weight, the relative position of a function node and the topological structure among its neighbor nodes) is taken into consideration in our approach. From the perspective of accumulating and propagating vulnerabilities, key function callers and function callees are mined respectively. Case studies on real software date show the utility of the approach. © 2015, ICIC International.

关键词： Software reliability

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