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2nd International Conference on Artificial Intelligence and computer Science, AICS 2020

作者： Chen, Jing Huang, Jincheng College of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China Software Engineering Laboratory in Hebei Province Qinhuangdao066004 China

In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN (Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities. © Published under licence by IOP Publishing Ltd.

关键词： Profitability

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Research on a Novel Influence Maximization Algorithm Based on Community Structure 2

Research on a Novel Influence Maximization Algorithm Based o...

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2nd International Conference on Artificial Intelligence and computer Science, AICS 2020

作者： Chen, Jing Liu, Jiangchuan College of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China Software Engineering Laboratory in Hebei Province Qinhuangdao066004 China

With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influence of seed nodes, this paper proposes an IMCS algorithm based on community structure. Firstly, we divide the community through the central node, and the quality of community division is ensured by defining community fitness and node contribution. Then through the analysis of the community division results, the seed node selects the one with the largest degree. Since most of the nodes activated by seed nodes of different communities also belong to different communities, this method solves the problem of overlapping influence to a certain extent. The experimental results show that the effectiveness of the IMCS algorithm is verified under the real network, cooperative network and artificial network, and the IMCS algorithm has a better effect than IEIR and Degree algorithms in most networks under the IC model. © Published under licence by IOP Publishing Ltd.

关键词： community division community structure independent cascade model influence maximization overlapping influence Seed node

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The study of a hierarchical hadoop architecture in multiple data centers environment

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Open Cybernetics and systemics Journal 2015年第1期9卷 131-137页

作者： Shengtao, Sun Aizhi, Wu Xiaoyang, Liu School of Information Science and Engineering Yanshan University QinhuangdaoHebei066004 China College of Vehicles and Energy Yanshan University QinhuangdaoHebei066004 China Data Industry Research Institute Limited Company QinhuangdaoHebei066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province QinhuangdaoHebei066004 China

Hadoop is a reasonable tool for cloud computing in big data era and MapReduce paradigm may be a highly successful programming model for large-scale data-intensive computing application, but the conventional MapReduce model and Hadoop framework limit themselves to implement jobs within single cluster. Traditional single-cluster Hadoop may not suitable for situations when data and compute resources are widely distributed this paper focuses on the application of Hadoop across multiple data centers and clusters. A hierarchical distributed computing architecture of Hadoop is designed and the virtual Hadoop file system is proposed to provide global data view across multiple data centers. The job submitted by user can be decomposed automatically into several sub-jobs which are then allocated and executed on corresponding clusters by location-aware manner. The prototype based on this architecture shows encouraging results. © Shengtao et al.

关键词： MapReduce

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A novel collaborative recommendation algorithm integrating probabilistic matrix factorization and neighbor model

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Journal of Information and Computational Science 2015年第5期12卷 2011-2019页

作者： Yu, Hongtao Dou, Lisha Zhang, Fuzhi The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao China

The existing collaborative recommendation algorithms suffer from lower recommendation precision due to the problem of data sparsity. To solve this problem, we propose a novel collaborative recommendation algorithm which integrates the probabilistic matrix factorization and neighbor models. We first propose a method to calculate the similarity between users or items based on the probabilistic matrix factorization model and construct a natural exponential function to compute the weighted similarity. Then we devise a collaborative recommendation algorithm to make recommendations for the target user, which dynamically adjusts the recommendation results for user-and item-based models by the balance adjustment factor. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms the existing algorithms in terms of prediction accuracy. Copyright © 2015 Binary Information Press.

关键词： Exponential functions

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Graph-cluster-mine: weighted closed key path mining in complex software executing network modules

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Journal of Computational Information systems 2015年第1期11卷 285-295页

作者： Ren, Jiadong Li, Weina Han, Gaowei College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration Qinhuangdao China

Previous key path mining algorithms have high computation complexity in the whole software executing network. But the network can be clustered into modules to find key paths in the module. If simplified strategy, weight and closed checking of paths are considered, interesting weighted closed key paths can be found more efficiently. Therefore, a novel algorithm Graph-Cluster-Mine is proposed. Firstly, weighted complex software executing network is constructed, definition of software executing network modules is given. Secondly, GN algorithm is improved to cluster the network into modules on limiting scale of a module. And DFS is adopted for each module to find paths. Path-Simplified-Bundle and Path-Simplified- Hierarchy strategies are devised to simplify paths. Thirdly, key-Path-Mine and Path-Closed-Checking are developed to mine weighted closed key paths. Experimental results indicate Graph-Cluster-Mine is more efficient in finding weighted closed key paths in complex software executing network. ©, 2014, Journal of Computational Information systems. All right reserved.

关键词： Complex networks

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A Robot Pick and Place Skill Learning Method Based on Maximum Entropy and DDQN Algorithm

A Robot Pick and Place Skill Learning Method Based on Maximu...

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2021 International Conference on Robotics Automation and Intelligent Control, ICRAIC 2021

作者： Wu, Peiliang Zhang, Yan Li, Yao Mao, Bingyi Chen, Wenbai Gao, Guowei School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Hebei Qinhuangdao066004 China School of Automation Beijing Information Science and Technology University Beijing100192 China

Pick and place (PAP) skill learning is a fundamental ability of intelligent robots, such as home service robot. Due to the NP-hard nature of the PAP problem, it takes a long time for an intelligent robot to learn the PAP skill based on current methods. In order to improve the learning efficiency of robot PAP skills, this paper proposes a Soft-DDQN-based PAP skill learning method. Firstly, the Soft-DDQN is proposed by introducing maximum entropy into robot DDQN framework, and the learning goal of Soft-DDQN is to maximize reward and information entropy. Secondly, PAP problem is modelled as a discrete form and Soft-DDQN is applied to solve the PAP problem. Finally, in order to verify the efficiency of Soft-DDQN-based PAP skill learning, comparisons have been given from two standard perspectives and shown that Soft-DDQN improves efficiency of PAP skill learning evidently. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Intelligent robots

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Optimal Subspace Analysis Based on Information-Entropy Increment 1

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8th China computer Federation International Conference on Big Data, CCF BIGDATA 2020

作者： Zhang, Zhongping Liu, Iiaojiao Zhang, Yuting Zhang, Jiyao Tian, Mingru College of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Hebei Education Examinations Authority Shijiazhuang050000 China

ISBN: (数字)9789811607059

ISBN: (纸本)9789811607042

The data structure is becoming more and more complex, and the scale of the data set is getting larger and larger. The strong limitations and instability in the high-dimensional data environment is showed in traditional outlier detection method. To solve the problems, an Optimal subspace Analysis based on Information-entropy Increment is proposed. The concepts such as mutual information and dimensional entropy to re-portrait the indicators that measure the pros and cons of subspace clustering, optimize the objective function of the clustering subspace, and obtain the optimal subspace. According to the idea of dividing the information entropy increment by one, the entropy outlier score is proposed as a metric to detect outliers in the optimal subspace. Finally, experiments verify the effectiveness of the algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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A filtering algorithm for scattered point cloud based on curvature features classification

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Journal of Information and Computational Science 2015年第2期12卷 525-532页

作者： Gu, Xiaoying Liu, Yongshan Wu, Qiong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China Data Industry Research Lab Qinhuangdao China

In order to remove the noise of three-dimensional scattered point cloud, and to smooth point cloud model effectively, an algorithm which uses different filtering strategies on different feature regions of a point cloud model is proposed. The algorithm firstly estimates the normal vectors of a point cloud model by using covariance analysis method, then estimates Gaussian curvatures of the point cloud model by using parabolic fitting method, and uses the average Gaussian curvature in the k-neighborhood as the threshold, to divide the point cloud model into flat neighborhood type regions and mutant neighborhood type regions, and, we use improved median filtering algorithm and bilateral filtering algorithm on different feature regions, so as to achieve the purpose of smoothing scattered point cloud. Experimental results show that it has a very good smoothing effect and maintains the detail features of the point cloud model, which has proved the algorithm is simple and effective. Copyright © 2015 Binary Information Press.

关键词： Three dimensional computer graphics

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Semantic recommendation through Reinforcement Learning and weighted meta-paths

Semantic recommendation through Reinforcement Learning and w...

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2022 International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2022

作者： Yu, Jing Lang, Yue Li, Xuewen Zhang, Jin Zheng, Shaojie Gong, Jibing School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9781510655133

Explainable recommendation systems, which can produce high-accuracy recommendations and help users make quick decisions, have become a hotspot in research field. Most of existing research algorithms committed to improving the accuracy of recommendation, but ignored the interpretability of recommendation. We propose a semantic recommendation algorithm through Reinforcement Learning and weighted meta-paths which analyzes and selects the meta-path, and uses Reinforcement Learning network to train the weight of meta-path. This method can effectively solve data sparsity, improve the accuracy of recommendation, and increase interpretability. Compared with the baseline method, indicators of the recommended methods for integrating Reinforcement Learning with Heterogeneous Information Networks have been greatly improved, as verified and compared on a real MovieLens 1M dataset. Experimental results prove that the algorithm can effectively improve the accuracy and interpretability of the recommendation. © 2022 SPIE.

关键词： Reinforcement learning

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Design of Irregular QC-LDPC Code Based Multi-Level Coded Modulation Scheme for High Speed Optical Communication systems

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China Communications 2019年第5期16卷 106-120页

作者： Liqian Wang Dongdong Wang Yongjing Ni Xue Chen Midou Cui Fu Yang Beijing University of Posts and Telecommunications Beijing 100876China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang 050081China Science and Technology on Communication Networks Laboratory Shijiazhuang 050081China College of Information Science and Engineering Yanshan UniversityQinhuangdao 066000China College of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang 050000China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066000China

In this paper, we focus on the design of irregular QC-LDPC code based multi-level coded modulation(MLCM) scheme by jointly optimizing the component code rate and the degree distribution of the irregular QC-LDPC component code. Firstly, the sub-channel capacities of MLCM systems is analyzed and discussed, based on which the optimal component code rate can be obtained. Secondly, an extrinsic information transfer chart based two-stage searching algorithm is proposed to find the good irregular QC-LDPC code ensembles with optimal component code rates for their corresponding sub-channels. Finally, by constructing the irregular QC-LDPC component codes from the designed ensembles with the aim of possibly enlarging the girth and reducing the number of the shortest cycles, the designed irregular QC-LDPC code based 16QAM and 64QAM MLCM systems can achieve 0.4 dB and 1.2 dB net coding gain, respectively, compared with the recently proposed regular QC-LDPC code based 16QAM and 64QAM MLCM systems.

关键词： quasi-cyclic low-density parity check (QC-LDPC) code irregular, extrinsic information transfer(EXIT) chart generalized mutual information(GMI) multi-level coded modulation(MLCM)

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