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An improved algorithm for community discovery in weighted social networks

Journal of Information and Computational Science 2015年第18期12卷 6873-6881页

作者： Guo, Jingfeng Liu, Miaomiao Liu, Linlin College of Information Science and Engineering Yanshan University QinhuangdaoHebei China Northeast Petroleum University DaqingHeilongjiang China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province QinhuangdaoHebei China

The problems of AGMA (Automatic Graph Mining Algorithm) are improved and a novel algorithm, namely CRMA (Clustering Re-clustering Merging Algorithm) is proposed which can realize more reasonable community division for weighted social networks. Firstly, when re-clustering nodes, AGMA neglected the clustering situation of its neighbors and didn't take into account the weight of the edge connecting the current node and its unclustered neighbor, which led to unreasonable clustering. Aiming to this, the concept of the connection compactness is introduced. Then according to the node weight, the edge weight and the friend coefficient, each node would be clustered into the community has the largest connection compactness with itself. Secondly, for community division in some unweighted networks and signed networks, if there are many nodes whose clustered neighbors is less than its unclustered neighbors, the division results of AGMA tend to produce many small communities, which would result in the lower modularity. In view of this problem, the concepts of positive edge weight density, the cluster density and the connection coefficient between clusters are proposed. Lastly, we merge communities based on the above three concepts and the modularity is effectively enhanced finally. The higher correctness and better generality of the improved algorithm are verified through experiments. © 2015 by Binary Information Press.

关键词： Clustering algorithms

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A Time-aware POI Recommendation Method Exploiting User-based Collaborative Filtering and Location Popularity

A Time-aware POI Recommendation Method Exploiting User-based...

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2017 2nd International Conference on Communications, Information Management and Network Security (CIMNS2017)

作者： Ya-li SI Fu-zhi ZHANG Wen-yuan LIU School of Information Science and Engineering Yanshan University School of Liren Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

Point-of-interest(POI) recommendation becomes an important research for location-based social networks, since it helps modern citizens to explore new locations in unvisited cites effectively according to their preferences. However, the current POI recommendation methods are lack of a deep mining in all time slots features and their effects on recommendation. To this end, in this paper we propose a POI recommendation method(called UPT) by combining time slot features, user-based collaborative filtering and spatial influence. Firstly, we extract time interval feature and time slot based popularity feature from history check-in datasets on LBSNs using probability statistical analysis method. Then, we devise a POI recommendation method based on the proposed temporal features to achieve better performance. In UPT, user-based collaborative filtering and smoothing technique are used by adding each time slot influence, and the overall popularity of a location is combined with each time slot feature. Our experimental results on Foursquare and Gowalla datasets show that UPT outperforms baseline POI recommendation methods in precision and recall.

关键词： Location-based social networks Point-of-interest recommendation User-based collaborative filtering Location popularity Temporal feature

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Object Tracking by Jointly Utilizing Template Updating and Relocation Mechanisms⋆

SSRN

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SSRN 2023年

作者： Wen, Jia Ren, Kejun School of Information Science and Engineering Yanshan University Qinhuangdao066099 China The Key Laboratory for Computer Virtual Technology System Integration of Hebei Province China

The Siamese network-based tracker has achieved competitive performance in the field of single target tracking because of its excellent tracking speed and tracking accuracy. When faced with target deformations, most Siamese trackers rely on fixed template features from the initial frame, severely constraining the tracker’s performance. To mitigate this situation, we introduce a novel tracking framework(SiamTAR) that allows adaptive template updates based on past tracking information. In addition, we design a feature refinement module which can capture the target information more accurately at the full scale on the search image through an integrated attention mechanism, thus enabling the tracker to pay less attention to redundant information. Notably, we introduce a relocalization mechanism to solve the problems of random drift of the tracking box and inaccurate scale estimation in online tracking. Extensive experiments on GOT-10K, UAV123, OTB100 and DTB70 datasets show that the tracker achieves good tracking results and real-time performance. © 2023, The Authors. All rights reserved.

关键词： Target tracking

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An algorithm based on bit complementary tree for mining closed frequent itemsets

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International Journal of Advancements in Computing technology 2012年第22期4卷 427-435页

作者： Ren, Jiadong Yang, Xuan Dong, Jun College of Information Science and Engineering Yanshan University Qinhuangdao City066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China

In recent years, closed frequent itemsets mining has become a hot topic. In this paper, we present an algorithm BCTCF, which is based on Bit complementary tree (BCTree) in order to mine closed frequent itemsets efficiently. First we adopt bit vectors to compress the database and define a novel structure, BCTree, in which a node stores two bit vectors that are complementary and each path is given a prime value. Based on the left-most bit in the bit vectors we adopt a divide-and-conquer strategy which handles the itemsets separately and then according to the prime unique feature we can get the closed frequent itemsets quickly and it makes us need not to mine all the frequent itemsets first. Both the divide-and-conquer strategy and prime unique can decrease the runtime. Experiment results show that BCTCF is very effective and scalable.

关键词： Forestry

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A clustering algorithm for data stream based on spatial directed graph with core

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Journal of Convergence Information technology 2012年第19期7卷 131-139页

作者： Dong, Jun Yuan, Xuemei Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao City 066004 China Key Laboratory for Computer Virtual Technology System Integration of Hebei Province Qinhuangdao City 066004 China

Due to the randomness of the partition of grids, the edge points of clusters might be partitioned into the sparse grids. These points would become noise information out of clusters when we cluster data stream by grid-density based algorithm. A data stream clustering algorithm based on spatial directed graph with core, SDGCStream, is proposed. It uses the spatial directed graph and the orthocenter of the sparse grids to handle the edge points of clusters. At first, the algorithm defines a structure SDGC (Spatial Directed Graph with Core) to store the summary statistics of data stream. The vertices of SDGC are maintained as the stream arriving. When the clustering quest comes, the edge information is generated. The initial clustering results are got through clustering on SDGC, then we judge whether the points of sparse grids which are adjacent to the border of a cluster belong to the cluster according to the orthocenter information and the border vertices of SDGC. At last, a strategy based on the distance between clusters is presented to adjust the clustering results after handling the border of clusters. The experimental results on synthetic and real datasets show the better validity of SDGCStream on handling the edge data points of clusters, and the scalability as the increasing of the length and dimensions of data stream.

关键词： Clustering algorithms

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Inuence maximization algorithm based on genetic algorithm

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Journal of Computational Information systems 2014年第21期10卷 9255-9262页

作者： Guo, Jingfeng Liu, Yuanying Shen, Jiali Wei, Zhenzhen Lv, Jiaguo College of Information Science and Engineering Yanshan University Qinhuangda China College of Information Technology Hebei University of Economics and Business Shijiazhuang China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

The inuence maximization problem is defined as providing a given initial integer k, mining top-k inuential nodes from a social network such that the spread of inuence in the network is maximized. Some existing studies are based on Greedy algorithm, but their time complexity is very high. In this paper, a different method based on Genetic Algorithms, denoted as MAGA is proposed. In the MAGA algorithm, the set of k nodes is seen as a candidate solution, and the expected inuence value as fitness. Use the genetic algorithm to get the optimal solution. Experiments show that the algorithm achieved a balance in inuence spread and running time.

关键词： Genetic algorithms

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VReading Multi-Sensory Reading Platform

VReading Multi-Sensory Reading Platform

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International Conference on virtual Reality and Visualization (ICVRV)

作者： Yuhan Liu Yong Tang Jiayin Song Jing Zhao Mengya Lv Qian Sun Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Yanshan University College of Economics and Management Qinhuangdao China

ISBN: (纸本)9781538684986;9781538684979

The form of books is constantly developing with the upgrading of carrying media, and the emergence of electronic books has greatly shaken the traditional paper books. In recent years, with the combination of artificial intelligence, virtual reality, high-speed network and digital reading, the concept of "VR" has been applied to more and more industries. The introduction of VReading multi-sensory reading platform will bring new ideas to digital reading industry.

关键词： Conferences virtual reality Visualization

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A Generalized Weighted Closed Sequential Pattern Mining Algorithm with Item Interval

A Generalized Weighted Closed Sequential Pattern Mining Algo...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Haitao Lu Shuo Li College of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Library of Yanshan University Yanshan University

ISBN: (纸本)9781510803084

The algorithm of this paper inserts pseudo items which are converted from item interval to obtain equal extended sequence database;it defines item-interval constraints, which are relative to the item weight, to prune the mining patterns. Through doing this, the algorithm avoids mining the patterns which users are not interested in and shortens the running time. It adopts histogram statistic pattern to get the standardization description to item interval of the mining patterns, making the mining sequences include the item interval information which is valuable to user decision.

关键词： weighted sequential patterns closed sequential patterns item interval

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Automatic simulated computation of remote sensing information model based on semantic

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Journal of Chemical and Pharmaceutical Research 2014年第7期6卷 521-529页

作者： Sun, Shengtao Wu, Aizhi The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao China College of Vehicles and Energy Yanshan University Qinhuangdao China

This paper discusses the simulated computation methods of remote sensing information model, and tries to put forward a more available solution. It presents our research works on the description and simulation methods of remote sensing information model: the service on demand method provides a basic methodology of model analysis, the semantic relationship description of information processing procedure supports the basic knowledge of resource selection and matching based on user’s demand, and the hierarchical mapping method of remote sensing information model shows the transformation from abstract model to executable data processing workflow. © 2014, Journal of Chemical and Pharmaceutical Research. All rights reserved.

关键词： Semantics

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Mining the important nodes of software based on complex networks

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ICIC Express Letters 2015年第12期9卷 3263-3268页

作者： Huang, Guoyan Zhang, Peng Li, Yanling Ren, Jiadong College of Information Science and Engineering Yanshan University No. 438 Hebei Ave Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 Hebei Ave Qinhuangdao China

The main purpose of studying the software structure is considered to understand and recognize the structure easily by also taking into account the quality of software. In this paper, we define and analyze the node importance based on the invoking dependency relationships among the nodes to mine the influential nodes for the software. Firstly, we construct a directed network to denote the software execution process depending on the invoking relationships. Secondly, according to the information of the Dependency Node Set (DNS) of each function node, we define the importance for the node. Finally, we present the algorithm Measure the Importance of Node (MIN) to evaluate the importance of each node. Comparing the value of nodes by the algorithm Mining the top-K Nodes (MKN), we can obtain the most influential nodes in the software network. In addition, with experiments results, the method MKN is verified to be effective for measuring the importance of the function nodes accurately. © 2015 ICIC International.

关键词： Complex networks

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