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2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Zhengyu Zhu Wei Zhu Shuai Li College of Computer Science Chongqing University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

Probabilistic tracking algorithms typically using linear structure to update the learning *** linear structure is not appropriate for long-term robust tracking as the occlusion and other challe

ISBN: (纸本)9781509053643;9781509053636

Probabilistic tracking algorithms typically using linear structure to update the learning *** linear structure is not appropriate for long-term robust tracking as the occlusion and other challe

关键词： Target tracking Adaptation models Robustness Computational modeling Bayes methods Context modeling

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Hybrid Recommendation Algorithm Based on Social Trust Clustering

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Ruan Jian Xue Bao/Journal of Software 2018年 29卷 21-31页

作者： Zhu, Jing-Hua Wang, Chao Ma, Sheng-Chao School of Computer Science and Technology Heilongjiang University Harbin150080 China Heilongjiang Province Key Laboratory for Database and Parallel Computing Harbin150080 China

Recommender system can solve the information overload problem effectively, and collaborative filtering (CF) is one of the techniques that is widely used in recommendation system. However, the traditional CF technology has problems such as poor scalability, sparse data, and low accuracy of recommendation results. In order to improve the quality of recommendations, this article integrates the trust relationship into the recommendation system in which the trust relationship is clustered by using the clustering (FCM) method. Using the trust cluster to predict implicit trust between users, the trust relationship is finally combined with the user-item relationship to give recommendations. The experimental results on the data set of Douban and Epinions show that compared with traditional CF algorithm, trust based recommendation algorithm and recommendation algorithm for user item clustering, the presented algorithm can greatly improve the recommendation quality and time efficiency. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Collaborative filtering

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Incremental Influence Maximization for Dynamic Social Networks

Incremental Influence Maximization for Dynamic Social Networ...

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The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Yake Wang Jinghua Zhu Qian Ming School of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

Influence maximization is one fundamental and important problem to identify a set of most influential individuals to develop effective viral marketing strategies in social network. Most existing studies mainly focus on designing efficient algorithms or heuristics to find Top-K influential individuals for static network. However, when the network is evolving over time, the static algorithms have to be re-executed which will incur tremendous execution *** this paper, an incremental algorithm DIM is proposed which can efficiently identify the Top-K influential individuals in dynamic social network based on the previous information instead of calculating from scratch. DIM is designed for Linear Threshold Model and it consists of two phases: initial seeding and seeds updating. In order to further reduce the running time, two pruning strategies are designed for the seeds updating phase. We carried out extensive experiments on real dynamic social network and the experimental results demonstrate that our algorithms could achieve good performance in terms of influence spread and significantly outperform those traditional static algorithms with respect to running time.

关键词： Influence maximization Dynamic social network Linear threshold model Pruning strategy

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Influence Equilibrium Problem Research Based on Common Interests in Mobile Social Network 12

Influence Equilibrium Problem Research Based on Common Inter...

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12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Li, Jinbao Han, Yue Zhu, Jinghua School of Computer Science and Technology Heilongjiang University China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781509056965

Influence maximization refers to the number of influenced nodes reach to maximum by finding a set of seed nodes in social network. Due to peoples' excessive concentration of attention, sometimes, negative effects will appear during the influence propagation. However, the negative effects on influence propagation are not involved in the existing research work. In this paper, we consider the influence equilibrium problem in mobile social network. The goal of influence equilibrium is to balance the influential range of different kinds of seed sets through the way of node scheduling. Our goal is to reduce the negative effects by transforming the status of influence from excessive concentration to relatively balanced. There are two main challenges: (1) which nodes should be scheduled in mobile social network in order to achieve a balanced effect. (2) how to schedule the nodes. To address the two challenges, we estimate the randomness of nodes and then schedule the nodes which have high randomness. The entropy-based random walk select balance algorithm(RSB) is proposed in this paper. To achieve the balanced effect, random walk procedure is adopted to choose the sets that can receive the scheduled nodes during the scheduling phase. However, new imbalance may arise during this procedure. Thus, the entropy-based max-min balance algorithm(MMB) is proposed to deal with the new imbalance caused by RSB algorithm. Theoretical analysis and experimental results show that the RSB and MMB algorithm can balance the influence range and MMB performs better than RSB. © 2016 IEEE.

关键词： Entropy

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Using gaussian mixture model to fix errors in SFS approach based on propagation 2nd

Using gaussian mixture model to fix errors in SFS approach b...

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

作者： Huang, Wenmin Ma, Jiquan Zhang, Enbin College of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9789811020520

A new Gaussian mixture model is used to improve the quality of propagation method for SFS in this paper. The improved algorithm can overcome most difficulties of propagation SFS method including slow convergence, interdependence of propagation nodes and error accumulation. To slow convergence and interdependence of propagation nodes, stable propagation source and integration path are used to make sure that the reconstruction work of each pixel in the image is independent. A Gaussian mixture model based on prior conditions is proposed to fix the error of integration. Good result has been achieved in the experiment for Lambert composite image of the front illumination. © Springer Science+Business Media Singapore 2016.

关键词： Errors

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CTS: Combine temporal influence and spatial influence for time-aware POI recommendation 2nd

CTS: Combine temporal influence and spatial influence for ti...

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

作者： Zhang, Hanbing Yang, Yan Zhang, Zhaogong School of Computer Science and Technology Heilongjiang University Harbin150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

ISBN: (纸本)9789811020520

As location-based social network (LBSN) services become more popular in people’s lives, Point of Interest (POI) recommendation has become an important research *** recommendation is to recommend places where users have not visited before. There are two problems in POI recommendation: sparsity and precision. Most users only check-in a few POIs in an LBSN. To tackle the sparse problem in a certain extent, we compute the similarity between the check-in datasets of different times. For the precision problem, we incorporate temporal information and geographical information. The temporal information will influence how the user chooses and allow the user to visit different distance point on different day. The geographical information is also used as a control for points which are too far away from the user’s check-in data. Our experimental results on real life LBSN datasets show that the proposed approach outperforms the other POI recommendation methods substantially. © Springer Science+Business Media Singapore 2016.

关键词： Collaborative filtering

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Research on F Statistic parallel Algorithm Based on GPU

Research on F Statistic Parallel Algorithm Based on GPU

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Xingkun Liu Jun Lu Ruiqing Jing Jinliang Zheng Jun Li College of Comp uter Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Comp uting of Heilongjiang Province

ISBN: (纸本)9781509001668

The experimental data is soybean gene variation information *** individual biological information is extracted from the soybean gene file and the value of the relationship between populations is *** the degree of differentiation of the two populations of wild soybean and cultivated soybean is ***,the easy parallel serial algorithm is *** accordi ng to the independence of the operational object,the partial algorithm can be paralleled is parallel processed with GPU multi thread ***,the result and the time of the serial/parallel algorithm are analyzed and *** experimental results show that the GPU algorithm efficiency is much higher than the serial algorithm *** pro vides a quick calculation scheme to handle massive biological data.

关键词： F statistics GPU differentiation mutation

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Tracking Nodes Selection Algorithm Based on Voronoi Structure in Sensor Networks

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Ruan Jian Xue Bao/Journal of Software 2017年 28卷 30-38页

作者： Ren, Qian-Qian Li, Jin-Bao Sun, Bei-Bei Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China Key Laboratory of Electronic Engineering Colleges of Heilongjiang Province Heilongjiang University Harbin150080 China School of Computer Science and Technology Heilongjiang University Harbin150080 China

Energy saving and the tracking performance are two important issues in moving target tracking. This paper presents a Voronoi structure-based nodes selection algorithm, which constructs a network model based on the property of Voronoi, and selects sensor nodes which are more close to the target to participate in tracking. This paper also presents a nodes scheduling mechanism, which minimizes the number of awaking nodes with tracking quality guarantee. Finally, a set of simulation experiments are made to analyze the effects of various parameters on the network performance. Experimental results show the excellent performance of the proposed algorithm in terms of energy saving and tracking quality. © Copyright 2017, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Target tracking

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CTS: Combine Temporal Influence and Spatial Influence for Time-Aware POI Recommendation

CTS: Combine Temporal Influence and Spatial Influence for Ti...

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The Second International Conference of Young Computer Scientists, Engineers and Educators,ICYCSEE 2016

作者： Hanbing Zhang Yan Yang Zhaogong Zhang School of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

As location-based social network(LBSN) services become more popular in people's lives, Point of Interest(POI) recommendation has become an important research *** recommendation is to recommend places where users have not visited before. There are two problems in POI recommendation:sparsity and precision. Most users only check-in a few POIs in an LBSN. To tackle the sparse problem in a certain extent, we compute the similarity between the check-in datasets of different times. For the precision problem, we incorporate temporal information and geographical information. The temporal information will influence how the user chooses and allow the user to visit different distance point on different day. The geographical information is also used as a control for points which are too far away from the user's check-in *** experimental results on real life LBSN datasets show that the proposed approach outperforms the other POI recommendation methods substantially.

关键词： Recommendation Point-of-interest Location-based social network Collaborative filtering Spatio-temporal

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Using Gaussian Mixture Model to Fix Errors in SFS Approach Based on Propagation

Using Gaussian Mixture Model to Fix Errors in SFS Approach B...

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The Second International Conference of Young Computer Scientists, Engineers and Educators,ICYCSEE 2016

作者： Wenmin Huang Jiquan Ma Enbin Zhang College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

A new Gaussian mixture model is used to improve the quality of propagation method for SFS in this paper. The improved algorithm can overcome most difficulties of propagation SFS method including slow convergence, inter-dependence of propagation nodes and error accumulation. To slow convergence and interdependence of propagation nodes, stable propagation source and inte-gration path are used to make sure that the reconstruction work of each pixel in the image is independent. A Gaussian mixture model based on prior conditions is proposed to fix the error of integration. Good result has been achieved in the experiment for Lambert composite image of the front illumination.

关键词： Shape from shading Propagation method Silhouette Gaussian mixture model Surface reconstruction

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