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16th IEEE International Conference on parallel and Distributed Systems, ICPADS 2010

作者： Li, Jinbao Zhang, Desheng Guo, Longjiang Ji, Shouling Li, Yingshu School of Computer Science and Technology Heilongjiang University Harbin Heilongjiang 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin Heilongjiang 150080 China Department of Computer Science Georgia State University Atlanta GA 30303 United States

ISBN: (纸本)9780769543079

In this paper, a duty cycle based multi-channel MAC protocol with multiple channel reservation, called M-cube, is proposed to tackle the triple hidden terminal problems. M-cube can make nodes to choose one actually idle channel from all the expected idle channels. Therefore, M-cube can avoid data packet collisions resulted by the triple hidden terminal problems. By minimizing the lower bound of the average number of times of channel switching in M-cube, the optimal duty cycle is obtained through theoretical analysis. To validate the effectiveness of multiple channel reservation and dynamic optimal duty cycling, extensive simulations and real test bed experiments were conducted. Both the simulation and experiment results show that when the number of channels is large or network loads are heavy, M-cube improves energy efficiency and throughput significantly compared with other works in the literature. © 2010 IEEE.

关键词： Energy efficiency

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Distributed multi-dimensional probabilistic Top-k query processing in sensor networks

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Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) 2012年第SUPPL.1期40卷 389-393+397页

作者： Zhu, Jinghua Guan, Xuemin Department of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing Heilongjiang Province Harbin 150080 China

A distributed multi-dimensional probabilistic Top-k (DMPT) query processing algorithm was proposed for sensor networks. DMPT exploited Skyline operator to get the Top-k results and reduced data transmissions and query delay through feedback and filtering mechanism. Data uncertainty, multi-dimensional attributes, distributed network and energy limitation were considered in DMPT, and the Top-k results were attained by computing Skylayers of data. Experiments on real and simulated data are conducted to demonstrate that DMPT has better energy efficiency and faster response compared with traditional algorithm.

关键词： Sensor networks

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Research on Neutral Mutation Algorithm in DNA Polymorphism Testing based on OpenMP

Research on Neutral Mutation Algorithm in DNA Polymorphism T...

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2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： Jun Li Jun Lu Ruiqing Jing Qiong Wang Cheng Chang College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

ISBN: (纸本)9781479919819

Soybean genotypes are adopted as the data source and the Tajima's D statistical method is parallel implemented by OpenMP *** method is used for testing neutral mutation hypothesis of DNA *** setting the sliding window size,all chromosomes containing the massive windows with multiple SNP sites are parallel processed,then the number of individual in sample,the number of segregating sites(S) and average number of nucleotide differences(π) are *** parameters filtering,the Tajima's D values for the effective SNP sites in each sliding window are parallel *** to the experimental data,the serial/parallel algorithms are compared and the conclusion shows that the efficiency of testing neutral mutation can be improved by using the OpenMP *** is a realistic significance for processing massive biological information data.

关键词： genotype Tajima's D OpenMP SNP

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Research on Jukes-Cantor Model parallel Algorithm Based on OpenMP

Research on Jukes-Cantor Model Parallel Algorithm Based on O...

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2015大数据技术及应用论坛

作者： Kang Wan Jun Lu Jiaju Zheng Zihan Ren College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

The parallel algorithm of the evolutionary distance between different species is implemented by using Open MP parallel technique in this paper. In order to get the best degree of algorithm parallelism, the method of making loop variable corresponding to the rower and column labels of distance matrix is adopted. It is to say that the double loop can be converted into single loop to improve the parallel efficiency. The serial algorithm and parallel algorithm are compared in this paper. The experiment result shows that the highest speedup is14.1. It improves the running efficiency of the program. It is a great significance to dealing with massive bioinformatics data.

关键词： OpenMP parallel Evolutionary distance Distance matrix

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An Incremental Partitioning Graph Similarity Search Based on Tree Structure Index

An Incremental Partitioning Graph Similarity Search Based on...

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2020国际计算机前沿大会

作者： Yuhang Li Yan Yang Yingli Zhong School of Computer Science Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

Graph similarity search is a common operation of graph database,and graph editing distance constraint is the most common similarity measure to solve graph similarity search problem. However, accurate calculation of graph editing distance is proved to be NP hard, and the filter and verification framework are adopted in current method. In this paper, a dictionary tree based clustering index structure is proposed to reduce the cost of candidate graph, and is verified in the filtering stage. An efficient incremental partition algorithm was designed. By calculating the distance between query graph and candidate graph partition, the filtering effect was further enhanced. Experiments on real large graph datasets show that the performance of this algorithm is significantly better than that of the existing algorithms.

关键词： Graphsimilaritysearch Similaritysearch Graphpartition

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Negative Influence Maximization in Social Networks

Negative Influence Maximization in Social Networks

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2018国际计算机前沿大会（原国际青年计算机大会）

作者： Jinghua Zhu Bochong Li Yuekai Zhang Yaqiong Li School of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

Influence maximization is one of the key research problems in social networks due to its wide applications like spread of ideas and viral marketing of products. Most of the existing work focus on social networks containing only positive relationships（e.g. friend or trust） between users, but in reality social networks containing both positive and negative relationships（e.g. foe or distrust）. Ignoring the negative relations may lead to over-estimation of positive influence in practical applications. Thus, in this paper, we study influence maximization problem with negative effects（NIM）. To address the NIM problem, we use the polarity Independent Cascade（IC-P） diffusion model which extends the standard Independent Cascade（IC） model with negative *** we propose the positive influence maximization algorithm（PIM） and negative influence maximization（NIM） problem. We prove that influence function of the NIM problem is monotonic and submodular, so we propose a CELF based algorithm（CELFIM） to solve it. Experiments results show that our algorithm has matching influence spread compared with greedy algorithm and achieves several orders of magnitude time improvement.

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PSHCAR: A Position-Irrelevant Scene-Aware Human Complex Activities Recognizing Algorithm on Mobile Phones

PSHCAR: A Position-Irrelevant Scene-Aware Human Complex Acti...

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2018国际计算机前沿大会（原国际青年计算机大会）

作者： Boxuan Jia Jinbao Li Hui Xu School of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

Recognizing human complex activities has become an essential topic in pervasive computing research area. With the growing popularity of mobile phones, more and more studies have been dedicated to identifying human complex activities using mobile phones in recent years. However, previous works often restrain the position and orientation of cell phones which limit the applicability of their methods. To overcome this limitation, we propose a novel position-irrelevant activities identification method named PSHCAR, which efficiently utilize information from multiple sensors on smartphones. Moreover,besides commonly-used features such as accelerometer and gyroscope,PSHCAR also employ the knowledge about scenes of activities, which is helpful but ignored by previous works, to identify complex activities of mobile phone users. Comparative experiments show that our method performs better than several strong baselines on the task of human complex activities recognition. In conclusion, our method achieves state-of-the-art performance without any limitation on position or orientation of mobile phones.

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Research on parallel F-statistics estimation algorithm based on OpenMP

Research on Parallel F-statistics estimation algorithm based...

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2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： Ruiqing Jing Jun Lu Jun Li Kang Wan Haoyu Zhang College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province

ISBN: (纸本)9781479919819

The genetic variation information file of glycine max is adopted as experimental data in this *** genetic variation information of soybean is processed and the corresponding algorithm is paralleled by the OpenMP parallel *** experiment extracted the corresponding biologic information of population individuals from genetic variation information,implemented corresponding process and *** relevant serial algorithm and parallel algorithm are compared and *** experiment results show that the efficiency of the data processing and analysis of F statistic are effectively improved by OpenMP parallel technology[l].It has actual significance on processing massive bioinformatic data.

关键词： F-statistics OpenMP population differentiation variation site

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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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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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