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, inte...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.展开更多
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 o...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 time. In 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.展开更多
As location-based social network (LBSN) services become more popular in people’s lives, Point of Interest (POI) recommendation has become an important research topic.POI recommendation is to recommend places where us...As location-based social network (LBSN) services become more popular in people’s lives, Point of Interest (POI) recommendation has become an important research topic.POI 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.展开更多
The parallel algorithm of the evolutionary distance between different species is implemented by using OpenMP parallel technique in this paper.In order to get the best degree of algorithm parallelism,the method of maki...The parallel algorithm of the evolutionary distance between different species is implemented by using OpenMP 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 is 14.1.It improves the running efficiency of the program.It is a great significance to dealing with massive bioinformatics展开更多
In many applications of mobile sensor networks, such as water flow monitoring and disaster rescue, the nodes in the network can move together or separate temporarily. The dynamic network topology makes traditional spa...In many applications of mobile sensor networks, such as water flow monitoring and disaster rescue, the nodes in the network can move together or separate temporarily. The dynamic network topology makes traditional spanning-tree-based aggregation algorithms invalid in mobile sensor networks. In this paper, we first present a distributed clustering algorithm which divides mobile sensor nodes into several groups, and then propose two distributed aggregation algorithms, Distance-AGG （Aggregation based on Distance）, and Probability-AGG （Aggregation based on Probability）. Both of these two algorithms conduct an aggregation query in three phases： query dissemination, intra-group aggregation, and inter-group aggregation. These two algorithms are efficient especially in mobile networks. We evaluate the performance of the proposed algorithms in terms of aggregation accuracy, energy efficiency, and query delay through ns-2 simulations. The results show that Distance-AGG and Probability-AGG can obtain higher accuracy with lower transmission and query delay than the existing aggregation algorithms.展开更多
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 grap...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.展开更多
文摘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.
文摘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 time. In 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.
文摘As location-based social network (LBSN) services become more popular in people’s lives, Point of Interest (POI) recommendation has become an important research topic.POI 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.
文摘The parallel algorithm of the evolutionary distance between different species is implemented by using OpenMP 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 is 14.1.It improves the running efficiency of the program.It is a great significance to dealing with massive bioinformatics
基金Supported by the National Natural Science Foundation of China (Nos. 61100048, 61033015, and 60803015)Programs Foundation of Ministry of Education of China for New Century Excellent Talents in University (No. NCET-11-0955)+4 种基金the Natural Science Foundation of Heilongjiang Province(No. F201038)Programs Foundation of Heilongjiang Educational Committee for New Century Excellent Talentsin University (No. 1252-NCET-011)Program for Group of Science and Technology Innovation of Heilongjiang Educational Committee (No. 2011PYTD002)the Science and Technology Research of Heilongjiang Educational Committee (Nos. 12511395 and 11551343)the Science and Technology Innovation Research Project of Harbin for Young Scholar (Nos. 2008RFQXG107, 2009RFQX080, and2011RFQXG028)
文摘In many applications of mobile sensor networks, such as water flow monitoring and disaster rescue, the nodes in the network can move together or separate temporarily. The dynamic network topology makes traditional spanning-tree-based aggregation algorithms invalid in mobile sensor networks. In this paper, we first present a distributed clustering algorithm which divides mobile sensor nodes into several groups, and then propose two distributed aggregation algorithms, Distance-AGG （Aggregation based on Distance）, and Probability-AGG （Aggregation based on Probability）. Both of these two algorithms conduct an aggregation query in three phases： query dissemination, intra-group aggregation, and inter-group aggregation. These two algorithms are efficient especially in mobile networks. We evaluate the performance of the proposed algorithms in terms of aggregation accuracy, energy efficiency, and query delay through ns-2 simulations. The results show that Distance-AGG and Probability-AGG can obtain higher accuracy with lower transmission and query delay than the existing aggregation algorithms.
基金The Natural Science Foundation of Heilongjiang Province under Grant Nos.F2018028.
文摘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.
