期刊：
International Journal of Modern Physics B,2017年31(15):1750121 ISSN：0217-9792
通讯作者：
Shen, Shaowu
作者机构：
[Hu, Fang; Shen, Shaowu; Shi, Yuan; Zhu, Youze] Hubei Univ Chinese Med, Coll Informat Engn, Wuhan 430065, Peoples R China.;[Cai, Jianchao] China Univ Geosci, Inst Geophys & Geomat, Hubei Subsurface Multiscale Imaging Key Lab, Inst Geophys & Geomat, Wuhan 430074, Peoples R China.;[Chen, Luogeng] Hubei Univ Chinese Med, Dept Sci & Technol, Wuhan 430065, Peoples R China.;[Hu, Fang] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Shen, Shaowu] H;Hubei Univ Chinese Med, Coll Informat Engn, Wuhan 430065, Peoples R China.
关键词：
Walktrap-SPM algorithm;overlapping community detection;modularity;normalized mutual information
摘要：
In this paper, based on Walktrap algorithm with the idea of random walk, and by selecting the neighbor communities, introducing improved signed probabilistic mixture (SPM) model and considering the edges within the community as positive links and the edges between the communities as negative links, a novel algorithm Walktrap-SPM for detecting overlapping community is proposed. This algorithm not only can identify the overlapping communities, but also can greatly increase the objectivity and accuracy of the results. In order to verify the accuracy, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks based on LFR benchmark. The experimental results indicate that this algorithm can identify the communities accurately, and it is more suitable for overlapping community detection. Compared with Walktrap, SPM and LMF algorithms, the presented algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SPM and LMF algorithms.
期刊：
Modern Physics Letters B,2017年31(29):1750262 ISSN：0217-9849
通讯作者：
Hu, Fang
作者机构：
[Hu, Fang; Wang, Yanran; Huang, Xiaoming; Chen, Luogeng; Hu, Mengyu] Hubei Univ Chinese Med, Coll Informat Engn, Wuhan 430065, Hubei, Peoples R China.;[Hu, Fang] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Hu, Fang] H;[Hu, Fang] C;Hubei Univ Chinese Med, Coll Informat Engn, Wuhan 430065, Hubei, Peoples R China.;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
关键词：
Community detection;SA-SOM algorithm;modularity;normalized mutual information;density;simulation test
摘要：
Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.
摘要：
In order to reduce Chinese text similarity calculation complexity and improve text clustering accuracy, this paper proposes a new text similarity calculation algorithm based on DF_LDA. First, we use DF method to realize feature extraction; then, we use LDA method to construct text topic model; finally, we use DF_LDA model obtained to calculate text similarity. Due to considering the text semantic and word frequency information, the new method can improve text clustering precision. In addition, DF_LDA method reduces text feature vector dimensions twice; it can efficiently save text similarity calculating time, and increases text clustering speed. Our experiments on TanCorp-12-Txt and FuDanCorp datasets demonstrate that the proposed method can reduce modeling time efficiently, and improves text clustering accuracy effectively.
期刊：
Lecture Notes in Computer Science,2013年8284 LNAI:28-37 ISSN：0302-9743
作者机构：
[Qian, Tieyun; Yao, Hongwei] State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China;[Chen, Li; Qian, Manyun; Mo, Xueyu] Department of Computer Science, Central China Normal University, Wuhan, China
会议名称：
1st International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2013
期刊：
PROCEEDINGS OF 2010 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY (ICCSIT 2010), VOL 7,2010年:401-404 ISSN：2381-3458
通讯作者：
Zhu Bian
作者机构：
[Chen Li; Zhu Bian; Tang Chao; Cui Huan-huan] HuaZhong Nonnal Univ, Dept Comp Sci, Wuhan, Peoples R China.
摘要：
Anonymous authentication is an important feature for trusted computing. Firstly, this paper describes two kinds of authentication model Trusted Computing Group (TCG) proposed -TTP model and DAA model, and then analyses their advantages and disadvantages. Secondly, the paper proposes an improved solution based on the elliptic curve algorithm (ECC) in the base of the original DAA model. Finally, the paper detailed analysis the performance of the improved solution.
期刊：
Lecture Notes in Electrical Engineering,2009年33 LNEE:15-28 ISSN：1876-1100
通讯作者：
Xu, H.
作者机构：
[Xiaoqiong Wu; Debao Xiao; Yanan Chang; Limiao Chen; Hui Xu] Institute of Computer Network and Communication, Huazhong Normal University, Wuhan, Hubei, 430079, China
摘要：
With evolution of the Internet, complexity of computer networks has greatly increased, when more and more network resources need to be effectively managed. The aim of this chapter is then to establish an evaluation framework to measure the capabilities of data modeling languages in adapting to the requirements of ever-evolving network management and apply it to examine the possibility of XML Schema and YANG as NETCONF-based data modeling languages for the purpose of standardization. KeywordsData modeling languages-NETCONF-based-Network management-Evaluation framework-XML Schema
摘要：
The data for the analysis of data may contain hundreds of features. Many of the features are irrelevant in data mining, So it is particularly important to find out the minimum set of features to improve the efficiency of data mining. A method of neural network features selection based on sensitivity analysis is presented in the paper. It avoids the deficiency of traditional neural network methods that needs to train a network using all features. It ranks the features of initial features set by using the method of sensitivity analysis, and then removes the secondary features to achieve dimension reduction. The feathers are selected by the BP neural network at last. The simulation results show the efficiency of this approach.
通讯机构：
[Liu, Jing] H;Huazhong Normal Univ, Dept Comp Sci, Wuhan, Peoples R China.
关键词：
Ant algorithm;Genetic algorithm;Task schedule;Grid computer;Parameters grouping
摘要：
Task scheduling is one of the core problems in grid computing. How to accomplish tasks quickly and efficiently to meet users' requirements has always been being a hot issue in the fileds of theoretical and applied research. The algorithm presented in this paper is based on the ant colony algorithm and genetic algorithm. It realizes scheduling optimization for grid tasks by studying and exploring optimization grouping of four parameters in ant colony algorithm with the quick global search randomly in genetic algorithm. In order to evaluate the performance, we design a simulating program to validate it after finishing the Gridsim study. Simulation results show that optimization grouping of parameters not only improve the efficiency of task distributing and scheduling but also balance the load. At last, further research direction is bringing forward.
期刊：
2008 INTERNATIONAL SYMPOSIUM ON INFORMATION PROCESSING AND 2008 INTERNATIONAL PACIFIC WORKSHOP ON WEB MINING AND WEB-BASED APPLICATION,2008年:533-537
通讯作者：
Yi, Chen
作者机构：
[Li, Chen; Yi, Chen; Rong, Li] Cent China Normal Univ, Dept Comp Sci, Wuhan, Peoples R China.;[Ge, Gao; Yi, Chen] Natl Engn Res Ctr, Multimedia Software, Wuhan, Peoples R China.
通讯机构：
[Yi, Chen] C;Cent China Normal Univ, Dept Comp Sci, Wuhan, Peoples R China.
会议名称：
ISIP 2008
会议时间：
2008-01-01
会议地点：
Moscow, Russia
会议论文集名称：
ISIP 2008
关键词：
wireless sensor networks;QoS;beacon exchange
摘要：
In wireless sensor networks, the network state beacon exchange rate is vital to determine the quality of service (QoS) performance of network state. It is important to schedule this parameter adaptively due to the dynamic nature of the networks. In this paper, an adaptive self-control model for wireless sensor (ASMW) is presented which model the whole wireless networks as a closed-loop system. The model is independent of the precision of the model parameters but operates adaptively. The simulation results show that the ASMW model is able to improve the performance of wireless networks effectively in adjusting the value of the beacon exchange rate to the reference value and uncomplicated to be implied in hardware.
