作者机构：
[He, Tingting; Hsu, Ching-Fang; Zhang, Maoyuan] Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.;[Harn, Lein] Univ Missouri, Dept Comp Sci Elect Engn, Kansas City, MO 64110 USA.
通讯机构：
[Hsu, Ching-Fang] C;Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.
关键词：
Group key transfer protocol;secret sharing;LSSS based on Vandermonde matrix;wireless sensor networks
摘要：
Special designs are needed for cryptographic schemes in wireless sensor networks (WSNs). This is because sensor nodes are limited in memory storage and computational power. The existing group key transfer protocols for WSNs using classical secret sharing require that a t-degree interpolating polynomial be computed in order to encrypt and decrypt the secret group key. This approach is too computationally intensive. In this paper, we propose a new group key transfer protocol using a linear secret sharing scheme and factoring assumption. The proposed protocol can resist potential attacks and also significantly reduce the computation complexity of the system while maintaining low communication cost. Such a scheme is desirable for secure group communications in WSNs, where portable devices or sensors need to reduce their computation as much as possible due to battery power limitations.
期刊：
IEEE Transactions on NanoBioscience,2014年13(2):80-88 ISSN：1536-1241
通讯作者：
Zhao, Junmin
作者机构：
[Zhao, Junmin] Henan Univ Urban Construct, Pingdingshan 467036, Peoples R China.;[He, Tingting; Shen, Xianjun; Hu, Xiaohua; Li, Peng; Zhang, Ming] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Zhao, Junmin] Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构：
[Zhao, Junmin] N;Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
关键词：
Biological network;gene co-express;protein complex;weighted PPI network
摘要：
Recent studies have shown that protein complex is composed of core proteins and attachment proteins, and proteins inside the core are highly co-expressed. Based on this new concept, we reconstruct weighted PPI network by using gene expression data, and develop a novel protein complex identification algorithm from the angle of edge (PCIA-GeCo). First, we select the edge with high co-expressed coefficient as seed to form the preliminary cores. Then, the preliminary cores are filtered according to the weighted density of complex core to obtain the unique core. Finally, the protein complexes are generated by identifying attachment proteins for each core. A comprehensive comparison in term of F-measure, Coverage rate, P-value between our method and three other existing algorithms HUNTER, COACH and CORE has been made by comparing the predicted complexes against benchmark complexes. The evaluation results show our method PCIA-GeCo is effective; it can identify protein complexes more accurately.
期刊：
Applied Mathematics and Computation,2014年249:436-443 ISSN：0096-3003
通讯作者：
Hsu, Chingfang
作者机构：
[Zhang, Maoyuan; Hsu, Chingfang] Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.;[Zeng, Bing] S China Univ Technol, Sch Software Engn, Guangzhou 510006, Guangdong, Peoples R China.
通讯机构：
[Hsu, Chingfang] C;Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.
关键词：
Big data security;DH key agreement;Group key transfer;Linear secret sharing schemes;Vandermonde Matrix
期刊：
IEEE Transactions on NanoBioscience,2014年13(2):89-96 ISSN：1536-1241
通讯作者：
Li, Peng
作者机构：
[He, Tingting; Shen, Xianjun; Hu, Xiaohua; Li, Peng; Zhang, Ming; Wang, Yan] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Zhao, Junmin] Henan Univ Urban Construct, Pingdingshan 467036, Peoples R China.
通讯机构：
[Li, Peng] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
关键词：
Algorithm CACE;connected affinity;effective and accurately;overlapping functional modules
摘要：
A novel algorithm based on Connected Affinity Clique Extension (CACE) for mining overlapping functional modules in protein interaction network is proposed in this paper. In this approach, the value of protein connected affinity which is inferred from protein complexes is interpreted as the reliability and possibility of interaction. The protein interaction network is constructed as a weighted graph, and the weight is dependent on the connected affinity coefficient. The experimental results of our CACE in two test data sets show that the CACE can detect the functional modules much more effectively and accurately when compared with other state-of-art algorithms CPM and IPC-MCE.
作者机构：
[李宏伟; 付丽华] School of Mathematics and Physics, China University of Geosciences, Wuhan, Hubei 430074, China;[张猛] Department of Computer, Central China Normal University, Wuhan, Hubei 430079, China
通讯机构：
School of Mathematics and Physics, China University of Geosciences, China
期刊：
2013 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM),2013年:386-391 ISSN：2156-1125
通讯作者：
Zhao, Junmin
作者机构：
[Zhao, Junmin] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[He, Tingting; Hu, Xiaohua; Li, Peng; XianjunShen; Zhang, Ming] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Engn, Philadelphia, PA USA.;[Zhao, Junmin] Henan Univ Urban Construct, Inst Comp Sci & Engn, Pingdingshan, Peoples R China.
通讯机构：
[Zhao, Junmin] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)
会议时间：
DEC 18-21, 2013
会议地点：
Shanghai, PEOPLES R CHINA
会议主办单位：
[Zhao, Junmin] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.^[Hu, Xiaohua;He, Tingting;Li, Peng;Zhang, Ming;XianjunShen] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Engn, Philadelphia, PA USA.^[Zhao, Junmin] Henan Univ Urban Construct, Inst Comp Sci & Engn, Pingdingshan, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
Protein Complex;Gene co-express;Biological network;Weighted PPI network
摘要：
Recent studies have shown that protein complex is composed of core and attachment proteins, and proteins inside the core are highly co-expressed. Based on this new concept, we reconstruct weighted PPI network by using gene expression data, and develop a novel protein complex identification algorithm from the angle of edge(PCIA-GeCo). First, we select the edge with high co-expressed coefficient as seed to form the preliminary cores. Then, the preliminary cores are filtered according to the weighted density of complex core to obtain the unique core. Finally, the protein complexes are generated by identifying attachment proteins for each core. A comprehensive comparison in term of F-measure, Coverage rate between our method and three other existing algorithms HUNTER, COACH and CORE has been made by comparing the predicted complexes against benchmark complexes. The evaluation results show our method PCIA-GeCo is effective; it can identify protein complexes more accurately.
期刊：
Journal of Convergence Information Technology,2012年7(2):160-166 ISSN：1975-9320
通讯作者：
Li, H.(fulihua9270@yahoo.com.cn)
作者机构：
[Fu, Lihua; Liu, Zhihui] School of Mathematics and Physics, China University of Geosciences, China;[He, Tingting; Zhang, Meng; Li, Hongwei] Department of Computer Science, Central China Normal University, China
摘要：
In leaf image classification or retrieval fields, hybrid features are widely used to represent the information in various aspects by combining a number of sub-features linearly. However, the importance degrees of sub-features are often ignored by assigning the weights in an ad-hoc fashion without a solid theoretical basis. In this paper, a new type of adaptive hybrid features is proposed by using kernel trick of support vector machine (SVM), in which the weights can be adaptively selected. All weights are obtained by solving an optimization problem to maximize the discriminability of features. Experimental results of leaf image classification show that SVMs with new features significantly outperform those with traditional ones in terms of test accuracy.
期刊：
Journal of Computers,2012年7(1):187-195 ISSN：1796-203X
通讯作者：
Li, H.(fulihua9270@yahoo.com.cn)
作者机构：
[Li, Hongwei; Fu, Lihua] School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China;[Zhang, Meng] Department of Computer Science, Central China Normal University, Wuhan 430079, China
摘要：
Peer-to-Peer (P2P) network is an important component to implement next generation Internet, how to quickly and efficiently search the resources in P2P networks has become one of the most critical issues, at the same time, this is one of greatest concern to users. This paper describes the basic Flooding Peer-to-Peer network search method, followed by analysis of several new search methods pros and cons, and then further analysis of these algorithms is proposed based on a cache-based search algorithm: When a node of the remaining load capacity is high, it will become the center node, and form a joint topology area with the nearby nodes together, then the center node and ordinary nodes also need to store the index cache, at the local region the overheating resources will be copied to the local (that is, the contents cache). The simulation shows that the algorithm can effectively improve the hit rates of resources searching, reduce the query delay.
作者机构：
[Yu, Shaoquan; Zhang, Meng] Department of Computer Science, Central China Normal University, No. 152, Luoyu Road, Wuhan 430079, China;[Fu, Lihua] School of Mathematics and Physics, China University of Geosciences, No. 388, Lumo Road, Wuhan 430074, China
通讯机构：
[Fu, L.] D;Department of Computer Science, Central China Normal University, No. 152, Luoyu Road, China
作者机构：
[李宏伟; 付丽华] School of Mathematics and Physics, China University of Geosciences, Wuhan, Hubei 430074, China;[张猛] Department of Computer, Central China Normal University, Wuhan, Hubei 430079, China
通讯机构：
School of Mathematics and Physics, China University of Geosciences, China
期刊：
Journal of Convergence Information Technology,2011年6(12):51-58 ISSN：1975-9320
通讯作者：
Li, H.(fulihua9270@yahoo.com.cn)
作者机构：
[Li, Hongwei; Fu, Lihua; Liu, Zhihui] School of Mathematics and Physics, China University of Geosciences, China;[Zhang, Meng] Department of Computer Science, Central China Normal University, China
摘要：
In this paper, a novel generalized Gaussian kernel function (GGKF) is constructed and introduced to the support vector machine (SVM). We find that this new kernel meets Mercer condition. Besides offering an alternative to support vector kernel, GGKF can induce a feature space, in which the mapped data set may be more in favor of solving classification problem than the standard Gaussian kernel function (SGKF). The numerical results on both artificial and real-world datasets show GGKF is capable of producing the SVM with both greater accuracy and better generalization ability than SGKF. Besides, the results also indicate that the SVM with GGKF is less sensitive to the parameters selection than SVM with SGKF.
作者机构：
[Fu, Lihua; Li, Hongwei] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.;[Zhang, Meng] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Zhang, Meng] C;Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
关键词：
multi-kernel;neural networks;radial function basis;nonlinear system identification
摘要：
While the conventional standard radial basis function (RBF) networks are based on a single kernel, in practice, it is often desirable to base the networks on combinations of multiple kernels. In this paper, a multi-kernel function is introduced by combining several kernel functions linearly. A novel RBF network with the multi-kernel is constructed to obtain a parsimonious and flexible regression model. The unknown centres of the multi-kernels are determined by an improved k-means clustering algorithm. And orthogonal least squares (OLS) algorithm is used to determine the remaining parameters. The complexity of the newly proposed algorithm is also analyzed. It is demonstrated that the new network can lead to a more parsimonious model with much better generalization property compared with the traditional RBF networks with a single kernel.
