Chongjun Wang

Illumination invariant face recognition based on neural network ensemble

An illumination invariant face recognition method based on neural network ensemble architecture is proposed. Given a face image with an arbitrary illumination direction, it can complete recognition in a uniform way with high performance without knowing or estimating the illumination direction. Experimental result shows that the recognition ratio of the ensemble architecture is higher than the conventional approach that uses a single neural network to recognize faces of a specific illumination direction.

Dynamic Hierarchical Distributed Intrusion Detection System Based on Multi-Agent System

At present, distributed intrusion detection systems are taking on more and more characteristic of distributing and intelligence. Multi-agent system (MAS), which shows its intelligence by the interaction of a group of intelligent agents, is a popular tool for building novel intrusion detection systems (IDSs). The BDI (belief-desire-intention) model is a well studied architecture of agency for intelligent agents situated in complex and dynamic environments. Traditional MAS-based distributed IDSs commonly do their detection work within a framework that employs the method of distributed data collection and hierarchical data analysis. This is a simple and strict method, but it restricts the characteristic of distributing, intelligence and real-time response badly. In this paper, we present a dynamically-created hierarchical framework, and then bring forward the basic algorithm system to support it. We introduce the opponent BDI model to our agents, and realize the detection based on multi-agent cooperation, which effectively improves the traditional distributed intrusion detection

A Factor-Based Model for Context-Sensitive Skill Rating Systems

Estimating agent’s skill ratings from team competition results has many applications in the real world. Existing models assume skills are the same for all contexts. However, skills are context-sensitive in a variety of cases. In this paper, we present a Factor-Based Context-Sensitive Skill Rating System(FBCS- SRS). Instead of estimating agent skills under every context, which is hard due to data sparisity, we propose a factor model where individual skills are modelled by the inner product of an agent factor vector and a context factor vector. Collapsed Gibbs sampling is used for approximate inference. We formulate the problem of sampling linear constraint factors as a variant of MAX-SAT, and solve it by linear programming algorithms . We validate our model on two real-world datasets. Experiments show that FBCS-SRS achieves significantly higher prediction accuracy than other skill rating systems. The improvement is even more obvious when there are a lot of contexts.

Overlapping Community Detection via Leader-Based Local Expansion in Social Networks

Most community detection algorithms are trying to obtain the global information of the network. But increasingly large scale of the current network makes accessing to global information very difficult. In the meanwhile, the network shows power-law distribution and sparse features. And local community mining algorithms which use these features have more advantages over global mining methods. In this paper, we proposed a local community detection algorithm based on the core members named LLCDA (Leader based Local Community Detecting Algorithm) which uses local structural information in the network to optimize a local objective function. A local community can be detected through continuous optimization of the function by expanding from an initial core member computed by a modified PageRank sorting algorithm. The proposed LLCDA algorithm has been tested on both synthetic and real world networks, and it has been compared with other community detecting algorithms. The experimental results validated our proposed LLCDA and showed that significant improvements have been achieved by this technique.

A Center-Based Community Detection Method in Weighted Networks

The study of community detection has received more and more attention in recent years, the problem is very difficult and of great importance in many fields such as sociology, biology and computer science. But most of the algorithms proposed so far could not utilize the weight information within weighted networks, and many of them are so time-consuming that they are not fit for the large-scale networks. We propose a new center-based method, which is especially designed for weighted networks. And the method is also suitable for large-scale network because of its low computational complexity. We demonstrate our method on a synthetic network and two real-world networks. The result shows the high efficiency and precision of our method.

Detecting Link Communities Based on Local Approach

Detecting communities from networks has been given great attention these years. The traditional approaches were always focusing on the node community, while some recent studies have shown great advantage of link community approach which partitions links instead of nodes into communities. We proposed a novel algorithm LBLC (local based link community) to detect link communities in the network based on local information. A local link community can be detected by maximizing a local link fitness function from a seed link, which was ranked by another algorithm previously. The proposed LBLC algorithm has been tested on both synthetic and real world networks, and it has been compared with other link community detecting algorithm. The experimental results showed LBLC achieves significant improvement on link community structure.

Entropy chain multi-label classifiers for traditional medicine diagnosing Parkinson's disease

Parkinson disease is a chronic, degenerative disease of the central nervous system, which commonly occurs in the elderly. Until now, no treatment has shown efficacy. Traditional Chinese Medicine is a new way for Parkinson, and the data of Chinese Medicine for Parkinson is a multi-label dataset. Classifier Chains(CC) is a popular multi-label classified algorithm, this algorithm considers the relativity between labels, and contains the high efficiency of Binary classification algorithm at the same time. But CC algorithm does not indicate how to obtain the predicted order chain actually, while more emphasizes the randomness or artificially specified. In this paper, we try to apply Multi-label classification technology to build a model of Chinese Medicine for Parkinson, which we hope to improve this field. We propose a new algorithm ETCC based on CC model. This algorithm can optimize the order chain on global perspective and have a better result than the algorithm CC.

Traditional Chinese Medicine formula evaluation using multi-instance multi-label framework

Traditional Chinese Medicine (TCM) is a holistic integrative medical approach. Exploring the relations between the herbal formulae and the symptoms is a crucial problem in researches of TCM. Unlike existing researches, we treat it as a both multi-instance learning and multi-label learning problem. In this paper, we propose a novel approach, which named Weighted Sampling based on Similar Herbs MIML (WSSH-MIML), to predict one formulas primary symptoms based on multi-instance multi-label framework. We compare the performance of our model with other three state-of-the-art multi-label learning algorithms and the experimental results indicate ours is superior. This study suggests that the MIML technique provides a new research paradigm for mining meaningful TCM information.

LSRB-CSR: A Low Overhead Storage Format for SpMV on the GPU Systems

Sparse matrix vector multiplication (SpMV) is a basic building block of many scientific applications. Several GPU accelerated SpMV algorithms for the CSR format suffer from workload unbalance for irregular matrices. In this paper, we propose a new auxiliary array assisted CSR format called local segmented reduction based CSR (LSRB-CSR), which enables synchronization free preprocessing and efficient SpMV algorithm with the light weight auxiliary arrays. It is efficient for both regular matrices and irregular matrices with tiny preprocessing overhead. We compare our LSRB-CSR based SpMV algorithm with the CSR-based SpMV from cuSPARSE, the SpMV algorithm based on segmented reduction adopted by CUDPP library, and the CSR5-based SpMV algorithm for both regular and irregular sparse matrices. Compared to cuSparse, our LSRB-CSR based SpMV algorithm could improve the performance by 26% on regular matrices and up to 4750% on irregular matrices. Compared to CUDPP, our LSRB-CSR based SpMV algorithm could improve the average SpMV performance by 210% on regular matrices and 250% on irregular matrices. Our LSRB-CSR based SpMV algorithm has comparable performance as the CSR5 based SpMV algorithm for regular matrices, and achieves better performance over the CSR5 based SpMV algorithm for irregular matrices. Experimental results show that the conversion overhead from the CSR to the LSRB-CSR is only 1/10 of the overhead from the CSR to the CSR5 on average.

Multi-label Classification: Dealing with Imbalance by Combining Labels

Data imbalance is a common problem both in single-label classification (SLC) and multi-label classification (MLC). There is no doubt that the predicting result suffers from this problem. Although, a broad range of studies associate with imbalance problem, most of them focus on SLC and for MLC is relatively less. Actually, this problem arising in MLCis more frequent and complex than in SLC. In this paper, we proceed from dealing with imbalance problem for MLC and propose a new approach called DEML. DEML transforms the whole label set of multi-label dataset into some subsets and each subset is treated as a multi-class dataset with balanced class distribution, which not only addressing imbalance problem but also preserving dataset integrity and consistency. Extensive experiments show that DEML possesses highly competitive performance both in computation and effectiveness.