Non-parametric Statistical Tests for Informative Gene Selection
This paper presents two non-parametric statistical test methods, called Kolmogorov-Smirnov (KS) and U statistic test methods, respectively, for informative gene selection of a tumor from microarray data, with help of the theory of false discovery rate. To test the effectiveness of these non-parametric statistical test methods, we use the support vector machine (SVM) to construct a tumor diagnosis system (i.e., a binary classifier) based on the identified informative genes on the colon and leukemia data. It is shown by the experiments that the constructed tumor diagnosis system with both the KS and U statistic test methods can reach a good prediction accuracy on both the colon and leukemia data sets.

title: A Neural Network Approach to Real-Time Pattern Recognition
abstract: This paper presents a new neural network approach to real-time pattern recognition on a given set of binary (or bipolar) sample patterns. The perceptive neuron of a binary pattern is defined and constructed as a binary neuron with a neighborhood perceptive field. Letting its hidden units be the respective perceptive neurons of the patterns, a three-layer forward neural network is constructed to recognize these patterns with minimum error probability in a noisy environment. The theoretical and simulation analyses show that the network is effective for pattern recognition and can be under strict real-time constraints.

title: MFCC and SVM Based Recognition of Chinese Vowels
abstract: The recognition of vowels in Chinese speech is very important for Chinese speech recognition and understanding. However, it is rather difficult and there has been no efficient method to solve it yet. In this paper, we propose a new approach to the recognition of Chinese vowels via the support vector machine (SVM) with the Mel-Frequency Cepstral Coefficients (MFCCs) as the vowel’s features. It is shown by the experiments that this method can reach a high recognition accuracy on the given vowels database and outperform the SVM with the Linear Prediction Coding Cepstral (LPCC) coefficients as the vowel’s features.

title: The Mahalanobis Distance Based Rival Penalized Competitive Learning Algorithm
abstract: The rival penalized competitive learning (RPCL) algorithm has been developed to make the clustering analysis on a set of sample data in which the number of clusters is unknown, and recent theoretical analysis shows that it can be constructed by minimizing a special kind of cost function on the sample data. In this paper, we use the Mahalanobis distance instead of the Euclidean distance in the cost function computation and propose the Mahalanobis distance based rival penalized competitive learning (MDRPCL) algorithm. It is demonstrated by the experiments that the MDRPCL algorithm can be successful to determine the number of elliptical clusters in a data set and lead to a good classification result.

title: The Stability of the Generalized Hopfield Networks in Randomly Asynchronous Mode
abstract: We present a study on the stability of the generalized Hopfield network in randomly asynchronous mode. First, the stability is investigated from the state space of the network. We introduce a concept of hole and define two kinds of stability in randomly asynchronous mode. By mathematical inductive method, we have proved that a generalized Hopfield network with non negative weights is strictly stable, that is, the network evolves to a simple hole-stable state with probability one when it starts at any initial state. For the general case, we made the simulation experiments on the networks with the number of neurons from 5 to 10. The empirical results have shown that almost any generalized Hopfield network with simple holes is strictly stable.

title: Further Results on the Asymptotic Memory Capacity of the Generalized Hopfield Network
abstract: This paper presents a further theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network (GHN) under the perceptron learning scheme. It has been proved that the asymptotic memory capacity of the GHN is exactly 2(n− 1), where n is the number of neurons in the network. That is, the GHN of n neurons can store 2(n− 1) bipolar sample patterns as its stable states when n is large, which has significantly improved the existing results on the asymptotic memory capacity of the GHN.

title: A Real Trading Model based Price Negotiation Agents
abstract: Sim proposed a market-driven negotiation agent model that makes adjustable amounts of concession by reacting to different market situations and trading constraints, and it was improved with an enhanced market-driven strategy by taking opponent eagerness into consideration. In both Sim's original model and improved model, however, it was implied that a negotiation agent has same behaviors and actions to all trading partners referring to a same trading issue. It is not quite true in a real world trading negotiation. Based on both models, this paper proposes a real trading model based price negotiation agents that take each trading partner as an individual with different strategies and actions. Moreover, negotiation actions between a negotiation agent and a trading partner are kept in secret and unknown to others

title: A P2P groupware system with decentralized topology for supporting synchronous collaborations
abstract: Network based groupware systems are for supporting collaborations among a group of people who are engaged in a common task or goal using computers connected by a variety of networks including the Internet. A synchronous groupware system supports group members' collaborative activities at the same time. Due to the almost all of current synchronous collaborative systems have been implemented either using a centralized topology or a hybrid topology, the research presented in this paper has been devoted to investigation, design and implementation of a peer-to-peer (P2P) groupware system, called DSC, using a decentralized topology. As it does not use any server at all, peers in a group need to coordinately manage their group by themselves, and each peer has to fully handle the correct message passing by itself. The DSC system is implemented using the JXTA technology and platform that enable peers to find each other, form groups and exchange messages across firewalls and NATs. It currently offers three shared objects of Web browser, file viewer and drawing pad as well as a text chat tool. The synchronous controls of a shared space, the objects, telepointer and so on are also provided. Its evaluations with a practical test environment are given in detail.

title: A Multiplatform P2P System: its implementation and applications
abstract: Peer-to-peer (P2P) computing offers many attractive features, such as collaboration, self-organization, load balancing, availability, fault tolerance and anonymity. However, it also faces many serious challenges. In our previous work, we implemented a synchronous P2P collaboration platform called TOMSCOP. Based on the elementary peer group services offered by the JXTA general framework, TOMSCOP provides the extra four types of services: synchronous message transportation, peer room administration, peer communication support and application space management. By using the four services, different kinds of shared applications for various specific purposes can be relatively easily developed and associated collaborative cyber spaces or communities can be quickly built across the JXTA virtual network overlaid on top of the existing physical networks. However, the TOMSCOP was implemented only in Windows XP OS. In this paper, we extend our previous work and present the implementation of a Multi-Platform P2P System (MPPS). The proposed system operates very smoothly in UNIX Solaris 9 OS, LINUX Suse 9.1 OS, Mac OSX, and Windows XP. In the future study, we would like to evaluate the implemented P2P platform and compare its performance with other P2P systems.

title: A Web-Based Teacher Assisting Agent System
abstract: In the conventional teaching, teaching assistances (TAs) play importance roles in supporting a teacher’s teaching activities. This paper presents a Web-based teacher assisting agent system in which a set of automated programs, so called agents in this context, is developed. It is described that how the agents are generated, where they reside, and how they work independently or collaborate with each other to assist a teacher. It is emphasized that a later developed working agent can be easily incorporated into the system under the developed system framework. It is addressed that both agent persistency and mobility in the system can be achieved by the proposed mechanisms.

title: A Robust Error Concealment Algorithm in Wireless Networks
abstract: Compressed video sequences are very vulnerable to channel disturbances when transmitted through the burst-packet-loss wireless channels. Transmission errors not only corrupt the current frame, but may also propagate to succeeding frames. We find that a number of current proposed post-processing error concealment methods exploit the spatial/temporal redundancy in the video stream can not actually conceal the transmission errors effectively, because they ignored the fact that the neighboring blocks can be easily lost by the burst packet losses and the correlation was considered only in the current frame, which all limited the ability to obtain an optimal recovery. In this paper, a novel error concealment algorithm is proposed for video transmissions in a wireless environment. Simulation proves that the proposed algorithm can be chosen as good trade-off between video quality and computational load in wireless networks.