Simplex Memory Neural Networks
The biological neural network-simplex memory neural network-is proposed to describe the mechanisms of pattern memory in the brain. A mathematical model of the simplex memory neural network is constructed to memorize any binary pattern with content-addressable memory function. Under Hebbian learning rule, the new network has some important functions in accord with the learning and memory behaviors of the brain.

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: 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: 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: 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: Unexpected means of protocol inference
abstract: Network managers are inevitably called upon to associate network traffic with particular applications. Indeed, this operation is critical for a wide range of management functions ranging from debugging and security to analytics and policy support. Traditionally, managers have relied on application adherence to a well established global port mapping: Web traffic on port 80, mail traffic on port 25 and so on. However, a range of factors - including firewall port blocking, tunneling, dynamic port allocation, and a bloom of new distributed applications - has weakened the value of this approach. We analyze three alternative mechanisms using statistical and structural content models for automatically identifying traffic that uses the same application-layer protocol, relying solely on flow content. In this manner, known applications may be identified regardless of port number, while traffic from one unknown application will be identified as distinct from another. We evaluate each mechanism's classification performance using real-world traffic traces from multiple sites.

title: A Shopping Negotiation Agent That Adapts to User Preferences
abstract: This paper describes a shopping negotiation agent that can adapt user preferences and automatically negotiate with its counter party on behalf of a user it represents. The agent is built on a basis of the proposed negotiation model, the enhanced extended Bazaar model, which is a sequence of decision making model of negotiation with exploiting common knowledge, public information, and game theory. Since different users can have different preferences, it is important for the agent to have adaptation to different user preferences. This can be achieved by acquiring user preferences, tracing user's behavior on Web and mapping the behavior to a set of the preference parameters, creating the negotiation model class, and generating an instance negotiation model object with new/updated preference parameters.

title: TOMSCOP - a synchronous P2P collaboration platform over JXTA
abstract: We focus on design and implementation of a synchronous P2P (peer-to-peer) collaboration platform called TOMSCOP (Technology Of Multiuser Synchronous Collaboration Platform). 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.

title: The design and implementation of a distributed Web document database
abstract: Distance learning has become a very important mechanism for virtual university operation. In order to realize such an operation smoothly, it is necessary to consider distance learning from three perspectives: administration, awareness, and assessment. We are currently implementing a virtual university environment according to these guidelines. In this paper, we propose a new Web documentation database as a supporting environment of the Multimedia Micro-University project. The design of this database facilitates a Web documentation development paradigm that we have proposed earlier. From a script description, to its implementation as well as testing records, the database and its interface allow the user to design Web documents as virtual courses to be used in a Web-savvy virtual library. The database supports object reuse and sharing, as well as referential integrity and concurrence. In order to allow real-time course demonstration, i.e. also propose a simple course distribution mechanism, which allows the pre-broadcast of course materials. The System is implemented as a three-tier architecture which runs under MS Windows and other platforms.

title: A Smart Ambient Sound Aware Environment for Be Quiet Reminding
abstract: This paper presents a prototype of a smart sound aware environment that captures ambient sounds, analyzes sound situations and gives a be-quiet reminder to someone who generates sound exceeding certain volume level and disturbing others in the same site. It shows the microphone net to sense sound data and the speaker net to send be-quiet reminders. All computers are interconnected with a P2P network atop of the TOMSCOP platform. It discusses how sound data is captured and processed, and when a be-quiet reminder decision is made with analyzing contexts of sounds and users in a room. Some preliminary experimental results are given