Learning to cluster web search results
Organizing Web search results into clusters facilitates users' quick browsing through search results. Traditional clustering techniques are inadequate since they don't generate clusters with highly readable names. In this paper, we reformalize the clustering problem as a salient phrase ranking problem. Given a query and the ranked list of documents (typically a list of titles and snippets) returned by a certain Web search engine, our method first extracts and ranks salient phrases as candidate cluster names, based on a regression model learned from human labeled training data. The documents are assigned to relevant salient phrases to form candidate clusters, and the final clusters are generated by merging these candidate clusters. Experimental results verify our method's feasibility and effectiveness.

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: Two Adaptive Matching Learning Algorithms for Independent Component Analysis
abstract: Independent component analysis (ICA) has been applied in many fields of signal processing and many ICA learning algorithms have been proposed from different perspectives. However, there is still a lack of a deep mathematical theory to describe the ICA learning algorithm or problem, especially in the cases of both super- and sub-Gaussian sources. In this paper, from the point of view of the one-bit-matching principle, we propose two adaptive matching learning algorithms for the general ICA problem. It is shown by the simulation experiments that the adaptive matching learning algorithms can efficiently solve the ICA problem with both super- and sub-Gaussian sources and outperform the typical existing ICA algorithms in certain aspects.

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: 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: Simulation Study of AODV Performance over IEEE 802.15.4 MAC in WSN with Mobile Sinks
abstract: Mobility has been incorporated into wireless sensor networks in recent years in order to improve energy efficiency and network coverage. In particular, at the cost of increased message delivery delay, the energy conservation can be achieved by limiting the hop count between the communicating sensor-sink pair. In this paper we allow multihop routing between one sensor and mobile sinks, and consider the delay*energy metric, which is a compound metric to evaluate the performance of diverse data collection schemes. Through extensive simulation experiments of AODV performance over IEEE 802.15.4 MAC with clustered wireless sensor network setting, we investigate the fundamental problems of AODV without information of 802.15.4 MAC, and analyze the influence of incorporating multiple sinks.

title: A genetic algorithm for optimal 3-connected telecommunication network designs
abstract: In this paper, a genetic algorithm for three connected telecommunication network designs is proposed. The genetic algorithm uses a new approach to solution representation, in which constraints such as diameter and connectivity constraints can be easily encoded and two-point crossover with the operation of swapping duplicated nodes ensures solutions generated through genetic evolution are all feasible so that both checking of the constraints and repair mechanism can be avoided. Both efficiency and effectiveness are increased due to no checking of the constraints and no repair mechanism conducted. In fact, our design algorithm for three-connected network design can he extended to a high-connectivity network design.

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 distributed Web document database and its supporting environment
abstract: 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, we 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 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.