On the correct convergence of the EM algorithm for Gaussian mixtures
It is well-known that the EM algorithm generally converges to a local maximum likelihood estimate. However, there have been many evidences to show that the EM algorithm can converge correctly to the true parameters as long as the overlap of Gaussians in the sample data is small enough. This paper studies this correct convergence problem asymptotically on the EM algorithm for Gaussian mixtures. It has been proved that the EM algorithm becomes a contraction mapping of the parameters within a neighborhood of the consistent solution of the maximum likelihood when the measure of average overlap among Gaussians in the original mixture is small enough and the number of samples is large enough. That is, if the initial parameters are set within the neighborhood, the EM algorithm will always converge to the consistent solution, i.e., the expected result. Moreover, the simulation results further demonstrate that this correct convergence neighborhood becomes larger as the average overlap becomes smaller.

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 asymptotic memory capacity of the generalized Hopfield network
abstract: This paper presents a theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network. The perceptron learning scheme is proposed to store sample patterns as the stable states in a generalized Hopfield network. We have obtained that (n-1) and 2n are a lower and an upper bound of the asymptotic memory capacity of the network of n neurons, respectively, which shows that the generalized Hopfield network can store the larger number of sample patterns than Hopfield network.

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 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: Finding diversity in remote code injection exploits
abstract: Remote code injection exploits inflict a significant societal cost, and an active underground economy has grown up around these continually evolving attacks. We present a methodology for inferring the phylogeny, or evolutionary tree, of such exploits. We have applied this methodology to traffic captured at several vantage points, and we demonstrate that our methodology is robust to the observed polymorphism. Our techniques revealed non-trivial code sharing among different exploit families, and the resulting phylogenies accurately captured the subtle variations among exploits within each family. Thus, we believe our methodology and results are a helpful step to better understanding the evolution of remote code injection exploits on the Internet.

title: Smart hyperspaces and project Ubikids
abstract: A smart space is an electronics-enhanced physical environment that can sense the existence of users and other entities, and provide them the right services, in the right way, at the right time. Such senses and services are based on collaborative work of many interconnected devices and computers, which may be small enough to be embedded to everyday objects in the space. So far, many creative researches have been conducted to develop and experiment a variety of smart spaces. The next research issue to emerge, we believe, is to interconnect and integrate these isolated smart spaces together into a higher level space known as a hyperspace. In this paper, after briefly reviewing the smart space concepts and related research, we describe what a smart hyperspace is all about and what related challenging issues exist. We then explain our vision and motivations towards building a specific smart hyperspace, called Ubikids, to assist parents and provide services to make them more convenient, prompt, reliable, precise, and can remotely take care of their kids. Finally, we show the fundamental approaches, conceptual designs, crucial problems and so on, regarding the project Ubikids.

title: The specification and implementation of a virtual university software system
abstract: Distance learning is one of the interesting research topics of distributed computing. This paper discusses a joint research project between the University of Aizu in Japan and Tamkang University in Taiwan. A software system supporting virtual university operations is proposed. The software architecture is designed based on three criteria of virtual university operations: administration, awareness and assessment. Specifications of each tools in the system are proposed, with some implementation details and solutions being discussed. We also point out some interesting research directions in the realization of a virtual university.

title: Spatial-Temporal Cross-Correlation Analysis: A New Measure and a Case Study in Infectious Disease Informatics
abstract: This paper aims to develop a new statistical measure to identify significant correlations among multiple events with spatial and temporal components. This new measure, K(r,t), is defined by adding the temporal dimension to Ripley’s K(r) function. Empirical studies show that the use of K(r,t) can lead to a more discriminating and flexible spatial-temporal data analysis framework. This measure also helps identify the causal events whose occurrences induce those of other events.

title: Rough Asymptotics of Forward-Backward Stochastic Differential Equations
abstract: This note is concerned with the asymptotic behavior of adapted solutions to forward-backward SDEs when the forward diffusion contains small noises. We prove the sample path Large Deviation Principle (LDP) for the adapted solutions to the FBSDEs under appropriate conditions stated in terms of a certain type of convergence of the solutions to the associated quasilinear PDEs. As an application we apply the LDP results to a problem of rare event simulation. We provide a necessary condition for the existence of an asymptotically efficient estimator amongst the class of importance sampling estimators.