Text classification without negative examples revisit
Traditionally, building a classifier requires two sets of examples: positive examples and negative examples. This paper studies the problem of building a text classifier using positive examples (P) and unlabeled examples (U). The unlabeled examples are mixed with both positive and negative examples. Since no negative example is given explicitly, the task of building a reliable text classifier becomes far more challenging. Simply treating all of the unlabeled examples as negative examples and building a classifier thereafter is undoubtedly a poor approach to tackling this problem. Generally speaking, most of the studies solved this problem by a two-step heuristic: first, extract negative examples (N) from U. Second, build a classifier based on P and N. Surprisingly, most studies did not try to extract positive examples from U. Intuitively, enlarging P by P' (positive examples extracted from U) and building a classifier thereafter should enhance the effectiveness of the classifier. Throughout our study, we find that extracting P' is very difficult. A document in U that possesses the features exhibited in P does not necessarily mean that it is a positive example, and vice versa. The very large size of and very high diversity in U also contribute to the difficulties of extracting P'. In this paper, we propose a labeling heuristic called PNLH to tackle this problem. PNLH aims at extracting high quality positive examples and negative examples from U and can be used on top of any existing classifiers. Extensive experiments based on several benchmarks are conducted. The results indicated that PNLH is highly feasible, especially in the situation where |P| is extremely small.

title: A template model for multidimensional inter-transactional association rules
abstract: Multidimensional inter-transactional association rules extend the traditional association rules to describe more general associations among items with multiple properties across transactions. “After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away” is an example of such rules. Since the number of potential inter-transactional association rules tends to be extremely large, mining inter-transactional associations poses more challenges on efficient processing than mining traditional intra-transactional associations. In order to make such association rule mining truly practical and computationally tractable, in this study we present a template model to help users declare the interesting multidimensional inter-transactional associations to be mined. With the guidance of templates, several optimization techniques, i.e., joining, converging, and speeding, are devised to speed up the discovery of inter-transactional association rules. We show, through a series of experiments on both synthetic and real-life data sets, that these optimization techniques can yield significant performance benefits.

title: Parameter Free Bursty Events Detection in Text Streams
abstract: Text classification is a major data mining task. An advanced text classification technique is known as partially supervised text classification, which can build a text classifier using a small set of positive examples only. This leads to our curiosity whether it is possible to find a set of features that can be used to describe the positive examples. Therefore, users do not even need to specify a set of positive examples. As the first step, in this paper, we formalize it as a new problem, called hot bursty events detection, to detect bursty events from a text stream which is a sequence of chronologically ordered documents. Here, a bursty event is a set of bursty features, and is considered as a potential category to build a text classifier. It is important to know that the hot bursty events detection problem, we study in this paper, is different from TDT (topic detection and tracking) which attempts to cluster documents as events using clustering techniques. In other words, our focus is on detecting a set of bursty features for a bursty event. In this paper, we propose a new novel parameter free probabilistic approach, called feature-pivot clustering. Our main technique is to fully utilize the time information to determine a set of bursty features which may occur in different time windows. We detect bursty events based on the feature distributions. There is no need to tune or estimate any parameters. We conduct experiments using real life data, a major English newspaper Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 31st VLDB Conference, Trondheim, Norway, 2005 in Hong Kong, and show that the parameter free feature-pivot clustering approach can detect the bursty events with a high success rate.

title: Reverse link capacity of SIR-based power-controlled CDMA systems with antenna arrays
abstract: In this paper, reverse link capacity, in terms of user capacity, of a code-division multiple access (CDMA) system with both beamforming and antenna diversity under a multipath environment is analyzed and compared. Signal-tointerference ratio (SIR) based power control is assumed in the system. Antenna elements are divided into several groups to achieve antenna diversity gain. Antenna elements in a group are closely spaced to perform beamforming in order to suppress the own-cell and other-cell interference, while those groups are widely spaced to achieve the diversity gain. Based on the comparison, a most efficient use of the antenna array according to the distribution of antenna elements among the groups is obtained. Instead of using tedious iterative methods to evaluate user capacity, a simple closed-form capacity expression with respect to antenna diversity and beamforming gains, a target SIR and the CDMA processing gain is derived. Numerical results indicate significant capacity improvement with the antenna array.

title: Reliability measurement: from theory to practice
abstract: The gap between theory and practice of reliability measurement in software design is discussed, and key issues that underlie reliability measurement's evolution from theory to practice are presented. A panel discussion in which reliability measurement's salient issues, basic concepts, and underlying theory are outlined is included. Reliability measurement's role in the development life cycle is also discussed.<<ETX>>

title: An Analysis of DoS Attacks on Wireless LAN

title: Evolutionary feature synthesis for facial expression recognition
abstract: Feature extraction is one of the key steps in object recognition. In this paper we propose a novel genetically inspired learning method for facial expression recognition (FER). Unlike current research on facial expression recognition that generally selects visually meaningful feature by hands, our learning method can discover the features automatically in a genetic programming-based approach that uses Gabor wavelet representation for primitive features and linear/nonlinear operators to synthesize new features. These new features are used to train a support vector machine classifier that is used for recognizing the facial expressions. The learned operator and classifier are used on unseen testing images. To make use of random nature of a genetic program, we design a multi-agent scheme to boost the performance. We compare the performance of our approach with several approaches in the literature and show that our approach can perform the task of facial expression recognition effectively.

title: Feature Synthesis Using Genetic Programming for Face Expression Recognition
abstract: In this paper a novel genetically-inspired learning method is proposed for face expression recognition (FER) in visible images. Unlike current research for FER that generally uses visually meaningful feature, we proposed a Genetic Programming based technique, which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. In this approach, the output of the learned composite operator is a feature vector that is used for FER. The experimental results show that our approach can find good composite operators to effectively extract useful features.