A Novel Dimensionality Reduction Technique Based on Independent Component Analysis for Modeling Microarray Gene Expression Data
DNA microarray experiments generating thousands of gene 
expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. But one challenge of microarray studies is the fact that the number n of samples collected is relatively small compared to the number p of genes per sample which are usually in thousands. In statistical terms this very large number of predictors compared to a small number of samples or observations makes the classification problem difficult. This is known as the ”curse of dimensionality problem”. An efficient way to solve this problem is by using dimensionality reduction techniques. Principle Component Analysis(PCA) is a leading method for dimensionality reduction of gene expression data which is optimal in the sense of least square error. In this paper we propose a new dimensionality reduction technique for specific bioinformatics applications based on Independent component Analysis(ICA). Being able to exploit higher order statistics to identify a linear model result, this ICA based dimensionality reduction technique 
outperforms PCA from both statistical and biological 
significance aspects. We present experiments on NCI 60 
dataset to show this result.

title: PC-Filter: A Robust Filtering Technique for Duplicate Record Detection in Large Databases
abstract: In this paper, we will propose PC-Filter (PC stands for Partitio n Comparison), a robust data filter for approximately duplicate record detection in large databases. PC-Filter distinguishes itself from all of existing methods by using the notion of partition in duplicate detection. It first sorts the whole database and splits the sorted database into a number of record partitions. The Partition Comparison Graph (PCG) is then constructed by performing fast partition pruning. Finally, duplicate records are effectively detected by using internal and external partition comparison based on PCG. Four properties, used as heuristics, have been devised to achieve a remarkable efficiency of the filter based on triangle inequity of record similarity. PC-Filter is insensitive to the key used to sort the database, and can achieve a very good recall level that is comparable to that of the pair-wise record comparison method but only with a complexity of O(N 4/3). Equipping existing detection methods with PC-Filter, we are able to well solve the ”Key Selection” problem, the ”Scope Specification” problem and the ”Low Recall” problem that the existing methods suffer from.

title: Gene Specific Co-regulation Discovery: An Improved Approach
abstract: Discovering gene co-regulatory relationships is a new but important research problem in DNA microarray data analysis. The problem of gene specific co-regulation discovery is to, for a particular gene of interest, called the target gene , identify its strongly co-regulated genes and the condition subsets where such strong gene co-regulations are observed. The study on this problem can contribute to a better understanding and characterization of the target gene. The existing method, using the genetic algorithm (GA), is slow due to its expensive fitness evaluation and long individual representation. In this paper, we propose an improved method for finding gene specific co-regulations. Compared with the current method, our method features a notably improved efficiency. We employ k NN Search Table to substantially speed up fitness evaluation in the GA. We also propose a more compact representation scheme for encoding individuals in the GA, which contributes to faster crossover and mutation operations. Experimental results with a real-life gene microarray data set demonstrate the improved efficiency of our technique compared with the current method.

title: A Color Indexing Scheme Using Two-level Clustering Processing for Effective and Efficient Image Retrieval
abstract: In this paper, we present a clustering-based color indexing scheme for effective amd efficient image retrieval, which is essentially an exploration on the application of clustering technique to image retrieval. In our approach, the color features are clustered automatically using a color clustering algorithm twice (called two-level clustering processing) and two color feature summarizations are obtained, e.g. Local Color Centriods (LCCs) and Global Color Centroids (GCCs). Based upon LCCs and GCCs, a three-level R-tree is bulit for indexing database images and performing effecitve and efficient image retrieval. The experiments show that this indexing scheme is effective and efficient in performing image retrieval.

title: Anomaly detection in high-dimensional network data streams: A case study
abstract: In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training data generation, anomaly classification and false positive reduction are proposed in this paper as well. Experimental results demonstrate that SPOT is effective in detecting anomalies from network data streams and outperforms existing anomaly detection methods.

title: XAR-miner: efficient association rules mining for XML data
abstract: In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document are first preprocessed to transform to either an Indexed Content Tree (IX-tree) or Multi-relational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection and AR mining. Task-relevant concepts are generalized to produce generalized meta-patterns, based on which the large ARs that meet the support and confidence levels are generated.

title: Proactive care-of address test for route optimization in FMIPv6
abstract: This paper proposes a practical scheme to run the care-of address test (part of the Return Routability test for Mobile IPv6 route optimization) in a proactive way in the context of the Fast Handovers for Mobile IPv6 (FMIPv6) protocol, so that the latency (normally one round-trip time between a mobile node and its correspondent node) caused by the care-of address test after movements can be removed or reduced. Proactive care-of address test can make the Return Rroutability protocol more suitable for delay sensitive applications especially when it is applied in conjunction with other Return Routability optimization schemes.

title: Factor analysis based anomaly detection
abstract: We propose a novel anomaly detection algorithm based on factor analysis and Mahalanobis distance. Factor analysis is used to uncover the latent structure (dimensions) of a set of variables. It reduces attribute space from a larger number of variables to a smaller number of factors. The Mahalanobis distance is used to determine the "similarity" of a set of values from an "unknown" sample to a set of values measured from a collection of "known" samples. Combined with factor analysis, Mahalanobis distance is extended to examine whether a given vector is an outlier from a model identified by "factors" based on factor analysis. We present a factor analysis-based network anomaly detection algorithm and apply it to DARPA intrusion detection evaluation data. The experimental results show that the proposed algorithm is able to detect network intrusions with relatively low false alarms.

title: Evolutionary Algorithm Based on Overlapped Gene Expression
abstract: Inspired by the overlap gene expression in biological study, this paper proposes a novel evolutionary algorithm-EAOGE i.e. Evolutionary Algorithm based on Overlapped Gene Expression. Different from existing works, EAOGE suggests a new expression structure of genes with probabilities of overlapped expression for some segments. The main contributions are: (1) Proposing a novel model and an algorithm of gene expression while borrowing some ideas from artificial immunity algorithm; (2) Analyzing the expressing space and encode characteristic of the new model; (3) The extensive experiments in function finding shows that new model is 2.8~9.7 times faster than usual GEP method, and in higher-degree polynomial function finding, the success rate of EAOGE is over 10 times than usual GEP.

title: Adaptive motion selection for online hand-eye calibration
abstract: While a robot moves, online hand–eye calibration to determine the relative pose between the robot gripper/end-effector and the sensors mounted on it is very important in a vision-guided robot system. During online hand–eye calibration, it is impossible to perform motion planning to avoid degenerate motions and small rotations, which may lead to unreliable calibration results. This paper proposes an adaptive motion selection algorithm for online hand–eye calibration, featured by dynamic threshold determination for motion selection and getting reliable hand–eye calibration results. Simulation and real experiments demonstrate the effectiveness of our method.

title: Comparative knowledge management: A pilot study of Chinese and American universities
abstract: Comparative study of knowledge management (KM) promises to lead to more effective knowledge use in all cultural environments. This pilot study compares KM priorities, needs, tools, and administrative structure components in large Chinese and American universities. General KM theory and literature related to KM in higher education are analyzed to develop the four components of the study. Comparative differences in KM practice at large Chinese and American universities are analyzed for each component. A correlation matrix reveals statistically significant co-variation among all but one of the study components. Four conclusions related to comparative KM and suggestions for future research are presented.