Gene Specific Co-regulation Discovery: An Improved Approach
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: Detecting Projected Outliers in High-Dimensional Data Streams
abstract: In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector (SPOT), to identify outliers embedded in subspaces. Sparse Subspace Template (SST), a set of subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. Multi-Objective Genetic Algorithm (MOGA) is employed as an effective search method for finding outlying subspaces from training data to construct SST. SST is able to carry out online self-evolution in the detection stage to cope with dynamics of data streams. The experimental results demonstrate the efficiency and effectiveness of SPOT in detecting outliers in high-dimensional data streams.

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: HOS-Miner: A System for Detecting Outlying Subspaces of High-dimensional Data
abstract: We identify a new and interesting high-dimensional outlier detection problem in this paper, that is, detecting the subspaces in which given data points are outliers. We call the subspaces in which a data point is an outlier as its Outlying Subspaces. In this paper, we will propose the prototype of a dynamic subspace search system, called HOS-Miner (HOS stands for High-dimensional Outlying Subspaces), that utilizes a sample-based learning process to effectively identify the outlying subspaces of a given point.

title: Generalized Semi-Infinite Optimization and its Application in Robotics' Path Planning Problem

title: Identification of initial rotor position of a micro-PMSM
abstract: This paper presents a method to identify initial rotor position of a PMSM (permanent magnet synchronous motor). The method applies a fuzzy phase controller (FPC) to control rotor position of a PMSM. In steady state, the FPC output is used to identify the absolute rotor position. The FPC is selected because of the non-linear plant behavior. The results demonstrate that the method can identify the initial rotor position exactly without moving rotor significantly from its initial position.

title: Name Disambiguation Using Atomic Clusters
abstract: Name ambiguity is a critical problem in many applications, in particular in the online bibliography systems, such as DBLP and CiteSeer. Previously, several clustering based methods have been proposed although, the problem still presents to be a big challenge for both research and industry communities. In this paper, we present a complementary study to the problem from another point of view. We propose an approach of finding atomic clusters to improve the performance of existing clustering-based methods. We conducted experiments on a dataset from a real-world system: Arnetminer.org. Experiments results show that significant improvements can be obtained by using the proposed atomic clusters finding approach (about +8% and +27% improvements depending on different clustering methods).

title: Topic-Level Random Walk through Probabilistic Model
abstract: In this paper, we study the problem of topic-level random walk, which concerns the random walk at the topic level. Previously, several related works such as topic sensitive page rank have been conducted. However, topics in these methods were predefined, which makes the methods inapplicable to different domains. In this paper, we propose a four-step approach for topic-level random walk. We employ a probabilistic topic model to automatically extract topics from documents. Then we perform the random walk at the topic level. We also propose an approach to model topics of the query and then combine the random walk ranking score with the relevance score based on the modeling results. Experimental results on a real-world data set show that our proposed approach can significantly outperform the baseline methods of using language model and that of using traditional PageRank.

title: The Effect of Organizational/Technological Factors and the Nature of Knowledge on Knowledge Sharing
abstract: This study investigates the dynamics of a knowledge sharing effort in New York State government that involved multiple organizations, divisions, and geographically separated offices in the development of the Multi- Using a case study approach, we address the question of how multiple organizational and technological factors — distributed leadership, alignment of issues and incentives, coordination of a number and variety of groups, trust, technology, and implementation strategy — interact with the nature of knowledge to influence the knowledge sharing process. A major contribution of this study is that it uses a multi-dimensional view of knowledge, examining the interactive impact of the nature of knowledge with multiple organizational and technological factors in public sector knowledge management research.

title: Towards Re-engineering Legacy Systems for Assured Dynamic Adaptation
abstract: Increasingly, software must adapt its behavior in response to changes in the supporting computing, communication infrastructure, and in the surrounding physical environment. Since most existing software was not designed to adapt, research on technique to make legacy software dynamically adaptive has gained increasing interest. Assurance is crucial for adaptive software to fulfill its intended purpose. Correctness is even more critical if it is to be applied in high assurance systems. This paper proposes a model-driven approach to introduce dynamic adaptation to non-adaptive legacy systems while maintaining assurance properties. An aspect-oriented technique is applied to achieve separation of concerns in the implementation.