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RESEARCH HIGHLIGHTS

Some of the project highlights of the Department are listed below:

Computational Intelligence

A Critiquing System for Enhancing Writing Competency Using Automatic Text Analysis
Staff Dr. CHEUNG, William Kwok Wai (Principal Investigator)
Abstract To build a web supported learning system with an automatic text analysis module in it, so that (1) teachers can monitor and manage individual student’s writing processes, and (2) students can submit drafts during their writing processes and receive just-in-time subtheme suggestions (critiques) from the system.

A Model-based Approach to Distributed Data Mining with Privacy Preservation
Staff Dr. CHEUNG, William Kwok Wai (Supervisor of PhD Student Mr. ZHANG, Xiaofeng)
Abstract To investigate a principled way of learning global statistical models (for clustering and manifold discovery) from physically distributed local data subsets. Research issues include deriving a proper local data abstractions, learning global generative models from the local abstractions, and enabling negotiation among the local data sources for optimizing the global model accuracy with a minimum cost.

Non-negative Matrix Factorization Based Belief Compression and Clustering for Solving POMDPs
Staff Dr. CHEUNG, William Kwok Wai (Supervisor of PhD Student Ms. LI, Xin)
Abstract The main objective of this project is to study how to integrate two approaches, namely belief compression and belief clustering, to address the intractability of POMDP problems. Techniques involved include data clustering, dimension reduction techniques and their integration into a value-directed framework for computing optimal policy for POMDPs.

Privacy Policy Enforcement in Data Analysis Workflows
Staff Dr. CHEUNG, William Kwok Wai (Supervisor of MPhil Student Mr. CHAN, Kai Kin)
Abstract To study a semantic approach for enforcing privacy policies in data analysis workflow systems. In particular, we introduce privacy preservation and analysis-relevant concepts as ontologies, incorporate them into a policy framework which represents polices, resolves potential conflicts among them, enforces them as well as provides suggestions on corrective actions.

Discrete integrable systems and their applications to convergence acceleration algorithms
Staff Dr. TAM, Hon Wah (Principal Investigator)
URL http://www.comp.hkbu.edu.hk/~tam/rgc09-10_index.html
Abstract This research includes: (1) studying the integrable bilinear study of the Heisenberg-type equations and their discrete analogues. The emphasis is placed on integrable discretizations of some Heisenberg-type equations such as the Myrzakulov equations. (2) investigating some properties of discrete integrable systems that are related to the properties of convergence acceleration algorithms. We expect that discretizing integrable PDEs will lead to new sequence transformations.

On Motion Based Visual Feature Selections for Lip-Reading
Staff Prof. CHEUNG, Yiu Ming (Principal Investigator)
Abstract Lip-reading has potential attractie applications in information security, speech recognition, and so forth. This project is to develop a new visual feature representation and extraction approach to the lip localization for lip-reading. The proposed method features high accuracy of lip localization and robust performance against the shadow. We build up a prototype of lip-reading system.

Autonomy-Oriented Computing (AOC), Self-Organized Computability, and Complex Data Mining
Staff Prof. LIU, Jiming (Principal Investigator)
Abstract Future challenges will lie in breakthroughs in scalable/robust computational solutions to complex data-mining problems that are: (1) Petabyte-scale (e.g., mining social networks), (2) dynamically-evolving (e.g., detecting social norms), (3) interaction-rich as well as trans-disciplinary (e.g., preventing economic/ecological crisis), and (4) highly-distributed (e.g., community evolution). This project concerns the development of an unconventional computing paradigm, called Autonomy-Oriented Computing (AOC).

Complex Emergent Behavior, Self-Organized Criticality, and Phase Transition in Multi-Agent Systems
Staff Prof. LIU, Jiming (Supervisor of MPhil Student Mr. HU, Bingcheng)
Abstract In this project, we address several issues on modeling complex or emergent behaviors in Multi-Agent Systems. In particular, we focus on the local-global relationship in a Self-Organizing Multi-Agent System (or SOMAS), a RoboNBA system, and study the phenomena of self-organized criticality (SOC) and phase transition in SOMAS.

Mechanism Design for Multi-Agent Systems in Distributed Data Mining
Staff Prof. LIU, Jiming (Principal Investigator)
Abstract Methods for governing competitive systems have been studied in the field of distributed artificial intelligence (DAI), e.g., for autonomous agents and multi-agent systems. Mechanism design theories have been proposed and applied to networking and agent-based e-commerce. In this project, we are interested in investigating the use and impacts of mechanism design in distributed data-mining (DDM).


Project in Different Research Areas:

Computational Intelligence Databases and Information Management Networking and Systems Pattern Recognition and Machine Learning Others
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Department of Computer Science, Hong Kong Baptist University
Hong Kong Baptist University