published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

No description available.

This thesis introduces the W2C Corpus which contains 97 languages with more than 10 million words for each of these languages, with the total size 10.5 billion words. The corpus was built by crawling the Internet. This work describes the methods and tools used for its construction. The complete process consisted of building an initial corpus from Wikipedia, developing a language recognizer for 122 languages, implementing a distributed system for crawling and parsing webpages and finally, the reduction of duplicities. A comparative analysis of the texts of Wikipedia and the Internet is provided at the end of this thesis. The analysis is based on basic statistics such as average word and sentence length, conditional entropy and perplexity. 1

by Li Wai Kit. / Bibliography: leaves 170-178 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1985

So, King-yan Oldfield. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 68-70). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Evolution of Communication Technologies --- p.1 / Chapter 1.2 --- The Internet --- p.2 / Chapter 1.3 --- The Distributed Virtual Environments --- p.2 / Chapter 1.3.1 --- Features of DVE --- p.3 / Chapter 1.3.2 --- Current and Potential Applications --- p.4 / Chapter 1.3.3 --- The Challenges --- p.5 / Chapter 1.4 --- Our Contributions --- p.6 / Chapter 2 --- System Architecture --- p.7 / Chapter 2.1 --- The SSDVE and MSDVE Architectures --- p.7 / Chapter 2.2 --- Issues in the MSDVE Architecture --- p.8 / Chapter 2.2.1 --- On the Server Side --- p.8 / Chapter 2.2.2 --- On the Client Side --- p.8 / Chapter 3 --- Balancing Work Load and Reducing Inter-server Communication --- p.10 / Chapter 3.1 --- Problem Formulation --- p.10 / Chapter 3.1.1 --- The Area of Interest --- p.11 / Chapter 3.1.2 --- The DVE Cells --- p.11 / Chapter 3.1.3 --- Expected Number of Avatars --- p.12 / Chapter 3.1.4 --- Cost Metrics in Different Types of Network --- p.13 / Chapter 3.1.5 --- Problem Definition --- p.14 / Chapter 3.1.6 --- Complexity --- p.18 / Chapter 3.2 --- Partitioning Algorithms --- p.19 / Chapter 3.2.1 --- A Simplified Case --- p.19 / Chapter 3.2.2 --- The General Case --- p.19 / Chapter 3.3 --- Experiments --- p.22 / Chapter 4 --- Communication Sub-graph --- p.31 / Chapter 4.1 --- Problem Formulation --- p.31 / Chapter 4.1.1 --- Optimization Metrics --- p.32 / Chapter 4.1.2 --- Design Considerations --- p.32 / Chapter 4.2 --- Communication Sub-graph Construction Algorithms --- p.34 / Chapter 4.2.1 --- The Minimum Diameter Sub-graph (MDS) --- p.34 / Chapter 4.2.2 --- The Core-based Tree (CBT) --- p.37 / Chapter 4.2.3 --- The Minimum Spanning Tree (MST) --- p.40 / Chapter 5 --- Synchronization --- p.42 / Chapter 5.1 --- Synchronization in a DVE System --- p.43 / Chapter 5.2 --- System Model --- p.46 / Chapter 5.2.1 --- Problem Definition --- p.47 / Chapter 5.2.2 --- The Markov Chain Model --- p.47 / Chapter 5.2.3 --- Deciding the Threshold Φ --- p.49 / Chapter 5.3 --- Optimal Synchronizing Interval --- p.50 / Chapter 5.3.1 --- "An ""on-average"" Guarantee" --- p.50 / Chapter 5.3.2 --- A Stochastic Guarantee --- p.52 / Chapter 5.3.3 --- Finding p with T and Φ --- p.52 / Chapter 5.3.4 --- Searching for r*p --- p.54 / Chapter 5.4 --- Experiments --- p.55 / Chapter 5.4.1 --- Simulation Results --- p.55 / Chapter 5.4.2 --- Theoretical Results --- p.58 / Chapter 6 --- Related Work --- p.63 / Chapter 6.1 --- Load Balancing on DVE --- p.63 / Chapter 6.2 --- Object State Synchronization Techniques --- p.63 / Chapter 6.3 --- Group Communication and Multicasting --- p.64 / Chapter 6.4 --- DVE System Development Toolkits --- p.64 / Chapter 6.5 --- Example DVE Systems --- p.65 / Chapter 7 --- Conclusion --- p.66 / Chapter 7.1 --- A Vision to the Future --- p.66 / Chapter 7.2 --- Conclusion --- p.66 / Bibliography --- p.68

Chan Ming-fei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 63-66). / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Challenging Issues --- p.2 / Chapter 1.2 --- Previous Work --- p.4 / Chapter 1.3 --- Organization of the Thesis --- p.5 / Chapter 2 --- Distributed Virtual Environment --- p.6 / Chapter 2.1 --- Possible Architectures --- p.6 / Chapter 2.2 --- Representations of Clients as Avatars --- p.7 / Chapter 2.3 --- Dynamic Membership --- p.9 / Chapter 3 --- Bandwidth and Computation Reduction Techniques --- p.11 / Chapter 3.1 --- Network Communication --- p.12 / Chapter 3.2 --- Dead Reckoning --- p.13 / Chapter 3.3 --- Message Aggregation --- p.15 / Chapter 3.3.1 --- Network-Based Aggregation --- p.15 / Chapter 3.3.2 --- Organization-Based Aggregations --- p.16 / Chapter 3.3.3 --- Grid-Based Aggregations --- p.16 / Chapter 3.4 --- Relevance Filtering --- p.17 / Chapter 3.4.1 --- Entity-Based Filtering --- p.17 / Chapter 3.4.2 --- Grid-Based Filtering --- p.19 / Chapter 3.5 --- Quiescent Entities --- p.20 / Chapter 3.6 --- Spatial Partitioning --- p.21 / Chapter 3.6.1 --- Necessity of Spatial Partitioning --- p.22 / Chapter 3.6.2 --- Binary Space Partitioning Tree --- p.23 / Chapter 3.6.3 --- BSP Tree Construction --- p.23 / Chapter 4 --- Partitioning Algorithm --- p.25 / Chapter 4.1 --- Problem Formulation --- p.25 / Chapter 4.2 --- Exhaustive Partition (EP) Algorithm --- p.28 / Chapter 4.3 --- Partitioning Algorithm --- p.29 / Chapter 4.3.1 --- Recursive Bisection Partition (RBP) Algorithm --- p.30 / Chapter 4.3.2 --- Layering Partitioning (LP) Algorithm --- p.32 / Chapter 4.3.3 --- Communication Refinement Partitioning (CRP) Algorithm --- p.38 / Chapter 4.4 --- Parallel Approach --- p.42 / Chapter 4.5 --- Further Observation --- p.43 / Chapter 5 --- Experiments --- p.44 / Chapter 5.1 --- Experiment 1: Small Virtual World --- p.45 / Chapter 5.2 --- Experiment 2: Large Virtual World --- p.46 / Chapter 5.3 --- Experiment 3: Moving of Avatars --- p.47 / Chapter 5.4 --- Experiment 4: Dynamic Joining and Leaving --- p.48 / Chapter 5.5 --- Experiment 5: Parallel Approach --- p.49 / Chapter 6 --- Implementation Considerations --- p.55 / Chapter 6.1 --- Different Environments --- p.55 / Chapter 6.2 --- Platform --- p.56 / Chapter 6.3 --- Lessons learned --- p.57 / Chapter 7 --- Conclusion --- p.59 / A Simplex Method --- p.60 / Bibliography --- p.63

by Chan Wai To. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 117-121). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Clustering --- p.3 / Chapter 1.2 --- Mobile Agent --- p.4 / Chapter 1.3 --- Contribution --- p.4 / Chapter 1.4 --- Outline of this Thesis --- p.5 / Chapter 2 --- Related Work --- p.6 / Chapter 2.1 --- Clustering --- p.6 / Chapter 2.1.1 --- K-Means Clustering --- p.6 / Chapter 2.1.2 --- A more efficient K-Means Clustering Algorithm --- p.3 / Chapter 2.1.3 --- K-Medoids Clustering Algorithms --- p.8 / Chapter 2.1.4 --- Linkage-based Methods --- p.11 / Chapter 2.1.5 --- BIRCH --- p.13 / Chapter 2.1.6 --- DBSCAN --- p.14 / Chapter 2.1.7 --- Other Clustering Algorithm --- p.17 / Chapter 2.2 --- Parallel Clustering and Distributed Clustering --- p.17 / Chapter 2.2.1 --- A Fast Parallel Clustering Algorithm for Large Spatial Databases --- p.17 / Chapter 2.3 --- Distributed Data Mining --- p.18 / Chapter 2.3.1 --- A Distributed Clustering Algorithm --- p.18 / Chapter 2.3.2 --- Efficient Mining of Association Rules in Distributed Databases --- p.19 / Chapter 2.4 --- Information Retrieval and Document Clustering --- p.20 / Chapter 2.4.1 --- Document and Document Set Representation --- p.20 / Chapter 2.4.2 --- TFIDF --- p.20 / Chapter 2.4.3 --- Similarity --- p.21 / Chapter 2.4.4 --- Partitional Document Clustering --- p.22 / Chapter 2.4.5 --- Hierarchical Document Clustering --- p.22 / Chapter 2.4.6 --- Document Clustering Application --- p.23 / Chapter 3 --- Distributed Clustering --- p.24 / Chapter 3.1 --- Problem Description --- p.24 / Chapter 3.2 --- Distributed k-Means Clustering Algorithm --- p.25 / Chapter 3.2.1 --- Initialization --- p.25 / Chapter 3.2.2 --- weighted k-Means procedure --- p.26 / Chapter 3.2.3 --- Refinement --- p.27 / Chapter 3.2.4 --- Example --- p.31 / Chapter 3.2.5 --- Communication Cost --- p.34 / Chapter 3.3 --- Grid k-Mean --- p.34 / Chapter 3.3.1 --- Runtime Splitting --- p.36 / Chapter 3.3.2 --- Initial Clusters --- p.38 / Chapter 3.3.3 --- Refinement --- p.38 / Chapter 3.3.4 --- Overall Algorithm --- p.39 / Chapter 3.3.5 --- Efficiency in Decomposition --- p.42 / Chapter 3.3.6 --- Example --- p.42 / Chapter 3.3.7 --- Comparison with previous k-Means method --- p.43 / Chapter 3.3.8 --- Communication Cost --- p.44 / Chapter 3.4 --- Experiment --- p.44 / Chapter 3.4.1 --- Performance --- p.46 / Chapter 3.4.2 --- Communication Cost --- p.47 / Chapter 3.4.3 --- Quality of Clustering --- p.49 / Chapter 3.4.4 --- Clustering in High Dimension --- p.49 / Chapter 3.4.5 --- Other Data Distributions --- p.52 / Chapter 4 --- Distributed DBSCAN --- p.54 / Chapter 4.1 --- Representative points of local candidate clusters --- p.55 / Chapter 4.2 --- Verification and Cluster Merging --- p.57 / Chapter 4.2.1 --- Clustering Result Quality --- p.59 / Chapter 4.3 --- Experiment --- p.62 / Chapter 5 --- Document Clustering --- p.72 / Chapter 5.1 --- Initialization --- p.73 / Chapter 5.2 --- Refinement --- p.76 / Chapter 5.3 --- Stopping criteria --- p.77 / Chapter 5.4 --- Message --- p.77 / Chapter 5.5 --- Algorithm --- p.78 / Chapter 5.6 --- Experiment --- p.82 / Chapter 5.6.1 --- Data Source and Experimental Setup --- p.82 / Chapter 5.6.2 --- Data Size --- p.34 / Chapter 5.6.3 --- Evaluation Metrics --- p.85 / Chapter 5.6.4 --- Experimental Result --- p.85 / Chapter 5.6.5 --- Comparison to Other Algorithms --- p.94 / Chapter 5.6.6 --- Conclusion --- p.94 / Chapter 5.7 --- Future Work --- p.95 / Chapter 6 --- Agent and Implementation Details --- p.96 / Chapter 6.1 --- Agent Introduction --- p.96 / Chapter 6.1.1 --- Reason to use Mobile Agent --- p.97 / Chapter 6.1.2 --- Grasshopper Overview --- p.97 / Chapter 6.1.3 --- Agent Scenario --- p.98 / Chapter 6.1.4 --- Another Agent Scenario --- p.99 / Chapter 6.2 --- Implementation Details --- p.100 / Chapter 6.2.1 --- Distributed k-Means --- p.100 / Chapter 6.2.2 --- Grid k-Means --- p.104 / Chapter 6.2.3 --- Distributed DBSCAN --- p.109 / Chapter 6.2.4 --- Distributed Document Clustering --- p.112 / Chapter 7 --- Conclusion

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript, etc. / Digitized by Kansas Correctional Industries