Adaptive user interface for vehicle swarm control

An algorithm to automatically generate behaviors for robotic vehicles has been created and tested in a laboratory setting. This system is designed to be applied in situations where a large number of robotic vehicles must be controlled by a single operator. The system learns what behaviors the operator typically issues and offers these behaviors to the operator in future missions. This algorithm uses the symbolic clustering method Gram-ART to generate these behaviors. Gram-ART has been shown to be successful at clustering such standard symbolic problems as the mushroom dataset and the Unix commands dataset. The algorithm was tested by having users complete exploration and tracking missions. Users were brought in for two sessions of testing. In the first session, they familiarized themselves with the testing interface and generated training information for Gram-ART. In the second session, the users ran missions with and without the generated behaviors to determine what effect the generated behaviors had on the users\u27 performance. Through these human tests, missions with generated behaviors enabled are shown to have reduced operator workload over those without. Missions with generated behaviors required fewer button presses than those without while maintaining a similar or greater level of mission success. Users also responded positively in a survey after the second session. Most users\u27 responses indicated that the generated behaviors increased their ability to complete the missions --Abstract, page iii

Knowledge-based document retrieval with application to TEXPROS

Document retrieval in an information system is most often accomplished through keyword search. The common technique behind keyword search is indexing. The major drawback of such a search technique is its lack of effectiveness and accuracy. It is very common in a typical keyword search over the Internet to identify hundreds or even thousands of records as the potentially desired records. However, often few of them are relevant to users\u27 interests. This dissertation presents knowledge-based document retrieval architecture with application to TEXPROS. The architecture is based on a dual document model that consists of a document type hierarchy and, a folder organization. Using the knowledge collected during document filing, the search space can be narrowed down significantly. Combining the classical text-based retrieval methods with the knowledge-based retrieval can improve tremendously both search efficiency and effectiveness. With the proposed predicate-based query language, users can more precisely and accurately specify the search criteria and their knowledge about the documents to be retrieved. To assist users formulate a query, a guided search is presented as part of an intelligent user interface. Supported by an intelligent question generator, an inference engine, a question base, and a predicate-based query composer, the guided search collects the most important information known to the user to retrieve the documents that satisfy users\u27 particular interests. A knowledge-based query processing and search engine is presented as the core component in this architecture. Algorithms are developed for the search engine to effectively and efficiently retrieve the documents that match the query. Cache is introduced to speed up the process of query refinement. Theoretical proof and performance analysis are performed to prove the efficiency and effectiveness of this knowledge-based document retrieval approach

Analysing the temporal association among financial news using concept space model.

Law Yee-shan, Carol.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 81-89).Abstracts in English and Chinese.Chapter CHAPTER ONE --- INTRODUCTION --- p.1Chapter 1.1 --- Research Contributions --- p.5Chapter 1.2 --- Organization of the thesis --- p.5Chapter CHAPTER TWO --- LITERATURE REVIEW --- p.7Chapter 2.1 --- Temporal data Association --- p.7Chapter 2.1.1 --- Association Rule Mining --- p.8Chapter 2.1.2 --- Sequential Patterns Mining --- p.10Chapter 2.2 --- Information Retrieval Techniques --- p.11Chapter 2.2.1 --- Vector Space model --- p.12Chapter 2.2.2 --- Probabilistic model --- p.75Chapter CHAPTER THREE --- AN OVERVIEW OF THE PROPOSED APPROACH --- p.16Chapter 3.1 --- The Test Bed --- p.19Chapter 3.2 --- General Concept Term Identification........................................……… --- p.19Chapter 3.3 --- Anchor Document Selection --- p.21Chapter 3.4 --- Specific Concept Term Identification --- p.22Chapter 3.5 --- Establishment of Associations --- p.22Chapter CHAPTER FOUR --- GENERAL CONCEPT TERM IDENTIFICATION --- p.24Chapter 4.1 --- Document Pre-processing --- p.25Chapter 4.2 --- Stopwording and stemming --- p.29Chapter 4.3 --- Word-phrase formation --- p.29Chapter 4.4 --- Automatic Indexing of Words and Sentences --- p.30Chapter 4.5 --- Relevance Weighting --- p.31Chapter 4.5.1 --- Term Frequency and Document Frequency Computation --- p.31Chapter 4.5.2 --- Uncommon Data Removal --- p.32Chapter 4.5.3 --- Combined Weight Computation --- p.32Chapter 4.5.4 --- Cluster Analysis --- p.33Chapter 4.6 --- Hopfield Network Classification --- p.35Chapter CHAPTER FIVE --- ANCHOR DOCUMENT SELECTION --- p.37Chapter 5.1 --- What is an anchor document? --- p.37Chapter 5.2 --- Selection Criteria of an anchor document --- p.40Chapter CHAPTER SIX --- DISCOVERY OF NEWS ASSOCIATION --- p.44Chapter 6.1 --- Specific Concept Term Identification --- p.44Chapter 6.2 --- Establishment of Associations --- p.45Chapter 6.2.1 --- Anchor document representation --- p.46Chapter 6.2.2 --- Similarity measurement --- p.47Chapter 6.2.3 --- Formation of a link of news --- p.48Chapter CHAPTER SEVEN --- EXPERIMENTAL RESULTS AND ANALYSIS --- p.54Chapter 7.1 --- Objective of Experiments --- p.54Chapter 7.2 --- Background of Subjects --- p.55Chapter 7.3 --- Design of Experiments --- p.55Chapter 7.3.1 --- Experimental Data --- p.55Chapter 7.3.2 --- Methodology --- p.55Anchor document selection --- p.57Specific concept term identification --- p.55News association --- p.59Chapter 7.4 --- Results and Analysis --- p.60Anchor document selection --- p.60Specific concept term identification --- p.64News association --- p.68Chapter CHAPTER EIGHT --- CONCLUSIONS AND FUTURE WORK --- p.72Chapter 8.1 --- Conclusions --- p.72Chapter 8.2 --- Future work --- p.74APPENDIX A --- p.76APPENDIX B --- p.78BIBLIOGRAPHY --- p.8

Similarity searching in sequence databases under time warping.

Wong, Siu Fung.Thesis submitted in: December 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 77-84).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.viChapter 1 --- Introduction --- p.1Chapter 2 --- Preliminary --- p.6Chapter 2.1 --- Dynamic Time Warping (DTW) --- p.6Chapter 2.2 --- Spatial Indexing --- p.10Chapter 2.3 --- Relevance Feedback --- p.11Chapter 3 --- Literature Review --- p.13Chapter 3.1 --- Searching Sequences under Euclidean Metric --- p.13Chapter 3.2 --- Searching Sequences under Dynamic Time Warping Metric --- p.17Chapter 4 --- Subsequence Matching under Time Warping --- p.21Chapter 4.1 --- Subsequence Matching --- p.22Chapter 4.1.1 --- Sequential Search --- p.22Chapter 4.1.2 --- Indexing Scheme --- p.23Chapter 4.2 --- Lower Bound Technique --- p.25Chapter 4.2.1 --- Properties of Lower Bound Technique --- p.26Chapter 4.2.2 --- Existing Lower Bound Functions --- p.27Chapter 4.3 --- Point-Based indexing --- p.28Chapter 4.3.1 --- Lower Bound for subsequences matching --- p.28Chapter 4.3.2 --- Algorithm --- p.35Chapter 4.4 --- Rectangle-Based indexing --- p.37Chapter 4.4.1 --- Lower Bound for subsequences matching --- p.37Chapter 4.4.2 --- Algorithm --- p.41Chapter 4.5 --- Experimental Results --- p.43Chapter 4.5.1 --- Candidate ratio vs Width of warping window --- p.44Chapter 4.5.2 --- CPU time vs Number of subsequences --- p.45Chapter 4.5.3 --- CPU time vs Width of warping window --- p.46Chapter 4.5.4 --- CPU time vs Threshold --- p.46Chapter 4.6 --- Summary --- p.47Chapter 5 --- Relevance Feedback under Time Warping --- p.49Chapter 5.1 --- Integrating Relevance Feedback with DTW --- p.49Chapter 5.2 --- Query Reformulation --- p.53Chapter 5.2.1 --- Constraint Updating --- p.53Chapter 5.2.2 --- Weight Updating --- p.55Chapter 5.2.3 --- Overall Strategy --- p.58Chapter 5.3 --- Experiments and Evaluation --- p.59Chapter 5.3.1 --- Effectiveness of the strategy --- p.61Chapter 5.3.2 --- Efficiency of the strategy --- p.63Chapter 5.3.3 --- Usability --- p.64Chapter 5.4 --- Summary --- p.71Chapter 6 --- Conclusion --- p.72Chapter A --- Deduction of Data Bounding Hyper-rectangle --- p.74Chapter B --- Proof of Theorem2 --- p.76Bibliography --- p.77Publications --- p.8

Contribution à la définition de modèles de recherche d'information flexibles basés sur les CP-Nets

This thesis addresses two main problems in IR: automatic query weighting and document semantic indexing. Our global contribution consists on the definition of a theoretical flexible information retrieval (IR) model based on CP-Nets. The CP-Net formalism is used for the graphical representation of flexible queries expressing qualitative preferences and for automatic weighting of such queries. Furthermore, the CP-Net formalism is used as an indexing language in order to represent document representative concepts and related relations in a roughly compact way. Concepts are identified by projection on WordNet. Concept relations are discovered by means of semantic association rules. A query evaluation mechanism based on CP-Nets graph similarity is also proposed.Ce travail de thèse adresse deux principaux problèmes en recherche d'information : (1) la formalisation automatique des préférences utilisateur, (ou la pondération automatique de requêtes) et (2) l'indexation sémantique. Dans notre première contribution, nous proposons une approche de recherche d'information (RI) flexible fondée sur l'utilisation des CP-Nets (Conditional Preferences Networks). Le formalisme CP-Net est utilisé d'une part, pour la représentation graphique de requêtes flexibles exprimant des préférences qualitatives et d'autre part pour l'évaluation flexible de la pertinence des documents. Pour l'utilisateur, l'expression de préférences qualitatives est plus simple et plus intuitive que la formulation de poids numériques les quantifiant. Cependant, un système automatisé raisonnerait plus simplement sur des poids ordinaux. Nous proposons alors une approche de pondération automatique des requêtes par quantification des CP-Nets correspondants par des valeurs d'utilité. Cette quantification conduit à un UCP-Net qui correspond à une requête booléenne pondérée. Une utilisation des CP-Nets est également proposée pour la représentation des documents dans la perspective d'une évaluation flexible des requêtes ainsi pondéreés. Dans notre seconde contribution, nous proposons une approche d'indexation conceptuelle basée sur les CP-Nets. Nous proposons d'utiliser le formalisme CP-Net comme langage d'indexation afin de représenter les concepts et les relations conditionnelles entre eux d'une manière relativement compacte. Les noeuds du CP-Net sont les concepts représentatifs du contenu du document et les relations entre ces noeuds expriment les associations conditionnelles qui les lient. Notre contribution porte sur un double aspect : d'une part, nous proposons une approche d'extraction des concepts en utilisant WordNet. Les concepts résultants forment les noeuds du CP-Net. D'autre part, nous proposons d'étendre et d'utiliser la technique de règles d'association afin de découvrir les relations conditionnelles entre les concepts noeuds du CP-Nets. Nous proposons enfin un mécanisme d'évaluation des requêtes basé sur l'appariement de graphes (les CP-Nets document et requête en l'occurrence)