Thesis (Ph. D.)--University of Washington, 1999. / Includes bibliographical references (p. 144-155).

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2008. / Title from title screen (site viewed Nov. 20, 2008). PDF text: xii, 176 p. : ill. ; 870 K. UMI publication number: AAT 3311253. Includes bibliographical references. Also available in microfilm and microfiche formats.

Thesis (M.S.)--Brigham Young University. Dept. of Physical and Mathematical Sciences, 2005. / Includes bibliographical references (leaves 89-93).

Thesis (M.A.)--University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 29, 2007) Includes bibliographical references.

Through their role of content proxy, in particular on search engine result pages, Web summaries play an essential part in the discovery of information and services on the Web. In their simplest form, Web summaries are snippets based on a user-query and are obtained by extracting from the content of Web pages. The focus of this work, however, is on indicative Web summarization, that is, on the generation of summaries describing the purpose, topics and functionalities of Web pages. In many scenarios — e.g. navigational queries or content-deprived pages — such summaries represent a valuable commodity to concisely describe Web pages while circumventing the need to produce snippets from inherently noisy, dynamic, and structurally complex content. Previous approaches have identified linking pages as a privileged source of indicative content from which Web summaries may be derived using traditional extractive methods. To be reliable, these approaches require sufficient anchortext redundancy, ultimately showing the limits of extractive algorithms for what is, fundamentally, an abstractive task. In contrast, we explore the viability of abstractive approaches and propose a nearest-neighbors summarization framework leveraging summaries of conceptually related (neighboring) Web pages. We examine the steps that can lead to the reuse and adaptation of existing summaries to previously unseen pages. Specifically, we evaluate two Text-to-Text transformations that cover the main types of operations applicable to neighbor summaries: (1) ranking, to identify neighbor summaries that best fit the target; (2) target adaptation, to adjust individual neighbor summaries to the target page based on neighborhood-specific template-slot models. For this last transformation, we report on an initial exploration of the use of slot-driven compression to adjust adapted summaries based on the confidence associated with token-level adaptation operations. Overall, this dissertation explores a new research avenue for indicative Web summarization and shows the potential value, given the diversity and complexity of the content of Web pages, of transferring, and, when necessary, of adapting, existing summary information between conceptually similar Web pages.

Cheng Tung Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 136-141). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.1.1 --- Information Retrieval --- p.1 / Chapter 1.1.2 --- Search Engines --- p.2 / Chapter 1.1.3 --- Data Merging --- p.3 / Chapter 1.2 --- Meta-search Engines --- p.3 / Chapter 1.2.1 --- Framework and Techniques Employed --- p.3 / Chapter 1.2.2 --- Advantages of meta-searching --- p.8 / Chapter 1.3 --- Contribution of the Thesis --- p.10 / Chapter 1.4 --- Organization of the Thesis --- p.12 / Chapter 2 --- Literature Review --- p.14 / Chapter 2.1 --- Preliminaries --- p.14 / Chapter 2.2 --- Fusion Methods --- p.15 / Chapter 2.2.1 --- Fusion methods based on a document's score --- p.15 / Chapter 2.2.2 --- Fusion methods based on a document's ranking position --- p.23 / Chapter 2.2.3 --- Fusion methods based on a document's URL title and snippets --- p.30 / Chapter 2.2.4 --- Fusion methods based on a document's entire content --- p.40 / Chapter 2.3 --- Comparison of the Fusion Methods --- p.42 / Chapter 2.4 --- Relevance Feedback --- p.46 / Chapter 3 --- Research Methodology --- p.48 / Chapter 3.1 --- Investigation of the features of the retrieved results from the search engines --- p.48 / Chapter 3.2 --- Types of relationships --- p.53 / Chapter 3.3 --- Order of Strength of the Relationships --- p.64 / Chapter 3.3.1 --- Derivation of the weight for each kind of relationship (criterion) --- p.68 / Chapter 3.4 --- Observation of the relationships between retrieved objects and the effects of these relationships on the relevance of objects --- p.69 / Chapter 3.4.1 --- Observation on the relationships existed in items that are irrelevant and relevant to the query --- p.68 / Chapter 3.5 --- Proposed re-ranking algorithms --- p.89 / Chapter 3.5.1 --- Original re-ranking algorithm (before modification) --- p.91 / Chapter 3.5.2 --- Modified re-ranking algorithm (after modification) --- p.95 / Chapter 4 --- Evaluation Methodology and Experimental Results --- p.101 / Chapter 4.1 --- Objective --- p.101 / Chapter 4.2 --- Experimental Design and Setup --- p.101 / Chapter 4.2.1 --- Preparation of data --- p.101 / Chapter 4.3 --- Evaluation Methodology --- p.104 / Chapter 4.3.1 --- Evaluation of the relevance of a document to the corresponding query --- p.104 / Chapter 4.3.2 --- Performance Measures of the Evaluation --- p.105 / Chapter 4.4 --- Experimental Results and Interpretation --- p.106 / Chapter 4.4.1 --- Precision --- p.107 / Chapter 4.4.2 --- Recall --- p.107 / Chapter 4.4.3 --- F-measure --- p.108 / Chapter 4.4.4 --- Overall evaluation results for the ten queries for each evaluation tool --- p.110 / Chapter 4.4.5 --- Discussion --- p.123 / Chapter 4.5 --- Degree of difference between the performance of systems --- p.124 / Chapter 4.5.1 --- Analysis using One-Way ANOVA --- p.124 / Chapter 4.5.2 --- Analysis using paired samples T-test --- p.126 / Chapter 5 --- Conclusion --- p.131 / Chapter 5.1 --- "Implications, Limitations, and Future Work" --- p.131 / Chapter 5.2 --- Conclusions --- p.133 / Bibliography --- p.134 / Chapter A --- Paired samples T-test for F-measures of systems retrieving all media's items --- p.140

This work studies something new in Web search to cater for users' cross-lingual information needs by using the common search interests found across different languages. We assume a generic scenario for monolingual users who are interested to find their relevant information under three general settings: (1) find relevant information in a foreign language, which needs machine to translate search results into the user's own language; (2) find relevant information in multiple languages including the source language, which also requires machine translation for back translating search results; (3) find relevant information only in the user's language, but due to the intrinsic cross-lingual nature of many queries, monolingual search can be done with the assistance of cross-lingual information from another language. / We approach the problem by substantially extending two core mechanics of information retrieval for Web search across languages, namely, query formulation and relevance ranking. First, unlike traditional cross-lingual methods such as query translation and expansion, we propose a novel Cross-Lingual Query Suggestion model by leveraging large-scale query logs of search engine to learn to suggest closely related queries in the target language for a given source language query. The rationale behind our approach is the ever-increasing common search interests across Web users in different languages. Second, we generalize the usefulness of common search interests to enhance relevance ranking of documents by exploiting the correlation among the search results derived from bilingual queries, and overcome the weakness of traditional relevance estimation that only uses information of a single language or that of different languages separately. To this end, we attempt to learn a ranking function that incorporates various similarity measures among the retrieved documents in different languages. By modeling the commonality or similarity of search results, relevant documents in one language may help the relevance estimation of documents in a different language, and hence can improve the overall relevance estimation. This similar intuition is applicable to all the three settings described above. / Gao, Wei. / Adviser: Kaw-Fai Wong. / Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 114-122). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Luo, Yiwen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 38-41). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Image Search by Multi-Class Query --- p.1 / Chapter 1.1.1 --- Related Work --- p.2 / Chapter 1.1.2 --- Our Method --- p.4 / Chapter 1.2 --- Image and Video Quality Assessment --- p.5 / Chapter 1.2.1 --- Related Work --- p.6 / Chapter 1.2.2 --- Our Method --- p.6 / Chapter 2 --- Multi-Class Query Image Search System --- p.8 / Chapter 2.1 --- System Description --- p.8 / Chapter 2.1.1 --- Multi-Query Search --- p.8 / Chapter 2.1.2 --- Image Annotation --- p.9 / Chapter 2.1.3 --- Image Re-ranking --- p.10 / Chapter 2.2 --- Algorithm Description --- p.10 / Chapter 2.3 --- Evaluation and Results --- p.12 / Chapter 3 --- Image and Video Quality Assessment --- p.15 / Chapter 3.1 --- Criteria for Assessing Photo Quality --- p.15 / Chapter 3.1.1 --- Composition --- p.15 / Chapter 3.1.2 --- Lighting --- p.16 / Chapter 3.1.3 --- Focus Controlling --- p.16 / Chapter 3.1.4 --- Color --- p.17 / Chapter 3.2 --- Features for Photo Quality Assessment --- p.18 / Chapter 3.2.1 --- Subject Region Extraction --- p.18 / Chapter 3.2.2 --- Clarity Contrast Feature --- p.20 / Chapter 3.2.3 --- Lighting Feature --- p.21 / Chapter 3.2.4 --- Simplicity Feature --- p.22 / Chapter 3.2.5 --- Composition Geometry Feature --- p.22 / Chapter 3.2.6 --- Color Harmony Feature --- p.23 / Chapter 3.3 --- Features for Video Quality Assessment --- p.24 / Chapter 3.3.1 --- Length of Subject Region Motion --- p.24 / Chapter 3.3.2 --- Motion Stability --- p.26 / Chapter 3.4 --- Classification --- p.26 / Chapter 3.5 --- Experiments --- p.27 / Chapter 3.5.1 --- Photo Assessment --- p.27 / Chapter 3.5.2 --- Video Assessment --- p.28 / Chapter 3.5.3 --- Web Image Ranking --- p.31 / Chapter 4 --- Conclusion --- p.36 / Bibliography --- p.38 / Chapter A --- Supplementary Materials of Photo Quality Assessment --- p.42 / Chapter A.l --- Photo Database --- p.42 / Chapter A.2 --- Web Image Ranking

Suen, Ho Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 99-104). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Organization --- p.4 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Related Works --- p.6 / Chapter 2.2 --- Background Study --- p.7 / Chapter 2.2.1 --- World Wide Web --- p.7 / Chapter 2.2.2 --- Distributed Denial of Service Attack --- p.11 / Chapter 2.2.3 --- Tools for Dimension Reduction --- p.13 / Chapter 2.2.4 --- Tools for Anomaly Detection --- p.20 / Chapter 2.2.5 --- Receiver operating characteristics (ROC) Analysis --- p.22 / Chapter 3 --- System Design --- p.25 / Chapter 3.1 --- Methodology --- p.25 / Chapter 3.2 --- System Overview --- p.27 / Chapter 3.3 --- Reference Profile Construction --- p.31 / Chapter 3.4 --- Real-time Anomaly Detection and Response --- p.32 / Chapter 3.5 --- Chapter Summary --- p.34 / Chapter 4 --- Reference Profile Construction --- p.35 / Chapter 4.1 --- Web Access Logs Collection --- p.35 / Chapter 4.2 --- Data Preparation --- p.37 / Chapter 4.3 --- Feature Extraction and Embedding Engine (FEE Engine) --- p.40 / Chapter 4.3.1 --- Sub-Sequence Extraction --- p.42 / Chapter 4.3.2 --- Hash Function on Sub-sequences (optional) --- p.45 / Chapter 4.3.3 --- Feature Vector Construction --- p.46 / Chapter 4.3.4 --- Diffusion Wavelets Embedding --- p.47 / Chapter 4.3.5 --- Numerical Example of Feature Set Reduction --- p.49 / Chapter 4.3.6 --- Reference Profile and Further Use of FEE Engine --- p.50 / Chapter 4.4 --- Chapter Summary --- p.50 / Chapter 5 --- Real-time Anomaly Detection and Response --- p.52 / Chapter 5.1 --- Session Filtering and Data Preparation --- p.54 / Chapter 5.2 --- Feature Extraction and Embedding --- p.54 / Chapter 5.3 --- Distance-based Outlier Scores Calculation --- p.55 / Chapter 5.4 --- Anomaly Detection and Response --- p.56 / Chapter 5.4.1 --- Length-Based Anomaly Detection Modules --- p.56 / Chapter 5.4.2 --- Characteristics of Anomaly Detection Modules --- p.59 / Chapter 5.4.3 --- Dynamic Threshold Adaptation --- p.60 / Chapter 5.5 --- Chapter Summary --- p.63 / Chapter 6 --- Experimental Results --- p.65 / Chapter 6.1 --- Experiment Datasets --- p.65 / Chapter 6.1.1 --- Normal Web Access Logs --- p.66 / Chapter 6.1.2 --- Attack Data Generation --- p.68 / Chapter 6.2 --- ROC Curve Construction --- p.70 / Chapter 6.3 --- System Parameters Selection --- p.71 / Chapter 6.4 --- Performance of Anomaly Detection --- p.82 / Chapter 6.4.1 --- Performance Analysis --- p.85 / Chapter 6.4.2 --- Performance in defending DDoS attacks --- p.87 / Chapter 6.5 --- Computation Requirement --- p.91 / Chapter 6.6 --- Chapter Summary --- p.95 / Chapter 7 --- Conclusion and Future Work --- p.96 / Bibliography --- p.99

Lin, Jianfeng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 79-83). / Abstracts in English and Chinese. / Abstract --- p.i / 内容摘要 --- p.ii / Acknowledgement --- p.iii / Table of Contents --- p.iv / List of Figures --- p.vi / List of Tables --- p.vii / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- Related Work --- p.6 / Chapter 2.1. --- Ontology Integration --- p.7 / Chapter 2.2. --- Schema Matching --- p.10 / Chapter 2.3. --- Taxonomy Integration as Text Categorization --- p.13 / Chapter 2.4. --- Cross-lingual Text Categorization & Cross-lingual Information Retrieval --- p.15 / Chapter Chapter 3. --- Problem Definition --- p.17 / Chapter 3.1. --- Mono-lingual Category Tree Integration --- p.17 / Chapter 3.2. --- Integration Operators --- p.19 / Chapter 3.3. --- Cross-lingual Category Tree Integration --- p.21 / Chapter Chapter 4. --- Mono-lingual Category Tree Integration Techniques --- p.23 / Chapter 4.1. --- Category Relationships --- p.23 / Chapter 4.2. --- Decision Rules --- p.27 / Chapter 4.3. --- Mapping Algorithm --- p.38 / Chapter Chapter 5. --- Experiment of Mono-lingual Category Tree Integration --- p.42 / Chapter 5.1. --- Dataset --- p.42 / Chapter 5.2. --- Automated Text Classifier --- p.43 / Chapter 5.3. --- Evaluation Metrics --- p.46 / Chapter 5.3.1. --- Integration Accuracy --- p.47 / Chapter 5.3.2. --- Precision and Recall and F1 value of the Three Operators --- p.48 / Chapter 5.3.3. --- "Precision and Recalls of ""Split""" --- p.48 / Chapter 5.4. --- Parameter Turning --- p.49 / Chapter 5.5. --- Experiments Results --- p.55 / Chapter Chapter 6. --- Cross-lingual Category Tree Integration --- p.60 / Chapter 6.1. --- Parallel Corpus --- p.61 / Chapter 6.2. --- Cross-lingual Concept Space Construction --- p.65 / Chapter 6.2.1. --- Phase Extraction --- p.65 / Chapter 6.2.2. --- Co-occurrence analysis --- p.65 / Chapter 6.2.3. --- Associate Constraint Network for Concept Generation --- p.67 / Chapter 6.3. --- Document Translation --- p.69 / Chapter 6.4. --- Experiment Setting --- p.72 / Chapter 6.5. --- Experiment Results --- p.73 / Chapter Chapter 7. --- Conclusion and Future Work --- p.77 / Reference --- p.79