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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
91

Visualizing Similarity in Subject Term Co-Assignment

Gabel, Jeff, Smiraglia, Richard P. January 2009 (has links)
The purpose of this research is to improve retrieval performance in systems that use assigned subject descriptors, such as library subject headings. We are looking for wider semantic boundaries surrounding summary headings assigned to documents by providing a means of identifying clustered headings that fall within the indexerâ s collective common perceptions of relevance. We are here experimenting with two techniques that can help increase both precision and recall. In earlier research citationâ chasing was employed to yield a fuller retrieval set than might have been found using subject headings alone. In the present study we are employing multiâ dimensional scaling to determine the best fit among works to which subject descriptors have been coâ assigned. A term co-occurrence matrix compiled from 19 LCSH subject headings assigned to works in the field of â language originâ is used to generate an MDS map of the semantic space. Two clusters emerge: language and languages, and evolution biology, sometimes termed evolingo. Results allow us to visualize how differing perceptions of indexers affect the semantic space surrounding assigned terms. In both cases - citation-chasing and term co-occurrence - and especially when combining the two techniques acting as thresholds for each other, it is possible to overcome the inverse relation between precision and recall.
92

A cooperative approach to networked information resource discovery

Roberts, Marcus James January 1996 (has links)
No description available.
93

Fundamental indexation and mean reversion on the Taiwanese equity market

Fongwa, Emmanuel C. January 2015 (has links)
Magister Commercii - MCom / The equity market has a long memory of indexing. The market portfolio is a capweighted index that weights stocks based on the market capitalisation of the stocks constituting the index and has been upheld by modern portfolio theory as the optimal portfolio, generating the highest return for given risk. Justification for the meanvariance efficiency of the market portfolio stems from the assumed efficiency of stock markets. However, Siegel (2006) states that, because of speculative trading in the market, which induces noise in stock prices, the prices of stocks deviate from their intrinsic value. The subsequent reversal of overweighting of overvalued stocks and underweighting of undervalued stocks to their intrinsic values by capitalisation weighting results in a return drag. Recent observations of portfolios constructed based on weighting methodologies other than capitalisation weighting have resulted in portfolios that generate excess riskadjusted returns over and above that of the market portfolio; casting doubt on the assumed efficiency of the market. One such weighting methodologies is fundamental indexation, under which stocks are weighted by their fundamental metrics of size. The concept was introduced by Arnott, Hsu and Moore (2005). Chen, Chen and Bassett (2007) also introduced the concept of smoothed cap weights (SCW) as a more reliable estimate of the intrinsic value of a stock. This research study applies the concept of fundamental indexation and SCW to investigate the relative performance of fundamental indices of different concentrations (top 50 and mid-100 stocks) against cap-weighted portfolios on the Taiwanese equity market. The research period runs from January 2001 to June 2014, using the TEJ database as the data source. The TAIEX is employed as the market proxy. The research also examines the performance attribution and robustness of fundamental indices against cap-weighted portfolios. The results indicate that most fundamental indices constructed from the top 50 stocks are less mean-variance efficient than the TAIEX but more mean-variance efficient than the cap-weighted reference portfolio. All fundamental indices of the mid-100 stocks are more mean-variance efficient than the TAIEX and the reference portfolio. The return drag observed in the cap-weighted TAIEX and reference portfolio evidences the presence of mean reversion of stocks. Moreover, the returns of fundamental indices of the top 50 stocks are partly influenced by size risk premium but the fundamental indices comprised of the mid-100 stocks display return variations with statistically significant factor loading on the small cap (size) risk premium and value risk premium. Fundamental indices, on average show a higher resilience against the cap-weighted portfolios in both bull and bear markets. The sales index and fundamental composite index are the most mean-variance efficient fundamental indices and generate statistically significant alphas post accounting for both size and value risk premia.
94

Scaling Geospatial Searches in Large Spatial Databases

Cary, Ariel 08 November 2011 (has links)
Modern geographical databases store a rich set of aspatial attributes in addition to geographic data. Retrieving spatial records constrained on spatial and aspatial attributes provides users the ability to perform more interesting spatial analyses via composite spatial searches; e.g., in a real estate database, "Find the nearest homes for sale to my current location that have backyard and whose prices are between $50,000 and $80,000". Efficient processing of such composite searches requires combined indexing strategies of multiple types of data. Existing spatial query engines commonly apply a two-filter approach (spatial filter followed by non-spatial filter, or viceversa), which can incur large performance overheads. On the other hand, the amount of geolocation data in databases is rapidly increasing due in part to advances in geolocation technologies (e.g., GPS- enabled mobile devices) that allow to associate location data to nearly every object or event. Hence, practical spatial databases may face data ingestion challenges of large data volumes. In this dissertation, we first show how indexing spatial data with R-trees (a typical data pre- processing task) can be scaled in MapReduce – a well-adopted parallel programming model, developed by Google, for data intensive problems. Close to linear scalability was observed in index construction tasks over large spatial datasets. Subsequently, we develop novel techniques for simultaneously indexing spatial with textual and numeric data to process k-nearest neighbor searches with aspatial Boolean selection constraints. In particular, numeric ranges are compactly encoded and explicitly indexed. Experimental evaluations with real spatial databases showed query response times within acceptable ranges for interactive search systems.
95

Matching Slides to Presentation Videos

Fan, Quanfu January 2008 (has links)
Video streaming is becoming a major channel for distance learning (or e-learning). A tremendous number of videos for educational purpose are capturedand archived in various e-learning systems today throughout schools, corporations and over the Internet. However, making information searchable and browsable, and presenting results optimally for a wide range of users and systems, remains a challenge.In this work two core algorithms have been developedto support effective browsing and searching of educational videos. The first is a fully automatic approach that recognizes slides in the videowith high accuracy. Built upon SIFT (scale invariant feature transformation) keypoint matching using RANSAC (random sample consensus), the approach is independent of capture systems and can handle a variety of videos with different styles and plentiful ambiguities. In particular, we propose a multi-phase matching pipeline that incrementally identifies slides from the easy ones to the difficult ones. We achieve further robustness by using the matching confidence as part of a dynamic Hidden Markov model (HMM) that integrates temporal information, taking camera operations into account as well.The second algorithm locates slides in the video. We develop a non-linear optimization method (bundle adjustment) to accurately estimate the projective transformations (homographies) between slides and video frames. Different from estimating homography from a single image, our method solves a set of homographies jointly in a frame sequence that is related to a single slide.These two algorithms open up a series of possibilities for making the video content more searchable, browsable and understandable, thus greatly enriching the user's learning experience. Their usefulness has been demonstrated in the SLIC (Semantically Linking Instructional Content) system, which aims to turnsimple video content into fully interactive learning experience for students and scholars.
96

Intute: from a distributed network to a unified database – lessons learned

Kerr, Linda 12 1900 (has links)
Intute (http://www.intute.ac.uk/) catalogues and describes the best Internet resources for education and research. It is funded by the Joint Information Systems Committee (JISC), and is primarily aimed at evaluating web resources suitable for undergraduate study. The service also offers Internet research skills tutorials, rss feeds of new resources added to the catalogue, a personalisation service (MyIntute), and a blog highlighting trends in Internet research skills and particularly good or topical subject-based resources. The current Intute catalogue of Internet resources is an aggregation of records from eight subject services previously funded by the JISC as the Resource Discovery Network (RDN). This paper describes the process and challenges of integrating these eight databases into one unified catalogue with one standard metadata schema, whilst continuing to satisfy the needs of different subject communities. The paper also outlines a current project to evaluate and compare the cost-effectiveness of manual and automatic metadata creation.
97

Indexing methods for multimedia data objects given pair-wise distances.

January 1997 (has links)
by Chan Mei Shuen Polly. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 67-70). / Abstract --- p.ii / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Definitions --- p.3 / Chapter 1.2 --- Thesis Overview --- p.5 / Chapter 2 --- Background and Related Work --- p.6 / Chapter 2.1 --- Feature-Based Index Structures --- p.6 / Chapter 2.2 --- Distance Preserving Methods --- p.8 / Chapter 2.3 --- Distance-Based Index Structures --- p.9 / Chapter 2.3.1 --- The Vantage-Point Tree Method --- p.10 / Chapter 3 --- The Problem of Distance Preserving Methods in Querying --- p.12 / Chapter 3.1 --- Some Experimental Results --- p.13 / Chapter 3.2 --- Discussion --- p.15 / Chapter 4 --- Nearest Neighbor Search in VP-trees --- p.17 / Chapter 4.1 --- The sigma-factor Algorithm --- p.18 / Chapter 4.2 --- The Constant-α Algorithm --- p.22 / Chapter 4.3 --- The Single-Pass Algorithm --- p.24 / Chapter 4.4 --- Discussion --- p.25 / Chapter 4.5 --- Performance Evaluation --- p.26 / Chapter 4.5.1 --- Experimental Setup --- p.27 / Chapter 4.5.2 --- Results --- p.28 / Chapter 5 --- Update Operations on VP-trees --- p.41 / Chapter 5.1 --- Insert --- p.41 / Chapter 5.2 --- Delete --- p.48 / Chapter 5.3 --- Performance Evaluation --- p.51 / Chapter 6 --- Minimizing Distance Computations --- p.57 / Chapter 6.1 --- A Single Vantage Point per Level --- p.58 / Chapter 6.2 --- Reuse of Vantage Points --- p.59 / Chapter 6.3 --- Performance Evaluation --- p.60 / Chapter 7 --- Conclusions and Future Work --- p.63 / Chapter 7.1 --- Future Work --- p.65 / Bibliography --- p.67
98

Indexing techniques for object-oriented databases.

January 1996 (has links)
by Frank Hing-Wah Luk. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 92-95). / Abstract --- p.ii / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- The Problem in Object-Oriented Database Indexing --- p.2 / Chapter 1.3 --- Contributions --- p.3 / Chapter 1.4 --- Thesis Organization --- p.4 / Chapter 2 --- Object-oriented Data Model --- p.5 / Chapter 2.1 --- Object-oriented Data Model --- p.5 / Chapter 2.2 --- Object and Object Identifiers --- p.6 / Chapter 2.3 --- Complex Attributes and Methods --- p.6 / Chapter 2.4 --- Class --- p.8 / Chapter 2.4.1 --- Inheritance Hierarchy --- p.8 / Chapter 2.4.2 --- Aggregation Hierarchy --- p.8 / Chapter 2.5 --- Sample Object-Oriented Database Schema --- p.9 / Chapter 3 --- Indexing in Object-Oriented Databases --- p.10 / Chapter 3.1 --- Introduction --- p.10 / Chapter 3.2 --- Indexing on Inheritance Hierarchy --- p.10 / Chapter 3.3 --- Indexing on Aggregation Hierarchy --- p.13 / Chapter 3.4 --- Indexing on Integrated Support --- p.16 / Chapter 3.5 --- Indexing on Method Invocation --- p.18 / Chapter 3.6 --- Indexing on Overlapping Path Expressions --- p.19 / Chapter 4 --- Triple Node Hierarchy --- p.23 / Chapter 4.1 --- Introduction --- p.23 / Chapter 4.2 --- Triple Node --- p.25 / Chapter 4.3 --- Triple Node Hierarchy --- p.26 / Chapter 4.3.1 --- Construction of the Triple Node Hierarchy --- p.26 / Chapter 4.3.2 --- Updates in the Triple Node Hierarchy --- p.31 / Chapter 4.4 --- Cost Model --- p.33 / Chapter 4.4.1 --- Storage --- p.33 / Chapter 4.4.2 --- Query Cost --- p.35 / Chapter 4.4.3 --- Update Cost --- p.35 / Chapter 4.5 --- Evaluation --- p.37 / Chapter 4.6 --- Summary --- p.42 / Chapter 5 --- Triple Node Hierarchy in Both Aggregation and Inheritance Hierarchies --- p.43 / Chapter 5.1 --- Introduction --- p.43 / Chapter 5.2 --- Preliminaries --- p.44 / Chapter 5.3 --- Class-Hierarchy Tree --- p.45 / Chapter 5.4 --- The Nested CH-tree --- p.47 / Chapter 5.4.1 --- Construction --- p.47 / Chapter 5.4.2 --- Retrieval --- p.48 / Chapter 5.4.3 --- Update --- p.48 / Chapter 5.5 --- Cost Model --- p.49 / Chapter 5.5.1 --- Assumptions --- p.51 / Chapter 5.5.2 --- Storage --- p.52 / Chapter 5.5.3 --- Query Cost --- p.52 / Chapter 5.5.4 --- Update Cost --- p.53 / Chapter 5.6 --- Evaluation --- p.55 / Chapter 5.6.1 --- Storage Cost --- p.55 / Chapter 5.6.2 --- Query Cost --- p.57 / Chapter 5.6.3 --- Update Cost --- p.62 / Chapter 5.7 --- Summary --- p.63 / Chapter 6 --- Decomposition of Path Expressions --- p.65 / Chapter 6.1 --- Introduction --- p.65 / Chapter 6.2 --- Configuration on Path Expressions --- p.67 / Chapter 6.2.1 --- Single Path Expression --- p.67 / Chapter 6.2.2 --- Overlapping Path Expressions --- p.68 / Chapter 6.3 --- New Algorithm --- p.70 / Chapter 6.3.1 --- Example --- p.72 / Chapter 6.4 --- Evaluation --- p.75 / Chapter 6.5 --- Summary --- p.76 / Chapter 7 --- Conclusion and Future Research --- p.77 / Chapter 7.1 --- Conclusion --- p.77 / Chapter 7.2 --- Future Research --- p.78 / Chapter A --- Evaluation of some Parameters in Chapter5 --- p.79 / Chapter B --- Cost Model for Nested-Inherited Index --- p.82 / Chapter B.1 --- Storage --- p.82 / Chapter B.2 --- Query Cost --- p.84 / Chapter B.3 --- Update --- p.84 / Chapter C --- Algorithm constructing a minimum auxiliary set of J Is --- p.87 / Chapter D --- Estimation on the number of possible combinations --- p.89 / Bibliography --- p.92
99

Rival penalized competitive learning for content-based indexing.

January 1998 (has links)
by Lau Tak Kan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 100-108). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Problem Defined --- p.5 / Chapter 1.3 --- Contributions --- p.5 / Chapter 1.4 --- Thesis Organization --- p.7 / Chapter 2 --- Content-based Retrieval Multimedia Database Background and Indexing Problem --- p.8 / Chapter 2.1 --- Feature Extraction --- p.8 / Chapter 2.2 --- Nearest-neighbor Search --- p.10 / Chapter 2.3 --- Content-based Indexing Methods --- p.15 / Chapter 2.4 --- Indexing Problem --- p.22 / Chapter 3 --- Data Clustering Methods for Indexing --- p.25 / Chapter 3.1 --- Proposed Solution to Indexing Problem --- p.25 / Chapter 3.2 --- Brief Description of Several Clustering Methods --- p.26 / Chapter 3.2.1 --- K-means --- p.26 / Chapter 3.2.2 --- Competitive Learning (CL) --- p.27 / Chapter 3.2.3 --- Rival Penalized Competitive Learning (RPCL) --- p.29 / Chapter 3.2.4 --- General Hierarchical Clustering Methods --- p.31 / Chapter 3.3 --- Why RPCL? --- p.32 / Chapter 4 --- Non-hierarchical RPCL Indexing --- p.33 / Chapter 4.1 --- The Non-hierarchical Approach --- p.33 / Chapter 4.2 --- Performance Experiments --- p.34 / Chapter 4.2.1 --- Experimental Setup --- p.35 / Chapter 4.2.2 --- Experiment 1: Test for Recall and Precision Performance --- p.38 / Chapter 4.2.3 --- Experiment 2: Test for Different Sizes of Input Data Sets --- p.45 / Chapter 4.2.4 --- Experiment 3: Test for Different Numbers of Dimensions --- p.49 / Chapter 4.2.5 --- Experiment 4: Compare with Actual Nearest-neighbor Results --- p.53 / Chapter 4.3 --- Chapter Summary --- p.55 / Chapter 5 --- Hierarchical RPCL Indexing --- p.56 / Chapter 5.1 --- The Hierarchical Approach --- p.56 / Chapter 5.2 --- The Hierarchical RPCL Binary Tree (RPCL-b-tree) --- p.58 / Chapter 5.3 --- Insertion --- p.61 / Chapter 5.4 --- Deletion --- p.63 / Chapter 5.5 --- Searching --- p.63 / Chapter 5.6 --- Experiments --- p.69 / Chapter 5.6.1 --- Experimental Setup --- p.69 / Chapter 5.6.2 --- Experiment 5: Test for Different Node Sizes --- p.72 / Chapter 5.6.3 --- Experiment 6: Test for Different Sizes of Data Sets --- p.75 / Chapter 5.6.4 --- Experiment 7: Test for Different Data Distributions --- p.78 / Chapter 5.6.5 --- Experiment 8: Test for Different Numbers of Dimensions --- p.80 / Chapter 5.6.6 --- Experiment 9: Test for Different Numbers of Database Ob- jects Retrieved --- p.83 / Chapter 5.6.7 --- Experiment 10: Test with VP-tree --- p.86 / Chapter 5.7 --- Discussion --- p.90 / Chapter 5.8 --- A Relationship Formula --- p.93 / Chapter 5.9 --- Chapter Summary --- p.96 / Chapter 6 --- Conclusion --- p.97 / Chapter 6.1 --- Future Works --- p.97 / Chapter 6.2 --- Conclusion --- p.98 / Bibliography --- p.100
100

An effective Chinese indexing method based on partitioned signature files.

January 1998 (has links)
Wong Chi Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 107-114). / Abstract also in Chinese. / Abstract --- p.ii / Acknowledgements --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction to Chinese IR --- p.1 / Chapter 1.2 --- Contributions --- p.3 / Chapter 1.3 --- Organization of this Thesis --- p.5 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Indexing methods --- p.6 / Chapter 2.1.1 --- Full-text scanning --- p.7 / Chapter 2.1.2 --- Inverted files --- p.7 / Chapter 2.1.3 --- Signature files --- p.9 / Chapter 2.1.4 --- Clustering --- p.10 / Chapter 2.2 --- Information Retrieval Models --- p.10 / Chapter 2.2.1 --- Boolean model --- p.11 / Chapter 2.2.2 --- Vector space model --- p.11 / Chapter 2.2.3 --- Probabilistic model --- p.13 / Chapter 2.2.4 --- Logical model --- p.14 / Chapter 3 --- Investigation of Segmentation on the Vector Space Retrieval Model --- p.15 / Chapter 3.1 --- Segmentation of Chinese Texts --- p.16 / Chapter 3.1.1 --- Character-based segmentation --- p.16 / Chapter 3.1.2 --- Word-based segmentation --- p.18 / Chapter 3.1.3 --- N-Gram segmentation --- p.21 / Chapter 3.2 --- Performance Evaluation of Three Segmentation Approaches --- p.23 / Chapter 3.2.1 --- Experimental Setup --- p.23 / Chapter 3.2.2 --- Experimental Results --- p.24 / Chapter 3.2.3 --- Discussion --- p.29 / Chapter 4 --- Signature File Background --- p.32 / Chapter 4.1 --- Superimposed coding --- p.34 / Chapter 4.2 --- False drop probability --- p.36 / Chapter 5 --- Partitioned Signature File Based On Chinese Word Length --- p.39 / Chapter 5.1 --- Fixed Weight Block (FWB) Signature File --- p.41 / Chapter 5.2 --- Overview of PSFC --- p.45 / Chapter 5.3 --- Design Considerations --- p.50 / Chapter 6 --- New Hashing Techniques for Partitioned Signature Files --- p.59 / Chapter 6.1 --- Direct Division Method --- p.61 / Chapter 6.2 --- Random Number Assisted Division Method --- p.62 / Chapter 6.3 --- Frequency-based hashing method --- p.64 / Chapter 6.4 --- Chinese character-based hashing method --- p.68 / Chapter 7 --- Experiments and Results --- p.72 / Chapter 7.1 --- Performance evaluation of partitioned signature file based on Chi- nese word length --- p.74 / Chapter 7.1.1 --- Retrieval Performance --- p.75 / Chapter 7.1.2 --- Signature Reduction Ratio --- p.77 / Chapter 7.1.3 --- Storage Requirement --- p.79 / Chapter 7.1.4 --- Discussion --- p.81 / Chapter 7.2 --- Performance evaluation of different dynamic signature generation methods --- p.82 / Chapter 7.2.1 --- Collision --- p.84 / Chapter 7.2.2 --- Retrieval Performance --- p.86 / Chapter 7.2.3 --- Discussion --- p.89 / Chapter 8 --- Conclusions and Future Work --- p.91 / Chapter 8.1 --- Conclusions --- p.91 / Chapter 8.2 --- Future work --- p.95 / Chapter A --- Notations of Signature Files --- p.96 / Chapter B --- False Drop Probability --- p.98 / Chapter C --- Experimental Results --- p.103 / Bibliography --- p.107

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