Thesis (M.S.)--Duquesne University, 2006. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p.38-40) and index.

The present study investigated identification, access and usage of periodicals in university libraries in Kenya, with a view of recommending a tool for assisting users to identify information. Using questionnaires completed by 316 university library users and 27 librarians, backed with participant observations, document analysis as well as interviews, it was found that usage of periodicals was low as most users browse through periodicals to identify information, a method that is not effective. In-house indexing was investigated and found to be an effective tool in facilitating access to relevant information. The study recommends establishment of in-house indexing programs and databases in university libraries; formulation of consistent indexing policies to achieve quality indexing; and that indexing should be focused on both content and user requirements by specifying points- of- view, and study methodologies to enhance retrieval of relevant information. / Information Science / M. A. (Information Science)

This study investigates the relationships between the use of a zoom tool, the terms they supply to describe the image, and the type of image being viewed. Participants were assigned to two groups, one with access to the tool and one without, and were asked to supply terms to describe forty images, divided into four categories: landscape, portrait, news, and cityscape. The terms provided by participants were categorized according to models proposed in earlier image studies. Findings of the study suggest that there was not a significant difference in the number of terms supplied in relation to access to the tool, but a large variety in use of the tool was demonstrated by the participants. The study shows that there are differences in the level of meaning of the terms supplied in some of the models. The type of image being viewed was related to the number of zooms and relationships between the type of image and the number of terms supplied as well as their level of meaning in the various models from previous studies exist. The results of this study provide further insight into how people think about images and how the manipulation of those images may affect the terms they assign to describe images. The inclusion of these tools in search and retrieval scenarios may affect the outcome of the process and the more collection managers know about how people interact with images will improve their ability to provide access to the growing amount of pictorial information.

published_or_final_version / Computer Science / Master / Master of Philosophy

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.

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.

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.

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A dissertation submitted for the degree of Master of Science School of Computer Science University of the Witwatersrand. Johannesburg, 2015. / Rock art is the archaeological term used to describe pre-historic artworks placed on natural stone and as one of the earliest known traces of modern human creativity, it is a major component of world history and human heritage. Archival records and the art itself, however, are rapidly decaying requiring the need to preserve them for future generations and humanity as a whole. In line with this, the Rock Art Research Institute digitised their collections of photographs and historical records of the rock art in southern Africa. This has resulted in the South African Rock Art Digital Archive, a collection of over 275 000 images of paintings depicting a wide variety of objects such as humans and animals. The problem with this archive, however, is that most of the images are not labelled with the objects that appear in them. Manual labelling is infeasible due to the size of the archive but rock art researchers require this information to perform text-based search queries. Therefore, in this research, we present the combination of the Viola Jones object detection framework and a Support Vector Machine to automatically classify rock art objects. To test it, we have created and assessed the performance of classi ers for eland, elephant, human, and rhebuck rock art objects. We have performed the experiments using ve-fold cross-validation and found the results to be promising considering the variation and deterioration of the paintings.