With the growing use of computers as tools for the automation of clerical tasks, there has come not only a proliferation of documentation, but the realization that computers could be employed in automating certain aspects of the production of documents — not only such documents as describe computer developments, but also papers, briefs, letters, etc. The runoff program, usually an adjunct to a text editing facility, has been in existence for a long time, but its use has been limited to computing installations and those directly involved with computing. The reason for this is two-fold: public unawareness, and the ad hoc nature of runoff program design have prevented wider use. This thesis is an attempt to present a reasoned design of a program which acts enough like a rather intelligent typewriter to be usable by members of the public, but gives the user with a greater computing background enough power of expression in terms of programming language and layout design, to overcome some of the limitations of earlier runoff programs. Previous work in the area of text processing which relates to document processing is examined in some detail. The underlying ideas common to existing document-processing facilities are brought forth, and examined in the light of what a user might reasonably expect of such a facility. The resulting design for a document processor is presented in an orderly fashion, outlining the reasons for design decisions and backing away respectfully from designs which are unfeasible for economic implementation. An entire chapter is devoted to a description of the resulting document processor, in the form of a somewhat rarified user's manual. Suggestions for and details of an implementation are given, based on the author's own experiences with implementation. A bibliography and a short glossary of the most important terms and those most likely to confuse are appended to the thesis. / Science, Faculty of / Computer Science, Department of / Graduate

The user perspective of image search remains poorly understood. the purpose of this study is to identify and investigate the key issues relevant to a user’s interaction with images and the user’s approach to image search. a deeper understanding of these issues will serve to inform the design of image retrieval systems and in turn better serve the user. Previous research explores areas of information seeking behavior, representation in information science, query formulation, and image retrieval. the theoretical framework for this study includes an articulation of image search scenarios as adapted from Yoon and O’Connor’s taxonomy of image query types, Copeland’s Engineering Design Approach for rigorous qualitative research, and Anderson’s Functional Ontology Construction Model for building robust models of human behavior. a series of semi-structured interviews were conducted with expert-level image users. Interviewees discussed their motivations for image search, types of image searches they pursue, and varied approaches to image search, as well as how they decide that an information need has been met and which factors influence their experience of search. a content analysis revealed themes repeated across responses, including a collection of 23 emergent concepts and 6 emergent categories. a functional analysis revealed further insight into these themes. Results from both analyses may be used as a framework for future exploration of this topic. Implications are discussed and future research directions are indicated. Among possibilities for future research are investigations into collaborative search and ubiquitous image search.

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We have examined the technique of persistent spectral holeburning as a method for analog optical data storage. Two types of materials were examined from a theoretical standpoint, ones which exhibit photochemical holeburning (PHB) and ones which exhibit nonphotochemical holeburning (NPHB). We have presented the conditions under which a PHB material shows a linear relation between hole depth and exposure intensity or exposure time. Also we show that a NPHB material has no such condition. We conclude that a PHB material may be useful for analog optical data storage, while a NPHB material would not. Experiments were conducted with a NPHB material, R' color centers in LiF, to support the NPHB analysis.

This investigation identifies end-user cognitive heuristics that facilitate judgment and evaluation during information retrieval (IR) system interactions. The study extends previous research surrounding relevance as a key construct for representing the value end-users ascribe to items retrieved from IR systems and the perceived effectiveness of such systems. The Lens Model of user cognition serves as the foundation for design and interpretation of the study; earlier research in problem solving, decision making, and attitude formation also contribute to the model and analysis. A self reporting instrument collected evaluative responses from 32 end-users related to 1432 retrieved items in relation to five characteristics of each item: topical, pertinence, utility, systematic, and motivational levels of relevance. The nominal nature of the data collected led to non-parametric statistical analyses that indicated that end-user evaluation of retrieved items to resolve an information problem at hand is most likely a multi-stage process. That process appears to be a cognitive progression from topic to meaning (pertinence) to functionality (use). Each step in end-user evaluative processing engages a cognitive hierarchy of heuristics that includes consideration (of appropriate cues), differentiation (the positive or negative aspects of those cues considered), and aggregation (the combination of differentiated cue aspects needed to render an evaluative label of the item in relation to the information problem at hand). While individuals may differ in their judgments and evaluations of retrieved items, they appear to make those decisions by using consistent heuristic approaches.

We build a joint multimodal model of text and images for automatically assigning illustrative images to journalistic articles. We approach the task as an unsupervised representation learning problem of finding a common representation that abstracts from individual modalities, inspired by multimodal Deep Boltzmann Machine of Srivastava and Salakhutdinov. We use state-of-the-art image content classification features obtained from the Convolutional Neural Network of Krizhevsky et al. as input "images" and entire documents instead of keywords as input texts. A deep learning and experiment management library Safire has been developed. We have not been able to create a successful retrieval system because of difficulties with training neural networks on the very sparse word observation. However, we have gained substantial understanding of the nature of these difficulties and thus are confident that we will be able to improve in future work.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

The aim of this thesis is to describe passage retrieval (PR), with basis in results from various empirical experiments, and to critically investigate different approaches in PR. The main questions to be answered in the thesis are: (1) What characterizes PR? (2) What approaches have been proposed? (3) How well do the approaches work in experimental information retrieval (IR)? PR is a research topic in information retrieval, which instead of retrieving the fulltext of documents, that can lead to information overload for the user, tries to retrieve the most relevant passages in the documents. This technique was investigated studying a number of central articles in the research field. PR can be divided into three different types of approaches based on the segmentation of the documents. First, you can divide the text considering the semantics and where the topics change. Second, you can divide the text based on the explicit structure of the documents, with help from e.g. a markup language like SGML. And third, you can do a form of PR, where you divide the text in parts containing a fixed number of words. This method is called unmotivated segmentation. The study showed that an unmotivated segmentation resulted in the best retrieval effectiveness even though the results are difficult to compare because of different kinds of evaluation methods and different types of test collections. A combination between full text retrieval and PR also showed improved results. / Uppsatsnivå: D

Li Kar Wing. / "July 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 127-139). / 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. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

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