For individuals requiring interactive access to online text, information storage and retrieval systems provide a way to retrieve desired documents and/or text passages. The CODER (COmposite Document Expert/effective/extended Retrieval) system is a testbed for determining how useful various artificial intelligence techniques are for increasing the effectiveness of information storage and retrieval systems. The system, designed previously, has three components: an analysis subsystem for analyzing and storing document contents, a central spine for manipulation and storage of world and domain knowledge, and a retrieval subsystem for matching user queries to relevant documents. This thesis discusses the implementation of the retrieval subsystem and portions of the spine and analysis subsystem. It illustrates that logic programming, specifically with the Prolog language, is suitable for development of an intelligent information retrieval system. Furthermore, it shows that system modularity provides a flexible research testbed, allowing many individuals to work on different parts of the system which may later be quickly integrated. The retrieval subsystem has been implemented in a modular fashion so that new approaches to information retrieval can be easily compared to more traditional ones. A powerful knowledge representation language, a comprehensive lexicon and individually tailored experts using standardized blackboard modules for communication and control allowed rapid prototyping, incremental development and ready adaptability to change. The system executes on a DEC VAX ll/785 running ULTRIX™, a variant of 4.2 BSD UNIX. It has been implemented as a set of MU-Prolog and C modules communicating through TCP/IP sockets. / Master of Science

no abstract provided by author / Master of Arts

There is a great need to automatically segment, categorize, and annotate video data, and to develop efficient tools for browsing and searching. We believe that the categorization of videos can be achieved by exploring the concepts and meanings of the videos. This task requires bridging the gap between low-level content and high-level concepts (or semantics). Once a relationship is established between the low-level computable features of the video and its semantics, the user would be able to navigate through videos through the use of concepts and ideas (for example, a user could extract only those scenes in an action film that actually contain fights) rat her than sequentially browsing the whole video. However, this relationship must follow the norms of human perception and abide by the rules that are most often followed by the creators (directors) of these videos. These rules are called film grammar in video production literature. Like any natural language, this grammar has several dialects, but it has been acknowledged to be universal. Therefore, the knowledge of film grammar can be exploited effectively for the understanding of films. To interpret an idea using the grammar, we need to first understand the symbols, as in natural languages, and second, understand the rules of combination of these symbols to represent concepts. In order to develop algorithms that exploit this film grammar, it is necessary to relate the symbols of the grammar to computable video features. In this dissertation, we have identified a set of computable features of videos and have developed methods to estimate them. A computable feature of audio-visual data is defined as any statistic of available data that can be automatically extracted using image/signal processing and computer vision techniques. These features are global in nature and are extracted using whole images, therefore, they do not require any object detection, tracking and classification. These features include video shots, shot length, shot motion content, color distribution, key-lighting, and audio energy. We use these features and exploit the knowledge of ubiquitous film grammar to solve three related problems: segmentation and categorization of talk and game shows; classification of movie genres based on the previews; and segmentation and representation of full-length Hollywood movies and sitcoms. We have developed a method for organizing videos of talk and game shows by automatically separating the program segments from the commercials and then classifying each shot as the host's or guest's shot. In our approach, we rely primarily on information contained in shot transitions and utilize the inherent difference in the scene structure (grammar) of commercials and talk shows. A data structure called a shot connectivity graph is constructed, which links shots over time using temporal proximity and color similarity constraints. Analysis of the shot connectivity graph helps us to separate commercials from program segments. This is done by first detecting stories, and then assigning a weight to each story based on its likelihood of being a commercial or a program segment. We further analyze stories to distinguish shots of the hosts from those of the guests. We have performed extensive experiments on eight full-length talk shows (e.g. Larry King Live, Meet the Press, News Night) and game shows (Who Wants To Be A Millionaire), and have obtained excellent classification with 96% recall and 99% precision. http://www.cs.ucf.edu/~vision/projects/LarryKing/LarryKing.html Secondly, we have developed a novel method for genre classification of films using film previews. In our approach, we classify previews into four broad categories: comedies, action, dramas or horror films. Computable video features are combined in a framework with cinematic principles to provide a mapping to these four high-level semantic classes. We have developed two methods for genre classification; (a) a hierarchical method and (b) an unsupervised classification met hod. In the hierarchical method, we first classify movies into action and non-action categories based on the average shot length and motion content in the previews. Next, non-action movies are sub-classified into comedy, horror or drama categories by examining their lighting key. Finally, action movies are ranked on the basis of number of explosions/gunfire events. In the unsupervised method for classifying movies, a mean shift classifier is used to discover the structure of the mapping between the computable features and each film genre. We have conducted extensive experiments on over a hundred film previews and demonstrated that low-level features can be efficiently utilized for movie classification. We achieved about 87% successful classification. http://www.cs.ucf.edu/-vision/projects/movieClassification/movieClmsification.html Finally, we have addressed the problem of detecting scene boundaries in full-length feature movies. We have developed two novel approaches to automatically find scenes in the videos. Our first approach is a two-pass algorithm. In the first pass, shots are clustered by computing backward shot coherence; a shot color similarity measure that detects potential scene boundaries (PSBs) in the videos. In the second pass we compute scene dynamics for each scene as a function of shot length and the motion content in the potential scenes. In this pass, a scene-merging criterion is used to remove weak PSBs in order to reduce over-segmentation. In our second approach, we cluster shots into scenes by transforming this task into a graph-partitioning problem. This is achieved by constructing a weighted undirected graph called a shot similarity graph (SSG), where each node represents a shot and the edges between the shots are weighted by their similarities (color and motion). The SSG is then split into sub-graphs by applying the normalized cut technique for graph partitioning. The partitions obtained represent individual scenes in the video. We further extend the framework to automatically detect the best representative key frames of identified scenes. With this approach, we are able to obtain a compact representation of huge videos in a small number of key frames. We have performed experiments on five Hollywood films (Terminator II, Top Gun, Gone In 60 Seconds, Golden Eye, and A Beautiful Mind) and one TV sitcom (Seinfeld) that demonstrate the effectiveness of our approach. We achieved about 80% recall and 63% precision in our experiments. http://www.cs.ucf.edu/~vision/projects/sceneSeg/sceneSeg.html

This research is an empirical investigation of human factors on the use of database systems. The problem motivating the study is the difficulty encountered by end-users in retrieving data from a database.

published_or_final_version / Social Work / Master / Master of Social Work

No description available.

M.Bibl. / Although written records have existed for a long time, the management of unpublished documentation or unpublished information has emerged only recently as part of the management of information as a resource to the organisation. The management of unpublished information is therefore the cost effective management of the document from the creation of the record to its disposal, so that the information contained in the document can be retrieved when required (Cook 1991:3) ...

Yuan Yu-mei Shirley. / Title also in Chinese. / Summary in Chinese. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1980. / Bibliography: leaf 194.

This study identified determinants existing within the academic environment of professional education faculty that influence the development and implementation of an information technology system to support their professional endeavors. Such a system would encourage these faculty to integrate technology into their curriculum and, consequently, produce teacher graduates who could employ information technology effectively in teaching. This study used a field survey research design with a mailed questionnaire. Both institutional and personal demographic factors were examined. The population exhibited a high level of homogeneity. This study indicated that ease of use, reliable operation, and capability to integrate technology into their personal work style were the most important concerns of faculty members. These factors reduce the emotional and intellectual costs of using technology.

The main contribution of this dissertation is a system for locating and indexing audio files on the World Wide Web. The idea behind this system is that the use of both web page and audio file analysis techniques can produce more relevant information for locating audio files on the web than is used in full-text search engines. / The most important part of this system is a web crawler that finds materials by following hyperlinks between web pages. The crawler is distributed and operates using multiple computers across a network, storing results to a database. There are two main components: a set of retrievers that retrieve pages and audio files from the web, and a central crawl manager that coordinates the retrievers and handles data storage tasks. / The crawler is designed to locate three types of audio files: AIFF, WAVE, and MPEG-1 (MP3), but other types can be easily added to the system. Once audio files are located, analyses are performed of both the audio files and the associated web pages that link to these files. Information extracted by the crawler can be used to build search indexes for resolving user queries. A set of results demonstrating aspects of the performance of the crawler are presented, as well as some statistics and points of interest regarding the nature of audio files on the web.