Moses Ma. / "December, 1982" / Bibliography: leaf [6] / "ONR/N00014-77-C-0532 (NR 041-519)

Victoria Yu-yu Jin, Alexander H. Levis. / "August 1985." / Bibliography: p. 20. / "Office of Naval Research ... Contract N00014-83-K-0185 (NR 247-349)" "Office of Naval Research ... Contract N00014-84-K-0519 (NR 649-003)"

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 95-97).

The diffusion of information and communication technology (ICT) into healthcare has been generally low. This varies with application and setting, but at the point of care clinical level it has been particularly slow. The ICT niche in clinics has been recognised in numerous publications, where it potential benefits are proclaimed. A reoccurring factor identified with criticism of design i information systems research (ISR) is the difficulty in integrating the different human and technical elements. Activity Theory (AT) has been proposed as a means of overcoming this by providing single theoretical framework able to represent relevant factors across all levels of operational abstraction. In this work the (practical) operational functionality of AT is employed (tested) as a basis for design and evaluation of ICT, applied to integration at the clinical level of the National Health Service (NHS) healthcare organisation. Chronic wound healing is a complex activity, with a long history and strong dependence on data, as observed and recorded by clinicians, to treat and heal patients. Wound clinics that are part of the NHS, which is currently actively pursuing a strategy for information technology (IT) integration in healthcare, afford the opportunity to develop specific ICT for wound data and consider issues of diffusion at different levels of the organisation. An Action Research paradigm, using methods borrowed from soft systems methodology (SSM), is applied to the problem of producing ICT to manage wound data in participating NHS clinics. Data are collected via naturalistic (participant) observation, 'in-depth' interviews and focus groups, and are recorded using ethnographic field notes, a research logbook and diary, and digital and analogue voice recordings. Activity models are generated, to interpret the research process and represent the activity at the action level of the clinic, situating the analysis, both within the network of supporting activities, and the influence and constraints of the administrative and the organisational levels. Practical findings highlight the potential of ICT in participating clinics, showing how this can be expanded to the chronic wound healing activity in general, and reporting the implications that this has for the NHS IT strategy at the level of the clinics involved with regards to integration of ICT. Theoretical findings support the suitability of the Action Research strategy and the relevance of AT both as a descriptive framework for information systems development (!SD), and as an evaluative framework for ISR.

The digital music industry has expanded dramatically during the past decades, which results in the generation of enormous amounts of music data. Along with the Internet, the growing volume of quantitative data about users (e.g., users’ behaviors and preferences) can be easily collected nowadays. All these factors have the potential to produce big data in the music industry. By utilizing big data analysis of music related data, music can be better semantically understood (e.g., genres and emotions), and the user’s high-level needs such as automatic recognition and annotation can be satisfied. For example, many commercial music companies such as Pandora, Spotify, and Last.fm have already attempted to use big data and machine learn- ing related techniques to drastically alter music search and discovery. According to musicology and psychology theories, music can reflect our heart and soul, while emotion is the core component of music that expresses the complex and conscious experience. However, there is insufficient research in this field. Consequently, due to the impact of emotion conveyed by music, retrieval and discovery of useful music information at the emotion level from big music data are extremely important. Over the past decades, researchers have made great strides in automated systems for music retrieval and recommendation. Music is a temporal art, involving specific emotion expression. But while it is easy for human beings to recognize emotions expressed by music, it is still a challenge for automated systems to recognize them. Although some significant emotion models (e.g., Hevner’s adjective circle, Arousal- Valence model, Pleasure-Arousal-Dominance model) established upon the discrete emotion theory and dimensional emotion theory have been widely adopted in the fi of emotion research, they still suffer from limitations due to the scalability and specificity in music domain. As a result, the effectiveness and availability of music retrieval and recommendation at the emotion level are still unsatisfactory. This thesis makes contribution at theoretical, technical, and empirical level. First of all, a hybrid musical emotion model named “Resonance-Arousal-Valence (RAV)” is proposed and well constructed at the beginning. It explores the computational and time-varying expressions of musical emotions. Furthermore, dependent on the RAV musical emotion model, a joint emotion space model (JESM) combines musical audio features and emotion tags feature is constructed. Second, corresponding to static musical emotion representation and time-varying musical emotion representation, two methods of music retrieval at the emotion level are designed: (1) a unified framework for music retrieval in joint emotion space; (2) dynamic time warping (DTW) for music retrieval by using time-varying music emotions. Furthermore, automatic music emotion annotation and segmentation are naturally conducted. Third, following the theory of affective computing (e.g., emotion intensity decay, and emotion state transition), an intelligent affective system for music recommendation is designed, where conditional random fi lds (CRF) is applied to predict the listener’s dynamic emotion state based on his or her personal historical music listening list in a session. Finally, the experiment dataset is well created and pro- posed systems are also implemented. Empirical results (recognition, retrieval, and recommendation) regarding accuracy compared to previous techniques are also presented, which demonstrates that the proposed methods enable an advanced degree of effectiveness of emotion-based music retrieval and recommendation. Keywords: Music and emotion, Music information retrieval, Music emotion recognition, Annotation and retrieval, Music recommendation, Affective computing, Time series analysis, Acoustic features, Ranking, Multi-objective optimization

A flourishing World Wide Web dramatically increases the amount of images up­loaded and shared, and exploring them is an interesting and challenging task. While content-based image retrieval, which is based on the low level features extracted from images, has grown relatively mature, human users are more interested in the seman­tic concepts behind or inside the images. Search that is based solely on the low level features would not be able to satisfy users requirements and not e.ective enough. In order to measure the semantic similarity among images and increase the accuracy of Web image retrieval, it is necessary to dig the deep concept and semantic meaning of the image as well as to overcome the semantic gap. By exploiting the context of Web images, knowledge base and ontology-based similarities, through the analysis of user behavior of image similarity evaluation, we established a set of formulas which allows e.cient and accurate semantic similarity measurement of images. When jointly applied with ontology-based query expansion approaches and an adaptive image search engine for deep knowledge indexing, they are able to produce a new level of meaningful automatic image annotation, from which semantic image search may be performed. Besides, the semantic concept can be automatically enriched in MPEG-7 Structured Image Annotation approach. The system is evaluated quantitatively using more than thousands of Web images with associated human tags with user subjective test. Experimental results indicate that this approach is able to deliver highly competent performance, attaining good precision e.ciency. This approach enables an advanced degree of semantic richness to be automatically associated with images and e.cient image concept similarity measurement which could previously only be performed manually. Keywords: Image Index, Image Retrieval, Semantic Similarity, Relevance Feed­back, Knowledge Base, Ontology, Query Expansion, MPEG-7 . . .

M.A. (Information Technology) / The Internet contains large amounts of information that researchers can use, however, finding the required relevant information can be a lengthy exercise. Internet search engines, such as Google, allow users to search the Internet but these search engines only supply lists of information that "could" be relevant thereby forcing us, the users, to manually examine the list to select the relevant information that we require. Instead of requiring users to use Internet search engines to find lists of possibly useful information, it would be advantageous to have a system that would give an academic user a research summary report for the specified research query. This research summary report can be formatted in such a manner that would allow the user to have a list of links to the relevant information obtained, which could be used as an aid in the researcher's research projects. The design of the Mobile Agent Information Processing (MAIP) model and prototype relied heavily on mobile software agents (MSA). These MSAs will move from the creator system (the user's computer) to other known peer computer systems participating in the MAIP system. Whilst the MSAs are on the remote computer systems, relevant information is extracted from any of the text documents that are acknowledged as available by the remote host system. The extracted information will be returned to the creator system by each MSA where it will be processed and used to create the research summary report. The MAIP model is designed to locate, retrieve, and summarise information that is relevant to a researcher's research query, it can, therefore, be said that the MAIP model meets all the stated research objectives. The new and innovative model is deemed to offer an effective and feasible technology solution to the problem of information overload that exists within the electronic environment.

Many important applications in the social, ecological, epidemiological, and biological sciences can be modeled as complex systems in which a node or variable interacts with another via the edges in the network. Community detection has been known to be important in obtaining insights into the network structure characteristics of these complex systems. The existing community detection methods often assume that the pairwise interaction data between nodes are already available, and they simply apply the detection algorithms to the network. However, the predefined network might contain inaccurate structures as a result of indirect effects that stem from the nodes' high-order interactions, which poses challenges for the algorithms upon which they are built. Meanwhile, existing methods to infer the direct interaction relationships suffer from the difficulty in identifying the cut point value that differentiates the direct interactions from the indirect interactions. In this thesis, we consider the overlapping community detection problem with determination and integration of the structural information of direct dependency interactions. We propose a new overlapping community detection model, named direct-dependency-based nonnegative matrix factorization (DNMF), that exploits the Bayesian framework for pairwise ordering to incorporate the structural information of the underlying network. To evaluate the effectiveness and efficiency of the proposed method, we compare it with state-of-the-art methods on benchmark datasets collected from different domains. Our empirical results show that after the incorporation of a direct dependency network, significant improvement is seen in the community detection performance in networks with homophilic effects.

M.Bibl. / Please refer to full text to view abstract

Call number: LD2668 .T4 1979 F37 / Master of Business Administration