The primary concern of this piece of work is to reconsider scientific realism debate in the philosophy of science. Accordingly, the overall aim is to come up with the clues of a viable scientific realist attitude in the face of anti-realist interpretations of scientific theories. To accomplish this aim, I make use of two modified versions of scientific realism, that is, &lsquo / epistemic structural realism&rsquo / and &lsquo / entity realism&rsquo / . Epistemic structural realism is a realist position of which proponents claim that the only knowable part of the reality is the structure of it which is expressed by the mathematical equations of our best scientific theories. On the other hand, according to entity realism, the only assured knowledge obtained from scientific theories is the existence of theoretical entities posited by these theories. I argue that a combination of the properly construed versions of these two positions might fulfill the afore-mentioned aim of this thesis.

Information Extraction (IE) is the process of extracting structured and important pieces of information from a set of unstructured text documents in natural language. The final goal of structured information extraction is to populate a database and reach data effectively. Our study focuses on named entity recognition (NER) which is an important subtask of IE. NER is the task that deals with extraction of named entities like person, location, organization names, temporal expressions (date and time) and numerical expressions (money and percent). NER research on Turkish is known to be rare. There are rule-based, learning based and hybrid systems for NER on Turkish texts. Some of the learning approaches used for NER in Turkish are conditional random fields (CRF), rote learning, rule extraction and generalization. In this thesis, we propose a learning based named entity recognizer for Turkish texts which employs a modified version of Bayesian learning as the learning scheme. To the best of our knowledge, this is the first learning based system that uses Bayesian approach for NER in Turkish. Several features (like token length, capitalization, lexical meaning, etc.) are used in the system to see the effects of different features on NER process. We also propose hybrid system where the Bayesian learning-based system is utilized along with a rule-based recognition system. There are two different versions of the hybrid system. Output of rule-based recognizer is utilized in different phases in these versions. We observed increase in F-Measure values for both hybrid versions. When partial scoring is active, hybrid system reached 91.44% F-Measure value / where rule-based system result is 87.43% and learning-based system result is 88.41%. The hybrid system can be improved by utilizing rule-based and learning-based components differently in the future. Hybrid system can also be improved by using different learning approaches and combining them with existing hybrid system or forming the hybrid system with a completely new approach.

No description available.

The goal of development of sound database for marine fishes in Taiwan not only preserves data, but also wants to provide a common ground of data sharing to increase the efficiency for the study of fish behavior, automatic recognition, localization, and tracking. In order to provide the sound quality in terms of signal-to-noise ratio to users, the fish sound recording will be analyzed before uploading. Because most available data were recorded either in the field or in fish tank, the fish sounds were extracted by using two different automatic detection methods. If fish sound recordings were from the field, the Time Endpoint Detection was applied by the processing a 0.5-s time frame with 50 % overlapping. Then the energy of the time frame was obtained by the sum of square of amplitude and the median of the energy plus a standard deviation was established as the threshold to extract fish sounds. If the recording was made in the fish tank, the Frequency Endpoint Detection was applied by 0.5-s time frame with 50 % overlapping. Then each time frame will be transformed into spectrum and the energy ratio of each frequency will be calculated from the spectrum. Finally the information entropy was obtained from the energy ratio and the detection threshold was set on standard deviation above the median of the information entropy. From two different automatic detection methods, the sound quality was presented in the signal-to-noise ratio, which was the average power of signal divided by average power of the background noise. The fish sound database was a 3-Tier system and developed by PHP and MySQL. In order to reduce the storage size and maintain the integrity of data, the Relational Model was applied. Firstly, the recording data were conceptually represented as Entity-Relationship Diagram(ERD). Secondly, the ERD was transformed to relational schemas. Thirdly, the schemas was normalized by first, second, and third forms. To improve the users¡¦ efficiency the sound database provides three interfaces. One was data uploading, another was data searching according to the keyword of creature name, recording area, and recording time, the other was data comparing by recording number.

This thesis focuses on entity and fact extraction from the web. Different knowledge representations and techniques for information extraction are discussed before the design for a knowledge extraction system, called WebKnox, is introduced. The main contribution of this thesis is the trust ranking of extracted facts with a self-supervised learning loop and the extraction system with its composition of known and refined extraction algorithms. The used techniques show an improvement in precision and recall in most of the matters for entity and fact extractions compared to the chosen baseline approaches.

Current developments of hardware devices have allowed the computer vision technologies to analyze complex human activities in real time. High quality computer algorithms for human activity interpretation are required by many emerging applications, such as patient behavior analysis, surveillance, gesture control video games, and other human computer interface systems. Despite great efforts that have been made in the past decades, it is still a challenging task to provide a generic gesture recognition solution that can facilitate the developments of different gesture-based applications. Human vision is able to perceive scenes continuously, recognize objects and grasp motion semantics effortlessly. Neuroscientists and psychologists have tried to understand and explain how exactly the visual system works. Some theories/hypotheses on visual perception such as the visual attention and the Gestalt Laws of perceptual organization (PO) have been established and shed some light on understanding fundamental mechanisms of human visual perception. In this dissertation, inspired by those visual attention models, we attempt to model and integrate important visual perception discoveries into a generic gesture recognition framework, which is the fundamental component of full-tier human activity understanding tasks. Our approach handles challenging tasks by: (1) organizing the complex visual information into a hierarchical structure including low-level feature, object (human body), and 4D spatiotemporal layers; 2) extracting bottom-up shape-based visual salience entities at each layer according to PO grouping laws; 3) building shape-based hierarchical salience maps in favor of high-level tasks for visual feature selection by manipulating attention conditions of the top-down knowledge about gestures and body structures; and 4) modeling gesture representations by a set of perceptual gesture salience entities (PGSEs) that provide qualitative gesture descriptions in 4D space for recognition tasks. Unlike other existing approaches, our gesture representation method encodes both extrinsic and intrinsic properties and reflects the way humans perceive the visual world so as to reduce the semantic gaps. Experimental results show our approach outperforms the others and has great potential in real-time applications. / PhD Thesis

No description available.

<p>This thesis describes and introduces novel methods for knowledge discovery and presents a software system that is able to extract information from biomedical literature, review interesting connections between various biomedical concepts and in so doing, generates new hypotheses. The experimental results obtained by using methods described in this thesis, are compared to currently published results obtained by other methods and a number of case studies are described. This thesis shows how the technology&nbsp / resented can be integrated with the researchers&rsquo / own knowledge, experimentation and observations for optimal progression of scientific research.</p>

<p>In South Africa today those who lead sport are facing increasing scrutiny which has inevitably resulted in questioning governance practices. The transition of many sports from<br /> predominantly volunteer administered organizations anchored in an amateur ethos, to professionally managed entities catering to a more sophisticated market place has created<br /> unique challenges for the governance of sport sponsorship. This evolution gives rise to possible conceptual linkage between sport sponsorship and corporate governance or none<br /> thereof. This study focuses on the ABSA-Premier Soccer League (PSL) sponsorship transaction as a case study to explore this linkage. The research is aimed at finding the nexus<br /> between sport sponsorship and corporate governance principles, namely accountability and transparency in the conclusion of this transaction.</p> <p><br /> The research consists of four phases / literature review which provides a detailed analysis of all primary and secondary material available on this topic. The second phase is a qualitative<br /> case study research methodology which comprises of in-depth interviews with targeted stakeholders. The third part presents the research findings and discussion section, and lastly<br /> the way forward through reflections and recommendations including highlighting potential future research areas.</p> <p><br /> This work is timely when there has been generally a heightened evolution in sport sponsorship and its focus was to explore the nexus and compliance to the founding principles<br /> of the organizations (laws of governance). Factually, good corporate governance aims at ensuring a higher degree of transparency in an organization by encouraging full disclosure of<br /> transactions in the company accounts as well as accountability. Academics have been exploring the notion of sports sponsorship and corporate governance with much of the<br /> debates grounded on understanding its commercial value.</p> <p><br /> The focus for this study is on the conceptual nexus or non-thereof, between sponsorship and corporate governance. Information was collected through in-depth and experience interviews,<br /> documented reviews and analysis (such as the review and analysis of the constitution of the South Africa football Association, PSL, financial/banking sector policy regulations, South<br /> African Sports Act, corporate governance reports, annual reports, scholarly journals, academic books, conference papers and Parliamentary Monitoring Group documents etc. to<br /> mention but a few.</p>

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal