No description available.

No description available.

No description available.

No description available.

No description available.

This research projects deals with the question of whether the paintings of artists who have committed suicide is reflective of their mental states both in terms of content and form. It specifically attempts to answer whether the deterioration in mental state from a time of better mental health to the time of their suicide is expressed graphically in the paintings of those artists and whether this can be reliably observed. It was discovered that paintings in the absence of contact with or interpretation by the artists, provided enough information to enable non-expert judges to make reliable global content-related judgements (e.g. destructiveness and hopelessness) as well as form-specific ratings (e.g. lack of detail) that distinguished between paintings created near the time of artists’ suicides and their paintings created at a time of better mental health as well as paintings from artists who were suffering from depression. It was also found that non-expert judges were able to correctly identify paintings that were created just before artists’ suicides as reflecting serious mental health problems. Furthermore, it was discovered that there was a general preference for paintings from depressed artists over the last paintings by artists who have taken their own lives. The implications of these findings for clinical work both in terms of assessment and treatment were discussed. Furthermore, several limitations of this research project were noted and suggestions for future research were provided.

This thesis describes work on using content to improve recommendation systems. Personalised recommendations help potential buyers filter information and identify products that they might be interested in. Current recommender systems are based mainly on collaborative filtering (CF) methods, which suffer from two main problems: (1) the ramp-up problem, where items that do not have a sufficient amount of meta-data associated with them cannot be recommended; and (2) lack of transparency due to the fact that recommendations produced by the system are not clearly explained. In this thesis we tackle both of these problems. We outline a framework for generating more accurate recommendations that are based solely on available textual content or in combination with rating information. In particular, we show how content in the form of social tags can help improve recommendations in the book and movie domains. We address the ramp-up problem and show how in cases where they do not exist, social tags can be automatically predicted from available textual content, such as the full texts of books. We evaluate our methods using two sets of data that differ in product type and size. Finally we show how once products are selected to be recommended, social tags can be used to explain the recommendations. We conduct a web-based study to evaluate different styles of explanations and demonstrate how tag-based explanations outperform a common CF-based explanation and how a textual review-like explanation yields the best results in helping users predict how much they will like the recommended items.

The phenomenal growth of Information Technology requires us to elicit, store and maintain huge volumes of data. Analyzing this data for various purposes is becoming increasingly important. Data mining consists of applying data analysis and discovery algorithms that under acceptable computational efficiency limitations, produce a particular enumeration of patterns over the data. We present two techniques based on using Logic programming tools for data mining. Data mining analyzes data by extracting patterns which describe its structure and discovers co-relations in the form of rules. We distinguish analysis methods as visual and non-visual and present one application of each. We explain that our focus on the field of Logic Programming makes some of the very complex tasks related to Web based data mining and dynamic content generation, simple and easy to implement in a uniform framework.

This dissertation focuses on media coverage and public opinion about United States foreign policy during a time of national crisis. It seeks to better understand the nature of news content by exploring the concept of press independence through the lens of two theories of news media: indexing and echoing. Focusing on the current U.S. military engagement with the Islamic State of Iraq and the Levant (ISIL), the present study tracks media coverage between June 2014 and June 2015 across six distinct print and online news outlets. This content analysis reveals that the press offered limited criticism of policies, particularly early in the intervention. Print and online news media covered U.S. policy in similar fashion, each relying more on nongovernmental sources than on Washington elites. Combat and non-combat policies were more likely to appear together in the same story in print news than in online news and print offered more justifications for policy positions than did online news. This dissertation examined how news media affects public opinion by experimentally manipulating news coverage of U.S. policy toward ISIL. Based on a national sample, the current work utilized a 2 (high/low in-group threat)X 2 (high/low in-group strength) experiment to explore the mediating role of group emotions on support for foreign policies. Guided by intergroup emotions theory, this study found that group anger mediated the relationships between in-group threat and a host of combat and non-combat policies, while group anxiety did not. On the other hand, in-group threat and in-group strength interacted to predict group anxiety, resulting in two moderated-mediation models, which predicted support for negotiating with ISIL and modern racism toward Muslims. This experiment demonstrates that these group emotions operate in divergent ways, and that group emotions on the whole function differently than individual emotions when predicting political attitudes.

The present study is an investigation of certain aspects of the relationship which exists between the development of language of a child and the speech of his mother. An attempt was made to investigate the evolving speech pattern of the child as he is influenced by the speech patterns of his mother. Can one determine the age at which infants begin to develop speech similar to the speech patterns (intonation, phonemic content, distinctive feature content, place and manner of articulation) of their mother's speech?