Includes bibliographical references (p. 62-67).

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Computer Science / Master / Master of Philosophy

Artificial Intelligence Lab, Department of MIS, Univeristy of Arizona / Fear about identity verification reached new heights since the terrorist attacks on Sept. 11, 2001, with national security issues related to detecting identity deception attracting more interest than ever before. Identity deception is an intentional falsification of identity in order to deter investigations. Conventional investigation methods run into difficulty when dealing with criminals who use deceptive or fraudulent identities, as the FBI discovered when trying to determine the true identities of 19 hijackers involved in the attacks. Besides its use in post-event investigation, the ability to validate identity can also be used as a tool to prevent future tragedies. Here, we focus on uncovering patterns of criminal identity deception based on actual criminal records and suggest an algorithmic approach to revealing deceptive identities.

The goal of this brief article is to explain the bibliomining process. Emphasis is placed on data warehousing and patron privacy issues because they are required before anything else can begin. It is essential to capture our data-based institutional records while still protecting the privacy of users. By using a data warehouse, both goals can be met. Once the data warehouse is in place, the library can use reporting and exploration tools to gain a more thorough knowledge of their user communities and resource utilization.

With the ever increasing popularity of emails, it is very common nowadays that people discuss specific issues, events or tasks among a group of people by emails. Those discussions can be viewed as conversations via emails and are valuable for the user as a personal information repository. For instance, in 10 minutes before a meeting, a user may want to quickly go through a previous discussion via emails that is going to be discussed in the meeting soon. In this case, rather than reading each individual email one by one, it is preferable to read a concise summary of the previous discussion with major information summarized. In this thesis, we study the problem of discovering and summarizing email conversations. We believe that our work can greatly support users with their email folders. However, the characteristics of email conversations, e.g., lack of synchronization, conversational structure and informal writing style, make this task particularly challenging. In this thesis, we tackle this task by considering the following aspects: discovering emails in one conversation, capturing the conversation structure and summarizing the email conversation. We first study how to discover all emails belonging to one conversation. Specifically, we study the hidden email problem, which is important for email summarization and other applications but has not been studied before. We propose a framework to discover and regenerate hidden emails. The empirical evaluation shows that this framework is accurate and scalable to large folders. Second, we build a fragment quotation graph to capture email conversations. The hidden emails belonging to each conversation are also included into the corresponding graph. Based on the quotation graph, we develop a novel email conversation summarizer, ClueWordSummarizer. The comparison with a state-of-the-art email summarizer as well as with a popular multi-document summarizer shows that ClueWordSummarizer obtains a higher accuracy in most cases. Furthermore, to address the characteristics of email conversations, we study several ways to improve the ClueWordSummarizer by considering more lexical features. The experiments show that many of those improvements can significantly increase the accuracy especially the subjective words and phrases.

In the absence of clinical markers for Chronic Fatigue Syndrome (CFS), research to find a biological basis for it is still open. Many data-mining techniques have been widely employed to analyze biomedical data describing different aspects of CFS. However, the inconsistency of the results of these studies reflect the uncertainty in regards to the real basis of this disease. In this thesis, we show that CFS has a biological basis that is detectable in gene expression data better than blood profile and Single Nucleotide Polymorphism (SNP) data. Using random forests, the analysis of gene expression data achieves a prediction accuracy of approximately 89%. We also identify sets of differentially expressed candidate genes that might contribute to CFS. We show that the integration of data spanning multiple levels of the biological scale might reveal further insights into the understanding of CFS. Using integrated data, we achieve a prediction accuracy of approximately 91%. We find that Singular Value Decomposition (SVD) is a useful technique to visualize the performance of random forests. / Thesis (Master, Computing) -- Queen's University, 2007-12-11 12:15:40.096

Thesis (Ph.D.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 78-81)

Thesis (Ph.D.)--George Mason University, 2008. / Vita: p. 134. Thesis director: Carlotta Domeniconi. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed Oct. 14, 2008). Includes bibliographical references (p. 128-133). Also issued in print.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2002. / Thesis advisor(s): Lyn R. Whitaker, Samuel E. Buttrey. Includes bibliographical references (p. 89-91). Also available online.