Treasures @ UT Dallas

Welcome to Treasures @ UT Dallas Institutional Repository, established in 2010. Treasures is a resource for our community to showcase, organize, share, and preserve research and scholarship in an Open Access repository.


Recent Submissions

Necessary Evils: the Role of Horror in Modern and Contemporary Literature
(2023-12) Cox, Gavin Lynn; Towner, Theresa M.; Wright, Benjamin; Brewer, Kenneth; Smith, Erin A.; Hatfield, Charles
In my extensive studies of horror, I have found that the genre of horror has typically not been taken seriously in its own right. This can be extended to the occurrence of horror in other types of literature. Horror, if recognized at all, is viewed as a component of the story, and not necessarily as significant or as relevant as other aspects of the book. This dissertation approaches the problem of how literary horror can, genre or otherwise, be recognized as a part of legitimate and influential academic study and why such study is important. To do so, I examine multiple works of both genre horror and literary horror, using established literary theories to analyze and understand these written works. I also examine multiple works not classified as horror yet contain instances of significant horror to show that horror exists past the genre. I utilize literary theories such as the uncanny, the monstrous, Kristeva’s theory of abjection and the Jungian shadow to show the literary merits of these works. To not read horror is to ignore aspects of life that act as a mirror reflecting society and individual fears at any point in time. Such willful evasion can be detrimental. Dismissing horror as merely entertainment avoids the social and cultural deceits it can expose. Horror is an interpretation of what is both desired and feared in our lives. Its omnipresence makes it critical to be understood as a vehicle used to acknowledge and understand our fears, and ultimately determine the best way to handle them.
Modern Iranian Fiction in the United States: Translation, Publication, and Promotion (1979–2022)
(2022-05) Saeidfar, Ghazal; Llamas Rodriguez, Juan; Schulte, Rainer; Hatfield, Charles; Wright, Benjamin; Gooch, John C.
My dissertation investigates the translation of modern Iranian writers in the United States. My particular focus is on the fictional works that have been written after the Iranian revolution in 1979. I examine what writers and what works have been translated and what is the place of these writers in the post-revolutionary literary landscape of Iran. I also examine the reception of this translated literature among the American readership through the analysis of the critical reviews in journals and magazines. In addition, I specifically explore the process of transmission and promotion of these literary works through an overview of the translators and the publishers who were involved. I investigate the criteria and motivations of these translators through a study of their educational and professional backgrounds as well as their knowledge of Persian and English. I also study the type of publishers and their editorial and publicity approaches that have played a significant role in the presentation of this literature in the United States. Additionally, I argue about the critical role of the Iranian-American community as well as academia in presenting and promoting this literature in the US. The findings of this research show that the representation of Iranian literature in the United States is an outcome of political, cultural, and economic factors. Based on the results of this study, I argue that although this literature is not under-represented in the American literary translation market, it has remained somehow invisible due to the stereotypical images of media about Iran, the financial challenges of the writers, translators, and publishers, and also the cultural and linguistic differences. However, the constant process of transmitting this literature has never been stopped thanks to the contributions of the Iranian-American community. This research has succeeded in drawing a clear picture of the challenges for the representation and promotion of the translated literature of a minor language in the American literary market.
Transfer Learning and Uncertainty Quantification in Natural Language Processing for Political Science and Cyber Security
(2023-08) Hu, Yibo; Khan, Latifur; Makris, Yiorgos; Ouyang, Jessica; Brandt, Patrick T.; Du, Xinya
Recent advancements in Natural Language Processing (NLP) driven by pretrained language models have revolutionized various fields reliant on large-scale text-based research through transfer learning. This dissertation presents efficient, reliable computational NLP applications to address real-world challenges, with a focus on political science, cyber security, and uncertainty quantification. The dissertation begins with interdisciplinary research in political science, where advanced NLP models are developed to track and analyze dynamics related to global political conflict. The creation of ConfliBERT, the first domain-specific sociological language model, enables improved performance on 18 downstream tasks, particularly in scenarios with limited data availability. Moreover, by leveraging transfer learning and existing expert knowledge, specific tasks such as political event extraction and classification are further optimized. One approach called Confli-T5 is a text generation model that augments labeled data by in- corporating achievable templates derived from political science knowledge bases. Another technique introduced is the Zero-Shot fine-grained relation classification model for PLOVER ontology (ZSP), which eliminates the need for labeled data by relying solely on an annotation codebook to classify intricate interactions between political actors. These strategies combine the power of transfer learning with domain-specific expertise to reduce the dependence on extensive labeled data, making them valuable tools in the field. In the field of cyber security, text generation techniques are employed for cyber deception, generating multiple fake versions of critical documents to deter malicious intrusion. A context-aware model called Fake Document Infilling (FDI) addresses the limitations of existing approaches by considering contextual awareness. FDI produces highly believable fake documents, protecting critical information and deceiving adversaries effectively. Finally, uncertainty quantification techniques are explored to enhance the reliability of NLP models in such interdisciplinary or cross-domain applications. A novel model, BERT-ENN, employees evidential theory to quantify multidimensional uncertainty in the data and calibrate uncertainty estimation in text classifiers. This approach achieves state-of-the-art out-of-distribution detection performance, thereby improving the reliability of NLP models.