Digital Commons Network^™

Human Vs Machine: Hyper-Realistic Avatars And Their Efficacy As A Communication Channel, Jill S. Schiefelbein

USF Tampa Graduate Theses and Dissertations

Hyper-realistic avatars (HRAs), a form of synthetic media, are custom-created digital embodiments of a human, created by capturing and combining that person’s video and vocal likeness. This is the first known study of the efficacy of videos delivered by hyper-realistic avatars as a communication channel in comparison to videos delivered by their human counterparts. An experiment testing how information retention, engagement, and trust vary between viewers of videos delivered by a real human, videos delivered by the HRA representing that same human, and videos delivered by the HRA that discloses to viewers that it is a hyper-realistic avatar is presented. …

A Psychometric Analysis Of Natural Language Inference Using Transformer Language Models, Antonio Laverghetta Jr.

USF Tampa Graduate Theses and Dissertations

Large language models (LLMs) are poised to transform both academia and industry. But the excitement around these generative AIs has also been met with concern for the true extent of their capabilities. This dissertation helps to address these questions by examining the capabilities of LLMs using the tools of psychometrics. We focus on analyzing the capabilities of LLMs on the task of natural language inference (NLI), a foundational benchmark often used to evaluate new models. We demonstrate that LLMs can reliably predict the psychometric properties of NLI items were those items administered to humans. Through a series of experiments, we …

Evaluation Of A Prototype Deep Learning-Based Autosegmentation Algorithm On A High Quality Database Of Head And Neck Cancer Radiotherapy Patients, Jihye Koo

USF Tampa Graduate Theses and Dissertations

This dissertation is devoted to the study of deep learning-based autosegmentation in head and neck radiotherapy. Much of the work presented here is motivated by the need to introduce a clinically useful autosegmentation model for head and neck organs at risk, with the aim of reducing inter-observer variation in structure segmentation and enhancing time efficiency of the treatment planning process. This dissertation describes autosegmentation approaches, introduces a prototype deep learning-based autosegmentation algorithm trained with carefully curated local gold data, and presents a series of comprehensive evaluations to verify the feasibility of implementing the prototype model in clinical settings.

One of …

Artificial Intelligence, Basic Skills, And Quantitative Literacy, Gizem Karaali

Numeracy

The introduction in November 2022 of ChatGPT, a freely available language-based artificial intelligence, has led to concerns among some educators about the feasibility and benefits of teaching basic writing and critical thinking skills to students in the context of easily accessed, AI-based cheating mechanisms. As of now, ChatGPT can write pretty convincing student-level prose, but it is still not very good at answering quantitatively rich questions. Therefore, for the time being, the preceding concerns may not be shared by a large portion of the numeracy education community. However, as Google and WolframAlpha are definitely capable of answering standard and some …