Computer Sciences Commons^™

A Pipeline For Creating Obfuscated Binary Samples To Train Ai-Powered Detection Models, Luka R.H. Wilmink

Theses and Dissertations

The analysis of binary files is a critical component of antivirus software and is one of the most important tools for incident response teams across the industry. In the field, malware is often obfuscated, a practice in which the compilation process is transformed with different techniques to hinder decompilation and reverse engineering. Artificial Intelligence and Machine Learning techniques can assist, but models need to be trained on well constructed datasets first. This paper outlines a pipeline for creating such a dataset and builds a proof-of-concept machine learning classification model. All associated data and code are supplied in the project GitHub …

Nash Equilibrium Strategies For Multicluster Pursuit–Evasion Game With Disturbances: A Prescribed-Time Convergence Approach, Lei Xue, Xian Yu, Yongbao Wu, Jian Liu, Changyin Sun, D. C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article investigates the problem of prescribed-time Nash equilibrium (NE) seeking for a multicluster pursuit–evasion game (PEG) subject to external disturbances. To mitigate the impact of disturbances and reach the NE within a user-defined prescribed time, a prescribed-time disturbance observer (PTDO) is devised to estimate and compensate for them. Based on this observation, a novel control algorithm is developed, which facilitates collaboration among multiple pursuers to capture multiple evaders within the prescribed time. It is theoretically demonstrated that the designed algorithm ensures prescribed-time convergence to the NE of the multicluster PEG with disturbances. Finally, numerical simulations are conducted to verify …

Reconstructing Lost Voices, Lana Tamim

Williams Honors College, Honors Research Projects

This project uses digital text mining tools (OCR, NLP, sentiment analysis, and topic modeling) to analyze 19th–20th-century newspaper archives, focusing on how marginalized groups (women, immigrants, or labor workers) were historically portrayed. Many historical newspapers were dominated by elite voices, so this project aims to recover silenced or misrepresented perspectives by identifying hidden patterns in language, frequency of coverage, sentiment, and shifts in public perception over time. Using machine learning and visualization tools, the project will create interactive maps and timelines showing how representation evolved across regions.

Pixel-To-World Mapping For Multi-Camera Warehouse Robot Localization, Mariam Faruque Sharif

College of Graduate Studies: Theses & Dissertations

This thesis presents a comprehensive framework for camera calibration and pixel-to-world coordinate mapping for multi-camera robot localization in a structured warehouse environment. The study is conducted in the APRN-ROWS laboratory, where four overhead cameras observe a planar grid of known barcode locations used as the reference coordinate system.

The proposed approach combines geometric modeling and optimization-based techniques to estimate camera parameters. Initially, camera extrinsic parameters, including position and orientation, are derived using physical measurements and geometric relationships. Principal point locations are estimated through a zoom-based alignment method, ensuring accurate correspondence between the optical axis and the world coordinate system. Intrinsic …

Artificial Elections: How Artificial Intelligence Can Affect Elections And Decide Our Future, Ryan D. Granger

Honors Undergraduate Theses

This thesis examines the growing role of artificial intelligence (AI) in democratic elections, highlighting both its transformative potential and its associated risks. Drawing on a qualitative analysis of existing literature, the study explores how AI is increasingly integrated into political campaigns, election administration, and voter engagement. Key benefits include enhanced data analysis, personalized political messaging, and improved efficiency in campaign operations. AI also supports real-time fact-checking and more accurate vote tabulation, which can strengthen transparency and trust in electoral processes. However, the thesis emphasizes that these advantages are accompanied by significant challenges. AI technologies enable the rapid creation and dissemination …

To Print A Remake: An Analysis Of Hollywood Remakes And Their Cultural Value, Connor F. Seaton

Honors Undergraduate Theses

In recent years, audiences and movie critics have expressed concern that Hollywood’s growing reliance on remakes, sequels, franchises, and similar adaptations has led to a broader worry that originality is fading from modern cinema and that the industry is instead focused on using adaptations to maximize profits. Although adaptation is often seen as a commercially driven framework for reproducing existing intellectual property in a new media format, this thesis argues that it should be recognized as an autonomous cultural category with its own artistic, historical, and social significance and merit. By analyzing adaptation scholarship and reviewing its complex historical development, …

Challenges And Applications Of Fine-Grained Temporal Action Understanding: Modeling Temporal Granularity And Data Efficiency, Halil I. Helvaci

Theses and Dissertations--Electrical and Computer Engineering

Fine-grained Temporal Action Segmentation (TAS) has become a cornerstone of video understanding, offering dense frame-level predictions essential for clinical assessment, surgical skill evaluation, and human-computer interaction. While TAS methods have delivered strong results on coarse-grained benchmarks, two fundamental challenges persist: (1) global attention mechanisms dilute boundary information critical for subsecond precision, a phenomenon we term the temporal granularity bottleneck, and (2) dense frame-level annotation remains prohibitively expensive, with most datasets requiring exhaustive labeling of lengthy untrimmed videos. These challenges are particularly pronounced in medical domains, where sub-second primitives define clinical outcomes while expert annotation remains scarce. In this dissertation, we …

Generative Ai Peer Tutoring To Support Peer-Reviewed Source Identification And Evaluation, Rae Mair, Michelle Kelley, Taylar Wenzel, Andrea C. Borowczak, Yuqing Li, Mike Borowczak

Faculty Scholarship and Creative Works

This chapter explores the design and evaluation of a generative artificial intelligence peer tutor prompt to support college students in identifying and evaluating peer-reviewed sources for academic research. Grounded in literature on peer tutoring, Socratic dialogue, and AI-supported learning, the authors describe an iterative prompt engineering process designed to transform large language models (LLMs) into Socratic-style peer tutors capable of scaffolding student reasoning without completing tasks for them. Five guiding criteria for an effective peer tutor shaped development and evaluation: cognitive congruence, step-by-step guidance, avoiding giving answers, adaptability to student level, metacognitive transparency, and following assignment directions. Across multiple human-centered …

Actor-Critic For Continuous Action Chunks: A Reinforcement Learning Framework For Long-Horizon Robotic Manipulation With Sparse Reward, Jiarui Yang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Existing reinforcement learning (RL) methods struggle with long-horizon robotic manipulation tasks, particularly those involving sparse rewards. While action chunking is a promising paradigm for robotic manipulation, using RL to directly learn continuous action chunks in a stable and data-efficient manner remains a critical challenge. This paper introduces AC3 (Actor-Critic for Continuous Chunks), a novel RL framework that learns to generate high-dimensional, continuous action sequences. To make this learning process stable and dataefficient, AC3 incorporates targeted stabilization mechanisms for both the actor and the critic. First, to ensure reliable policy improvement, the actor is trained with an asymmetric update rule, learning …

Interpretable Machine Learning For In-Home Mild Cognitive Impairment Detection, Budhitama Subagdja, Shanthoshigaa D, Ah-Hwee Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

This paper introduces a novel system for in-home cognitive health assessment using ambient sensors and a machine learning technology that can robustly detect mild cognitive impairment (MCI) despite limited available data. The learned model can explain the aspects of individuals’ daily lives led to the prediction, while reliably predicting MCI, providing more insights to healthcare workers for further clinical interventions. We developed the robust transparent machine learning model, based on fusion adaptive resonance theory (Fusion ART) neural network to learn individuals’ daily patterns of activity from continuous sensor data in terms of a suite of digital biomarkers reflecting four key …

Cross-Modal Proxy Evolving For Ood Detection With Vision-Language Models, Hao Tang, Yu Liu, Shuanglin Yan, Fei Shen, Shengfeng He, Jing Qin

Research Collection School Of Computing and Information Systems

Reliable zero-shot detection of out-of-distribution (OOD) inputs is critical for deploying vision-language models in open-world settings. However, the lack of labeled negatives in zero-shot OOD detection necessitates proxy signals that remain effective under distribution shift. Existing negative-label methods rely on a fixed set of textual proxies, which (i) sparsely sample the semantic space beyond in-distribution (ID) classes and (ii) remain static while only visual features drift, leading to cross-modal misalignment and unstable predictions. In this paper, we propose CoEvo, a training- and annotation-free test-time framework that performs bidirectional, sample-conditioned adaptation of both textual and visual proxies. Specifically, CoEvo introduces a …

Constrained Reinforcement Learning: From Single-Agent Safety To Multi-Agent Coordination, Hao Jiang

Dissertations and Theses Collection (Open Access)

Real-world decision-making systems such as autonomous driving and largescale ride-pooling must operate under strict safety and resource constraints. Traditional Reinforcement Learning (RL) methods, while powerful in simulation, often fail to guarantee such constraints, limiting their real-world deployment. The fundamental challenge lies in integrating constraint satisfaction with long-term reward optimization, especially when outcomes are stochastic and interdependent across multiple agents.

This dissertation advances the field of Constrained Reinforcement Learning (CRL) from both single-agent safety and multi-agent coordination perspectives. In the single-agent setting, we introduce a Reward Penalty framework that augments the state space with cumulative cost and penalizes only trajectories that …

The Devil Is In The Det[Ai]Ls: Ai Agents, Ghost Students, And The Crisis Of Verified Presence In An Agentic Ai World, Aras Bozkurt, Helen Crompton, Caroline Fell Kurban

STEMPS Faculty Publications

The transition from reactive Generative Artificial Intelligence (GenAI) to agentic AI systems marks a categorical shift in digital education, moving beyond simple content generation to goal-oriented, autonomous execution. This paper explores the emergence of the “ghost student”: a digital surrogate created by the coupling of Large Language Models (the “mind”) and agentic AI browsers (the “body”). These entities are capable of navigating Learning Management Systems (LMS), engaging with content, and completing assessments with human-like mimicry, often rendering the actual learner’s presence optional. We argue that this phenomenon creates a verification gap that traditional proctoring and detection tools are structurally unable …

Instructional Designers' Reflections On Generative Ai Use For Scenario-Based And Performance-Centered Learning: A Collective Autoethnography, Nour El Houda Maache, Dan V. Dao, Jiyoon Jung, Jayanth Nadheri, Nari Kim, Chikezie Ozuzu, Xinyue Ren

STEMPS Faculty Publications

This study examines how instructional designer-instructors (IDIs) use and evaluate generative artificial intelligence (GenAI) when designing scenario-based and performance-centered authentic learning in higher education. Using a collective autoethnography (CAE) approach, the study draws on semi-structured interviews and reflective narratives from five IDIs with varied professional experience. Findings indicate that GenAI enhanced design capacity by accelerating scenario development, translating complex content, and supporting scenario-based and performance-based task construction. At the same time, participants reported limitations related to contextual misalignment, output unreliability, and the cognitive demands of prompt refinement. Across cases, effective integration depended on sustained human oversight, disciplinary judgment, and ethical …

Chatgpt In Secondary English Language Learning: Educators' And Students' Literacy, Perceptions, And Experiences, Victoria Brenes, Sierra Outerbridge, Xinyue Ren

STEMPS Faculty Publications

The increasing use of generative artificial intelligence (GenAI) has shown the potential of transforming teaching and learning practices in various educational settings, such as in English language learning (ELL). As English language learners (ELLs) often experience many challenges and barriers in schools in the United States, it is urgent to leverage the educational affordances of GenAI in fostering the effectiveness of ELL. Given the limited research investigating GenAI adoption, especially ChatGPT literacy within K-12 ELL, this convergent mixed methods research aims to investigate students' and teachers’ perceptions of using ChatGPT and their ChatGPT literacy in secondary ELL contexts. We will …

Redefining Educational Technology: A Critical Collaborative Inquiry, Aras Bozkurt, Helen Crompton, Robert Farrow, Agnes Kukulska-Hulme, Jon Dron, Richard West, Agnieszka (Aga) Palalas, Matth Bower, Junhong Xiao, Ahmed Tlili, Danah Henriksen, Angelica Pazurek, Henk Huijser, Thomas K. F. Chiu, Petar Jandrić, Katy Jordan, John Curry, Royce Kimmons, Mutlu Cukurova, Thomas Reeves, Gwo-Jen Hwang, Peter Shea, Jason Lodge, Martin Weller, Davy Ng, Tutaleni Iita Asino

STEMPS Faculty Publications

Educational technologists have not settled on a fixed definition of the field and likely never will. However, attempting to define the field helps to understand the epistemological meanings that shape what the field sees, values, and considers worth pursuing. Through a critical historical review spanning over a century, alongside theoretical engagement with the concepts of entanglement and distributed agency, this paper identifies three key insufficiencies in current educational technology frameworks. These are the persistence of an instrumental-facilitative paradigm that treats technology as a resource deployed by human agents; the theoretical dissolution of the pedagogy-technology dichotomy that existing definitions have not …

A Comparison Of Conversational Chatbots And The Internet For Consumer Information Search, Wondwesen Tafesse, Yoseph Mamo

STEMPS Faculty Publications

This study compares consumer perceptions of conversational chatbots and the internet for information search. While the internet is a mature platform, conversational chatbots represent an emerging technology, and insight into how consumers view them in relation to the internet for information search is lacking. Drawing on the information source utility perspective, the study builds a comparative model based on four key dimensions: information currency, information customisation, information trustworthiness, and media richness. Additionally, the study investigates consumers’ prior experience with conversational chatbots as a moderating factor. Data was collected from 191 respondents recruited through MTurk. Paired sample t-tests assessed mean differences …

A Large Language Model-Based Academic Advising Assistant For Engineering Technology Students, Murat Kuzlu, Vukica M. Jovanovic, Katherine Smith, Otilia Popescu, Adel El-Shahat, Ryan Cotton, Kayla Marie Seegers, William Austin Henderson

Engineering Technology Faculty Publications

In recent years, Artificial Intelligence (AI)-based solutions, particularly Large Language Models (LLMs), have been applied to a variety of domains, such as energy, finance, transportation, healthcare, and education. Among these domains, education has become increasingly popular due to strong interest among educators and students. This study proposes an academic advising assistant system that uses LLMs to help Engineering Technology (ET) students plan their course load based on their educational history, departmental course offerings, and personal constraints, such as their preferred semester course load. The proposed LLM-based academic advising assistant system maintains a database of students' course histories and upcoming course …

Improving Medical Diagnostics With Vision-Language Models: Convex Hull-Based Uncertainty Analysis, Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick, Michel Audette

Engineering Technology Faculty Publications

In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs’ consistency and uncertainty, particularly in critical applications such as healthcare, which demand a high level of trust and reliability. This paper proposes a novel approach to evaluate uncertainty in VLMs’ responses using a convex hull approach on a healthcare application for visual question answering (VQA). For any VLM, temperature refers to a sampling parameter used in probabilistic generation, which controls the randomness of the model’s output. The LLM-CXR model is selected as the medical …

Artificial Intelligence (Ai) In Educating Next Generation Of Engineering Technology Students, Adel El-Shahat, Murat Kuzlu, Vukica M. Jovanovic, Katherine Smith, Abdullah Al Mamun, Otilia Popescu

Engineering Technology Faculty Publications

Artificial Intelligence (AI) is transforming education, particularly for electrical engineering technology (EET) students, by presenting adaptive learning, immediate responses, and unconventional tools. Therefore, this paper proposes investigating modern learning to employ AI in educating future electrical engineering technology students. Firstly, the paper explores how to shape AI knowledge for EET students, supplying them with hands-on skills in AI tasks, clarifying coding, data analysis, and AI ethical usage. Then, as educators, what are the efficient AI tools to utilize in teaching, such as tailored tutoring, automated code assessment, AI-driven design/simulation, lecture dictation, and smart content creation? Key tools, for instance, Google …

A Methodological Framework For Modernizing Engineering Course Content With Large Language Models, Katherine Smith, Dalya Ismael, Otilia Popescu, Murat Kuzlu, Adel El-Shahat, Vukica M. Jovanovic

Engineering Technology Faculty Publications

The rapid evolution of technology presents challenges for engineering educators. While the core engineering methods often remain relevant over time, course materials rapidly become outdated in presentation and pedagogical approach. This paper presents a methodological framework for using large-language models (LLMs) to modernize engineering course content with a case study in an advanced technical analysis course.

The methodology follows a phased approach that is designed to be repeatable and verify the accuracy and completeness of course content. During the first phase, an LLM is used to map outdated text-heavy content to a modern format using a LaTeX template. The second …

Algorithmic Trading In Idiosyncratic-Payoff Markets: A Multi-Agent System For On-Chain Prediction Contracts, Saif Aldeen A.K. Agha

CMC Senior Theses

This thesis documents the design, deployment, and forward-test evaluation of an evolutionary multi-agent algorithmic trading system on Polymarket, the largest decentralized prediction market. The system pairs a locally-hosted 72-billion-parameter language model with a gradient-boosted statistical filter and an evolutionary selection mechanism that maintains a population of approximately 500 autonomous trading agents. Each agent generates a probability estimate for an event, compares it to the prevailing market price, and trades the resulting disagreement.

The central empirical exercise estimates a panel regression of trade-level profit on the absolute disagreement between the agent's probability estimate and the market price, controlling for agent identity, …

Geovig And Purevig: Geometry-Aware Architectures For Efficient Computer Vision, Omar Ismail

Theses and Dissertations (Comprehensive)

Deploying deep learning models for medical image analysis on mobile devices requires a balance between inference latency, memory footprint, and delineating anatomical boundaries with high accuracy. While Convolutional Neural Networks (CNNs) and mobile Vision Transformers (ViTs) offer efficiency, they often struggle to model the irregular, non-local geometric structures inherent in biological tissues without incurring prohibitive computational costs. In this thesis, we introduce GeoViG (Geometric Vision Graph), an architecture that bridges the gap between efficient grid-based processing and explicit Geometric Deep Learning. GeoViG introduces a novel transition from high-resolution pixel grids to low-resolution dynamic graphs via a SpreadEdgePool operator, a geometry-aware …

Ocular Surface Disease Following Lasik And Cataract Surgery: A Review Of Their Interrelated Complications, Matthew D. Spangler, Nila Kirupaharan, John D. Sheppard

Department of Ophthalmology Faculty Publications

Background: Ocular surface disease is a multifactorial condition that is very commonly caused by dry eye disease (DED). Ophthalmic procedures intended to improve visual outcomes, laser-assisted in situ keratomileusis (LASIK) and cataract surgery, can paradoxically cause or exacerbate underlying ocular surface disease. This results in worsening vision and quality of life.

Areas covered: This review examines the pathophysiological mechanisms contributing to ocular surface disease development following LASIK and cataract surgery. Both procedures are associated with the transection of corneal nerves, leading to decreased tear production, surface instability, altered neurotrophin production, and impairment of the blink reflex. Furthermore, these incisional procedures …

Promptable Segmentation For Adaptive And Data-Efficient Medical Image Analysis, Tyler Ward

University of Kentucky Doctoral Dissertations

Image segmentation is a fundamental task in computer vision. While segmentation models have traditionally been trained in a fully-supervised manner, recent approaches have leveraged large-scale pre-training and prompting mechanisms to great effect. However, the performance of such approaches often degrades when applied to domain-specific tasks like medical image analysis. A major reason for this lies is that these models are trained on large, labeled datasets of natural images, which have drastically different characteristics compared to medical images, limiting the generalizability of the methods when applied to medical data. This dissertation presents several data-efficient, adaptive, and promptable medical image segmentation models. …

Ai-Assisted Surface-Enhanced Raman Spectroscopy For Cardiovascular Diagnostics: From Plasmonic Materials To Clinical Translation, Anju Joshi, Gymama Slaughter

Center for Bioelectronics Publications

Raman spectroscopy (SERS) has emerged as a powerful analytical technique, offering molecular fingerprint specificity and ultrasensitive detection of cardiac biomarkers. Recent advances in plasmonic nanostructures, surface functionalization strategies, and flexible sensing platforms have significantly improved the analytical performance of SERS-based biosensors. In parallel, the integration of artificial intelligence (AI) and machine learning has enabled robust interpretation of complex spectral datasets, facilitating automated biomarker classification and improved diagnostic accuracy in heterogeneous biological environments. Despite these advances, the field remains fragmented, with limited integration between nanomaterial design, biomarker selection, and data-driven analysis, and persistent challenges related to reproducibility, standardization, and clinical validation. …

Image-Derived 3d Hepatic Lobule Modeling Of Acetaminophen-Induced Hepatotoxicity, Rebecca Lauren Strauss

Selected Full-Text Master Theses 2021-

The liver’s highly structured vascular microarchitecture governs blood perfusion, metabolic zonation, and the spatial distribution of xenobiotic toxicity. Current computational models of hepatic drug metabolism often oversimplify this geometry, limiting their ability to capture realistic flow dynamics and cellular injury patterns. This study develops a multiscale computational framework to predict acetaminophen-induced hepatotoxicity using image-derived, three-dimensional hepatic lobule geometries. The model integrates computational fluid dynamics (CFD) with a mechanistic cellular injury module to simulate the interplay between perfusion, metabolism, and hepatocellular viability.

Realistic vascular reconstruction was achieved from histopathology liver slices, and the resulting geometry was meshed and solved using ANSYS …

Deep Learning For Eeg-Based Emotion Recognition With Temporal And Spectral Interpretability, Shruti Rameshbhai Shingala

Selected Full-Text Master Theses 2021-

Electroencephalography (EEG)-based emotion recognition has emerged as a critical component of affective computing and clinical neuroscience. Existing approaches to this problem primarily reduce the multi-dimensional EEG time series to a single averaged feature vector, thereby discarding the temporal structure of the emotional response. The present work addresses three identified gaps in the literature: the absence of temporal interpretability, the uniform use of frequency bands, and the use of single-scale temporal feature extraction. A deep learning architecture, MST-Mamba-Asym, is proposed, comprising four components: Asymmetry Attention, which encodes hemispheric asymmetry by computing signed left–right channel differences, FreqBandAttention, which learns differential weights across …

Large Language Model Communication And Data Transfer Across A Simulated Telephone Line, Jared Reyes

Dissertations and Theses

This thesis presents a proof-of-concept communication system in which two local large language model endpoints communicate across a simulated analog telephone line using legacy USB modems. The project combines modem voice mode, modem data mode, local speech processing, and structured machine messaging into a single staged session. During the voice phase, one endpoint places a call, the other answers, speech generated by a locally hosted LLaMA-family model is synthesized with Piper, transmitted through the modem voice path, captured on the remote side, and transcribed with Faster-Whisper to drive the next response. After the voice exchange, the system transitions to a …

Deep Learning-Based Co-Current Upward Gas-Liquid Two-Phase Flow Regime Identification In An Annular Conduit, Joshua Robert Macomber

Graduate Theses, Dissertations, and Problem Reports (ETD)

Flow regime identification in co-current upward gas-liquid flow through annular conduits remains a significant challenge in petroleum engineering, with major safety and operational implications. It is also important across industries involving the transport of multiphase fluids. Misidentifying flow regimes can introduce major operational risk, yet regime boundaries in annular gas-liquid flow are often visually complex and context dependent.

The objective of this study was to evaluate the utility of convolutional neural network (CNN) classifiers for flow regime identification. The CNN was trained using annular flow image dataset published by Texas A&M University. The dataset consists of approximately 947 RGB images …