Computer Sciences Commons^™

Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool, Thomas Kimmeth, Avijit Roy, Vivek Sharma

Publications and Research

Propagandistic content increasingly circulates through online news and social media, where readers often encounter it with limited scrutiny, highlighting the need for reliable and fine-grained detection. This paper introduces Propasafe-Hybrid, a sentence-level system that integrates a fine-tuned transformer classifier with LLM-based technique classification to identify, label, and explain specific propaganda strategies. The pipeline generates actionable outputs, including highlighted sentences, technique assignments, and concise rationales, so users can immediately understand why a sentence was flagged and how each label was determined. To control inference cost, Propasafe-Hybrid employs a cost-aware pre-filtering stage that forwards only high-likelihood sentences to LLMs, reducing token usage …

Security Assessment Of A Machine Learning Approach To Generate And Validate Digital Signatures, Juan Ortiz Couder

Doctoral Dissertations and Master's Theses

Cybersecurity has become a global concern as cyber-attacks have become more common, and the cost of the damage caused by them continues to increase. There are several approaches to improve the cyber security of systems such as Digital Signatures, hashing, watermarking, and encryption among others. Digital Signatures are a cryptographic technique used to verify the authenticity and integrity of digital messages or documents. Digital Signatures use a combination of hashing and public-private key encryption to verify the authenticity and integrity of videos, just as they are used for documents and messages. As a result of using a combination of other …

Machine Learning For Handwritten Character Recognition, Hannah Freitag

Honors Capstones

Handwritten character recognition remains a challenging problem in machine learning due to the high variability of handwriting across individuals and the visual similarity between certain character classes. This project explores whether Singular Value Decomposition (SVD)-based dimensionality reduction can serve as an effective preprocessing step for a fully connected neural network trained on the EMNIST Balanced dataset, a 47-class benchmark of handwritten digits and letters. By projecting 784- dimensional pixel inputs onto the top 70 principal components, approximately 90% of the total variance is preserved while reducing input dimensionality by 91%. The resulting SVD-based model achieves approximately 94% test accuracy, outperforming …

Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool (Ila 2026 Presentation), Thomas Kimmeth, Avijit Roy, Vivek Sharma

Publications and Research

This presentation introduces Propasafe-Hybrid, a hybrid system for sentence-level propaganda detection that combines offline transformer-based classification with selective large language model (LLM) explainability. The system employs a two-stage pipeline in which a local BERT-based classifier evaluates all input text and filters non-propagandistic content, while only high-confidence candidates are forwarded to an LLM for rhetorical technique labeling and explanation. This design enables cost-aware, privacy-conscious, and scalable analysis by reducing unnecessary reliance on external models.

Propasafe-Hybrid identifies propagandistic techniques such as loaded language, obfuscation, and appeal to fear, and generates concise natural language rationales that make these techniques interpretable to users. By …

A Survey On Knowledge-Enhanced Healthcare Question Answering Systems, Junnan Su, Pu Han, Jianxiang Wei

Journal of Scientific Information Research

[Purpose/significance] This paper aims to review the research progress and applications of knowledge enhancement techniques in healthcare question answering systems, in response to the limitations of traditional systems in knowledge representation and reasoning, as well as challenges faced by current large language model-based systems, such as insufficient domain knowledge, privacy concerns, and hallucination. The review provides a systematic reference for improving the precision and knowledge reliability of such systems. [Process/method] Focusing on knowledge enhancement strategies, this paper firstly outlines their fundamental concepts and overall framework. The strategies are then categorized into explicit and implicit types, with an analysis of their …

Research On Text Translation Model Based On Large Language Model And Knowledge Enhancement Framework, Chuanming Yu, Haoxuan Li

Journal of Scientific Information Research

[Purpose/significance] This paper aims to improve the translation quality of large language models and effectively alleviate the translation illusion problem, thereby enhancing cross-linguistic information retrieval capabilities. [Method/process] A translation generation method based on a knowledge enhancement framework is proposed. This framework optimizes the translation process from multiple dimensions, such as style, focus, and cultural adaptability, by combining external knowledge provided by the translation context building module and the knowledge base building and retrieval module, and then utilizing the guidance of the text attention module. [Result/conclusion] Experimental results show that the proposed method effectively enhances model performance. Specifically, on the WikiLingua, …

Augmented Reality In Fashion Retail: A Walmart Unlimited Study, Chloe A. Mcpherson

Apparel Merchandising and Product Development Undergraduate Honors Theses

As technology continues to evolve, augmented reality (AR) has become increasingly common within the retail and fashion industries. This study explored Gen Z consumers’ perceptions of immersive AR shopping experiences through Walmart Unlimited, an interactive digital shopping platform. The purpose of this research was to better understand how younger consumers respond to AR-enhanced shopping environments and whether these technologies influence attitudes toward convenience, engagement, and sustainability in retail.

A quantitative research design was used for this study. Participants completed the Walmart Unlimited shopping experience and then responded to a Qualtrics survey measuring areas such as immersion, satisfaction, ease of use, …

Sequential Robustness In Adversarial Reinforcement Learning, Roman Lok-Ming Belaire

Dissertations and Theses Collection (Open Access)

My goal is to build autonomous systems that expand the reach of human capability in challenging domains such as undersea and space exploration, disaster response, and large-scale infrastructure. In everyday settings, these systems will increasingly appear in safety-critical applications such as autonomous driving, robotics, and industrial manufacturing. A central requirement for these systems is the ability to operate reliably under uncertainty, particularly when the environment behaves in unanticipated ways.

The robust handling of unforeseen environment dynamics is therefore a technical cornerstone of autonomous decision-making; Adversarial attacks provide a useful and principled lens through which to study this problem. Adversarial \textit{robustness}, …

Perspectives On Interpretability For Neural Text Representations, Jia Peng Lim

Dissertations and Theses Collection (Open Access)

In this dissertation, we investigate interpretability in the three elements of learning neural text representations: inputs, passed into models, to produce probabilistic outputs. We emphasise perspectives as we present alternative novel methods to mine and organise meaning in this work.

Models. We initiate our investigation by examining Neural Topic Models (NTM), proposing an alternate angle of interpreting its word-topic distribution, producing better topic representations for interpretation. Our method maps the problem of finding these better interpretations to classical NP-hard graph problems, enabling examination of topic distributions in a composite manner. Next, we apply our previous findings to extract interpretations from …

Pyspqr: A Python Package For Density Estimation Using Deep Learning, Cameron Eddy, Reetam Majumder

Electrical Engineering and Computer Science Undergraduate Honors Theses

Splines are used for representing complex functions. In statistics, splines can be used for distributional shapes that are difficult to model by traditional parametric approaches. Ramsay (1) uses M-Spline bases to estimate continuous distributions. Semi-Parametric Quantile Regression (SPQR), developed by Xu and Reich (2), models conditional distributions where a neural network is used to estimate the basis function weights that depend on covariates. (3) implements a package for SPQR in R. We build on this by implementing a version of SPQR in Python with PyTorch. By using PyTorch, we can use more sophisticated deep learning architectures than those available in …

Towards Multi-Hop Retrieval Using Bipartite Question-Oriented Graphs, Micah Mccollum

Electrical Engineering and Computer Science Undergraduate Honors Theses

Accurately answering multi-hop questions requires full retrieval of multiple, interdependent passages and is a long-standing problem in the area of natural language question answering (QA). While retrieval-augmented generation (RAG) helps address single-hop questions, many retrievers presently focus on semantic similarity in a dense vector space, which is insufficient for handling multi-hop questions specifically. To ameliorate this, we propose constructing a bipartite question- oriented graph composed of hypothetically generated questions connected to passages at index time. The construction of the graph is guided by a large language model (LLM) to prioritize the formation of edges that signal whether a question can …

A Case Study On Using Large Language Models To Drive Convergence-Oriented Literature Discovery In Critical Infrastructure Security, Caden J. Williamson

Electrical Engineering and Computer Science Undergraduate Honors Theses

In the world of cybersecurity, the rapid development of artificial intelligence proposes a constant challenge for researchers to defend critical infrastructure. Attacks on critical infrastructure can be catastrophic, and emerging strategies of cyber-adversaries that implement leading AI models can expose vulnerabilities in critical infrastructure much faster than previous tools. To defend against this emerging threat, the Cybersecurity Research Working Group at the University of Arkansas is aiming to develop a cross-domain and cross-discipline center of excellence. To support this effort, the group is writing a literature review on the topics of AI and critical systems security. Literature review is an …

​Autonomous Agentic Orchestration For Physics-Aware Scientific Discovery: An Integrative Multimodal Framework For 2d Material Characterization​, Sankalp Pandey

Electrical Engineering and Computer Science Undergraduate Honors Theses

The advancement of next-generation semiconductor and quantum technologies relies on the scalability of the fabrication of two-dimensional (2D) van der Waals heterostructures. However, this process is severely bottlenecked by characterization workflows. Optical microscopy provides high-throughput imaging of 2D material flakes, but lacks the explicit physical priors required for the discernment of sub-nanometer thickness variations, such as distinguishing monolayers from bilayers. The use of computer vision models to automate the localization and characterization process of the flakes was proposed. As a part of this effort, we develop QuantumFlake, an open-source framework to streamline the integration and deployment of computer vision models …

A Generative Ai Method For Minority Class Handling In Anomaly Detection With Drift And Explainability Analysis, Kelvin J. Mwiga, Mussa A. Dida, Ahmad Mohsin, Iqbal H. Sarker

Research outputs 2022 to 2026

Artificial Intelligence, particularly machine learning (ML) algorithms, plays a crucial role in detecting cyberattacks, including anomalies and intrusions. However, machine learning models trained on imbalanced cybersecurity datasets often struggle to accurately detect minority data instances and potential threats, thereby weakening overall system security. Despite extensive research, a persistent challenge is the inadequate explanation for model predictions concerning minority data classes. This study aims to address these limitations by developing a generative AI-based approach to manage minority classes in anomaly detection, incorporating concept drift handling and explainability analysis. We introduce an over-sampling technique, CGGReaT, designed to enhance the presence of minority …

Robustness Of Vision Language Models For Pedestrian Detection Tasks, Ostonya K. Thomas

All Theses

Autonomous vehicle (AV) systems typically employ modular systems in which discrete components handle separate tasks such as perception, computation, and path planning. While flexible, this approach allows errors to propagate and compound across the pipeline, and many AI systems offer little transparency into their internal decision-making. Such limitations are particularly concerning in safety-critical domains where failures can carry lethal consequences. Vision Language Models (VLMs) have emerged as a promising alternative because they support end-to-end implementations that bypass compounding error risks and provide natural language explanations of their outputs. Despite these advantages, prior research has demonstrated that both computer vision systems …

Processing Conditions In Narrative Interpretation: A Structural Account Of Cf/Ef Divergence In "The Clerk's Tale”, Griselda Poe

Publications and Research

This paper applies the framework established in Paper 19, "Cognition Is Not Content: A Structural Account of Processing Conditions," to re-describe Chaucer's "The Clerk's Tale" from The Canterbury Tales.

The same narrative produces two incompatible interpretations: a record of domestic violence, and a story of genuine love. This divergence does not arise from differences in ethical judgment or emotional response. It arises from structural differences in the conditions under which information is reconstructed.

This paper does three things. First, it analyzes the characters Walter and Griselda in terms of CF (Core-foregrounded) and EF (Modulation-foregrounded) processing conditions. Second, it describes …

Improving Fairness On Semantic Segmentation Using Large Language Models, Samuel E. Burggraf

Electrical & Computer Engineering Projects for D. Eng. Degree

As machine learning systems are increasingly integrated into critical decision-making processes, ensuring fairness in their design and implementation has become a significant concern. While fairness research has primarily focused on specific protected attributes, less attention has been given to spatial fairness, which can affect individuals at specific locations. If fairness is not addressed, models may systematically underperform in certain regions or across populations which can lead to unequal access to accurate predictions and potentially biased decision-making. Fairness considerations should extend across all machine learning applications to align with the National Institute of Standards and Technology (NIST) guidelines of fair and …

From Mobilenet To Repvit: A Survey Of Edge-Optimized Computer Vision Architectures, Eli A. Bosch

Electrical Engineering and Computer Science Undergraduate Honors Theses

Edge-optimized computer vision is a constantly evolving field where the definition of efficiency has changed repeatedly. This thesis presents a literature survey of four recent Convolutional Neural Network (CNN) families, all analyzed through a consistent framework of accuracy, parameter count, and Multiply-Accumulate Operations (MACs), alongside a survey of five CNN and Vision Transformer (ViT) hybrid models to examine the direction of the field. It was found that accuracy follows a logarithmic curve with respect to parameter count, exhibiting diminishing returns as models scale. This suggests that architectural design contributes more to performance gains than parameter count alone. Theoretical efficiency metrics …

Benchmarking Gaslighting Attacks Against Speech Large Language Models, Jinyang Wu, Bin Zhu, Xiandong Zou, Qiquan Zhang

PhD Student’s Publications Collection

As Speech Large Language Models (Speech LLMs) become increasingly integrated into voice-based applications, ensuring their robustness against manipulative or adversarial input becomes critical. Although prior work has studied adversarial attacks in text-based LLMs and vision-language models, the unique cognitive and perceptual challenges of speech-based interaction remain underexplored. In contrast, speech presents inherent ambiguity, continuity, and perceptual diversity, which make adversarial attacks more difficult to detect. In this paper, we introduce gaslighting attacks, strategically crafted prompts designed to mislead, override, or distort model reasoning as a means to evaluate the vulnerability of Speech LLMs. Specifically, we construct five manipulation strategies: Anger, …

Teacher-Student Diffusion Model For Text-Driven 3d Hand Motion Generation, Ching Lam Cheng, Bin Zhu, Shengfeng He

PhD Student’s Publications Collection

Generating realistic 3D hand motion from natural language is vital for VR, robotics, and human-computer interaction. Existing methods either focus on full-body motion, overlooking detailed hand gestures, or require explicit 3D object meshes, limiting generality. We propose TSHaMo, a model-agnostic teacher-student diffusion framework for text-driven hand motion generation. The student model learns to synthesize motions from text alone, while the teacher leverages auxiliary signals (e.g., MANO parameters) to provide structured guidance during training. A co-training strategy enables the student to benefit from the teacher’s intermediate predictions while remaining text-only at inference. Evaluated using two diffusion backbones on GRAB and H2O, …

Sql Query Optimization - Human Vs. Chatgpt, Hailey Dennis

All Graduate Reports and Creative Projects, Fall 2023 to Present

Large Language Models (LLMs) such as ChatGPT have become ubiquitous tools for working professionals in the software industry. Many engineers are finding new ways to increase productivity by offloading tasks onto LLMs, while others are finding it difficult to trust code produced artificially, even after review. Taking a look at both perspectives, this study aims to compare a human’s ability to optimize SQL queries to that of an LLM and assess the experience using both methods.

Manual query optimization is a tedious task that relies heavily on statistics, heuristics, and good intuition. The SQL developer must search for the optimal …

Teaching Machines To Deter: Exploring Strategic Deterrence In Ai Models, Will Taylor

Theses/Capstones/Creative Projects

This capstone project investigates whether deterrence can emerge as a meaningful strategy within a zero-sum stochastic game using multi-agent reinforcement learning (MARL). After outlining core concepts in game theory and deterrence, the study models a simplified deterrence environment in which two minimax-Q agents repeatedly interact under uncertainty and adversarial incentives. The agents learn from rewards shaped by escalation costs, unilateral vulnerability, and the stabilizing benefits of restraint. Results show that both agents consistently converge toward a conservative, status-quo strategy, overwhelmingly selecting the Maintain action while avoiding both escalation and restraint in most scenarios. This behavior reflects the risk-averse logic of …

The Quality Assurance Machine – A Software Quality Assurance Architecture For Ml-Enabled Systems, Shane E. Downing

All-Inclusive List of Electronic Theses and Dissertations

This dissertation evaluates whether a reusable assurance architecture, the Quality Assurance Machine (QAM), can provide effective product and process quality assurance for ML-enabled software platforms. The QAM is a system-level SQA architecture that turns plans and policies into versioned configurations, executes them in controlled environments, and produces preserved run evidence that supports traceability, auditability, and controlled change. The study follows Design Science Research and evaluates the instantiated artifact using eight assurance requirements (AR1–AR8) synthesized from standards-based guidance, including IEEE 730 and ISO/IEC/IEEE 15026. A four-year longitudinal evaluation combines two methods. First, operational evidence from routine regression and release-validation runs, defect …

Autonomous Deficiency Detection And Vision-Language Summarization For Underground Infrastructure On Embedded Edge Systems, Johny Lopez

LSU New Orleans Theses and Dissertations

Aging underground infrastructure poses significant risks to public health and environmental safety, yet structural condition assessment remains bottlenecked by labor-intensive manual CCTV inspections. This thesis proposes a comprehensive algorithmic framework enabling fully autonomous, real-time deficiency detection, geometric assessment, and natural language reporting on resource- constrained edge computing platforms. Three core components address this challenge. First, RAPID-SCAN, a novel semantic segmentation architecture utilizing a Dynamic Feature Pyramid Network and Channel-Spatial Attention, achieves real-time, pixel-precise defect localization with dramatically reduced parameters. Second, an Edge-Optimized Vision-Language Model pipeline employing LoRA and 4-bit QLoRA quantization compresses Phi-3.5 for local deployment, en- abling autonomous technical …

Improving Online Political Discussion With Automated Bot Intervention, Bethanie E. Hackett

Departmental Honors & Graduate Capstone Projects

The quality of political discussions occurring on online platforms or social media sites has been deemed quite poor. To address this issue, I investigated whether a Large Language Model (LLM) can be used to promote civil and productive political discussions by identifying and responding to unproductive dialogue. I fine-tuned an existing LLM to detect elements of problematic dialogue, namely misinformation, misrepresentation of sources, logical fallacies, bias, and toxic language, and then respond in a corrective yet non-confrontational manner. The resulting model is referred to as FroBot and was evaluated through an experiment in which a human participant was placed in …

Using Ai For Data Loss Prevention, Camden A. Wright

Theses/Capstones/Creative Projects

Data Loss Prevention (DLP) systems play a critical role in protecting modern systems that handle sensitive information from both accidental and malicious exposure. Traditional DLP approaches often rely on static rules and methods that can struggle to adapt to complex and evolving data patterns. This paper presents a hybrid DPL system that integrates machine learning-based message classification, rule based policy enforcement, and context-aware access control to improve both detection accuracy and decision reliability. In addition, the system introduces a second stage access control model that evaluates user context, including role of clearance level and job title to determine whether access …

Evaluating Modern Neural Network Architectures For Suicide Prediction, Kyle Brown

Master's Theses

Suicide remains a leading cause of death among adolescents despite more access to healthcare information than ever before. Medical professionals struggle to make accurate diagnoses and catch warning signs with the overwhelming amount of data available. Machine learning algorithms, including neural networks, have previously been employed for this task, yet it remains an understudied domain.

This research aims to evaluate the capabilities of Multi-Layer Perceptron (MLP) and a selection of its successors, ResNet and MLP with a category embedding layer, at the task of predicting suicidal ideation among high-school students. This research finds ResNet to be the most capable at …

Privacy Protection In Machine Learning: Methods For Structured And Unstructured Data, Karuna Bhaila

Graduate Theses and Dissertations

As machine learning models become increasingly integrated into data-driven decision-making, the protection of sensitive information throughout the model lifecycle is a paramount concern. As these models process and memorize sensitive, proprietary, or personal data, they risk leaking information through their outputs or internal states, especially in domains such as healthcare and finance. The protection of data in machine learning has thus been a crucial field of study. Within this paradigm, researchers have studied theoretical and application-oriented mechanisms for realizing privacy protections for various data formats. Nonetheless, privacy in machine learning still has many open problems, especially with the emergence of …

Integrating Ai Into Collection Development Workflows: Prompts, Rubrics, And Responsible Use, Ivan Portillo, David Carson, Margaret Puentes

Library Presentations, Posters, and Audiovisual Materials

AI technologies are advancing at a rapid pace and offer new opportunities for library advancement. This session highlights practical ways AI can support collection development and discusses opportunities to improve library workflows.  Attendees will also learn how AI can strengthen library resource management by optimizing decision making and use of resources.

Learning Outcomes: 

• Attendees will learn about approaches to integrating artificial intelligence into collection development
• Attendees will learn about artificial intelligence tools and their applicability to collections 
• Attendees will learn about the ethical use of artificial intelligence tools 

Calibrating Human Trust In The Age Of Generative Ai: An Examination Of Ethical And Social Challenges, Abigail M. Mondido

Honors Theses

Generative AI (GenAI), a set of AI technologies with the ability to generate original, human-like outputs, is beginning to transform the way that information is distributed, composed, published, obtained, analyzed, and consumed. GenAI has seen massive adoption by internet users, businesses, and organizations in recent years despite the persistence of major ethical concerns and social implications. In particular, existing research has identified multiple critical trust-related issues associated with AI in general, including widespread mistrust and distrust, overreliance on AI, and a lack of trustworthiness of AI. There remains a need for a broader understanding of these issues as they relate …