Digital Commons Network^™

Beyond English: Auditing And Mitigating Cross-Lingual Data Contamination In Multimodal Large Language Models, Pavan Dharma Adapa

Theses and Dissertations

This thesis extends data contamination auditing for multimodal large language models to multilingual settings. Using LLaVA 1.5 and a high-fidelity French parallel dataset derived from ScienceQA, the study evaluates how performance changes when identical image-question pairs are translated from English to French. The resultsshow a substantial cross-lingual performance decline and frequent flips from correct English predictions to incorrect French predictions, indicating that benchmark performance can depend heavily on memorized English-specific patterns rather than stable multimodal reasoning. To address this weakness, the thesis introduces an inference-time mitigation strategy based on perturbation ensembling and cross-lingual consistency aggregation. The proposed method reduces instance-level …

A Pedagogically Effective Conceptual Framework For The Resilience Of Unit Test Suites To Refactoring, Daniel Paul Knight

Theses and Dissertations

Unit test suites are intended to support safe and efficient source code refactoring, yet in practice they can hinder rather than help when tests are tightly coupled to implementation details. Such non-resilient tests require frequent co-evolution, consume valuable engineering time, and may disincentivize beneficial code improvements. While concepts such as passive and active resilience, test smells, and technical debt have been studied individually, they have not been integrated into a single actionable framework, nor has their pedagogical value been systematically evaluated. This dissertation introduces a novel conceptual framework for the resilience of unit test suites to refactoring, grounded in resilience …

From Natural Language To Cryptographic Protocol Specifications: Evaluating Llms For Cpsa Generation, Martin Duclos

Theses and Dissertations

Formal verification can prove the security properties of cryptographic protocols, but translating natural language specifications into formal models requires specialized expertise, limiting the broader adoption of formal verification methods. This dissertation investigates whether large language models (LLMs) can lower this barrier by automatically generating Cryptographic Protocol Shapes Analyzer (CPSA) models from natural language protocol specifications. We evaluate three complementary strategies for improving LLM-based CPSA code generation through systematic experimentation across 104 protocols and 15 language models. First, we analyze prompt engineering and find that moderate structured guidance yields the most accurate outputs, while excessive prompt complexity degrades performance. Second, we …

Evaluation And Mitigation Of Bias And Toxicity In Open-Source Large Language Models Using Crows-Pairs And Bold, Sai Harika Gade

Theses and Dissertations

This thesis evaluates bias and harmful language generation in five open-source language models and tests practical mitigation methods that do not require retraining. Two masked models are assessed with a sentence-pair benchmark for stereotype preference, and three generative models are assessed with a prompt-based benchmark for harmful continuations across demographic domains. The study uses a unified experimental workflow to compare model behavior, summarize differences across bias categories, and measure changes after intervention. Results show that the masked models favor stereotypical content above a random baseline, while the generative models usually produce low average toxicity but still show uneven risk across …

Integrating Augmented Reality Visualizations Into Data Science Notebooks Using Microsoft Hololens 2, Derek Willis

Theses and Dissertations

Data-science notebooks support iterative analysis but are limited to two-dimensional (2D) displays. This work presents an approach to extend such environments with rapid augmented reality (AR) visualization while preserving conventional 2D workflows. An opensource R package was developed to convert notebook objects into three-dimensional (3D) models, export them in the Graphics Language Transmission Format (glTF), and transfer directly to a Microsoft HoloLens 2 via a USB connection for viewing in the native 3D Viewer application. The proposed workflow eliminates manual conversion and transfer steps required by earlier methods. A user study employing a post-session questionnaire indicated that participants found the …

Iterative Silver-Label Refinement For Temporal Information Extraction In Biomedical Literature, Chan Lee

UNLV Theses, Dissertations, Professional Papers, and Capstones

Temporal information extraction plays a critical role in the biomedical domain, where the ability to identify events and their temporal relationships is central to interpreting research findings. However, annotated corpora for this task remain scarce and costly to produce and the existing models developed for clinical text do not transfer well. This work bridges that gap through iterative silver-label refinement. A temporal model originally trained on news-domain data is adapted to biomedical text through cycles of automatic labeling, targeted correction, and retraining without the need for comprehensive manual annotation.

Key contributions include a practical iterative refinement methodology demonstrating that the …

An Evaluation Of Artificial Intelligence Chatbots As Alternatives To Specialized Software In Teaching Bayesian Pharmacokinetic Analysis, Reza Mehvar

Pharmacy Faculty Articles and Research

Objective

Methods

A Comparative Performance Analysis Of Task Allocation Methods In Uav-Sdn Networks, Yasameen Ali Al Janabi, Ahmed Mhdi Al-Salih

Journal of Intelligent Informatics, Networking, and Cybersecurity

Task allocation is really important for how unmanned aerial vehicle-software-defined networking networks work and how much energy they use. This is especially true when the network is big or small and when things around it change. We looked at three ways to assign tasks: giving them out randomly giving them out in a manner that maintains load balance, and using something called ``particle swarm optimization'' We tried these methods in two situations: when the unmanned aerial vehicle-software-defined networking network is in open space and when it has to deal with obstacles. We used several unmanned aerial vehicles and tasks: 10 …

Agentic Synthetic Data Generation For Automated Model Development, Frederick Eugene Diehl

Theses and Dissertations

Large Language Model research has made large strides in capabilities from sentiment analysis to writing code. These advancements have been realized thanks to research into specific capabilities such as prompting techniques. Language models today have demonstrated the ability to create content, transform, and classify. These capabilities are not limited to academic exploration but also found in commercial products that are positioning themselves from application augmentation to personal assistants. These commercial products tend to steer towards single actions such as “summarize this article” or “write a function that performs action...” In parallel research has continued to advance towards more advanced constructs …

Pwnagotchi: Deauthentication Attacks, Wpa Handshakes, And Wireless Network Security, Emily Musgrove

Computer Science Honors Papers

Wireless networks are the foundation of modern device communication infrastructure. This project examines the security implications of automated WPA/WPA2 handshake collection using the Pwnagotchi, a portable Wi-Fi network auditing and penetration testing device. The paper provides a technical analysis of WPA and WPA2 authentication mechanisms, including the structure of the 4-way handshake, the creation of cryptographic keys, and the role of deauthentication attacks in forcing reconnections for handshake capture. Additionally, the project explores the vulnerabilities associated with using legacy wireless security protocols such as WEP and TKIP to allow for older technologies that continue to require such encryption methods. This …

Agentic Scientific Machine Learning For Autonomous Model Discovery In Systems Pharmacology, Nazanin Ahmadi, George Karniadakis

Biology and Medicine Through Mathematics Conference

No abstract provided.

Synthergy: Social Deduction And Deception In Llm-Powered Agents, Lauren Campbell, Andrew Forney

Honors Thesis

Synthergy is an online social deduction game designed to enable comparative analysis of how large language model-powered agents engage in social deduction and deception under conditions of asymmetric information. Inspired by social deduction games such as Town of Salem, Throne of Lies, and Mafia, the game consists of two factions, Harmony and Discord, to which agents are secretly assigned. Agents must infer others’ affiliations through dialogue, in-game abilities, and voting behavior. To evaluate agent behavior, we conducted 100 simulated games across six agent types: a random baseline agent (RandomSynth), an LLM-based agent (Synth), a chain-of-thought agent (CoT Synth), a Bayesian …

A Governance-Aware Multi-Agent Framework For Enhancing Fairness & Temporal Accuracy In Disaster Response Systems, Md. Ashfaqur Rahman

Theses and Dissertations

Large Language Models (LLMs) have demonstrated significant potential in disaster-response decision support, however, their deployment in high-stakes humanitarian settings raises critical concerns regarding factual reliability, fairness, temporal validity, and governance compliance. Hallucinated outputs, demographic bias, and outdated recommendations can directly impact vulnerable populations and undermine public trust. This dissertation proposes a governance-aware multi-agent framework designed to enhance fairness and temporal accuracy in disaster-response systems through structured Retrieval-Augmented Generation (RAG), verification-driven orchestration, and adaptive correction mechanisms.The proposed architecture decomposes response generation into specialized agents responsible for real-time retrieval, fact-checking, bias auditing, temporal validation, threshold-based correction, and monitoring. By embedding governance constraints …

Deep Learning Compilers, Raffi Khatchadourian

Open Educational Resources

These lecture slides introduce deep learning compilers for a graduate compiler-construction course (CSc 81010). Building on the classical compiler pipeline, they show how modern machine-learning systems compile tensor programs: static tensor and type analysis (illustrated by a WALA/Ariadne-based refactoring of imperative TensorFlow code to graph mode), MLIR-based end-to-end compilation with IREE, and the PyTorch 2.x stack—TorchDynamo graph capture, AOTAutograd, PrimTorch operator decomposition, and TorchInductor lowering to Triton (GPU) and C++/OpenMP (CPU). The slides are a self-contained HTML (W3C Slidy) deck with editable Pandoc Markdown source. Part of a two-session unit on advanced compiler topics; see also "LLMs in Compiler Construction."

Llms In Compiler Construction, Raffi Khatchadourian

Open Educational Resources

These lecture slides survey the use of large language models (LLMs) in compiler construction for a graduate compiler course (CSc 81010). They situate LLMs across the compiler pipeline and examine representative work: foundation models trained on LLVM IR and assembly (Meta's LLM Compiler), LLM-driven code optimization, binary decompilation (LLM4Decompile), and LLM-assisted automated refactoring—alongside the challenges of applying probabilistic models to tasks that demand correctness. The slides are a self-contained HTML (W3C Slidy) deck with editable Pandoc Markdown source. Part of a two-session unit on advanced compiler topics; see also "Deep Learning Compilers."

Uncovering The Impact Of Youtube's Hidden Algorithm On Its Users, Oscar Perez

COD Library Student Research and Award Symposium

YouTube is a well-known platform that offers users endless hours of news, entertainment, and education. This research seeks to understand how the algorithm functions and uncover the effects of allowing a system to curate content for viewers. The research combines academic sources with fieldwork to understand the impact of YouTube's algorithm.

Faculty Sponsor:  Professor Jacqueline McGrath

Energy-Efficiency Optimization And Comparison For Irs-Assisted Bidirectional Relay And Direct Transmissions, Caixia Cai, Jiayao Zhang, Fuli Zhong, Han Hai, Yayu Yang, Sunil Chinnadurai, Anwer Al-Dulaimi

All Works

Intelligent reflecting surface (IRS) has emerged as a promising technique for achieving high-transmission rate and low-power consumption transmission to meet the requirements of future beyond 5G and 6G communication. In this paper, we optimize and compare the energy-efficiency (EE) of IRS-assisted bidirectional relay transmission (BRT) and bidirectional direct transmission (BDT). In specific, we firstly consider and give the IRS-assisted BRT and BDT models. Then, we give the analyses of signal transmission models and EE for both IRS-assisted BRT and BDT. In addition, to optimize the EE, we give the joint optimization problems for both IRS-assisted BRT and BDT, which incorporate …

Meeting The Moment With Ai-Employer Informed Education, Brent Terwilliger, John Faraca

Publications

As artificial intelligence transforms aviation, aerospace, and autonomy-related sectors, higher education must adapt to meet evolving workforce demands. This session shares emerging findings from a nationwide study led by Embry-Riddle Aeronautical University, focused on employer perceptions of AI adoption, responsible use, and workforce preparedness in domains including uncrewed systems, space systems, robotics, and advanced air mobility. Based on a structured survey and follow-up interviews, the presentation explores how organizations are using AI tools, from generative platforms to enterprise systems, and defining effective and inappropriate use in operational contexts. Participants will gain insight into critical concerns (e.g., data privacy, compliance, security, …

Dynamic Trust Calibration, Bruno Miranda Henrique

Dartmouth College Ph.D Dissertations

Trust calibration between humans and Artificial Intelligence (AI) is crucial for optimal decision-making in collaborative settings. Excessive trust can lead users to accept AI-generated outputs without question, overlooking critical flaws, while insufficient trust may result in disregarding valuable insights from AI systems, hindering performance. Despite its importance, there is currently no definitive and objective method for measuring trust calibration between humans and AI. Current approaches lack standardization and consistent metrics that can be broadly applied across various contexts, and they don’t distinguish between the formation of opinions and subsequent human decisions. This thesis brings a novel and objective method for …

Sage: Contention-Aware Sensitivity Adjustment For Spin Lock Exiting In Vms, Sam Thomas Stowers

Theses - ALL

To address the severe multi-threaded performance degradation caused by lock contention in oversubscribed cloud environments, this thesis examines and improves on the Pause Loop Exit (PLE) mechanism used by hypervisors to deschedule unproductive virtual CPUs (vCPUs). These oversubscribed environments introduce contention which frequently leads to Lock Holder Preemption (LHP), where a hypervisor preempts a vCPU holding a critical spinlock, causing waiting vCPUs to waste cycles spinning. To mitigate LHP, hardware mechanisms trigger PLE events to inform hypervisors of spinning vCPUs, relying on a PLE_gap parameter to differentiate spinlock instances. The PLE_gap dictates spinlock detection sensitivity—a component of the virtualization stack …

Stridevision: Automated Detection Of Running Form Deviations From 2d Pose Estimation And Machine Learning, Paulina Eguibar Ortega

Honors Theses

Running gait analysis plays a critical role in injury prevention and performance optimization, however, existing approaches often rely on specialized laboratory equipment or wearable sensors with limited interpretability. Recent advances in computer vision, particularly 2D human pose estimation, enable markerless motion analysis from standard video. However, progress remains constrained by the lack of publicly available datasets designed for running form analysis.

In this work, we introduce a preliminary dataset and benchmark for stride-level running gait analysis. The dataset consists of 73 treadmill running videos from 15 participants with varying experience levels, annotated with over 4,600 stride-level labels across multiple biomechanical …

Development And Launch Of Abn: An Adventist Freelance Web Application, Abishur Moses-Pakkianathan

MS in Computer Science Project Reports

The Seventh-day Adventist community often relies on personal networks and word-of-mouth for Adventist business services. Many of these businesses meet and provide services to their clients primarily through church connections and community recommendations. As the community and businesses grow, traditional networking methods become increasingly difficult to maintain. General marketplaces lack the trust framework shared by Adventists, and faith-based directories are often static or outdated without booking capabilities. Our solution provides a reliable and modern platform for Adventists to create service listings, receive and manage bookings, and connect with faith-aligned clients.

Nutrilog: Design And Development Of A Full-Stack Web Application For Holistic Health Tracking, Jaromir J. Saloni

Honors Theses

NutriLog is a web-based fitness and nutrition tracking application designed to help users record, organize, and interpret personal health data in one centralized platform. Many existing applications focus primarily on either nutrition tracking or exercise performance, which can make it difficult for users to understand how food intake and physical activity interact. NutriLog addresses this gap by combining food logging, exercise logging, calorie adjustment, nutrition summaries, and historical tracking into a single dashboard-based interface.

The application was developed using React and TypeScript for the frontend and Supabase for authentication, database storage, and user-specific data management. Nutrition data is supported through …

High-Frequency Vr-Native Eye Tracking: From Data Collection To Machine Learning Models, Meherun Nesa Shraboni, Aryabrata Basu

Research and Creative Works Expo

No abstract provided.

A Socio-Computational Framework For Understanding Information Campaigns Through A Collective Action Perspective, Sayantan Bhattacharya

Theses and Dissertations

In a time when social media significantly influences public dialogue, grasping the elements that contribute to the success of information campaigns has become vital for understanding modern social movements and political engagement. This dissertation explores the essential factors that affect the efficacy of information campaigns across digital platforms, addressing a notable gap in existing research that frequently neglects the systematic connection between information spread and outcomes of collective action. Instead of viewing these as distinct phenomena, this study constructs an integrated framework that highlights three crucial dimensions of successful information campaigns: the human factor, which emphasizes the role of influential …

Reflective Telemetry Replay For Performance Analysis In Extended Reality, Atit Kharel

Theses and Dissertations

Extended Reality (XR) environments are increasingly used for training, simulation, and skill development. However, conventional feedback mechanisms such as summary performance scores or first-person video recordings often lack the spatial and contextual detail necessary to support effective reflective learning. As a result, users may observe what occurred during an immersive task without fully understanding how their movement strategies and navigation decisions influenced performance outcomes. This work presents DataEcho, a telemetry-driven replay framework designed to capture, store, and reconstruct structured XR session data for interactive performance review and behavioral analysis. The system records frame-level telemetry from Unity-based XR applications, including object …

Automated Evaluation Of Web Accessibility In Cybersecurity Tools, William Robert Cox

Theses and Dissertations

Screen reader users face significant barriers when using web-based cybersecurity tools, however, the accessibility of these interfaces has received minimal systematic research attention. Existing automated evaluation tools assess Web Content Accessibility Guidelines (WCAG) conformance but do not measure the practical operability of complex and domain-specific interfaces for users of assistive technology. This dissertation presents the Deciphering Interfaces for Your Accessibility (DIYA) framework, an open-source automated auditing framework that evaluates cybersecurity tool web interfaces across eight normalized dimensions: WCAG conformance, Accessible Rich Internet Applications (ARIA) usage, semantic structure, keyboard operability, form accessibility, dynamic content accessibility, interaction cost, and screen reader readiness. …

Spaceforge: Spatial Reconstruction For Signal Simulations, Compton Ross

Honors Theses

Signal simulation environments require accurate three dimensional representations of physical spaces, yet current methods for generating these representations, including Light Detection and Ranging (LiDAR) scanning, manual 3D modeling, and commercial photogrammetry, are both costly and time intensive. SpaceForge addresses this gap with a prompt guided pipeline that takes an ordinary indoor photograph and a configurable set of simulation relevant object categories as input and produces a voxelized 3D scene compatible with downstream signal simulation workflows. The pipeline proceeds through five major stages: open set object detection and segmentation, object level preprocessing, single image 3D mesh reconstruction, heuristic pose estimation and …

Toward Vehicle-Agnostic Driving Signatures For Cognitive Impairment Prediction From Naturalistic Driving Data, Aadarsha Gopala Reddy

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis studies whether naturalistic driving data can help predict binary Clinical Dementia Rating (CDR) status while accounting for differences across vehicles. The final analytic dataset comprised 26,968 participant-weeks from 304 participants. Weekly driving features were derived from real-world telematics data and combined with four demographic covariates. Primary model comparisons used leave-one-participant-out (LOGO) cross-validation, with one individual held out at a time and pooled participant-level metrics used as the main reporting surface.

The main comparison includes six model families evaluated on the same dataset under a shared LOGO framework. Performance remained modest overall. GRU-DANN had the highest participant-level ROC AUC …

Improving Semantic Precision In Text-To-Image Diffusion Models Via Latent-Space Optimization And Semantically-Parsed Evaluation, Mohammad Rouie Miab

McKelvey School of Engineering Graduate Student Theses & Dissertations

Text-to-image diffusion models can produce visually impressive images from natural-language prompts, but they often fail to satisfy the detailed semantic constraints expressed in compositional prompts. Typical failure modes include omitted objects, merged entities, incorrect quantities, incorrect attribute binding, and leakage of one entity's attributes onto another. This thesis studies the problem of semantic precision in text-to-image generation: how faithfully a generated image satisfies the structured meaning of its prompt.   The thesis makes two linked contributions. First, it presents a training-free inference-time refinement method for diffusion-based image generation. The method operates directly in latent space during denoising and uses noun-phrase-aware cross-attention …