Digital Commons Network^™

Zero-Knowledge Proofs For Verifiable Machine Unlearning, Bidur Niroula

Open Access Theses & Dissertations

Machine unlearning aims to remove the influence of deleted data from a trained machine learning model. This is important because privacy regulations such as GDPR, CCPA, and PIPEDA give individuals the right to request the deletion of their personal data. Since unlearning can be expensive, a dishonest server may skip it or return an incorrect model and this motivates verifiable machine unlearning. Existing cryptographic approaches prove that the server executed a specific training or unlearning algorithm on the committed dataset, so the proof is tied to the algorithm's trajectory. Empirical verifiers avoid this cost but are known to be circumventable …

Log Anomaly Detection With Parameter-Efficient Tiny Language Models: From Centralized Fine-Tuning To Privacy-Preserving Federated Learning, Isaiah Thompson Thompson Ocansey

Open Access Theses & Dissertations

System logs are a primary source of information for identifying faults, misconfigurations, and security incidents in computing infrastructure. As these logs grow in volume and complexity, manual inspection becomes impractical, and automated detection methods are needed. This thesis investigates how compact language models can be applied to the task of log anomaly detection under two operational conditions: when log data is centralized and when it is located at different data sites.

In the first part, we propose LogTinyLLM, which applies parameter-efficient fine-tuning through Low-Rank Adaptation and adapter-based techniques to adapt tiny language models for identifying contextual anomalies in log sequences. …

Comparative Case Studies Of How General Chemistry Ii Students Explain Entropically-Driven Phenomena, Raymundo Aragonez

Open Access Theses & Dissertations

Understanding how students conceptualize entropy remains a central challenge in chemistry education. Often referred to as a "driving force" of chemical reactions, the dispersal of energy as a process proceeds is indicative of a process's spontaneity or thermodynamic favorability. Students frequently default to discussing phenomena in deterministic terms, potentially reflecting the language choice and heuristics selected by the instructor such as "disorder". In this study, assessment prompts about entropy from a convenience sample of general chemistry textbooks were characterized as to their potential to elicit three-dimensional learning, and students' responses to assessment prompts from a curriculum aligned with three-dimensional learning …

From Covid-19 To Influenza: Transforming Public Health Surveillance Through Freeze-Drying Innovations, Rui Dong

Open Access Theses & Dissertations

Public health surveillance increasingly relies on wastewater-based epidemiology (WBE) as a non-invasive, community-level early warning system for infectious diseases like COVID-19 and influenza. However, detecting minute viral concentrations in complex wastewater matrices remains a significant analytical challenge. Traditional concentrating methods, such as ultrafiltration, are limited by high performance variability and significant viral signal loss due to necessary pretreatment steps, such as pre-clarification, which inadvertently discard solid-associated viral fractions. To address these limitations, this dissertation introduces and evaluates freeze-drying as a novel, pretreatment-free concentrating method for wastewater viral surveillance. By sublimating water, freeze-drying uniquely retains both liquid and solid-phase viruses within …

Impaired Tunability Of Brain State And Motor Dysfunction In A Mouse Model Of Rett Syndrome, Victoria Kindler Norman

Graduate Theses and Dissertations

Rett syndrome (RTT) is a rare neurological disorder, caused by disrupted function of the MECP2 gene, resulting in impaired cognitive and motor functions. Previous studies suggest that although MECP2 has important functions throughout the body, the etiological origins of RTT-related dysfunction should be sought within the brain. However, it remains a mystery how neural population dynamics are altered in the RTT brain and how these alterations relate to motor dysfunction. Previous studies using an RTT mouse model point to abnormal correlations among firing rates of neurons. Here we hypothesize that such disrupted neural activity correlation could be caused by abnormal …

Spike-Sorting Algorithm For Neuropixel Probes, Luis David Davila

Open Access Theses & Dissertations

Modern extracellular neural probes like Neuropixels, allow for high-density extracellular neural probes to record terabytes of electrophysiological brain data. Spike sorting is the process of isolating individual neurons on this data through various clustering and filtering techniques. Modern spike sorting algorithms are held back by their need for human intervention to separate high-quality from low quality clusters, due to the volume of data that can be collected a completely automated approach is required. To address this requirement, we have developed a framework to preprocess, cluster, grade, and separate high/low quality clustering results using custom models trained on the grades. To …

Development Of A High-Throughput Framework For Studying Choice-Movement Dynamics In Freely Moving Rats Across Motivational And Pharmacological States, Atanu Giri

Open Access Theses & Dissertations

This thesis develops and applies high-throughput behavioral assays and analysis pipelines to study how external contingencies and internal state shape rodent decision-making. First, it introduces RECORD (Reward-Cost in Rodent Decision-making), a modular platform that combines 3D-printed arenas, microcontroller-based control, and closed-loop trial structure to deliver graded rewards and costs in a foraging-like environment. RECORD supports scalable data collection across multiple arenas and a software stack for parsing, databasing, and extracting spatiotemporal behavioral features and psychometric choice functions. Using this framework, the thesis quantifies how animals integrate sucrose reward magnitude with aversive light cost, revealing structured individual differences and sex-dependent movement …

Three Essays On Workplace Ethics And Ai: Conceptualizing, Scale Development, And Validation Of Self-Value Actualization, And Ai-Enabled High-Performance Work Systems, Mansura Nusrat

Open Access Theses & Dissertations

This dissertation advances understanding of workplace ethics and responsible AI integration across three interconnected essays unified by social cognitive theory. My first essay introduces self-value actualization (SVA), a novel construct defined as the dynamic, integrated psychological process through which individuals continually strive to align and fulfill their core moral values through observable professional behavior. Drawing on social cognitive theory and emotional intelligence theory, I conceptualize SVA as a higher-order self-regulatory orientation comprising three mutually reinforcing processes: value congruence, moral reflection, and adaptive morality. I developed and validated a 9-item SVA scale across four studies using expert panels, exploratory factor analysis, …

Understanding Machine Learning Model Behavior Under Fairness And Privacy Constraints, David Anthony Sanchez

Open Access Theses & Dissertations

Machine learning systems deployed in high-stakes domains are increasingly expected to satisfy demands beyond predictive accuracy-including fairness across demographic groups, protection of sensitive information, and explanations that human stakeholders can inspect and trust. This thesis investigates how those demands can be met through learning frameworks that explicitly govern the relationship between data and models, arguing that trustworthiness is a design problem rather than a post hoc correction. The thesis is organized around three studies, each targeting a distinct point of data-facing control. The first develops CondFairGen, a fairness-aware conditional generator for tabular data that improves subgroup equity by dynamically reweighting …

Eocene To Oligocene Continental Arc Migration In Ecuador And The Origin Of Spatiotemporal Trends In Zircon Lu-Hf Radiogenic Isotopes, Muriel M. Sandoval Vazquez

Open Access Theses & Dissertations

The modern continental arc system in Ecuador is well understood but less is known about arc systems in the past, hindering our understanding of the tectonic evolution of the northern Andes. The Macuchi arc, located in the Western Cordillera of Ecuador, was thought to be the main continental arc system during the Eocene, but new zircon U-Pb LA-ICPMS dates from intrusive arc rocks in central Ecuador indicate a more complex history. During the mid-Eocene, the footprint of arc magmatism broadened and expanded into the Inter-Andean Valley and Eastern Cordillera. During the late Eocene to Oligocene, the arc migrated back toward …

Riemannian Manifold-Coupled Joint Sparse Graphical Models For Spatially Structured Precision Matrix Estimation, Abdul Rahman Adam

Open Access Theses & Dissertations

This thesis introduces a novel geometry-aware framework for joint precision matrix estimation in spatially structured data. Unlike existing joint Gaussian graphical model estimators that rely on Euclidean coupling mechanisms such as fused or group graphical lasso, our approach incorporates intrinsic Riemannian geometry through geodesic distance penalties on the symmetric positive definite manifold.

The method measures similarity between precision matrices of adjacent spatial regions using the affine-invariant Riemannian distance, capturing deformations of concentration ellipsoids rather than entrywise differences. This is particularly suited for spatial transcriptomics, where regions may share dependence structures despite differences in scale or composition.

An efficient Alternating Direction …

Environmental Movements In El Paso And Ciudad Juarez: Possibilities And Limitations Of Cross-Border Organization At The U.S.-Mexico Border, Vanessa Maria Almada

Open Access Theses & Dissertations

At the U.S.-Mexico border, the anthropogenic and nationalistic construction of the landscape has accelerated urbanization and unequal development without establishing effective binational institutions for governance over a shared commons. Borderlanders, then, contest unsustainable development with the added complexity of navigating a barrier which creates diverse socioeconomic and political realities that challenge cross-border cooperation. This qualitative research analyzes environmental organizations in El Paso and Ciudad Juarez. Through ethnographic study of mobilization in response to environmental legislation and administrative action in each city, the immediate barriers to cross-border cooperation and limitations of nationalistic institutions in the borderlands can be better understood.

Bayesian Deep Learning For Photovoltaic Power Forecasting: A Probabilistic Framework For Uncertainty Quantification And Grid Reliability Optimization, Pablo Abraham Bustamante

Open Access Theses & Dissertations

The global energy landscape is undergoing a profound transformation, driven by the urgent need to decarbonize power systems, enhance energy security, and meet growing electricity demands. Solar photovoltaic (PV) power has emerged as a critical component of future energy infrastructure due to its abundance, scalability, and cost-effectiveness. However, PV generation is inherently variable and weather-dependent, introducing significant uncertainty into grid operations and complicating the task of balancing supply and demand. Accurate forecasting of PV power generation-particularly on day-ahead and hour-ahead horizons-has become a strategic necessity for grid stability, economic efficiency, and environmental sustainability. PV output is influenced by numerous factors, …

Timing And Synchronization: Software And Hardware Approaches For Precision Maximizing, Christina Alicia Alejandra Carreon Jimenez

Open Access Theses & Dissertations

This thesis explores the design of synchronization mechanisms that allow multiple FPGA boards to operate as if they were a single coherent timing domain. The solutions span three layers: a software PLL for E1 telecommunication links, a PCIe-based timestamp exchange protocol, and a hardware-level multi-FPGA mesh architecture. Across these layers, the work demonstrates that practical, low-cost synchronization can achieve sub-microsecond accuracy without the burden of specialized hardware such as GPSDOs or White Rabbit infrastructure. Experiments show stable alignment within the nanosecond-microsecond range, validating the feasibility of the proposed methods for SDR and telecom applications.

Advancing Apparel Education Through 3d Simulation In Vstitcher: Confidence, Competence, And Career Readiness In Higher Education, Jonah Diniakos Graham

Graduate Theses and Dissertations

The rapid adoption of 3D garment simulation in the apparel industry has created new demands of higher education to better prepare students with both technical proficiency and confidence within digital design platforms to better enable them for many career avenues. This study examines the integration of Browzwear’s VStitcher in an undergraduate apparel production course, addressing the need to assess both competence and self-confidence in digital garment construction while also fostering broader career readiness competencies. Guided by a combination of Self-Determination Theory (SDT) and Situated Expectancy-Value Theory (SEVT), the study emphasizes the role of autonomy, competence, and relatedness in the shaping …

Quantifying The Short-Term Hydrogeologic Responses Of Highway Construction In Hot Springs, Arkansas, Billy Jack Summerford

Graduate Theses and Dissertations

In late 2020, excavation for the Highway 5 Bypass in Hot Springs, Arkansas exposed seepage faces along roadcut walls. Groundwater levels at nearby wells decreased by up to 20.9 ft and have remained ~20 ft below pre-construction values on average. This study quantifies changes in storage in the unconfined aquifer within the Hot Springs thermal recharge zone associated with the seepage faces using a numerical groundwater model calibrated to pre-construction head at well ARb4. Model scenarios are used to evaluate whether construction-induced seepage or drought best explains the decrease from late 2020 to early 2021. The numerical groundwater simulation represented …

The Impact Of Quantum Computing On Ransomware, Byron Denham

Graduate Theses and Dissertations

Ransomware is one of the most pervasive and dangerous threats to cybersecurity today. Attacks involving ransomware have been responsible for the disruption of critical services in many fields including healthcare, education, and government. In the current paradigm, crypto ransomware relies on asymmetric cryptography to perform its operations in a way that ensure the victim’s only chance for file recovery is by paying the attacker’s requested ransom payment. However, advancements in quantum computing threaten the asymmetric cryptography that ransomware relies on. It has been shown that, once developed, a sufficiently powerful quantum computer will have the ability to break asymmetric cryptography …

Collective Dissipative Charging And Energy Extraction In Quantum Batteries, Sagar Pokhrel

Graduate Theses and Dissertations

In this dissertation, we investigate the charging and discharging dynamics of a quantum battery composed of a collectively driven ensemble of $N$ identical two level systems coupled to a structured electromagnetic environment. The system is described using collective angular momentum operators in the Dicke basis, which provides a compact and physically transparent framework for analyzing collective light-matter interactions. Rather than treating dissipation as an unwanted source of loss, we explicitly use it as a mechanism for energy storage and work extraction. Starting from a coherently driven ensemble coupled to common reservoirs, we derive a master equation in Lindblad form under …

Modeling Causal Interactions Across Brain Functional Systems For Population-Specific Disease Analysis, Alissen Moreno

Theses and Dissertations

Functional brain connectivity provides critical insight into neural mechanisms underlying neurodegenerative and affective disorders. Traditional neuroimaging studies rely on undirected or region-specific connectivity measures developed predominantly using racially homogeneous cohorts, limiting fairness and generalizability across diverse populations.

We propose a population-aware framework modeling directed causal interactions across large-scale brain functional subnetworks. Resting-state fMRI data from the HABS-HD cohort were used to construct subject-level causal connectivity matrices via ICA-LiNGAM, aggregated into interpretable system-level hyper-connectomes representing interactions among eleven canonical brain subsystems. These features trained nonlinear models for Alzheimer’s disease stage classification and trait worry prediction.

Results demonstrate that MLP models outperform …

Agentcite: Trustworthy Ai Agents For Information Verification Across Referenced Documents, Eymen Yigit

Master's Theses

This thesis presents AgentCite, a multi-agent framework for automated verification of referenced numerical data in research documents. The framework employs a hybrid design in which LLM-based agents handle document understanding and evidence retrieval, while deterministic components manage structured parsing and value comparison, improving consistency and reproducibility while reducing token consumption and execution time compared to fully agentic approaches.

AgentCite consists of three autonomous agents: a Negotiator, a Main Document Agent, and a Source Documents Agent, coordinated through a fixed tool-call pipeline. The Negotiator orchestrates verification by extracting tabular data from the main document, retrieving evidence from per-source vector stores, and …

Federated Neuromorphic Intelligence: Advancing Robustness, Efficiency, And Continual Adaptation In Edge Environments, Manh V. Nguyen

Dissertations

This dissertation investigates how spiking neural networks (SNNs) can improve federated edge intelligence by advancing three interconnected goals: communication efficiency, adversarial robustness, and continual adaptation. As edge computing deployments expand across Internet of Things (IoT), sensing, and privacy-sensitive applications, conventional federated learning approaches built around artificial neural networks (ANNs) face growing limitations in power consumption, bandwidth demand, and resilience to real-world uncertainty. SNNs offer an alternative computational paradigm based on event-driven, sparse, and temporally structured processing that is naturally suited to constrained edge environments. However, their behavior in practical federated settings remains insufficiently understood.

To address this gap, this dissertation …

X-Raying The Material Surrounding Active Supermassive Black Holes, Isaiah S. Cox

All Dissertations

It is important to understand the nature of the gas and dust immediately surrounding the supermassive black hole (SMBH) in active galactic nuclei (AGN), because this material feeds the accretion disk and couples the activities going on at the AGN scale to the host galaxy. However, there remain large uncertainties on basic properties about this material such as its location and distribution. This is primarily due to the difficulties in resolving spatial information on these scales at extragalactic distances.

AGN emit a tremendous amount of radiation all across the electromagnetic spectrum, including X-rays. The X-rays come from a very small …

Conditioning Hierarchical Diffusion Transformers On Rf Circuit Parameters, Ethan Morton

McKelvey School of Engineering Graduate Student Theses & Dissertations

As modern ML techniques have become increasingly advanced, they have begun to be integrated into wireless RF systems for classification, identification, and spectrum management. Deep Neural Networks (DNNs) enable RF system operators and designers to design more flexible systems with greater robustness to errors and attacks. However, neural networks require significant amounts of properly annotated data to train. Current data labeling methods lack the ability to obtain reliable true labels for circuit properties such as carrier frequency offset (CFO), power amplifier (PA) non-linearity, and in-phase/quadrature (IQ) imbalance. This thesis investigates the efficacy of a novel architecture, RF-Diffusion, for generating high-quality …

Low Methane Emissions From Nutrient-Rich Former River Channels In A Hydrologically Managed Dryland Delta, Siena Stassi, Jude A. Benavides, Chu-Lin Cheng, Marcelo Gomes Da Silva, Nicholas E. Ray, Nathan O. Barros, Rafael M. Almeida

School of Earth, Environmental, & Marine Sciences Faculty Publications

Anthropogenic stresses in dryland regions can alter hydrology, salinization, and biogeochemical processes. How these pressures influence methane (CH4) cycling and emissions from aquatic ecosystems remains poorly understood. Over the course of 1 year, we measured diffusive and ebullitive CH4 emissions from resacas—shallow, hydrologically managed distributary channels prevalent throughout the Lower Rio Grande Valley along the US–Mexico border. Despite warm, nutrient-rich waters, CH4 emissions from resacas were low, and more comparable to rates observed in estuarine systems. CH4 fluxes were positively related to temperature but negatively related to salinity and rainfall. Elevated salinity, driven by evaporation and source water from the …

Analysis Of Volumetric Reconstruction Methods In Archaeology, Cade O'Fallon

All-Inclusive List of Electronic Theses and Dissertations

The use of Structure from Motion (SfM) photogrammetry in archaeological projects is entering a period of transition; a method of producing 3D data that is traditionally embraced as tool for documentation is being explored for its analytic potential. One such way SfM photogrammetry can be used analytically is through the calculation of volumes using photogrammetric data. The tools exist for archaeologists to be able to create and quantify volumetric models; however, the discourse on these methods is still so new there is no consensus on the best method for conducting volumetric work. Different methods of creating and isolating volumetric space …

Market Reactions To Deceptive Language In Fake News: Implications From Language Expectancy Theory And Transfer Learning, Ka Chung Ng, Ping Fan Ke, Ping Fan, Mike So, Tam, Kar Yan

Research Collection School Of Computing and Information Systems

The advent of generative artificial intelligence (AI) has heightened the proliferation of fake news. A key challenge is the limited real-world data to investigate the societal impact of fake news produced by generative AI. In this paper, we examine stock market reactions to financial news articles that exhibit stylometric similarity to human-crafted and AI-crafted fake financial news. Grounded in language expectancy theory, we employ a style-based transfer learning model, pre-trained to recognizing deceptive language employed in various types of fake news intricacies. We then apply this model to a comprehensive dataset of financial news, assigning a “veracity style score” to …

Enhancing Traffic Safety Through Ai-Driven, Privacy-Preserving, And Secure Impaired Driving Detection Systems, Razan Alsulieman

Dissertations

Drunk driving remains a major threat to road safety worldwide, contributing significantly to traffic injuries and fatalities each year. Traditional detection approaches are largely reactive and vehicle-centric, relying on in-vehicle sensors, breathalyzers, or post-incident enforcement. These methods often depend on driver cooperation, intrusive hardware installations, or limited monitoring environments, restricting their scalability and effectiveness in large transportation systems. At the same time, modern cities increasingly deploy roadside cameras, surveillance networks, and drone- based monitoring systems, creating new opportunities for proactive intoxication detection at the infrastructure level. However, leveraging such external monitoring introduces challenges related to secure data collection, reliable AI-based …

Mease: Multi-Agent Episodic Action Sequence Explanation, Phyo Wai Khaing, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning (MARL) achieves remarkable performance in complex coordination tasks, yet interpreting the emergent behaviors of trained agents remains a fundamental challenge. Most current explainability methods focus on individual agent decisions, overlooking the critical interplay of joint strategiesand temporal coordination patterns that define successful multi-agent policies. We present MEASE (Multi-agent Episodic Action Sequence Explanation), a novel explainable MARL (XMARL) framework that explains trained MARL policies as human-interpretable emergent cooperative joint behaviors. MEASE employs a cognition-inspired episodic memory model to learn spatio-temporal multi-agent interaction patterns, coupled with abstraction algorithms that identify significant cooperative agent behaviors. We evaluate MEASE on diverse …

Machine-Learning Landslide Susceptibility And Runout Modeling In The Nolichucky River Gorge After Hurricane Helene, Grace Braver

Electronic Theses and Dissertations

Extreme rainfall from Hurricane Helene (September 2024) triggered widespread landslides across the southern Appalachian region, highlighting the need for rapid landslide susceptibility assessments that capture both landslide initiation and downstream runout. Traditional susceptibility models often focus solely on initiation zones, limiting their ability to identify which slopes will generate destructive landslides or where material will travel. This study addresses that gap by (1) integrating Geographic Information System (GIS)-based machine learning susceptibility modeling using ArcGIS Pro: Maximum Entropy (MaxEnt) and Random Forest-Based and Boosted Classification and Regression (FBBC) and (2) the U.S. Geological Survey (USGS) Grfin (Growth, Flow, and Inundation) runout …

Final Silver Bow Creek Conservation Area Materials Management Plan, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.