Digital Commons Network^™

Understanding Bias And Fairness In Large Language Models: An Empirical Study, Joshua Johnson

Electrical Engineering and Computer Science Undergraduate Honors Theses

This thesis investigates demographic bias in large language models (LLMs) through the use of evaluating outcome disparities when utilized in decision making tasks as well as underlying associations that could contribute to furthering these disparities. Using profiles from the Adult dataset, we analyze how Gemini 2.0 Flash performs in an income prediction task using zero-shot and few-shot prompting methods. Our findings show that models exhibit measurable differences in demographic parity and false positive rates, with the use of few-shot prompting reducing these disparities. Alongside this line of testing, we tested associational bias in Qwen 2.5 using probability based association tests …

Language Learner Literature: A Mixed Methods Study, Esmeralda Mora

Dissertations

This dissertation, Language Learner Literature: A Mixed Methods Study, by Esmeralda Mora, Ed.D., of National Louis University, specializing in Curriculum, Advocacy, and Policy, investigated the impact of shifting a Midwest high school world language department from a traditional, grammar-focused model to a proficiency-based approach centered on Language Learner Literature (LLL). Employing a Modified Explanatory Sequential Mixed Methods Design, the study first analyzed longitudinal AAPPL proficiency scores from 1,134 high school students (2020–2025) using T-tests and Multiple Linear Regressions to establish measurable outcomes (the "what"). This quantitative phase demonstrated significant and consistent gains in expressive skills (Writing and Speaking) post-LLL implementation, …

Evaluating The Effectiveness Of A Fully Immersive Esol Elementary Program: A Mixed-Methods Study, Equitia D. Armour

Dissertations

With the enrollment of students who are English Language Learners (ELL) steadily rising across the United States, this research sought to understand how immersive models support both language acquisition and academic achievement. This study examined the effectiveness of a fully immersive English Speaker of Other Languages (ESOL) program in one elementary school. The purpose was to identify the qualities of successful ESOL programming, and the conditions needed for sustained growth. The primary research question for this study was: What are the qualities of a successful fully immersive ELL elementary program? Using a mixed-methods design, data were drawn from surveys, interviews, …

Machine Learning-Based Detection Of Covert Data Exfiltration Via Electromagnetic Side-Channel Emissions From Computer Memory In Air-Gapped Systems, Oladapo Akintoye Ajike

All Theses

Air-gapped computer systems are physically isolated from unsecured networks. Though isolated, they remain vulnerable to covert data exfiltration via electromagnetic side channels and other covert-channel attacks. This research presents a comprehensive approach to detecting electromagnetic data exfiltration by establishing a controlled laboratory environment using low-cost, readily available hardware components. The study shows a proof-of-concept covert data transmission system that exploits electromagnetic emissions from computer memory access patterns through software-controlled Random Access Memory (RAM) operations.

The research methodology involved developing a C++ transmitter program that modulates CPU and memory-intensive operations to generate detectable electromagnetic signals at 100 MHz frequency, and implementing …

Energy-Efficient Task Offloading Frameworks For Mixed Reality And Extended Reality In Edge Ai Environments, Mahin Khan Mahadi

All Theses

Mixed Reality (MR) and Extended Reality (XR) technologies are increasingly integrated into real-time applications such as remote maintenance, spatial navigation, and immersive learning. However, the computational intensity of deep learning workloads, especially object detection models like YOLO, poses significant challenges for wearable XR devices with limited processing power and battery life. To address these challenges, this thesis presents a comprehensive study of adaptive offloading strategies that optimize task execution between XR devices and edge servers.

We first proposed the Binary Spatial Allocation Framework (BSAF), which introduced a real-time binary decision mechanism for XR systems. The BSAF framework evaluates scene complexity, …

Comparative Evaluation Of Power And Timing Simulation-Based Side Channels For Hardware Trojan Detection, India Shazuliyah Jade Elkhazin

All Theses

Hardware Trojans are covert modifications to integrated circuits that alter function or leak information while avoiding traditional verification. This thesis presented a simulation-based side-channel study of the AES-T1800 benchmark, utilizing power and timing analyses. The methodology aligned value change dump signals with waveform windows to correlate internal switching with external power signatures and compared timing slack distributions between a Trojan Free build and a Trojan Intruded build after implementation in the design tool.

Power analysis revealed a clear and repeatable fingerprint during Trojan activation. In filtered traces, the Trojan Free run rose from about 0.8×10⁻⁷ W to a steady level …

Design And Analysis Of Low-Cost Lora Mesh Network For Reliable And Secured Precision Agriculture, Yamini Swetha Nadella

All Theses

Rural precision agriculture requires reliable, low-cost, and energy-efficient communication systems that can cover large farmland areas. Traditional wireless technologies, such as Wi-Fi, cellular, and satellite communication, are either expensive, consume too much power or are even unreliable in maintaining connectivity in large farmlands, which is a common case in rural precision agriculture. LoRa-based networks support long-range communication with extremely low power consumption, making them an appropriate solution for precision agriculture applications in rural farmland areas. Current LoRaWAN systems employ star topology, which also limits their ability to offer efficient, multi-hop connectivity in rural environments.

This thesis presents the design, implementation, …

Integrating Semi-Supervised Learning And Ensemble Deep Learning For Low-Resource Deep Knowledge Tracing, Uchechukwu Melody Okechukwu

All Theses

As learning platforms scale, two obstacles prevent Deep Knowledge Tracing (DKT) from thriving in practice: the scarcity of labeled interactions and the opacity of predictions. We introduced a semi-supervised, ensemble DKT pipeline that trains diverse architectures, including Knowledge Proficiency Tracing (KPT), Exercise-Correlated KPT (EKPT), and Dynamic Key-Value Memory Networks (DKVMN) on a mixture of limited labeled and abundant unlabeled learner interactions, then aggregates predictions via majority voting to stabilize learning and reduce variance. Evaluated on benchmark datasets such as ASSISTments, the combined approach yields consistent gains in AUC, accuracy, and precision over strong supervised baselines while revealing influential interactions, surfacing …

Integrating Gis And Machine Learning To Uncover Spatial, Temporal, And Contributing Factors To Bicycle Crashes, Precious Kamsiyo Ejikeme

All Theses

The recent drive for a cleaner environment has led to an increase in greener modes of transportation, including the use of bicycles. These emerging trends have led to an increase in bicycle-related crashes. According to the CDC, an estimated 1,150 cyclists were killed in 2023, with 120,000 sustaining non-fatal injuries. Transportation planners and agencies are under pressure to make cycling safer by providing dedicated bike lanes and improving existing infrastructure. To accomplish this, it is very important to understand which factors contribute to bicycle crash severity across different roadways.

Despite recent advancements in modeling bicycle crash severity, most studies rely …

Integrating Gis And Machine Learning To Uncover Spatial, Temporal, And Contributing Factors To Bicycle Crashes, Precious Kamsiyo Ejikeme

All Theses

The recent drive for a cleaner environment has led to an increase in greener modes of transportation, including the use of bicycles. These emerging trends have led to an increase in bicycle-related crashes. According to the CDC, an estimated 1,150 cyclists were killed in 2023, with 120,000 sustaining non-fatal injuries. Transportation planners and agencies are under pressure to make cycling safer by providing dedicated bike lanes and improving existing infrastructure. To accomplish this, it is very important to understand which factors contribute to bicycle crash severity across different roadways.

Despite recent advancements in modeling bicycle crash severity, most studies rely …

The Crumbling Flsa Collective Action: How Bristol-Myers Squibb Shapes The Reach Of Federal Wage Claims, Elijah Phillips

Southern Illinois University Law Journal

The Fair Labor Standards Act (FLSA) was meant to help level the playing field and help workers create a healthy working environment for themselves. However, today, due to the Supreme Court’s decision in Bristol-Myers Squibb v. Superior Court of California, San Francisco County and lower federal courts’ eagerness to expand personal jurisdiction, the FLSA is crumbling. Workers now face great difficulty in combining their claims in collective actions through the FLSA. When workers from many states do combine their claims, the workers often face dismissal from the action because not all claims have sufficient connections with the chosen litigation forum. …

Beyond The Csu Ai Initiative Rollout: Exploring The Continued Uses, Gratifications, And Literacy Of Generative Artificial Intelligence Integration At Cal Poly, San Luis Obispo, Marshall Piros, Kaylie Marrs

Communication Studies

This case study examines the use of ChatGPT on the California Polytechnic State University, San Luis Obispo (hereafter referred to as Cal Poly or Cal Poly, San Luis Obispo) campus as part of the system-wide CSU AI Initiative. This is a follow-up study to previous work in which the researchers examined AI usage and gratifications during the early rollout of the CSU AI Initiative during Spring 2025. Using a qualitative approach, we used semi-structured interviews (N = 24) to gather in-depth perspectives on AI usage, gratifications, concerns, and definitions of AI literacy from students and faculty. Findings indicate that while …

Mozgus, Damian Cerda, Madison Lopez

Computer Science and Software Engineering

The indie game market is flooded with genre experiments, yet few successfully combine fast-paced action with meaningful strategic decision-making. Our project aims to fill this gap by creating a game that fuses top-down action combat with resource-management tycoon mechanics. We found that in many games, the management phases lack mechanical stakes. Our goal was to intertwine these systems so that choices made in one phase meaningfully impact the other.

Integrating Machine Learning With An Fps Aim Trainer For Optimal Sensitivity Finding, Sharan Krishna

Computer Science and Software Engineering

First-person shooter (FPS) games often demand high levels of skill in aiming, which leads players to look for external tools to improve their performance. This is where the concepts of aim training and aim trainers come in, becoming an easily accessible outside source for players to strengthen their performance with custom scenarios outside a set game. While many aim trainers exist, they offer limited insight into player performance metrics or adaptability to varying aiming styles. Furthermore, most existing aim trainers lack a standardized way of correlating aim skill with real-world performance or personalized feedback. This aim trainer addresses these limitations …

Divine Detachment: Beachy V. Assemblies Of God And A New Interpretation Of The Ecclesiastical Abstention Doctrine, Juarez Mcleod Johnson

MC Law Review

In the landmark case of Beachy v. Mississippi District Council for Assemblies of God, the Mississippi Supreme Court redefined its interpretation of the ecclesiastical abstention doctrine—marking a pivotal shift in church-state jurisprudence. Traditionally, this doctrine instructed courts to defer to the highest internal authority of a church when deciding disputes within hierarchical religious organizations. However, the court’s decision in Beachy significantly departed from this principle by designating the local pastor and congregation as the supreme authority over local church affairs, even within a hierarchical structure.

This Casenote explores the profound implications of the Supreme Court’s ruling, focusing on its …

Auer Deference: He Who Writes The Law Must Not Adjudge Its Violation, William G. Kennedy

MC Law Review

This Comment explores Auer deference and its application to the Sentencing Guidelines, the Sentencing Commission, and its commentary. Auer deference, in its simplest form, requires courts to give controlling weight to an agency’s interpretation of its own regulations unless it is plainly erroneous or inconsistent with the regulation. Following the Court’s decision in Kisor—expressly limiting Auer’s application, the circuit courts have disagreed on the applicability of Auer to the Sentencing Commission’s commentary regarding career offender designation. This disagreement begs for an appearance before the Court of Last Resort, but until that day, the courts must rigidly apply Kisor to …

The Mississippi Court Of Appeals 30th Anniversary Commemoration And Panel Discussion, Donna M. Barnes

MC Law Review

No abstract provided.

Content And Consequences: Impact Of Representation In Stem Higher Education Instructional Content On Marginalized Students, Nichole Ventura

Doctorate in Education

This qualitative study examined representation of historically marginalized students in STEM instructional content at the higher education level and its impact on their learning experiences. Despite growing diversity initiatives in STEM enrollment, curricular materials often fail to reflect the identities of underrepresented students. Using critical theory and interpretivist approaches, this research investigated how representation—or its absence—shapes students' sense of belonging, academic identity formation, and persistence. Through semi-structured interviews with undergraduate students from historically marginalized backgrounds, and purposeful sampling, this study captured the lived experiences of students engaging with STEM instructional materials. Interview protocols explored how students perceive their representation in …

Contracted Out Of Justice: The Role Of Arbitration Clauses In Undermining Consumer Rights, Brooke Shifflett

USF St. Petersburg campus Honors Program Theses (Undergraduate)

This thesis examines the growing use of mandatory arbitration clauses in adhesion contracts and argues that their widespread enforcement has undermined consumer and employee access to justice. Through an analysis of contract law, the Federal Arbitration Act (FAA), Supreme Court jurisprudence, empirical research, and ethical theory, the study explores how arbitration clauses have evolved from a voluntary dispute resolution mechanism into a tool frequently used by corporations to limit liability, suppress collective legal action, and avoid public accountability. The thesis evaluates the doctrine of unconscionability as a critical safeguard against unfair contractual provisions, highlighting how procedural and substantive inequalities often …

Dynamic Wellbore-Integrity Framework For Post-Blowout Capping Of Offshore Wells, Abdelhakim Khouissat

Theses and Dissertations

In the aftermath of a blowout, a well undergoes a period of unrestricted fluid discharge (de facto primary recovery) followed by pressure buildup after its shut-in, which can adversely impact wellbore integrity. The quintessential example is Union Oil’s 1969 “A-21” well blowout in California’s Santa Barbara Channel, where seafloor-broaching incidents (oil “boilups”) kept taking place from the sides of the well following several failed well-capping attempts, until reservoir depletion eventually allowed a successful shut-in.Analytical and numerical reservoir depletion models are coupled with near-wellbore geomechanics to quantify yardsticks related to the post-blowout discharge utilized to indicate dangers for underground blowouts, in …

Optimizing Virtual Reality User Experience With Balanced Integration Of Quality Attributes, Ananth Ramaseri

Theses and Dissertations

This dissertation presents a real-time adaptive framework for mitigating cybersickness and enhancing user experience in virtual reality (VR) environments by dynamically adjusting key visual rendering parameters in response to users’ head movements. Conventional VR systems typically rely on fixed or manually configured settings that do not account for individual motion patterns or varying susceptibility to discomfort, leading to suboptimal usability and degraded immersion. In response, this work integrates software architecture tactics with data-driven machine learning models to proactively manage cybersickness while preserving rendering performance.

The proposed system employs a client–server architecture based on the Model-View-Controller (MVC) pattern. We conducted a …

Developing Python-Based Software For Neutrosophic Set Operations, D. Vidhya, E. Subha

Neutrosophic Sets and Systems

No abstract provided.

Ne³-Dynamics: A Neutrosophic Dynamical Model For Measuring Performance In College Innovation And Entrepreneurship Education, Xuguang Dai, Ziwei Zhou, Jiachen Liu, Dun Hu

Neutrosophic Sets and Systems

No abstract provided.

Mapping Of Wetland And Riparian Habitat For The National Wetland Inventory: Wetland 38 Utah East And Co, Ryhan T. Sempler, Emma R. Chantegros, Carver E. Butterfield, Ella S. Hartshorn, Melissa L. Moser, Kay L. Hajek, Faith O. Hardin, Celine A. Knudsen, Adam R. Scheirer, Rob M. Clark

EMMA Publications

This report details the wetland and riparian mapping methodologies implemented for the Wetland 38 Utah East and CO project. The project area consists of 167 1:24,000 US Geological Survey (USGS) quads across Western Colorado, with quads across Larimer, Gunnison, Eagle, La Plata, Pitkin, Garfield, Montezuma, Rio Blanco, Mesa, Saguache, Dolores, Rio Grande, Pueblo, Grand, Moffat, Teller, Hinsdale, Jackson, Montrose, Routt, and Huerfano counties. There are also quads across three Wyoming counties (Sweetwater, Albany, and Uinta) and 8 Utah counties (Duchesne, Daggett, Grand, Carbon, Uintah, Emery, San Juan, and Utah).

Realism, Truth, And Commitment: An Exposition And Critique Of Michael Devitt's Constitutive Account Of Realism, Samuel Elias Hobbs

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Michael Devitt argues in Realism and Truth that positive semantic issues are not constitutive of realism concerning the mostly impersonal external world. This is motivated by his second maxim which prescribes that the metaphysical (ontological) issue of realism is to be (sharply) distinguished from any (positive) semantic issue. Devitt’s argument is that his view of realism, entitled ‘Realism’, doesn’t entail and isn’t entailed by any doctrine of truth.

This dissertation presents a critique of the argument indicated above and, thereby, Devitt’s second maxim. I present Devitt’s account of Realism and his overview of theories of truth. I next present Devitt’s …

Evaluation Of Distractor Model Specifications On Assessment Quality Within The Framework Of Assessment Engineering, Sunhyoung Lee

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Responding to the high demand for new test items, model-based item development has emerged as a promising approach for efficient item creation. Item models within Assessment Engineering (AE) offer a systematic framework that effectively generates a large volume of new items. In the development of multiple-choice questions, well-constructed distractors are essential for ensuring item quality, although creating them requires significant time and resources. A distractor model is a sub-model within an item model, producing correct answers and distractors aligned with all potential items to be generated. Various types of distractor models have different characteristics and offer efficient methods for generating …

Reinforcement Learning Based Security Schemes For Distributed Ai Systems, Ashan Chamath Gunawardena

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Distributed machine learning (DML) is a component of modern intelligent systems, enabling collaborative training across devices such as mobile clients, vehicles, and edge networks. However, the decentralized nature of these systems introduces vulnerabilities, particularly data poisoning attacks that compromise model integrity and degrade performance. Traditional defenses, such as statistical filtering, robust aggregation, and privacy-preserving techniques, often struggle to adapt to overwhelming adversaries or operate under strict privacy and real-time constraints. This dissertation proposes the use of reinforcement learning (RL) and deep reinforcement learning (DRL) based misbehavior detection schemes that dynamically identify poisoning attempts in distributed AI systems, including federated learning, …

Investigating Programming Behaviors To Understand Student Engagement And Experience In Introductory Programming Courses, Marcus Eugene Gubanyi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Introductory programming courses are foundational to developing students’ problem-solving abilities and shaping their persistence in computing pathways. Engagement with programming tasks plays a central role in student learning and experience. Many research measures, including self-reports and code submissions, offer only a limited view of student engagement with programming tasks. This dissertation leverages programming process data, consisting of keystrokes and compilation events, to capture the programming process as it unfolds and to investigate observable programming behaviors. Guided by educational theories, three studies examine how students’ programming behaviors vary across instructional and assessment contexts, how they relate to motivational profiles, and how …

Making Deep Neural Networks Trustworthy: Intelligibility And Safety Through Symbolic Methods, Eleanor Catherine Quint

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The rapid adoption of deep learning has come at the cost of properties long valued in artificial intelligence: intelligibility and safety. This dissertation develops methods that restore these properties by coupling neural networks with symbolic structure.

First, for supervised classification, I propose a differentiable decision tree integrated with a supervised variational autoencoder. The resulting model maintains competitive accuracy and generative performance while exposing clear macro-features in its latent space, improving interpretability.

Second, for reinforcement learning, I extend constrained Markov decision processes by specifying constraints in formal languages. This formal language constrained MDP enables the use of automata for state augmentation, …

Amplifying Mother Leaders’ Voices: A Feminist Inquiry Into Gender Inequity In Higher Education Leadership, Katherine Bard

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Women now comprise more than half of mid- and upper-level administrators in higher education, yet they remain underrepresented in the most senior leadership roles. For mothers in leadership, this underrepresentation is compounded by gendered structures, expectations, and norms that shape their personal and professional lives in distinct ways. While scholarship has examined working motherhood broadly and gender inequity in higher education separately, little research has focused on how mother leaders experience and interpret gender inequity in higher education leadership. This study sought to address that gap by exploring the lived experiences of ten mother leaders working at research-intensive (R1) universities …