Digital Commons Network^™

Zeroizing Trust In A Naïve Federated Zero Trust Environment, Keith E. Strandell

Theses and Dissertations

The position of the United States on the global stage is predicated on information dominance and the ability to project power through cooperative engagements with mission partners in both wartime and peacetime. Recent cyber-attacks highlighted the need for a more robust cybersecurity posture. As the United States progresses toward the adoption of Zero Trust, it is incumbent on the Department of Defense to assess the impact to the ability to share data across strategic partnerships while securing the data of both the United States and its partners. This paper proposes research into ensuring an environment rooted in Zero Trust and …

Synthetic Data Augmentation For Retinoblastoma Using Diffusion, Andrew Thompson

Theses and Dissertations

Many AI models rely on large and high quality datasets for optimal training. In certain cases, data can be difficult or expensive to obtain, making training difficult. Rare medical conditions are one of these cases. Datasets for retinoblastoma are severely lacking in quantity. Diffusion has been used to create synthetic data in the industrial, medical, and financial domains. By applying the latest Diffusion methods to retinoblastoma, this work seeks to improve predictive model performance on identifying retinoblastoma.

The Confluence, Volume 4, Issue 1, Full Issue

The Confluence

No abstract provided.

Exploring The Facets Of Responsible Ai: Interpretability, Biases, And Morality Of Large Language Models, Sean Xie

Dartmouth College Ph.D Dissertations

This thesis investigates critical aspects of responsible artificial intelligence (AI) — specifically model interpretability, bias detection and mitigation, and moral alignment in large language models (LLMs) — due to their pivotal role in the deployment of transparent, fair, and ethical AI systems. By addressing these dimensions of responsible AI, we hope to foster the increased trust and understanding necessary for wider AI adoption.

We begin by surveying the existing landscape of interpretability metrics and critically assess the effectiveness of interpretability methods designed to generate reliable explanations. Building upon this evaluation, we introduce novel model architectures and frameworks explicitly developed to …

Elevating Education: Leveling Up Individual Learning Plans, Maximum Mgrdich-Ararat Sirabian

UNLV Theses, Dissertations, Professional Papers, and Capstones

This three-article dissertation investigated the effectiveness, implementation quality, and automation of Individual Learning Plans (ILPs) in promoting college and career readiness. Article 1 analyzed High School Longitudinal Study of 2009 data and found that ILPs did not significantly guide course alignment. Article 2 examined ILP implementation across Nevada high schools, revealing inconsistent quality, limited standardization, and few culturally responsive practices. These findings informed the creation of a new high-quality ILP framework. Article 3 employed a convergent parallel mixed methods design to assess an automated ILP prototype based on this framework. Participants in the automated group reported significantly higher scores in …

Developments On Abbreviations Towards Machine Reading Comprehension, Sing Choi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine reading comprehension is a critical step in development of applications that require the semantic understanding of human speech-to-text driven work. Many devices such as smart home appliances like the Amazon Echo Dot, Google Home, or smart assistants like Apple Siri or Microsoft Cortana are examples of these applications. The comprehension task involves a deeper understanding and recognition of named entities such as person names, locations, medicals codes, quantities, abbreviations, and acronyms in speech or text data. In this dissertation, we explore and extend the different approaches and techniques in modern research that tackles the problem of recognition and definition …

Algorithms To Estimate Contours: Two Applications Of Analytical Tools In Differential Geometry And Topology, Mohammad Abirul Islam

Computer Science ETDs

We develop distributed robotics algorithms with analytical tools needed to define and analyze angle turned and distance traversed by robots executing geometric algorithms. We then use these analytical tools to obtain information, via sensor measurements, about an a priori unknown surface. Our contributions are threefold. First, we develop the Sketch Algorithm, which estimates the boundary of any unknown contour and is asymptotically optimal in terms of distance traversed and angle turned. Second, we present experimental field work that validates the Sketch Algorithm. Finally, we propose an approach to find multiple sources of a surface with potential applications to approximate that …

Memory-Augmented Llm Agent For Predicting Locomotion Modes In Construction Activities, Ehsan Ahmadi

LSU Doctoral Dissertations

The construction industry faces significant challenges, including labor shortages, high physical demands, and safety risks, necessitating advanced assistive technologies like exoskeletons to enhance worker efficiency and reduce injuries. However, effective exoskeleton control in dynamic construction environments requires accurate locomotion prediction, a task complicated by the diversity of activities and reliance on supervised learning methods that struggle to generalize. This study investigates a multimodal approach to locomotion prediction, leveraging speech commands and visual data from smart glasses to enable adaptive and safe human-exoskeleton interaction. The research unfolds in two stages: the first develops a framework to evaluate the zero-shot capability and …

Integrating Artificial Intelligence In Orthopedic Care: Advancements In Bone Care And Future Directions, Rahul Kumar, Kyle Sporn, Joshua Ong, Ethan Waisberg, Phani Paladugu, Swapna Vaja, Tamer Hage, Tejas C. Sekhar, Amar S. Vadhera, Alex Ngo, Nasif Zaman, Alireza Tavakkoli, Mouayad Masalkhi

SKMC Student Presentations and Publications

Artificial intelligence (AI) is revolutionizing the field of orthopedic bioengineering by increasing diagnostic accuracy and surgical precision and improving patient outcomes. This review highlights using AI for orthopedics in preoperative planning, intraoperative robotics, smart implants, and bone regeneration. AI-powered imaging, automated 3D anatomical modeling, and robotic-assisted surgery have dramatically changed orthopedic practices. AI has improved surgical planning by enhancing complex image interpretation and providing augmented reality guidance to create highly accurate surgical strategies. Intraoperatively, robotic-assisted surgeries enhance accuracy and reduce human error while minimizing invasiveness. AI-powered smart implant sensors allow for in vivo monitoring, early complication detection, and individualized rehabilitation. …

Efficient Eeg Epilepsy Classification And Feature Selections Based On Hellinger Distance, Muhammed Sadiq

Theses and Dissertations

Accurate and efficient detection of epileptic seizures from EEG signals remains a critical challenge due to high-dimensional data, class imbalance, and the limitations of standard classifiers. This thesis introduces two novel models to address these challenges. The first model presents a new classifier based on the Hellinger Distance, specifically designed to enhance discriminative capability and robustness against imbalanced datasets. By integrating the Hellinger Distance Classifier with Particle Swarm Optimization (PSO) for feature selection, this model significantly improves classification performance while reducing computational complexity. Experimental evaluations on the Bonn dataset demonstrate an accuracy of 96.25%, an F1-score of 97.74%, a recall …

Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam

Engineering Faculty Articles and Research

Anemia, characterized by low blood hemoglobin (Hgb) levels, afflicts >2 billion individuals worldwide. Here, we report real-world data generated by a smartphone app that noninvasively screens for anemia using only “fingernail selfies.” App data for anemia screening were obtained from >1.4 million uses across the United States enabling geographic mapping of Hgb levels. Of those, 9,061 users also self-reported complete blood count Hgb levels for comparison, resulting in accuracy and performance that match gold standard laboratory testing and a sensitivity and specificity of 89% and 93%, respectively, when using an anemia cutoff of 12.5 g/dL. Geotagged data enabled construction of …

Applications Of The Mathieu Groups And Information Theory In Dna Encoding Functions, Juan C. Nava Jr

Theses and Dissertations

A foundational idea in mathematics lies in breaking down existing components into their bare fundamentals. As evidenced by prime numbers and composites, we learn this idea at an early age. Categorizing these broken-down components into their simplest form allows mathematicians to construct proofs from emergent patterns. John Conway’s Atlas of Finite Groups in the 1990s was particularly concerned with the categorization of structures known as groups. There are certain axioms a group must adhere to, which amount to the retention of symmetry; ultimately a group helps us to better understand symmetric actions performed on a set with a binary operation. …

Computational Modeling And Structural Generation Of Piano Music In The Classical Style, Yijing Feng

Dartmouth College Ph.D Dissertations

Listening to fast-tempo piano sonatas of the Classical period (circa 1750-1820) has been shown to have therapeutic effects for neurological disorders such as epilepsy. The limited existing repertoire of music in this style motivates the creation of more long-form, coherent compositions with clearly defined structure. Despite the long history of computer-based music generation and recent progress in deep learning, particularly transformer-based models, generating structurally coherent long-form music remains a major challenge. This difficulty stems from the scarcity of reliable structural annotation datasets, the computational demands of modeling very long musical sequences, and the lack of effective structural encoding in both …

Computational Complexity Of Soundness Verification For Neural Networks, Scott Sirri

McKelvey School of Engineering Graduate Student Theses & Dissertations

Neural networks are an increasingly ubiquitous tool in systems of varying complexity across a range of domains. While these tools can be used to learn and predict complex functions, their opaque nature limits the scope of their acceptable applications. In particular, a lack of performance guarantees means that they are unsuitable for safety-critical applications such as self-driving cars and scheduling systems. Neural networks trained to solve NP-complete problems, in particular, are unlikely to be able to solve the problem exactly. However, a weaker soundness guarantee may be sufficient for some systems, e.g., that positive instances of the problem may be …

Beyond Boundaries: A Comprehensive Survey Of Transferable Attacks On Ai Systems, Guangjing Wang, Ce Zhou, Yuanda Wang, Bocheng Chen, Hanqing Guo, Qiben Yan

Computer Science Faculty Research & Creative Works

As Artificial Intelligence (AI) systems increasingly underpin critical applications, from autonomous vehicles to biometric authentication, their vulnerability to transferable attacks presents a growing concern. These attacks, designed to generalize across instances, domains, models, tasks, modalities, or even hardware platforms, pose severe risks to security, privacy, and system integrity. This survey delivers the first comprehensive review of transferable attacks across seven major categories, including evasion, backdoor, data poisoning, model stealing, model inversion, membership inference, and side-channel attacks. We introduce a unified six-dimensional taxonomy: cross-instance, cross-domain, cross-modality, cross-model, cross-task, and cross-hardware, which systematically captures the diverse transfer pathways of adversarial strategies. Through …

Cyber-Physical Security Through The Lens Of Ai-Enabled Systems, Zhiyuan Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cyber-physical systems (CPS), powered by emerging artificial intelligence (AI) technologies, have become integral to various critical domains such as the Internet of Things (IoTs), medical devices, and autonomous vehicles. A unique aspect of these systems lies in their interactions with the physical world, by perceiving environments through heterogeneous modalities (perception), processing digital data with human-in-the-loop intelligence algorithms (computing), and autonomously actuating controls that affect physical processes (actuation). While this intricate fusion of cyber and physical components has unlocked unprecedented capabilities, it has also introduced new security challenges. However, traditional security measures often fall short in addressing these multifaceted threats. This …

Clinical Value Of Chatgpt For Epilepsy Presurgical Decision-Making: Systematic Evaluation Of Seizure Semiology Interpretation, Yaxi Luo, Meng Jiao, Neel Fotedar, Jun-En Ding, Ioannis Karakis, Vikram R. Rao, Melissa Asmar, Xiaochen Xian, Orwa Aboud, Yuxin Wen, Jack J. Lin, Fang-Ming Hung, Hai Sun, Felix Rosenow, Feng Liu

Engineering Faculty Articles and Research

Background: For patients with drug-resistant focal epilepsy, surgical resection of the epileptogenic zone (EZ) is an effective treatment to control seizures. Accurate localization of the EZ is crucial and is typically achieved through comprehensive presurgical approaches such as seizure semiology interpretation, electroencephalography (EEG), magnetic resonance imaging (MRI), and intracranial EEG (iEEG). However, interpreting seizure semiology is challenging because it heavily relies on expert knowledge. The semiologies are often inconsistent and incoherent, leading to variability and potential limitations in presurgical evaluation. To overcome these challenges, advanced technologies like large language models (LLMs)—with ChatGPT being a notable example—offer valuable tools for …

Deep Learning Classification Of Drainage Crossings Based On High-Resolution Dem-Derived Geomorphological Information, Michael Edidem, Bill Xu, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang

Computer Science Faculty Publications and Presentations

High-resolution digital elevation models (HRDEMs) from LiDAR and InSAR technologies have significantly improved the accuracies of mapping hydrographic features such as river boundaries, streamlines, and waterbodies over large areas. However, drainage crossings that facilitate the passage of drainage flows beneath roads are not often represented in HRDEMs, resulting in erratic or distorted hydrographic features. At present, drainage crossing datasets are largely missing or available with variable quality. While previous studies have investigated basic convolutional neural network (CNN) models for drainage crossing characterization, it remains unclear if advanced deep learning models will improve the accuracy of drainage crossing classification. Although HRDEM-derived …

Towards The Next Generation Of Storage Stack For Nand Flash Memory-Based Systems, Ziyang Jiao

Dissertations - ALL

The explosive growth of data has led to increased attention on NAND flash-based solid-state drives (SSDs), which offer high performance, low power consumption, and significant capacity per unit volume when compared to traditional hard-disk drives (HDDs). However, as NAND flash memory density continues to scale, modern SSDs suffer from what is known as fail-slow symptoms, and their performance degrades over time as they wear out. In this dissertation, we focus on understanding and addressing the performance, reliability, and sustainability challenges of modern flash-based storage systems by modeling key metrics, analyzing the design tradeoffs between these metrics, and optimizing existing storage …

Automated Flaw Discovery In Decentralized Systems Via Semantic Fuzzing Tools, Yibo Wang

Dissertations - ALL

This dissertation studies the security challenges of blockchain transaction processing before consensus. While prior work has focused on consensus protocols and smart contract bugs, the pre-consensus infrastructure, such as the mempool and off-chain batching, remain underexplored. This dissertation aims to bridge that gap by systematically analyzing both transaction processing in the mempool and off-chain transaction batching for cost-optimization. This dissertation focuses on two core contributions. First, it presents MPFUZZ, an automated software fuzzing tool designed to uncover denial-of-service vulnerabilities in Ethereum mempools. This work is the first to formally define the mempool fuzzing problem and introduce bug oracles that detect …

Harnessing Llms To Detect Hate Speech, Weibin Cai

Theses - ALL

Hate is a sentiment, while hate speech refers to the expression of hate in a form that targets and attacks specific groups, such as race, religion, or gender. With the rise of the internet and social media, hate speech has spread rapidly, gaining wide exposure and posing threats to individual well-being, the profits of major tech companies, and social stability. As a result, both industry and academia have turned their attention to the study of hate speech. One of the most active areas is hate speech detection, which involves training models to predict whether a given piece of content is …

Bridging Cattle Farming And Technology: The Development Of Moomanager, Matthew Hayes

Honors College Theses

Small-scale cattle producers face persistent challenges in adopting digital tools for herd management, often due to barriers such as limited digital literacy, software complexity, and poor alignment with practical workflows. Existing literature highlights the potential benefits of mobile applications in agricultural contexts, yet adoption rates remain low among smaller operations. This thesis investigates how a streamlined, mobile-first application can address these adoption barriers while supporting essential farm management tasks. The study details the design and development of MooManager, a mobile application built with React Native and Supabase and structured around core features such as cattle tracking, beef sales logging, and …

Optimized Student Grouping For Enhanced Classroom Performance, Kathryn E. Reardon

Honors Theses

Effective grouping methods enhance classroom collaboration and allow for a student-centered teaching approach; however, traditional grouping methods are time-consuming, subjective, and can create inconsistent group dynamics. This project addresses these challenges by employing a data-driven approach to optimize student groups based on academic performance, behavior, attendance, language barriers, and teacher preferences. The minimum viable product is a web application with an algorithm-driven system to group students and a database storage for group results. During the initiation phase, a problem was defined with a proposed solution. During the planning phase, potential design choices and grouping methods were researched and assessed. During …

Scaling Quantum Systems: Quantum Networks, Distributed Quantum Computing, And Security, Zebo Yang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Recent advancements in quantum computing have opened up new possibilities across various fields, offering significant potential to enhance computation, communication, cryptography, and applications in areas like sensing, medicine, and chemistry. However, the capabilities of individual quantum devices are still limited, and scaling quantum hardware monolithically presents substantial challenges. Interconnecting quantum systems offers a promising alternative by aggregating capabilities across a network, thereby enabling quantum advantages for larger and more practical problems. This interconnection is achieved through quantum networking, which links external quantum systems, and Distributed Quantum Computing (DQC), which connects quantum processors within a system, such as in quantum data …

System Security Foundations For Ai-Enabled Systems, Yuhao Wu

McKelvey School of Engineering Graduate Student Theses & Dissertations

AI is being deployed broadly, from conventional computing systems like IoT systems to more advanced agentic systems. It is shifting from being a specialized component responsible for specific functions to becoming the core of agentic systems, where it drives autonomous decision-making and task execution. These AI-enabled systems bring tremendous benefits. For example, large language models can interpret user intent, select appropriate tools, and access data to complete tasks with minimal human guidance. However, they also introduce new security, privacy, and safety risks. These risks arise not only from the models themselves but also from the broader system design and integration. …

Towards Secure And Privacy-Preserving Machine Learning Systems, Han Liu

McKelvey School of Engineering Graduate Student Theses & Dissertations

In recent years, machine learning (ML) has advanced at an unprecedented pace, driving the widespread adoption of increasingly sophisticated models across a broad range of real-world applications, including healthcare, finance, autonomous systems, and critical infrastructure. While these models have delivered remarkable benefits and transformed numerous industries, they remain inherently vulnerable to a variety of security and privacy threats. Given their growing role in safety-critical domains, ensuring their security and privacy has become imperative. Systematically addressing these vulnerabilities requires comprehensive adversarial analyses to uncover weaknesses and inform the design of robust defenses. This dissertation systematically investigates ML vulnerabilities in adversarial settings …

Trustworthy Autonomy Through Robust Control And Alignment, Junlin Wu

McKelvey School of Engineering Graduate Student Theses & Dissertations

As artificial intelligence systems are increasingly applied in safety critical domains such as robotics, autonomous driving, and decision making under uncertainty, ensuring their trustworthiness has become a central challenge. This dissertation addresses two major facets of trustworthy AI: reliable control through formal guarantees and alignment against adversarial manipulation. The first part of the dissertation focuses on provably stable, robust, and safe control for nonlinear systems using learning based methods. We introduce the first general framework for synthesizing neural Lyapunov controllers in discrete time systems. This method combines a sound verifier based on mixed integer linear programming with gradient based counterexample …

Understanding And Mitigating Timing Issues In Autonomous Systems, Ao Li

McKelvey School of Engineering Graduate Student Theses & Dissertations

Autonomous systems, such as self-driving cars and drones, have become a part of our daily lives. Since these systems operate in and interact with the physical world, their correctness depends on both functional and temporal aspects. However, the increasing complexity of modern computing hardware and software often leads to unpredictable timing behavior in these systems, making temporal properties particularly challenging to ensure. This dissertation proposes novel approaches to specify and enforce temporal properties based on a comprehensive empirical study of real-world issues. The first half of this dissertation presents an empirical study that dissects the timing issues. The dissection begins …

Identification Of Subtypes Of Post-Stroke And Neurotypical Gait Behaviors Using Neural Network Analysis Of Gait Cycle Kinematics, Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison Mckenzie, Yuxin Wen, Natalia Sánchez

Physical Therapy Faculty Articles and Research

Gait impairment post-stroke is highly heterogeneous. Prior studies classified heterogeneous gait patterns into subgroups using peak kinematics, kinetics, or spatiotemporal variables. A limitation of this approach is the need to select discrete features in the gait cycle. Using continuous gait cycle data, we accounted for differences in magnitude and timing of kinematics. Here, we propose a machine-learning pipeline combining supervised and unsupervised learning. We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. Then, we used unsupervised time-series k-means and Gaussian Mixture Models to identify gait clusters. We tested …

Magic: The Gathering Deck Testing And Optimization, Ian B. Watson

Honors Theses

The goal of this project is to provide a tool for players of the trading card game Magic: the Gathering to determine whether or not a given deck is optimally built by outputting relevant information regarding its optimality. This is done through a simulator that plays a one-sided game, recording what cards are played, what turn they are played, and how much mana is left over at the end of every turn. The simulator was tested with both optimized and unoptimized decks to show how it can be used to diagnose both.