Digital Commons Network^™

Addressing Cybersecurity Data & Workforce Scarcity With Troy: Testbed For Resilient Operational Systems, Henry Oliver Schmidt

Graduate Theses and Dissertations

Machine learning has seen an explosive rise in the past decade. Companies, organizations, and governments are racing to pursue the advancements and insight provided by machine learning powered tools. However, to get effective and meaningful insights from machine learning models a significant amount of detailed data is required to train them. This poses a problem in fields where data is not openly available, such as cybersecurity. Entities are often unwilling to give out network or system data to the public for machine learning and cybersecurity research since that data can contain sensitive or proprietary information. The risk simply outweighs the …

Enhancing Smart Grid Security And Resilience Using Programmable Networks, Zheng Hu

Graduate Theses and Dissertations

The security and resilience of smart grids are essential to ensuring reliable and efficient energy distribution, especially as these cyber-physical systems grow more interconnected and complex. Supervisory Control and Data Acquisition (SCADA) systems play a critical role in smart grid operations by enabling essential infrastructure control and real-time monitoring. However, SCADA systems are highly vulnerable to modern cyber threats, which target weaknesses in industrial protocols and real-time data requirements.

This dissertation investigates the potential of programmable network technologies, with a focus on P4 (Programming Protocol-independent Packet Processors) switch, to deliver adaptable, in-network security solutions tailored to the needs of smart …

Employee Development And Employee Resilience In The Post-Covid-19 Workplace: A Quantitative Study Of High School Educators, Amy Keiser Maffei

Graduate Theses and Dissertations

The problem addressed by this research is the need for protective factors to support teacher resilience. Organizations that fail to develop employee resilience risk failing to enjoy the positive outcomes associated with it. Research-based methods to improve resilience may benefit organizations, including school districts. The purpose of this study was to examine the relationship between employee development provided by a school district and the resilience of its high school teachers in the post-Covid-19, in-person workplace. Conservation of Resources theory served as the framework to guide the research. Study participants completed a questionnaire with Likert-style response options, with items to assess …

Modeling, Synthesis, And Experimental Characterization Of Rhenium Disulfide For Optoelectronic Devices, Solomon Opeyami Ojo

Graduate Theses and Dissertations

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have garnered considerable interest for their potential in optoelectronics and photonics owing to their remarkable combination of electronic, optical, and mechanical properties. These characteristics distinguish them from other 2D materials, including graphene, making them particularly valuable for next-generation technologies. This dissertation focuses on ReS_₂, a 2D TMD with a unique anisotropic 1T distorted crystal structure. The research aims to elucidate its fundamental properties through a combination of theoretical modeling, material synthesis, and experimental characterization techniques, with the goal of optimizing ReS_₂ for device applications. A theoretical investigation utilizing Density Functional Theory …

Intersectionality And Career Persistence Of African American Women In Cybersecurity Careers: A Narrative Inquiry, Monica Marquita Jones Prewitt

Graduate Theses and Dissertations

The purpose of this narrative inquiry is to examine the experiences of African American women working in cybersecurity, a male-dominated field. An intersectional lens will be used in the study to provide insight into their positionality as women of color in a male-dominated career field. The study will examine the stories of the women who persisted in the field and the existing influences or interventions that supported their retention. This pre-registered study will provide the framework for this study to be completed at the University of Arkansas. The researcher will use a descriptive phenomenological approach and semi-structured interviews to understand …

Two-Photon Imaging For Characterizing Tumor Microenvironmental Changes In Non-Small Cell Lung Cancer Associated With Metastatic Recurrence, Paola Monterroso Diaz

Graduate Theses and Dissertations

Lung cancer remains the leading cause of cancer deaths, comprising nearly 25% of all cancer deaths (Sung et al., 2021a). The five-year survival rate of patients with non-small cell lung carcinoma (NSCLC) remains significantly low given that over half present with locally advanced or metastatic disease at time of diagnosis, and experience tumor recurrence following therapeutic intervention (Tamura et al., 2015; Uramoto & Tanaka, 2014). Current evaluation techniques to assess treatment response are lacking, given they are implemented several weeks after treatment completion and are solely based on anatomical changes in tumor size, forgoing other criteria such as functional or …

Causal Discovery In Time Series Data Using Deep Learning Techniques, Saima Zahin Farhana Absar

Graduate Theses and Dissertations

Causal structure learning from observational data has been an active field of research over the past decades. In the literature, different algorithms and models have been proposed, such as constrained-based methods and score-based methods including the emerging deep learning-based methods. However, most of the approaches apply to static and non-dynamic data only. In many applications, the data is temporal. For example, monitoring systems, weather surveillance systems, and stock data, to name but a few. Incorporating temporal information is an important extension of the causal discovery field. With the growth of observational data these days, the discovery of causal relationships from …

Investigation Of Conductive Die-Top Thermal Capacitors During Short Circuit Operation Of Silicon Carbide Devices, Youssef Abotaleb

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Silicon carbide (SiC) power devices have garnered significant attention in recent years due to their superior thermal and electrical properties compared to traditional silicon devices. However, SiC power devices suffer from severe reliability issues arising from their mechanical properties. This is because the Young's modulus of SiC is about three times larger than that of silicon, which correspondingly raises the mechanical stresses acting on the bonding structure, often leading to device failure under power shock events.

Traditional packaging materials and designs tend to constrain SiC performance, as hot-spot temperature resulting from a power shock is one of the key factors …

Identifying Challenges Faced By Small And Medium Enterprises (Smes) In The Middle East And North Africa (Mena) Region Through Total Quality Management, Mohamad Atef

Theses and Dissertations

Recently, Small and Medium Enterprises (SMEs) have become recognized as an important driver of global economic growth and innovation. This phenomenon is particularly evident in the MENA region, where small and medium-sized enterprises (SMEs) make a significant contribution to the businesses. SMEs offer significant benefits in terms of job creation, revenue generation, and fostering economic diversity. Although SMEs have a vital contribution in the economy of the MENA region, SMEs face many challenges that hinder their development and long-term viability. Focusing on these challenges is very important for fostering a vibrant and resilient economic environment. The objective of this thesis …

Triadic Temporal-Semantic Alignment For Weakly-Supervised Video Moment Retrieval, Jin Liu, Jialong Xie, Fengyu Zhou, Shengfeng He

Research Collection School Of Computing and Information Systems

Video Moment Retrieval (VMR) aims to identify specific event moments within untrimmed videos based on natural language queries. Existing VMR methods have been criticized for relying heavily on moment annotation bias rather than true multi-modal alignment reasoning. Weakly supervised VMR approaches inherently overcome this issue by training without precise temporal location information. However, they struggle with fine-grained semantic alignment and often yield multiple speculative predictions with prolonged video spans. In this paper, we take a step forward in the context of weakly supervised VMR by proposing a triadic temporalsemantic alignment model. Our proposed approach augments weak supervision by comprehensively addressing …

An Aggregate Matching And Pick-Up Model For Mobility-On-Demand Services, Xinwei Li, Jintao Ke, Hai Yang, Hai Wang, Yaqian Zhou

Research Collection School Of Computing and Information Systems

This paper presents an Aggregate Matching and Pick-up (AMP) model to delineate the matching and pick-up processes in mobility-on-demand (MoD) service markets by explicitly considering the matching mechanisms in terms of matching intervals and matching radii. With passenger demand rate, vehicle fleet size and matching strategies as inputs, the AMP model can well approximate drivers’ idle time and passengers’ waiting time for matching and pick-up by considering batch matching in a stationary state. Properties of the AMP model are then analyzed, including the relationship between passengers’ waiting time and drivers’ idle time, and their changes with market thickness, which is …

Unsupervised Modality Adaptation With Text-To-Image Diffusion Models For Semantic Segmentation, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Bo Li, Yang Tang, Pan Zhou

Research Collection School Of Computing and Information Systems

Despite their success, unsupervised domain adaptation methods for semantic segmentation primarily focus on adaptation between image domains and do not utilize other abundant visual modalities like depth, infrared and event. This limitation hinders their performance and restricts their application in real-world multimodal scenarios. To address this issue, we propose Modality Adaptation with text-toimage Diffusion Models (MADM) for semantic segmentation task which utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model’s cross-modality capabilities. Specifically, MADM comprises two key complementary components to tackle major challenges. First, due to the large modality gap, using one modal data to generate …

3d Snapshot: Invertible Embedding Of 3d Neural Representations In A Single Image, Yuqin Lu, Bailin Deng, Zhixuan Zhong, Tianle Zhang, Yuhui Quan, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.e., 3D Snapshot). Our idea is to distill a pre-trained neural rendering model (e.g., NeRF) into a visualizable image form that can then be easily inverted back to a neural network. To this end, we first present a neural image distillation method …

Self-Supervised Fine-Tuning For Neural Expert Finding, Budhitama Subagdja, Dan Sanchari, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Expert finding systems allow ones to find individuals who have expertise in specific fields or domains. Traditional expert finding are mostly based on topic modeling or keyword search methods that are limited in their capability to encode contextual knowledge from natural language. To address the limitation, this paper presents Neural Expert Finder (NEF), a novel method that takes a transfer learning approach based on transformer encoder networks to leverage the rich seman-tic and syntactic patterns of language encoded in pre-trained language models (PLMs). We propose a self-supervised learning approach utilizing contrastive training using both positive and automatically generated negative samples …

Long-Tailed Out-Of-Distribution Detection Via Normalized Outlier Distribution Adaptation, Wenjun Miao, Guansong Pang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Onekeychallenge in Out-of-Distribution (OOD) detection is the absence of groundtruth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD samples) to train OOD detectors. However, we find empirically that the outlier samples often present a distribution shift compared to the true OOD samples, especially in LongTailed Recognition (LTR) scenarios, where ID classes are heavily imbalanced, i.e., the true OOD samples exhibit very different probability distribution to the head and tailed ID classes from the outliers. In this work, we propose a novel approach, namely normalized outlier …

A Report Summarizing Hsc Faculty Participants: Ithaka S+R Coordinated Study Making Ai Generative For Higher Education, Laura J. Hall, Robyn Gleasner

Publications

This report presents findings from the University of New Mexico Health Sciences Center’s (HSC) participation in the ITHAKA S+R study Making AI Generative for Higher Education. Seven faculty members from HSC disciplines including internal medicine, pathology, psychiatry, nursing, radiology, and population health were interviewed to explore their perspectives on generative AI (GenAI) in teaching and learning, research and scholarship, and institutional support. A unique theme of “practice” emerged, reflecting how GenAI is influencing clinical care, bench-to-bedside translation, and the preparation of students for future professional practice. The report highlights faculty experiences, ethical concerns, and support needs, offering insight into how …

Divlog: Log Parsing With Prompt Enhanced In-Context Learning, Junjielong Xu, Ruichun Yang, Yintong Huo, Chengyu Zhang, Pinjia He

Research Collection School Of Computing and Information Systems

Log parsing, which involves log template extraction from semistructured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing supervised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose DivLog, an effective log parsing …

University ‘Pay-For-Grades’: The Bait And Switch Search Engine Optimization Strategies Of Contract Cheating Websites In The United States, Timothy M. Daly, James C. Ryan

All Works

This paper presents the first systematic investigation into the search engine optimization practices of major contract cheating websites in the United States. From a business perspective, visibility in organic search engine results is considered one of the top client recruitment tools. The current understanding of student recruitment strategies by these companies remains largely unexplored in both academic literature and popular press. Replicating the business research practices used in the search engine optimization industry, comprehensive search engine ranking and traffic data was obtained for the 38 largest contract cheating websites in the US. The overall objective was to illuminate the strategies …

Exploring The Cognitive Sense Of Self In Ai: Ethical Frameworks And Technological Advances For Enhanced Decision-Making, Emily Barnes, James Hutson

Faculty Scholarship

The burgeoning field of Artificial Intelligence (AI) increasingly focuses on developing systems capable of self-awareness, merging technological innovation with deep ethical and philosophical considerations. This article explores the cognitive sense of self within AI, examining mechanisms through which AI systems may mirror human-like consciousness and self-perception. Despite significant advances, substantial gaps remain in the understanding and practical implementation of self-aware characteristics in AI, particularly in applying theoretical models and ethical frameworks to real-world scenarios. There is a pressing need for comprehensive research to explore these theoretical underpinnings and translate them into operational systems capable of ethical and adaptable behaviors. This …

Single Droplet Combustion Of Alternative Fuels, Karah Powell

Masters Theses and Doctoral Dissertations

As the need to reduce greenhouse gas emissions grows, research into alternative fuels is becoming increasingly important. Biofuels, such as biodiesel and ethanol, are already used in the transportation sector. Rising biodiesel production generates byproducts like glycerol and surplus alcohols, which are also candidates for combustion applications. This study explores both conventional and alternative fuels, including butanol and biocrude from Algal. Droplet combustion experiments were performed to analyze the combustion characteristics of the fuels. Using the suspended droplet method, various droplet sizes were tested. The results show that combustion progresses through distinct stages: preheating, swelling, and burning. While hydrocarbon fuels …

Radial, Vortex, And Spiral Solutions To The Nonlinear Schrödinger Equation And Other Reaction--Diffusion Systems, James Redmon Cummins

Masters Theses and Doctoral Dissertations

This dissertation explores various solutions to nonlinear reaction-diffusion systems, focusing primarily on the nonlinear Schrödinger equation. Three types of solutions are investigated: radial solutions (with no angular dependence), vortex solutions (with angular dependence but no radial phase dependence), and spiral solutions (which have radial phase dependence). The study considers free particles without potential and trapped particles inside the cylindrical potential with an impenetrable barrier. We show that spiral solutions to the nonlinear Schrödinger equation only exist for a radially constant phase by considering a related system of nonlinear ordinary differential equations. This system is shown to be a special case …

Learning To Represent Temporal Dynamics And Generative Factors For Intelligent Visual Navigation, Sahand Khoshdel

All Theses

Visual navigation systems are crucial in various applications, including autonomous driving, unmanned aerial systems (UAS), and industrial automation. For these systems to operate efficiently in dynamic environments, they must not only interpret complex surroundings but also anticipate changes over time. Temporal prediction—forecasting environmental changes like moving obstacles or shifting lighting conditions—enables navigation systems to act proactively, enhancing both safety and performance. This dissertation investigates representation learning methods both as a backbone feature extractor for RL agents as well as a proxy for systems oriented for Explainable AI (XAI). Two main projects are presented as case studies to achieve the aforementioned …

The Impact Of In Ovo Administration Of Probiotic Lactobacilli On The Gut Health And Immune Competence Of Broiler Chickens, Shreeya Sharma

All Theses

Due to the incomplete development of their immune system, newly hatched chicks are vulnerable to a wide range of pathogenic microorganisms, including enteric bacterial pathogens during the early stage of life. To boost their immune system during this critical period, numerous studies suggest early feeding, either in ovo or immediately post-hatch, of nutrients and immunomodulators such as prebiotics, probiotics, phytobiotics, Toll-like receptors (TLRs) ligands, vitamins and minerals. Probiotics, defined as live, non-pathogenic microorganisms that confer beneficial effects on the host, have been extensively studied for their effects on poultry health. Supplementation of probiotics to chickens has demonstrated a broad spectrum …

Using Nano-Ities For Detection Of Metal Ions In Complex Aqueous Solutions, Mohammed Muzammil Nishar Ahmed

Theses and Dissertations

Recent urbanization has induced a rapid increase in heavy metal exposure to humans. The exploitation of heavy metals for economic gains coupled with poor and economically infeasible recycling methods have led to poor management of heavy metal wastes, which has opened up several pathways for heavy metals to enter the human body. Such heavy metals target many vital organs, but the early symptoms are common and difficult to diagnose. Severe symptoms appear in later stages and are very expensive to treat and the damage caused to the health are not typically reversable in such later stages. To counter this issue, …

Learning-Based Feature Identification For Rendezvous And Capture Of Non-Cooperative Space Objects, Trupti Mahendrakar

Theses and Dissertations

In recent years, On-Orbit Servicing (OOS) and Active Debris Removal (ADR) have attracted increasing interest due to growing concerns about space debris. This debris poses a significant risk to spacecraft, as collisions can catastrophically end missions and potentially trigger a cascading chain reaction of more debris. Space debris ranges from tiny paint chips to large non-functional spacecraft and even launch vehicle components. One way to mitigate the formation of additional space debris is to reduce the total number of large non-cooperative resident space objects present in operational orbits. This can be achieved through two approaches: de-orbiting these objects as part …

A Comparative Analysis Of Early Algebraic Thinking Activities From U.S. And Singapore Primary Textbooks, Christian Joel Hernandez

Theses and Dissertations

Many studies highlight the challenges students face when transitioning to algebra at the secondary level. Introducing algebraic concepts and fostering algebraic thinking at the primary level can help mitigate these difficulties. Prior to formal algebra instruction, early algebra can be cultivated as a mode of thinking known as algebraic thinking. Several international curricula, such as Singapore Math, incorporate early algebraic thinking into the early stages of schooling. Singapore Math, renowned for its high performance in international assessments, has been widely adopted by schools seeking to replicate its success.

This study compares two primary-level mathematics curricula—CCSSM-aligned textbooks and Singapore Math—specifically focusing …

Investigation Of Reproductive Inhibition During Chronic Heat Stress In C. Elegans, Macy Makenna Nielander

Theses and Dissertations

Nearly all organisms experience heterogeneity in environmental conditions that can negatively impact fitness. A common adaptive strategy to reduce the impact of this stress is called facultative diapause, where development and reproduction are temporarily arrested to promote survival. Facultative diapause is characterized by three major criteria, the arrest must be (i) induced by an unfavorable environmental condition, (ii) reversible, and (iii) driven by genetically regulated mechanisms. The aim of this thesis was to determine whether chronic heat-induced inhibition of egg laying in Caenorhabditis elegans represents a novel form of facultative diapause. It was found that the inhibition of egg-laying is …

Machine Learning Methods For Quantification Of Glacier Variations Through Satellite Imagery, Robert D. Breininger

Theses and Dissertations

Glaciers around the world have experienced a trend of recession within the past century. Quantification of glacier variations using satellite imagery is of great interest due to the importance of glaciers as freshwater resources and as indicators of climate change. The potential methods to quantify glacier variations with increasing complexity include detecting the terminus location, quantifying the glacier surface area, and measuring glacier volume. Although there are methods in literature designed purposefully for glacier area segmentation that have achieved acceptable results, they are often localized to the region where their training data were acquired and further rely on training sets …

Morphometric Based Roughness Estimates For Wind Loading Applications In The Coastal Transition Zone, Hadley Rose Besing

Theses and Dissertations

This project was developed to support the Florida Tech Wireless Sensor Network System (WSNS) hurricane research team in determining wind loads during high-impact wind events. The WSNS team objectives included estimating the critical pressure (𝐶_p) from observed 3-s gusts during high impact weather events (tropical cyclones). The field work was conducted at a single-story residential home in Satellite Beach, FL approximately 400 m from the coast. During data collection periods, more than 1-million 20-second LiDAR wind profiles were observed.

The bulk roughness at the test residence is characterized utilizing an existing morphometric technique with geometry that is derived …

Integration Of Blockchain Technology In Industrial Applications, Sanjana Sakinala

Theses

This research explores the integration of Hyperledger Fabric., a framework of blockchain in industrial applications, particularly focusing on supply chain management. By addressing the limitations of traditional blockchains - such as high transaction latency, scalability challenges, and privacy concerns - this research demonstrates how Hyperledger fabrics overcomes them. Through simulation, we show significant enhancements in transaction speed, data privacy and scalability, making the Fabric a viable solution for industrial systems.