Physical Sciences and Mathematics Commons^™

Quantitative Bounds On Resource Usage Of Probabilistic Programs, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Cost analysis, also known as resource usage analysis, is the task of finding bounds on the total cost of a program and is a well-studied problem in static analysis. In this work, we consider two classical quantitative problems in cost analysis for probabilistic programs. The first problem is to find a bound on the expected total cost of the program. This is a natural measure for the resource usage of the program and can also be directly applied to average-case runtime analysis. The second problem asks for a tail bound, i.e. ‍given a threshold t the goal is to find …

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 …

Open Source Software Development Tool Installation: Challenges And Strategies For Novice Developers, Larissa Salerno, Christoph Treude, Patanamon Thongtanunam

Research Collection School Of Computing and Information Systems

As the world of technology advances, so do the tools that software developers use to create new programs. In recent years, software development tools have become more popular, allowing developers to work more efficiently and produce higher-quality software. Still, installing such tools can be challenging for novice developers at the early stage of their careers, as they may face issues such as compatibility problems (e.g., with operating systems) and unclear instructions. Therefore, this work aims to investigate the challenges novice developers face when installing software development tools and the strategies they employ to overcome them. To investigate these, we conducted …

Natural Adversaries: Fuzzing Autonomous Vehicles With Realistic Roadside Object Placements, Yang Sun, Haoyu Wang, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

The emergence of Autonomous Vehicles (AVs) has spurred research into testing the resilience of their perception systems, i.e., ensuring that they are not susceptible to critical misjudgements. It is important that these systems are tested not only with respect to other vehicles on the road, but also with respect to objects placed on the roadside. Trash bins, billboards, and greenery are examples of such objects, typically positioned according to guidelines developed for the human visual system, which may not align perfectly with the needs of AVs. Existing tests, however, usually focus on adversarial objects with conspicuous shapes or patches, which …

Fighting Against Recruitment Scams: Theory-Driven Supervised Learning And Empirical Analysis For Digital Fraudulent Recruitment Posting Behavior, Tom (Tianteng) Wang, David (Jingjun) Xu, Keng Siau, Zhongju (John) Zhang

Research Collection School Of Computing and Information Systems

The number of recruitment postings on digital recruitment hiring platforms has increased since the COVID-19 pandemic. However, the weak surveillance and operations of these platforms, combined with the fact that most job seekers have relatively low vigilance and a strong desire for recruitment offers, enable scammers to easily deceive job seekers for their money and confidential information. In this work, we combine prevailing text mining techniques (i.e., ChatGPT with prompting engineering and supervised machine learning) with interpersonal deception theory (IDT) from social science to design an interpretable IT system to predict fraudulent recruitment postings on digital recruitment-hiring platforms. We compare …

Enhancing Action And Ingredient Modeling For Semantically Grounded Recipe Generation, Guoshan Liu, Bin Zhu, Yian Li, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in Multimodal Large Language Models (MLMMs) have enabled recipe generation from food images, yet outputs often contain semantically incorrect actions or ingredients despite high lexical scores (e.g., BLEU, ROUGE). To address this gap, we propose a semantically grounded framework that predicts and validates actions and ingredients as internal context for instruction generation. Our two-stage pipeline combines supervised fine-tuning (SFT) with reinforcement fine-tuning (RFT): SFT builds foundational accuracy using an Action-Reasoning dataset and ingredient corpus, while RFT employs frequency-aware rewards to improve long-tail action prediction and ingredient generalization. A Semantic Confidence Scoring and Rectification (SCSR) module further filters and …

Optimization Of The Baffle Geometry Of A Circular Culvert Outlet Structure To Maximize Energy Dissipation, Kiarash Shirmahi

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

High-velocity flow at culvert outlets can lead to significant soil erosion and damage to downstream infrastructure, particularly in agricultural regions where land protection is critical. Circular culverts, commonly used in transportation systems, are especially susceptible to this issue due to flow acceleration caused by geometric constraints and installation angles. Despite extensive research on energy dissipation in rectangular culverts, limited attention has been given to circular configurations. This study addresses this gap by evaluating the hydraulic performance of baffle systems designed to reduce outlet energy in circular culverts.

A series of laboratory experiments was conducted using a scaled physical model of …

Leveraging Code Embeddings To Identify And Address Blind Spots In Benchmark Creation, Charles Moloney

School of Computing: Dissertations, Theses, and Student Research

Formal software verification remains critical for early vulnerability detection, yet benchmarking these tools is costly and often reliant on centralized datasets such as SV-COMP. While such repositories enable standardized evaluation, they introduce risks of overfitting and bias, particularly due to first-party benchmark contributions. To address these limitations, we extend ARG-V, our tool for generating SV-COMP-compatible benchmarks from real-world Java code, with a novel approach of using code embedding techniques to selectively sample from mined code. By leveraging Nomic Embed Code and a cosine-based Minimum Hyperspherical Energy (MHE) objective, we systematically select and transform benchmarks from scraped GitHub code that …

Enhancing Food Security In Stann Creek. Belize Using Meat Rabbits, Lindsey Garretson

Crop, Soil and Environmental Sciences Undergraduate Honors Theses

In recent years, food insecurity and the prevalence of hunger have rapidly grown throughout the world, but especially within Latin America and the Caribbean. Along with growing rates of food insecurity and hunger there are increases in medical conditions including negative impacts the functioning of every major organ system, increased rates of mental illness, both overweight and underweight children, wasting in both children and adults, and anemia. Availability and access to healthy, nutritional food options can help lower the risk of these conditions. With these considerations, the goal of this project was to create an all-encompassing manual and construction guide …

Measurements And Scaling Of Ion Propulsion Impulse During Driven Magnetic Reconnection, Fatima Ebrahimi, Nicholas A. O'Gorman, Kush Maheshwari, Jongsoo Yoo, Alexandre Sainterme, Hantao Ji

Faculty Publications

Impulse scaling during magnetic reconnection, the magnetic energy conversion to kinetic energy, via direct Mach probe measurements in Magnetic Reconnection Experiment is examined. Ion exhaust velocity and impulse scalings with reconnecting magnetic field during the push phase of driven reconnection are presented. The outflows and impulse measurements are compared with global MHD simulations. Both measurements and simulations reveal a favorable scaling, greater than linear, of impulse with reconnecting field. These scaling results establish that magnetic reconnection could be utilized for plasma propulsion.

Herbivory Of Phragmites Australis Along A Salinity Gradient In Connecticut Wetlands, Sydney H. Kolz

Honors Scholar Theses

Invasive Phragmites australis (common reed) is widely distributed in New England wetlands, where it often outcompetes native plants and decreases biodiversity throughout the food web. Controlling its invasion is a priority, especially in salt marsh habitat, but current management techniques are inefficient. Leveraging natural processes may improve control strategies, but in order to do so, we must first better understand how abiotic and biotic stressors interact and affect Phragmites. This study focuses on herbivory and salinity, two factors limiting Phragmites’ fitness. I measured soil salinity and quantified the damage of three herbivore guilds (i.e. gall-formers and stem-borers, leaf-chewers, and …

Benchmarking Gaslighting Attacks Against Speech Large Language Models, Jinyang Wu, Bin Zhu, Xiandong Zou, Qiquan Zhang

PhD Student’s Publications Collection

As Speech Large Language Models (Speech LLMs) become increasingly integrated into voice-based applications, ensuring their robustness against manipulative or adversarial input becomes critical. Although prior work has studied adversarial attacks in text-based LLMs and vision-language models, the unique cognitive and perceptual challenges of speech-based interaction remain underexplored. In contrast, speech presents inherent ambiguity, continuity, and perceptual diversity, which make adversarial attacks more difficult to detect. In this paper, we introduce gaslighting attacks, strategically crafted prompts designed to mislead, override, or distort model reasoning as a means to evaluate the vulnerability of Speech LLMs. Specifically, we construct five manipulation strategies: Anger, …

Teacher-Student Diffusion Model For Text-Driven 3d Hand Motion Generation, Ching Lam Cheng, Bin Zhu, Shengfeng He

PhD Student’s Publications Collection

Generating realistic 3D hand motion from natural language is vital for VR, robotics, and human-computer interaction. Existing methods either focus on full-body motion, overlooking detailed hand gestures, or require explicit 3D object meshes, limiting generality. We propose TSHaMo, a model-agnostic teacher-student diffusion framework for text-driven hand motion generation. The student model learns to synthesize motions from text alone, while the teacher leverages auxiliary signals (e.g., MANO parameters) to provide structured guidance during training. A co-training strategy enables the student to benefit from the teacher’s intermediate predictions while remaining text-only at inference. Evaluated using two diffusion backbones on GRAB and H2O, …

Autonomous Deficiency Detection And Vision-Language Summarization For Underground Infrastructure On Embedded Edge Systems, Johny Lopez

LSU New Orleans Theses and Dissertations

Aging underground infrastructure poses significant risks to public health and environmental safety, yet structural condition assessment remains bottlenecked by labor-intensive manual CCTV inspections. This thesis proposes a comprehensive algorithmic framework enabling fully autonomous, real-time deficiency detection, geometric assessment, and natural language reporting on resource- constrained edge computing platforms. Three core components address this challenge. First, RAPID-SCAN, a novel semantic segmentation architecture utilizing a Dynamic Feature Pyramid Network and Channel-Spatial Attention, achieves real-time, pixel-precise defect localization with dramatically reduced parameters. Second, an Edge-Optimized Vision-Language Model pipeline employing LoRA and 4-bit QLoRA quantization compresses Phi-3.5 for local deployment, en- abling autonomous technical …

From Data Digitization To Personalized Care: Deep Learning And Decision Analytics In Healthcare Systems, Ahla Ko

Graduate Theses and Dissertations

This dissertation traces a progression from foundational data infrastructure to individualized clinical decision-making, developing and evaluating three computational frameworks that advance healthcare efficiency and personalization through deep learning and decision analytics. The first study presents an end-to-end pipeline for automated recognition of handwritten medical forms, addressing persistent challenges in health data digitization. Integrating a YOLO-based field detection model, a Convolutional Recurrent Neural Network (CRNN) for text transcription, and a confidence-based human-in-the-loop quality assurance framework, the system achieved 99.75\% Exact Match Accuracy and a 0.13\% Character Error Rate on medical forms collected from a tuberculosis research project in Moldova. A GPU-accelerated …

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 …

Ai And Statistics: An Examination Of The Capabilities Of Ai Models To Perform Data Science, Erik Rosine

Undergraduate Theses and Capstone Projects

Artificial intelligence has become one of the fastest growing modern technologies, with publicly available AI chatbots becoming particularly popular since the 2022 launch of OpenAI’s ChatGPT. Due to their seemingly endless applications and unrivalled ease of use, these AI models present a new and often highly efficient method of approaching problems and increasing efficiency. However, with so many marketed models available, it is challenging to know which is the best one to use. This experiment tests five of the most popular AI models at performing data science tasks with given datasets, a common application of AI. These models are OpenAI’s …

The Short Term Water Quality Impacts Of A Low-Head Dam Removal On Urban Stream Water Quality In Central Virginia, Usa: Is It The Dam Or The Urban Environment?, Lorraine L. Upton

Undergraduate Theses and Capstone Projects

Dam removal in the United States has become a common practice for stream restoration and community safety. Studies have shown removals to restore an ecosystem to its natural flow, but in urban environments, dams can serve as a retention basin that help to mitigate increased amounts of stormwater and high pollution loads from increased percentages of impervious surfaces. A central Virginia watershed located in an urbanized community recently experienced a low-head dam removal and its conversion to a constructed wetland. This study assesses the short-term impacts of the dam removal through water quality sampling based on its chemical, biota, and …

Crispr/Cas-Mediated Genome Editing Of Staphylococcus Aureus For Studying Phage-Pathogenicity Island-Host Interactions, Annalise Williams

Undergraduate Theses and Capstone Projects

Staphylococcus aureus is a gram-positive cocci that is a common member of our microbiota, living frequently on our skin and in our nasal passages. It is known for its wide variety of clinical manifestations, including endocarditis, scalded skin syndrome, toxic shock syndrome, food poisoning, sepsis, folliculitis, impetigo, and many other infections. It is widely recognized for being acquired in both hospital and community settings and is often resistant to multiple drugs. This poses a major problem, both in the United States and globally, as many treatments are no longer effective. MRSA, or Methicillin-Resistant Staphylococcus, is one example of the many …

Age And Characteristics Of Quaternary Fluvial And Dune Deposits Along The Arkansas And Cimarron Rivers In Osage And Pawnee Counties, Oklahoma, Barry A. Duncan

Graduate Theses and Dissertations

Quaternary aeolian and fluvial deposits along the Arkansas and Cimarron Rivers in Osage and Pawnee counties of central Oklahoma, record Late Pleistocene and Holocene fluvial landscape history in the eastern Osage Plains. Sediment supply, climate variability, channel incision, and channel migration control this landscape evolution. Sediment supply of both streams is dominantly sand. Late Pleistocene and Holocene precipitation have varied with periods of drought alternating with increased precipitation and warming. The study area is near the eastern boundary of substantial aeolian activity with drought and aeolian reworking of fluvial sand more severe to the west. This study examines the sediment, …

Geospatial Analysis Of The Influence Of Wastewater Disposal On The Induced Seismicity In Oklahoma, Usa, Kristine Nagy

Graduate Theses and Dissertations

Seismic records over the past decade and a half have indicated a significant increase of earthquakes in the state of Oklahoma and the evidence has strongly suggested the rise is likely caused by substantial wastewater disposal operations, a subsequent process of oil and natural gas extraction. This study investigated the relationship between wastewater disposal via injection wells, and its influence on inducing earthquakes in Oklahoma by employing Geographic Information System (GIS) tools for geospatial data integration and spatial analysis using United States Geological Survey (USGS) earthquake data and Underground Injection Control (UIC) Class II well data sourced from the Oklahoma …

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 …

Impacts Of Climate-Smart Agriculture On Greenhouse Gas Emissions In Southeast Arkansas Cotton, Cassandra Seuferling

Graduate Theses and Dissertations

Increasing concerns of climate change have led to the evaluation of greenhouse gas (GHG) emissions [i.e., carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O)] from agroecosystems, particularly cotton (Gossypium hirsutum) production. Technology improvement and conservation agriculture methods [i.e., reduced-tillage (RT) instead of conventional tillage (CT) and the use of cover crops (CC)] require further evaluation regarding GHG emissions from Arkansas cotton production. The first objective of the study was to evaluate four gas-flux-determination methods [i.e., linear or exponential regression models, with negative fluxes (WNF) included in the dataset or replacing negative fluxes (RNF)] on CO2, CH4, and N2O fluxes …

Privacy Protection In Machine Learning: Methods For Structured And Unstructured Data, Karuna Bhaila

Graduate Theses and Dissertations

As machine learning models become increasingly integrated into data-driven decision-making, the protection of sensitive information throughout the model lifecycle is a paramount concern. As these models process and memorize sensitive, proprietary, or personal data, they risk leaking information through their outputs or internal states, especially in domains such as healthcare and finance. The protection of data in machine learning has thus been a crucial field of study. Within this paradigm, researchers have studied theoretical and application-oriented mechanisms for realizing privacy protections for various data formats. Nonetheless, privacy in machine learning still has many open problems, especially with the emergence of …

Groundwater Responses To Tidal Restoration At Pelican Marsh And Sea-Level Rise In Elkhorn Slough, California, Julie E. Nelson

Graduate Theses and Dissertations

Coastal wetlands represent a dynamic but vulnerable terrestrial-aquatic interface with future resilience controlled by historic hydrologic alterations, sea-level rise, and groundwater extraction. In diked coastal settings, poor tidal connectivity can degrade habitat from the impoundment of freshwater or hypersaline surface waters. Restoring tidal connectivity could foster marsh revitalization, but altering the surface hydrologic system could lead to unwanted salinity intrusion in shallow aquifers. Saline groundwater intrusion degrades freshwater quality and, while inevitable with sea-level rise, could be accelerated with tidal marsh restoration. To investigate this, I developed a steady-state groundwater flow model of Pelican Marsh, a diked, muted tidal marsh …

Sedimentary Provenance Of The Pliocene – Pleistocene Giant Foresets Formation, Offshore Taranaki Basin, Aotearoa New Zealand, Anurag Kulkarrni

Graduate Theses and Dissertations

Sediment input and dispersal in the Taranaki Basin, offshore New Zealand’s North Island, was influenced by a complex interplay of ocean currents and sea level fluctuations over the late Neogene. This period of time was recorded in the Pliocene-Pleistocene Giant Foresets Formation (GFF) that represents a rapid progradation of the western continental shelf into the basin. High sedimentation rates during this time were likely a major contributing factor to the emplacement of multiple, large-scale (up to ~3,500 km3) mass-transport deposits (MTD) that were caused by shelf and slope collapse within the GFF of similar scale to that of the well-studied …

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 …

Developmental Mathematics As A Potential Barrier To Degree Completion In Community Colleges, Cynthia Bernice Fletcher

Graduate Theses and Dissertations

Developmental mathematics is often seen as a barrier to student progression in community colleges, especially for students pursuing Associate of Science degrees requiring math coursework. This quantitative, ex post facto, non-experimental study examined how developmental mathematics factors predicted Associate of Science degree completion at a two-year community college in the West South-Central United States. Specifically, it assessed how academic performance in developmental mathematics, placement method, math pathway, and number of developmental math courses related to Associate of Science degree completion, as well as differences across student subgroups. Archival institutional data were used for first-time-in-college students across four cohorts: 2018, 2019, …

Effects Of Wetting And Drying On Nutrient Mobilization In Aquatic Systems, Karessa Grace De La Paz

Graduate Theses and Dissertations

Wetting and drying cycles are a fundamental, but underexplored, control on nutrient mobilization and transport in aquatic ecosystems. Such intermittency impacts approximately 60% of the world's streams and rivers, and the extent is expected to increase with future climate change. Additionally, aging reservoir infrastructure is driving restoration efforts, like large-scale drawdowns, that can expose benthic sediments to extended dry periods before refilling. Despite the prevalence of wet-dry cycles, how they influence nutrient transport and mobilization across different aquatic ecosystems remains poorly understood. To address this gap, this thesis presents two complementary studies exploring nutrient dynamics in Arkansas aquatic ecosystems that …

A Spatial Analysis Of Socioeconomic Characteristics And Community Vulnerability To Natural Hazards In Arkansas, Emma Steuber, Brad Peter

Geosciences Undergraduate Honors Theses

Natural hazards have become more frequent in recent years, and because of their destructive impacts, it is crucial to locate regions that are most affected historically by hazardous events and identify what socioeconomic characteristics co-occur spatially. While many studies have analyzed this relationship at national and state scales, few have focused specifically on Arkansas, and even fewer have incorporated multiple socioeconomic characteristics. Comparing the spatial distribution of community characteristics in Arkansas with zones of common natural hazards is essential for identifying who is most at risk. This study utilizes Geographic Information Science (GISci) to examine and map the spatial relationship …