Computer Sciences Commons^™

Managing Graphical Fidelity With Stylized Shaders For Independent Game Development, Benjamin A. Edens

Electrical Engineering and Computer Science Undergraduate Honors Theses

This paper outlines work performed by the author within the Unity3D game engine to gain preliminary experience with technical art implementation and suggests design choices that could be useful to other students or independent game developers to manage complexity within their games while maintaining visual appeal. The final product of the discussed project is a small game consisting of an outdoor urban city environment as well as an interior aquarium environment. This paper begins with the author’s motivations and goals for the project before describing the implementation of specific aspects of technical art, including 3D modeling, rigging, animation, level design, …

Securing Distributed Energy Resources: A Secure Gateway For Modbus To Solid Communication Using A Raspberry Pi, Donna R. Thakadipuram

Electrical Engineering and Computer Science Undergraduate Honors Theses

As distributed energy resources (DERs) such as solar panels, wind turbines, and battery storage systems become more common, securing their communications has become increasingly important. Many of these systems still rely on legacy communication protocols such as Modbus, which were not designed with cybersecurity in mind. This project addresses this challenge by developing a secure communication gateway that allows Modbus RTU devices to interface with decentralized Solid pods, which are personal data storage units that give users control over their information. This system is built on a Raspberry Pi 4, and it translates telemetry data from Modbus into a Solid-compatible …

Leveraging P4 Programmable Switches For Resilient Operation And Design Of Phasor Measurement Unit Networks, Eva Casto

Electrical Engineering and Computer Science Undergraduate Honors Theses

The power grid utilizes a device called the phasor measurement unit (PMU), allowing power system administrators to remotely monitor and manage the state of the grid in Wide Area Monitoring Systems (WAMS). The advantages of PMUs – such as fine-grained, time-synchronized measurements and efficient, decentralized monitoring – are what make them key devices in the power grid. However, PMU technology also comes with new threats of the digital age, like malfunctions and cyberattacks, which can result in missing and faulty measurements that compromise power grid observability. P4 programmable networks can be used to detect faulty PMU data in a decentralized, …

Defend: A 1m Dataset Foundation Model For Tobacco Analysis, Matthew J. Shepard

Electrical Engineering and Computer Science Undergraduate Honors Theses

The study of tobacco imagery and marketing is a complex challenge that involves extremely large datasets. It also demands a detailed analysis of the so- cial context and specific types of tobacco being marketed. Despite major recent advances in computer vision and foundation model technology, this still poses a substantial challenge. Through the DEFEND model, we aspire to address these obstacles by integrating features such as multimodal learning, hierarchical under- standing, and feature extraction to develop a foundation model designed to handle the unique challenges of tobacco image analysis. One of the core elements of DE- FEND is the Tobacco …

Multimodal Learning For Visual Perception And Robotic Action, Taisei Hanyu

Electrical Engineering and Computer Science Undergraduate Honors Theses

Multimodal learning aims to weave information from images, language, depth, and other sensors into one coherent representation, much as people naturally combine sight, speech, and sound. Progress toward that goal is slowed by three gaps: vision encoders that cannot balance crisp object boundaries with global context, 3-D semantic maps that are computationally prohibitive for real-time, open-vocabulary queries, and vision-language-action pipelines that depend on large token pools with weak relational grounding.

We first introduce AerialFormer, a lightweight hybrid of convolutional and Transformer layers that captures long-range structure without sacrificing fine detail. On the large-scale iSAID benchmark it reaches 69.3% mean IoU, …

Analyzing Unmanned Aircraft System (Uas) Incidents From Nasa Asrs Data Using Unsupervised Machine Learning, Kacey Haws

Electrical Engineering and Computer Science Undergraduate Honors Theses

The NASA Aviation Safety Reporting System (ASRS) assembles voluntarily submitted aviation safety incident reports in their database to act on the information provided. This database allows the government, companies, and citizens to submit incident or situational reports to its database to discern recurring issues in the National Aviation System (NAS) so that the proper officials can act [1]. The narratives provided in these reports are text-based, resulting in large amounts of data to process. Previous work in the University of Arkansas Aerospace Systems Engineering and Transportation Laboratory (ASYST) lab involved parsing unmanned aircraft system (UAS) incident reports manually. While these …

An Introductory-Level Undergraduate Cs Course That Introduces Parallel Computing, Tia Newhall, Kevin C. Webb, Vasanta Chaganti, Andrew Danner

Computer Science Faculty Works

We present the curricular design, pedagogy, and goals of an introductory-level course on computer systems that introduces parallel and distributed computing (PDC) to students who have only a CS1 background. With the ubiquity of multicore processors, cloud computing, and hardware accelerators, PDC topics have become fundamental knowledge areas in the undergraduate CS curriculum. As a result, it is increasingly important for students to learn a common core of introductory parallel and distributed computing topics and to develop parallel thinking skills early in their CS studies. Our introductory-level course focuses on three main curricular goals: 1) understanding how a computer runs …

Diversity-Augmented Training For Generalizable Ai Agents, Wenjun Li

Dissertations and Theses Collection (Open Access)

Deep Reinforcement Learning (RL) has achieved remarkable success over the past decade, from superhuman performance in video games to real-world applications like robotics. However, RL models often lack generalization, making them unreliable when deployed in unfamiliar scenarios. For example, robots must adapt to varying terrains with different slopes and obstacles, yet standard RL training does not explicitly promote such adaptability. While various methods have been proposed to enhance RL robustness, achieving reliable generalization remains an open challenge.

This dissertation focuses on improving the generalization capability of agents in three major settings: infinite horizon RL agents, finite horizon RL agents, and …

Using Gaussian Process Regression To Learn Thermodynamic Equations Of State With Uncertainty Quantification, Austen T. Lee

Chemical Engineering Undergraduate Honors Theses

This study investigates the use of derivative-informed Gaussian Process (GP) models to estimate thermodynamic behavior across temperature and density by building a Helmholtz-based equation of state. Argon, a stable monatomic gas, was chosen as a case study within the vapor region. The GP model was trained using values of experimentally measurable properties found by taking first and second derivatives of the original potential function. Results show that while the GP model offered uncertainty quantification and informed thermodynamic behavior, it predicted values that deviated from the ground truth depending on the property. The model exhibited high confidence in regions with substantial …

Deepfakes On Trial: Developing A High-Accuracy, Court-Admissible Ai Pipeline For Deepfake Detection In Corporate Fraud Litigation, Aiden J. Green

Honors College Theses

As deepfake technology advances, cybercriminals are increasingly using AI-generated videos and audios to impersonate executives and carry out sophisticated CEO fraud schemes. These synthetic forgeries target human trust and corporate communication systems, creating an urgent need for forensic tools capable of authenticating digital evidence with legal accuracy. This thesis presents a forensic-grade AI deepfake detection pipeline designed for this purpose, emphasizing courtroom admissibility, reproducibility, and evidentiary integrity. Built entirely with free, opensource tools, the framework combines metadata analysis, AI-powered spectrogram analysis, neural artifact detection, and facial manipulation recognition into a transparent workflow that accurately identifies synthetic media. It was trained …

Exploring The Robustness Of The Effect Of Evo On Intention Valuation Through Replication: Supplemental Material, Yesugen Baatartogtokh, Kaitlyn Cook, Alicia M. Grubb

Computer Science: Faculty Publications

Supplemental material for the paper: "Exploring the Robustness of the Effect of EVO on Intention Valuation through Replication"

Accelerating Knowledge Graph And Ontology Engineering With Large Language Models, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Large Language Models bear the promise of significant acceleration of key Knowledge Graph and Ontology Engineering tasks, including ontology modeling, extension, modification, population, alignment, as well as entity disambiguation. We lay out LLM-based Knowledge Graph and Ontology Engineering as a new and coming area of research, and argue that modular approaches to ontologies will be of central importance.

Education In The Era Of Neurosymbolic Ai, Chris Davis Jaldi, Eleni Ilkou, Noah Schroeder, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Education is poised for a transformative shift with the advent of neurosymbolic artificial intelligence (NAI), which will redefine how we support deeply adaptive and personalized learning experiences. The integration of Knowledge Graphs (KGs) with Large Language Models (LLMs), a significant and popular form of NAI, presents a promising avenue for advancing personalized instruction via neurosymbolic educational agents. By leveraging structured knowledge, these agents can provide individualized learning experiences that align with specific learner preferences and desired learning paths, while also mitigating biases inherent in traditional AI systems. NAI-powered education systems will be capable of interpreting complex human concepts and contexts …

Evaluation Of Pre-Trained Vision Language Models In Challenging Contexts, Kankan Zhou

Dissertations and Theses Collection (Open Access)

The rapid advancement and proliferation of pre-trained vision-language models (VLMs) have heralded a new era in the realm of artificial intelligence (AI), opening up unprecedented opportunities and challenges alike. This dissertation sets forth on an ambitious and comprehensive journey to critically evaluate the performance and limitations of pre-trained VLMs, particularly in complex and challenging contexts that test the bounds of their capabilities. Our focus is twofold: to rigorously assess the extent of bias embedded in these models, and to meticulously scrutinize their reasoning abilities, highlighting parallels and disparities between machine and human cognition.

We initiate our exploration with a targeted …

Enhancing Sub-Optimal Trajectory Stitching: Spatial Composition Rvs For Offline Rl, Sheng Zang, Zhiguang Cao, Bo An, Senthilnath Jayavelu, Xiaoli Li

Research Collection School Of Computing and Information Systems

Reinforcement learning via supervised learning (RvS) has been known as a burgeoning paradigm for offline reinforcement learning (RL). While return-conditioned RvS (RvS-R) predominates across a wide range of datasets pertaining to the offline RL tasks, recent findings suggest that goal-conditioned RvS (RvS-G) outperforms in specific sub-optimal datasets where trajectory stitching is crucial for achieving optimal performance. However, the underlying reasons for this superiority remain insufficiently explored. In this paper, employing didactic experiments and theoretical analysis, we reveal that the proficiency of RvS-G in stitching trajectories arises from its adeptness in generalizing to unknown goals during evaluation. Building on this insight, …

Worldcuisines: A Massive-Scale Benchmark For Multilingual And Multicultural Visual Question Answering On Global Cuisines, Genta Indra Winata, Et. Al

Research Collection School Of Computing and Information Systems

Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date. It includes tasks for identifying dish names and their origins. We provide evaluation datasets in two sizes (12k and 60k instances) alongside …

Full-Stack Web Applications: Industry Standard Frameworks, Libraries & Technologies, Yassine Chahid, Patrick Slattery

Publications and Research

This research explores emerging full-stack web development technologies across front-end, back-end, and DevSecOps domains. It evaluates modern tools including Django, React, and TypeScript—focusing on their key features such as compile-time error checking—through to the development of a web application. By examining documentation for the frameworks Node.js, Next.js, Tailwind CSS, and others, along with the deployment tools Docker and Git for version/release control, the study analyzes how these innovations speed up development, improve existing practices, and have often replaced older technologies. Cloud solutions for tasks such as authentication and deployment will also be evaluated, along with various web-application technology stacks and …

Does The Transit Industry Understand The Risks Of Cybersecurity And Are The Risks Being Appropriately Prioritized?, Scott F. Belcher, Terri Belcher, James Grimes, Lusa Holmstrom, Andy Souders

Mineta Transportation Institute

The intent of this study is to assess the readiness, resourcing, and capabilities of public transit agencies to detect, identify, be protected from, respond to, and recover from cybersecurity vulnerabilities and threats. This study is an update of the 2020 Mineta Transportation Institute (MTI) study, “Is the Transit Industry Prepared for the Cyber Revolution? Policy Recommendations to Enhance Surface Transit Cyber Preparedness.” In the previous study, the authors found that the transit industry was ill-prepared for cybersecurity attacks. Unfortunately, after four years and the development of new, and often free, resources, the situation has not markedly improved. In fact, this …

Managing Software Dependency Risks In Web Applications, Christopher Alan Scott

Electronic Theses and Dissertations

Web applications commonly rely on third-party software dependencies to reduce development time. This thesis examines how vulnerabilities in a dependency chain propagate to compromise an application. It analyzes two vulnerable Markdown libraries from the npm and Composer dependency ecosystems, both of which are used for managing packages in applications developed with JavaScript and PHP. The analysis demonstrates how each library’s sanitizing functions—intended for removing unsafe user input when transforming Markdown text to HTML—are defeated to achieve a cross-site scripting exploit and take control of the application. The paper discusses potential business impacts of a compromise, underscoring the need for security …

The Playground: Redefining The Artistic Experience And Expression With Technology, Meghan Hutto

Undergraduate Honors Thesis Collection

Throughout the artistic community, many creators are exploring new ways to generate innovative works for growing audiences. With the emergence of endless technology, dance makers are searching for mediums to include new lighting techniques and music composition. Across this past year, I have researched and explored new frontiers of artistic mediums to further my project’s artistic expression. By developing and investigating new systems of lighting and musical composition, I created a fifteen-minute work called “Metaphysical Mindscape”. When solidifying the through-line that carried the expression of my piece, I uncovered a piece of myself. The themes that I explored were thoughts …

Automation Of Vulnerability And Patch Management: Information Extraction, Association, And Optimization, Kylie Mcclanahan

Graduate Theses and Dissertations

Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout …

From The Bleachers To The Browser: Redefining Fan Experience With Ar And Ai In Smaller Teams, Jennifer Lee Wunder

Theses

This project documents the creation and deployment of HootyHoo, an interactive augmented reality (AR) mascot experience designed for the O’Fallon Hoots, a small-scale collegiate summer baseball team. Built using accessible, open-source tools such as WebXR, Mixamo, Meshy, Botpress, Claude and ChatGPT, this prototype merges AI-driven conversation with animated 3D avatar interaction—redefining how fans engage with sports organizations digitally. Unlike enterprise-level applications used by professional franchises, HootyHoo is entirely browser-based, eliminating the need for app downloads and ensuring maximum accessibility for families and new fans with smartphones. The experience centers on Hooty, the team mascot, who answers questions about baseball and …

Cross-Dataset Fairness Evaluation Of Transformer-Based Sentiment Models, Sara Zuiran

Theses and Dissertations

With the growing exploration of Natural Language Processing (NLP) systems in decision-making environments, it is essential to evaluate technical and ethical aspects of the dataset and the NLP model to improve fairness. To assess fairness, the thesis examines demographic imbalances in sentiment classification models by evaluating transformer-based models fine-tuned on the Stanford Sentiment Treebank version 2 dataset (SST-2) against the demographically annotated Comprehensive Assessment of Language Model dataset (CALM). This work identifies performance disparities in sentiment prediction across demographic groups by examining sensitive attributes such as gender and race. The study evaluates both the RoBERTa and MentalBERT transformer models using …

Bridging The Gap: Enhancing Devops Security Through Comprehensive Threat Modeling, Ashutosh Jagdish Sonar

Theses and Dissertations

Today, security is an essential component of software development, especially in DevOps environments where rapid and continuous product release cycles are common. Systems are vulnerable to new attacks because traditional security approaches often cannot keep up with the pace of change. The threat modeling approaches used in DevOps are examined in this thesis, along with their advantages, disadvantages, and suitability for use in current software development processes. Well-known frameworks including STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service (DoS), and Elevation of Privilege), Attack Trees, LINDDUN (Linking, Identifying, Non-Repudiation, Detecting, Data Disclosure, Unawareness, and Non-Compliance.), Practical Threat Analysis (PTA), …

How Do Simulated Phishing Attacks Impact Cybersecurity Awareness And The Enhancement Of Security Protocols Among Faculty Members In A University Environment?, Navnoor Sandhu

University Honors Program Senior Projects

Phishing attacks are cyber threats where attackers deceive users into performing actions that compromise the user’s security and benefit the attacker. In 2024 alone, phishing attacks have resulted in estimated damages of around 800 million dollars [1]. In response, many institutions have implemented internal simulated phishing attacks to enhance their employees' cybersecurity awareness. This training exercise has been proven beneficial in improving cybersecurity awareness on an enterprise scale[4]. This study aims to evaluate the potential effectiveness of a simulated phishing attack within a university setting, which is a relatively unseen practice thus far. Universities, like other secure organizations, store sensitive …

How Will Artificial Intelligence Impact The Roles, Skills, And Design Processes Of Ux Professionals In The Next 5 Years?, Olivia Behan

Informatics

Artificial intelligence (AI) is changing the way we think about user experience (UX) design. This thesis explores how AI could impact the roles, skills, and design processes of UX professionals over the next five years. The research conducted consists of a mixed-methods approach, combining an online survey administered through Qualtrics to 50 survey respondents and a case study, which included individually interviewing 3 UX professionals over Zoom, to gather data and personal insights. The results show that while AI has the power and potential to make some parts of UX work faster, like research analysis, prototyping, and automating redundant tasks, …

Reducing Stigma Around Neurodiversity Through The Use Of Celebratory Technology Ice Breakers In First-Year Undergraduate Classrooms, Briana Craig

Electrical Engineering and Computer Science (MS) Theses

Celebratory technology for Neurodiversity is a new paradigm in the field of human computer interaction; it focuses on reducing stigma surrounding neurodivergent labels and behaviors. Celebratory technology aims to highlight the strengths of neurodiversity rather than fixing socially undesired traits, shifting the responsibility for change from neurodivergent individuals to society's attitudes. Stigma reduction can be accomplished through providing high quality interactions, where anyone can meet and learn about positive traits in others as well as learn of interests' others have in common, thus reframing neurodivergence as inclusion in human diversity rather than a condition to be stigmatized or objectified. This …

Implementation Of Residual Tandem Neural Networks For Photonic Inverse Design, Ponthea A. Zahraii

Electrical Engineering and Computer Science (MS) Theses

Deep-learning approaches can greatly benefit the modeling and design of nanophotonic and optical structures. Traditional full-wave simulations are time and resource-intensive, which can act as a bottleneck in photonic design. On the other hand, deep-learning approaches for designing the response of nanophotonic geometries can be computationally inexpensive and produce accurate and efficient results. In this project, we specifically investigate the case of optical forces near meta-structures. We propose using an inverse design approach with residual blocks to account for the deep nature of this architecture and inherently address the non-uniqueness problem. A tandem approach, which consists of two interconnected models, …

Survey On Application Of Large Language Models In Network Attack And Defense, Prisha Purohit

Honors Theses

The emergence of Large Language Models (LLMs) has significantly transformed the technological and cybersecurity landscape, introducing both unprecedented opportunities and formidable challenges. With the public release of ChatGPT in 2022, LLMs have gained global prominence, redefining natural language processing capabilities and enabling advancements across various fields. In cybersecurity, these models represent a dual-use technology: while they offer powerful tools for threat detection, automated analysis, and security training, they also pose risks when leveraged by malicious actors for phishing, social engineering, and the creation of evasive malware. This thesis presents a comprehensive literature review exploring the dual roles of LLMs in …

Reconfigurable Python Autopilot Software For Rc Aircraft, Kate Doiron

Honors Theses

No abstract provided.