Digital Commons Network^™

Tutortech: A Web App For A Smarter And More Efficient Tutoring System, Smarika Pathak

2025 Spring Honors Capstone Projects - Archive

The Computer Science and Engineering (CSE) department faces challenges with managing its tutoring services, especially tracking attendance, booking sessions, and overall management of the tutoring system - all of which severely limits the ability for tutors to connect and engage with students. To help overcome these issues, TutorTech, a web-based application that provides improved management of the tutoring system and supports more engaging learning experiences between students and tutors was designed. Through this project, the aim was to optimize the TutorTech search capabilities - assisting students to find tutors based on skills, while also considering the effect of user interface …

Generating Motivational Messages For Behavior Change: Encouraging Users To Be More Physically Active, Hananeel Pankaj

2025 Spring Honors Capstone Projects - Archive

High levels of sedentary lifestyles can cause adverse effects in individuals’ health. This has prompted researchers to analyze ways to increase physical activity, including the use of Large Language Models (LLMs) to generate motivational messages. While research has found LLMs to be feasible for this task, the findings are limited in availability and scope given that the research focuses on a conversational, chatbot setting—which is not ideal in the real world. This research assesses OpenAI’s GPT-4o mini’s (one of several models powering ChatGPT) ability to tailor messages towards a user. This is done by passing user health data to the …

Navigation Of Unmanned Aerial Vehicle Using Computer Vision In Raytheon Drone Competition, Joseph R. Pavlik Iii

2025 Spring Honors Capstone Projects - Archive

A major problem with using GPS to navigate an unmanned aerial vehicle is that GPS signals do not accurately work while inside a building. This work presents the usage of the Simultaneous Localization and Mapping library, ORB-SLAM2, in C++ to solve this issue. By using the camera attached to the unmanned aerial vehicle, a map of the area covered by the drone will be created, and landmarks in area will be utilized to navigate throughout the interior of the building without the GPS. Based on previous studies, this navigation method should be viable. Preliminary tests show that this method will …

Triple-Valued Neutrosophic Set, Quadruple-Valued Neutrosophic Set, Quintuple-Valued Neutrosophic Set, And Double-Valued Indetermsoft Set, Takaaki Fujita

Neutrosophic Systems with Applications

Concepts such as Fuzzy Sets, Neutrosophic Sets, Rough Sets, and Plithogenic Sets have been extensively studied to address uncertainty, finding diverse applications across various fields. A Double-Valued Neutrosophic Set (DVNS) extends traditional neutrosophic sets by introducing two distinct indeterminacy components: one leaning towards truth and the other towards falsity. In this paper, we explore Triple-Valued Neutrosophic Sets, Quadruple-Valued Neutrosophic Sets, and Quintuple-Valued Neutrosophic Sets, as well as an extension of the Indetermsoft Set, termed the Double-Valued Indetermsoft Set. Note that related concepts such as the Multi-Valued Neutrosophic Set and the n-Valued Refined Neutrosophic Set have already been established.

Supporting Novice Programmers With Scaffolded And Open-Ended Generative Ai Interfaces: Insights From A Design-Based Research Study, Justin Olmanson, Azadeh Hassani, Gretchen K. Larsen

Department of Teaching, Learning, and Teacher Education: Faculty Publications

In this study, we explore student experiences in coding and learning programming with scaffolded and unscaffolded generative AI interfaces. Specifically, we supported higher education students in using ChatGPT, an open ended interface for interacting with generative AI; and Giuseppe, a specialized interface with an OpenAI backend service that offers personalized supports specifically for helping students overcome early-stage challenges in learning to code, and working on education technology prototyping projects. This study contributes to the field by offering design insights for scaffolding initial learning interactions between generative AI interfaces and novice programmers. Our findings suggest that those new to coding welcome …

Air And Missile Defense Threat Scenario Variation To Reduce Pretest Sensitization, Video Games As A Case Study, Julie Renee Szekerczes

All-Inclusive List of Electronic Theses and Dissertations

This study uses fixed and variable video game types to measure pretest sensitization as a proxy for repeated and varied threat test scenarios in system performance testing of air and missile defense systems. The pretest sensitization phenomenon exists when repeated exposure to a test condition influences the participant's response. Research shows air and missile defense development correlates with video games, resulting in similar interfaces and computer operating environments. Department of Defense acquisition test and evaluation results must reflect system performance without prior knowledge of the threat scenarios confounding the results. System performance results inform acquisition decisions, such as further funding …

Usage Of Natural Language Processing And Deep-Learning Techniques On Thematic Apperception Tests To Predict Big Five Personality Traits, Blayten Jones

Electrical Engineering and Computer Science Undergraduate Honors Theses

The usage of personality as a method of behavioral prediction and outcomes of success has grown considerably over the last few decades. This project explores predicting user personality profiles via the Big Five personality index through the integration of advanced natural language processing techniques as well as neural networks. Using a dataset provided by Dr. James W. Pennebaker, participants analyze an image—formally referred to as a thematic apperception test—and write a thorough paragraph describing the details. This free-form text, along with their personality test results, is captured in a structured dataset. Many deep-learning and machine learning models have been used …

A Novel Approach To Attention-Based Models In Image Completion: Weighted Spatial-Attention Using Radial Distance, Tyler D. Kuper

Electrical Engineering and Computer Science Undergraduate Honors Theses

Humans infer missing visual information by focusing on spatial relationships in the context of their surroundings. Machine learning aims to replicate this skill through image completion, a fundamental task in current computer vision research. While advances in self-attention layers have recently enhanced generative machine learning models for text, these mechanisms still currently lack the capability to handle sparse image completion efficiently. We introduce a distance-based attention mechanism that uses radial-based weights to efficiently reconstruct an image. We compare this attention mechanism with self-attention and a fully connected network on an image completion task using the MNIST dataset. Our results show …

Fairness In Recommender Systems: Balancing Bias In Academic Paper Selection, Zachary Bergin

Electrical Engineering and Computer Science Undergraduate Honors Theses

Bias in academic paper selection remains a consistent issue, even within processes designed to promote fairness, such as double-blind peer review, bias stays persistent. In this paper we investigate demographic bias while particularly focusing on racial bias in the process of selecting academic papers and explore the impact of fairness aware recommender systems on the demographic parity. To build an effective system our focus is on the Special Interest Group on Computer Human Interaction (SIGCHI) a pillar in the community, we develop a neural network-based recommender system that uses real demographic data collected by other systems withing the context of …

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, …

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 …

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 …

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 …

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 …

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 …

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.

Computer Vision In Soccer: Yolov11 Analytics Engine For Quantifying Game Strategy, Connor S. Maurer

Data Science Undergraduate Honors Theses

Single-shot object detection capabilities significantly reduce computational overhead for real-time computer vision in sports analytics at 60 FPS. YOLO11’s lightweight CNN gives promising accuracy while meeting the low-latency demand of dynamic soccer matches. As data-driven approaches take over the sport of soccer, efficient player tracking systems become critical for informing coach’s strategies. I prototype the ETL (Extract, Transform, Load) process of data collected from a single- shot detection program and evaluate its viability for estimating player fatigue. YOLO11 detects players, the ball, and other characteristics, with the output transformed by homography to estimate the positions in the real world. These …

Reinforcement Learning, Modeling Markets, And Professional Basketball Free Agency, Jacob Cohn

Computational and Data Sciences (PhD) Dissertations

This dissertation presents a reinforcement learning-based approach to modeling and optimizing decision-making in professional basketball free agency and related economic environments. A Markov Decision Process (MDP) framework is introduced to capture the strategic interactions of NBA teams bidding for free agents under budgetary and roster constraints. To address computational scalability challenges, a reinforcement learning (RL) environment is developed, leveraging Proximal Policy Optimization (PPO) to approximate optimal policies for team decision-making.

Empirical results demonstrate that the RL agent successfully learns strategic bidding behavior that aligns with dynamic programming benchmarks in simplified settings while scaling effectively to larger, intractable environments. The study …

Capturing The Digital Scene: Applying Routine Activity Theory To Iot Smart Frames, Jordan Bakar

Theses/Capstones/Creative Projects

This project investigates the forensic risks and investigative challenges posed by smart frames, which are WiFi-enabled Internet of Things (IoT) devices used to store, display, and share digital media. These devices often collect and synchronize sensitive media, metadata, and behavioral logs across cloud ecosystems that lack adequate transparency and privacy safeguards. Routine Activity Theory (RAT) provides a criminological framework for examining how the convergence of a motivated offender, a suitable target, and the absence of capable guardianship creates opportunities for misuse and forensic exploitation. Smart frames represent ideal targets because of weak default security configurations, passive data synchronization, and limited …