Digital Commons Network^™

A Hybrid Llm-Srgm Framework For Ai-Enabled Reliability Assessment In Safety-Critical Software Systems, Caleb Stone, Shrenik Jadhav

Discovery Day - Daytona Beach

Ensuring the reliability of software intensive and safety critical systems is a persistent challenge across aerospace, defense, transportation, and other mis- sion focused domains. Traditional software relia- bility growth models (SRGM) provide useful quanti- tative insight into defect discovery trends, but they rely mostly only on numerical failure data and do not use the rich contextual information contained in test logs, anomaly reports, and engineering notes. This paper presents a hybrid framework that com- bines semantic features extracted by a large lan- guage model (LLM) with a non-homogeneous Pois- son process (NHPP) based software reliability growth model. The LLM analyzes …

Cars Imass - Comparing Llm Vs Human Operator Effectiveness In Multi-Agent Swarm Coordination, Gatlin Nelson

Discovery Day - Daytona Beach

Title: Dual-Perspective Risk Analysis for Human-LLM Decision Comparison in UAV Swarm Navigation   Unmanned aerial vehicle (UAV) swarms operating in low-altitude wireless network environments encounter localized disruptions that degrade positioning and navigation metrics. These disruptions are modeled as geographic failure zones with defined boundaries. A UAV discovers a zone by entering it and observing degraded performance on its onboard systems. This work assumes that affected UAVs can autonomously retreat to safety using onboard sensors and focuses on the subsequent rerouting decision. Once recovered, the system generates candidate repositioning points surrounding the vehicle, each scored using Conditional Value-at-Risk (CVaR). A human operator …

Social Attributions Of Moving Shapes: Comparing Qualitative Analyses Of Humans Vs. Ai, Angel Hinojosa

Discovery Day - Daytona Beach

As technology becomes more intelligent, the relationship between humans and machines is rapidly shifting. Whether a machine is perceived as a capable partner or a source of wariness often depends on the intentions and abilities, we attribute to it. My research in the InTeRACT Lab seeks to empirically assess these perceptions by comparing how we view different nonhuman agents, ranging from animals to robots.   This study analyzes qualitative data from a task where participants viewed animations of moving triangles. While the videos remained the same, participants were told the shapes represented either humans, robots, dogs, or inanimate shapes. Previous quantitative …

A Survey On Machine Learning Applications For Operating System Fingerprinting, Siri Siqveland

Discovery Day - Daytona Beach

In the modern age of computers and interconnected networks, cybersecurity and cyber-attackers are evolving in tandem to exploit each other’s vulnerabilities. One technique used by both parties is Operating System Fingerprinting (OSF): with the knowledge of what Operating System a target system is running, innate vulnerabilities can be identified and patched or exploited. Historically, OSF utilizes two main methods: passive and active—the former trades accuracy with undetectability while the latter is generally more detectable but more accurate. However, recent work has combined OSF with Machine Learning (ML) to improve accurate identification. The work presented here is a survey for the …

Phaëthon System, Brady Roudabush, Lauren Gallo, Emelia Thompson, Jacob Woods

Discovery Day - Daytona Beach

Phaëthon System is the project name for the Search and Rescue Drone Initiative. This initiative will improve the current search and rescue drone industry by introducing new techniques to get through dense forest canopies and other places where an overhead view is not useful. The Phaëthon System uses a swarm of drones that can penetrate under the tree canopy to map and search with the utmost efficiency and safety for rescuers. A command drone is launched to survey the overall search area, and set up a communications and data link. The next component is then released, which is a swarm …

Stress-Triggered Automation Reliance, Jazmin Elek

Discovery Day - Daytona Beach

Automation is widely used in complex systems and includes any process that replaces human motor, sensory, or cognitive functions with machines or computers (Norman, 1996). As automation becomes more common, understanding how humans trust and interact with these systems is critical. Trust can be measured by whether users override automation or blindly follow its prompts (Norman, 1996).   Artificial intelligence (AI) introduces additional complexity by enabling systems to learn patterns from data it generates. AI performs tasks with the ability to learn from experience (NASA, 2024). AI builds internal databases that can mimic human-like responses (Norman, 1996). However, AI systems can …

Modeling Doppler-Shifted Solar Spectra From Simulated Asteroidal Dust Populations Using Orbital Evolution Codes, Skylar G. Butler, Jarrett Dieterle

Discovery Day - Daytona Beach

We investigate how orbital properties of interplanetary dust particles produce Doppler-shifted solar absorption lines using synthetic spectra generated from particle outputs of a numerical orbital evolution code. The code is based on the Ipatov dynamical model, written in Fortran and using the SWIFT integration package to track the evolution of dust particles originating from asteroid and comet populations. The program reads input files containing particle orbital elements and heliocentric positions, along with planetary parameters and integration settings, and computes the time evolution of particle trajectories under gravitational perturbations. The resulting particle states are then used to generate synthetic spectra by …

Dcat - Distributed Computing And Analysis Tool, Asher Zwickel, Jacob Burdge

Discovery Day - Daytona Beach

This project uses distributed computing to process and analyze large datasets related to cyber breaches and attacks. Its main goal is to find patterns between initial cyber incidents and what happens next. It looks at whether responses tend to escalate, calm down, or stay about the same over time. Understanding this helps explain how digital conflicts develop and whether they follow predictable paths. The project was built as part of university research and runs on custom software across a cluster of 17 Chromebooks. While the system can study many topics, it is currently focused on cyber activity. The software uses …

Investigating The Spatial Scales Of Ionospheric Irregularities Using Wavelet Analysis, Nash Mcleod

Discovery Day - Daytona Beach

Investigating the Spatial Scales of Ionospheric Irregularities Using Wavelet Analysis:   Ionospheric radio wave scintillation arises from plasma density irregularities in Earth’s ionosphere. Consequently, rapid fluctuations occur in the phase and amplitude of Global Navigation Satellite System (GNSS) signals and can impact communication and navigation systems. These irregularities span from a wide range of spatial and temporal scales and evolve dynamically under the influence of magnetosphere-ionosphere (MI) processes. We investigate phase and amplitude scintillation events using Continuous Wavelet Transform (CWT) to study the spatial evolution of ionospheric irregularities. These irregularities are thought to be formed via different plasma mechanisms such as …

Stormtrack: A Regime-Aware Classifier-Router Architecture For Multi-Horizon Kp Index Forecasting, John Rendleman

Discovery Day - Daytona Beach

STORMTRACK: A Regime-Aware Classifier-Router Architecture for Multi-Horizon Kp Index Forecasting Current algorithms in operational space weather face extreme difficultly predicting the Kp geomagnetic index beyond 24 hours, a lead time that is critical for protecting high-frequency communications and infrastructure. Most regression models are optimized for quiet conditions, which dominate the data, leading to systematic underpredictions of storm events that cripple space infrastructure. Probabilistic approaches and physics-based numerical models also falter due to the same class imbalance plaguing standard regressors at multi-day lead times. The ICARUS 6 architecture addresses this by splitting the forecasting component into quiet and storm regimes, which …

Accelerating Search And Rescue Response: A Simulation Study On The Dynamic Efficiency Of Flocking-Enabled Drone Swarms, Sophia Beckwith, Carys Del Prete

Discovery Day - Daytona Beach

This project explores how imitations observed in animal group behavior, specifically flocking in birds, can be applied to the functionality of autonomous drone systems to aid in search and rescue efforts. The goal is to demonstrate how incorporating code based on the Boids, Vicsck and predictive control linear algebraic mathematical models for drone flight controls and the collective behaviors of flocks will increase the efficiency of drone maneuvers, allowing them to reorganize and fill gaps when one is removed. A MATLAB-based simulation was developed to model the behaviors using research conducted on the symmetric and synchronized behaviors observed from flocks …

Bridging The Gap: Cybersecurity And Occupational Safety Frameworks In Ai Data Centers, Athena Leader

Discovery Day - Daytona Beach

Bridging the Gap: Cybersecurity and Occupational Safety Frameworks in AI Data Centers   As artificial intelligence infrastructure expands, AI data centers represent a critical and underexamined convergence of cybersecurity and occupational safety risk. Existing frameworks such as NIST, OSHA, and ISO standards were largely developed in isolation, leaving significant gaps in how organizations manage risks that are simultaneously digital and physical in nature. This study investigates the gaps and overlaps between cybersecurity and occupational safety frameworks as they apply specifically to AI data center environments. Drawing on a targeted literature review of established regulatory and standards-based frameworks, this research identifies where …

Demonstrating Superresolution In Radar Range Estimation Using A Denoising Autoencoder, Robert Czupryniak, Abhishek Chakraborty, Andrew N. Jordan, John C. Howell

Mathematics, Physics, and Computer Science Faculty Articles and Research

We apply machine learning methods to demonstrate radar range superresolution using a denoising autoencoder trained without supervision. Focusing on the estimation of a single physical parameter, the separation between two scatterers in the subwavelength regime, we constrain the network to a one-dimensional bottleneck layer with its size matched to the parameter dimensionality. We find that the bottleneck layer forms a reproducible, monotonic mapping with the true separation, showing that the network learns a low-dimensional representation directly aligned with the underlying physical parameter. We further show that this representation preserves the Fisher information of the signal, indicating that the network recovers …

The Use Of Machine Learning Models For Predicting The Dielectric Strength Of Gases, Matthew Mileski, Paul W. Groth, Timothy S. Wolfe, Adib J. Samin

Faculty Publications

Technological advancements in high voltage systems have pushed sulfur hexafluoride (SF6) to its operational limits. Furthermore, this gas has other drawbacks including a high liquefaction temperature and a high global warming potential. Therefore, there has been an urgent need to find alternative gases with high dielectric strength (DS). In this work, density functional theory (DFT) is used to calculate molecular descriptors that are fed into an artificial neural network (ANN) and a random forest (RF). These machine learning (ML) models are then used to predict the DS for hundreds of molecules. A finite element model (FEM) is also used to …

Measuring The Tokenization Premium: A Cost Audit For Underserved Language Communities, Avijit Roy, Proma Roy, Hrishitva Patel

Publications and Research

Large language models are increasingly deployed as general-purpose educational and technical assistance systems, but their basic infrastructure does not treat languages equally. One underexamined source of disparity is tokenization: semantically equivalent content can require substantially different token counts across languages, affecting API cost, latency, and usable context length before a model is even invoked. We introduce the Tokenization Equity Audit (TEA), a reproducible benchmark for measuring tokenization premiums in technical tutoring content. TEA evaluates three widely used tokenizers, GPT-4o’s o200k base, Qwen2.5-7B, and Mistral-7B, on a 120-item Python debugging corpus translated from English into Bengali, Hindi, Arabic, Tamil, and Yoruba. …

Enabling Asl Digital Communication Under Poor Internet Access, Swann Thantsin

Student Theses

Video conferencing degrades asymmetrically. When bandwidth falls, a hearing caller loses picture quality and keeps the conversation; a deaf and hard of hearing signer, whose language is carried entirely in the visual modality, loses the conversation. This thesis asks whether signed video reduced to the rates at which commercial platforms fail can be reconstructed at the receiver well enough to keep signing legible. A twostage reduction pipeline crops to the signer and transmits the face and hands at higher fidelity than their surroundings, achieving a reduction of approximately 99%; reconstruction uses a recurrent bottleneck mixer architecture, trained both conventionally and …

Adaptive Phishing Url Detection Using Hybrid Fuzzy C-Means Clustering And Xgboost, Muntadher Mohammed Kareem, Rawaa I. Farhan

Karbala International Journal of Modern Science

Phishing attacks continue to evolve in sophistication, rendering static detection methods increasingly ineffective. Existing URL-based approaches suffer from limited adaptability to emerging phishing patterns, mislabeled training data, and insufficient validation protocols. This paper proposes a hybrid phishing URL detection system that integrates Fuzzy C-Means (FCM) clustering with XGBoost classification, enhanced by a novel Micro Adaptive Feature Extractor (MAFE). The system employs a multi-stage pipeline: feature engineering generating 36 statistical and interaction features, MAFE producing 15 adaptive features through class-aware dynamic weighting, micro-pattern detection, and entropy analysis, and FCM with K=2 clusters providing soft membership features to XGBoost. A two-pass confidence-based …

Wip: Developing A Generative Ai Autoethnography Assistant, Jyoti Suhag, Jennifer Drewyor, Kathryn Bugbee, Michelle Jarvie-Eggart, Lynn Albers, Leo Ureel

Michigan Tech Publications

Background: Generative AI (genAI) is transforming educational research, offering new possi-bilities for conducting interviews while local sandboxing minimizes data privacy risks and hallucinations. Purpose: This work-in-progress presents the AI Autoethnography Assistant, a project investigating how large language models (LLMs) can support autoethnographic interview design and execution. Approach: Using prompt engineering grounded in Interpretative Phenomenological Anal-ysis, paraphrasing techniques, and structured follow-up questions, we developed protocols that incorporate po-sitionality and prompt reflection on origin stories and pivotal life moments. Outcomes: Conversations conclude at the user’s discretion, with the AI generating a thematic summary. We tested refined prompts across four platforms (Gemini, Claude, …

Modeling And Simulation Of Solar Battery Charge Controller Using Adaptive Particle Swarm Optimization Mppt Algorithm, Mustafa Sacid Endiz, Göksel Gökkus

Mathematical Modelling and Numerical Simulation with Applications

Implementing an effective Maximum Power Point Tracking method is crucial for optimizing solar energy harvesting against environmental fluctuations like solar radiation and temperature. This paper introduces a novel approach for modeling and simulating a solar battery charge controller, using a modified Particle Swarm Optimization algorithm. The power stage of the system is based on a SEPIC converter, which is employed to manage the power conversion and improve the energy transfer to the battery. The developed circuit model is evaluated under various radiation levels at a constant temperature, as well as under different temperature levels at a constant radiation. Simulations are …

Rockin’ Rover On The Rainbow Road, Michael Kolta, Lawrence Burgee, Ying Yuan

Transformations

This paper presents a progressive series of age-appropriate lesson plans for grades K-12 that all use the same interdisciplinary activity to educate students about Science, Technology, Engineering, Art, and Mathematics (STEAM) simultaneously. Technology from Texas Instruments (TI) was employed including a TI Nspire graphing calculator that can run Python programs, a TI Innovator Hub, and a TI Rover. The TI Rover is a small, robotic car that has sensors and is controlled by the calculator via the Hub hardware interface. A Python program was developed that uses the color sensor in the Rover to detect the color on colored paper …

Real Bullets, Plastic Guns: Evaluating The Strength Of 3-D Printed Gun Parts, Maria Latenia Mayol

Student Theses

Privately made firearms (PMFs), often referred to as “ghost guns,” are firearms manufactured or assembled by individuals rather than federally licensed manufacturers. Although the terms are frequently used interchangeably, “ghost gun” more specifically describes an unserialized firearm, whereas PMFs include a broader range of firearms produced through nontraditional manufacturing methods. PMFs may be entirely 3-D printed, assembled from partially completed firearm kits, or constructed by integrating additively manufactured components with commercially manufactured firearm parts. The increasing accessibility of additive manufacturing and widespread dissemination of computer-aided design files have raised concerns about concealment, regulation, and forensic evasion, particularly when factory-manufactured components …

A Granularity-Centered Taxonomy Of Personalized Federated Learning, Ei Ei Nyein Chan, Sergei Chuprov, Pretom Roy Ovi, Kamrul Hasan

Computer Science Faculty Publications

Personalized Federated Learning (PFL) has emerged as a key approach to address performance degradation in FL systems under heterogeneous client data. While existing surveys typically categorize PFL methods based on optimization strategies or system-level mechanisms, they often overlook a fundamental question: where is personalization embedded within the model architecture? In this survey, we bridge this knowledge gap and introduce a granularity-centered taxonomy that organizes PFL approaches according to the structural depth of personalization, ranging from head-layer and layer-wise adaptation to model-wise and parameter-wise customization. This novel perspective helps practitioners select appropriate personalization strategies based on model architecture, data heterogeneity, and …

Closing The Awareness–Behavior Gap: A Role-Based Phishing Training Framework For Higher Education, Ranylene O. Olaybal, Ryan A. Olaybal

Journal of Cybersecurity Education, Research and Practice

Phishing remains one of the most persistent cybersecurity threats facing higher education institutions, where diverse user populations and highly connected digital environments increase exposure to social engineering attacks. Although cybersecurity awareness initiatives are widely implemented, high awareness does not always translate into secure behavior. This study examined phishing awareness, phishing-related practices, phishing susceptibility, and phishing experiences among college students, teaching faculty, and administrative staff in a private higher education institution in the Philippines. Using a quantitative cross-sectional design, data were collected from 553 respondents through a validated survey instrument and analyzed using descriptive statistics, one-way analysis of variance, Tukey's honestly …

Ai And The Music Industry: Its Current Status And A Speculative Projection Of Its Evolutionary Trajectory, Rhett D. Morris, Clayton Rosati, Stefan Fritsch

Honors Projects

Music serves as one of society's biggest cultural outlets, allowing millions to share in what used to be a uniquely human form of expression. The commodification of music has built a huge industry full of companies and platforms that have used technology and property laws to shape music's relationship with the public. This study aims to look into the future to see how AI and its implementation could affect the structure of the music industry. To look into the future, this piece establishes two of the most pressing kinds of AI technology for the music industry and looks to contextualize …

Clinical Utility Of An Fda-Authorized Artificial Intelligence Imaging Platform In Interstitial Lung Disease Diagnosis, Arjun Prakash Tambe, Ryan Boente, Gautam George, Fayez Kheir, Omid Tahamtani Omran, Kavitha Selvan

Division of Pulmonary, Allergy, and Critical Care Medicine Faculty Papers

Background/Objectives: The diagnosis of interstitial lung disease (ILD) is challenging and frequently delayed. Clinically accessible and minimally invasive diagnostic tools are needed to expedite the diagnosis of ILD while minimizing risk to patients. Fibresolve is an imaging artificial intelligence (AI) tool recently approved by the Food and Drug Administration (FDA) for use in ILD diagnosis and made available to clinicians. The objective of this study was to describe its utility in clinical practice. Methods: We conducted a prospective, observational study of patients across the United States (US) in whom Fibresolve was utilized during routine clinical practice between July 2024 and …

Dynamind: A Dynamic Learned Index For Update-Intensive Workloads, Jingxian Cheng, Yingfang Wang, Tianqing Zhu, Xu Yang, Ningning Cui, Jianxin Li

Research outputs 2022 to 2026

Learned indexes leverage machine learning models to approximate data distributions and predict key positions, offering better performance than traditional index structures such as B+Trees. As data in real-world applications evolve rapidly, the timely and efficient updating of learned indexes has become an increasingly important research problem, attracting growing attention in recent studies. However, under update-intensive workloads with frequent insertions and deletions, existing learned indexes cannot update the model in a timely manner. Moreover, they ignore the impact of deletions on model accuracy. These limitations lead to degraded prediction accuracy and increased query latency, undermining the core advantage of learned indexes. …

A Comprehensive Study And Performance Optimization Of Shared Virtual Memory, Bennett Cooper

All Dissertations

High performance computing (HPC) is dominated by heterogeneous systems that mainly derive performance from GPU accelerators. A majority of HPC applications have gravitated towards GPUs, which require explicit programming. Historically, programming explicitly to take advantage of GPUs has proven a significant barrier in fully utilizing GPU acceleration. Unified Memory (UM) is a technology designed to lower the barrier of entry to GPU programming by merging all memory domains of a system. While UM provides easier access to the capabilities of heterogeneous systems, the performance cost of UM greatly detracts from the benefit. Additionally, UM is implemented on a vendor-by-vendor basis …

Painting A Scene: 3d Painterly Rendering From Curves To Rebelle, William M. Luttrell

All Theses

Stylized 3D rendering has seen much development and success over the past few years. From Spider-Man: Across the Spider-Verse to The Bad Guys, many studios have developed tools to incorporate stylistic elements from graphic novels, comic books, watercolor paintings, and more into their productions. This stylization process incorporates the pacing, visual style, and themes from the source medium into the animated work, allowing a much greater freedom of expression for artists and directors.

Inspired by these films as well as the needs of the short film Kate Shelley and the Bridge of Darkness currently in production, This paper presents …

Causal Discovery In Photospheric Magnetic Field Time Series For Interpretable Solar Flare Prediction, Nathan W. Nelson

All Graduate Theses and Dissertations, Fall 2023 to Present

Solar flares are capable of damaging many valuable resources, including satellites, power grids, and even human lives. Being able to predict solar flares can allow for proactive measures to be taken that can prevent that damage. Many new deep learning methods for predicting solar flares have shown promise in this task, but the decisions they make are harder to explain to humans. This makes understanding why these models make mistakes difficult, which in turn makes fixing and maintaining them more challenging. We test a recent deep learning method that helps discover relationships between different measurements of the Sun as they …

Graph Perturbation Analysis For Subgraph Counting, Hanhua Xiao, Yuchen Li, Kyriakos Mouratidis

PhD Student’s Publications Collection

Subgraph counting, which involves determining the frequency of a query graph within a data graph, has numerous applications such as query optimization, fraud detection, and evaluating the expressiveness of graph neural networks. Despite its importance, there has been no systematic study on the impact of adversarial graph perturbations on subgraph counts. In this work, we examine the kSub problem, which aims to identify k edge additions that maximize the count of a query graph. We prove that kSub is intractable due to its NP-hardness, even for constant approximation. To address this, we relax the problem into a top-k selection, termed …