Digital Commons Network^™

“Made Classes Easier Than A Coloring Sheet”: Student Perceptions And Uses Of Genai, Jessalyn I. Vallade, Renee Kaufmann, Trenton Upchurch

Human-Machine Communication

Student use of GenAI is growing and so are the faculty concerns. With research providing mixed suggestions and approaches, this study sought to understand the student perspective on ethical uses, their own motives and perceived benefits of GenAI use, the risks involved, and their perceptions of susceptibility and severity related to unethical use (i.e., plagiarism/cheating). Results revealed the complexity of student considerations regarding the uses, risks, and benefits of GenAI and its potential for personalized learning enhancement. Students generally view the likelihood of being caught submitting AI-generated work as their own as high and the consequences as severe. The viability …

Use Matters: How Different Ways Of Using Chatgpt Drive Ai Acceptance And Solutionism, Florian Golo Flaßhoff, Fabian Anicker, Frank Marcinkowski

Human-Machine Communication

Artificial intelligence is central to solutionism—the vision of a world where all major problems are solved through technology. This study theorizes about how human–AI communication shapes attitudes toward AI and influences the formation of public opinion, sparking solutionist imaginaries. We empirically examine the attitude formation resulting from the non-simulated use of an unmanipulated conversational model in a controlled laboratory experiment. Using a between-subjects design, participants engaged in three semi-structured 20-minute sessions with ChatGPT, providing a novel perspective on the effects of its use. The findings reveal that mere use of ChatGPT causally increases AI acceptance; however, its impact significantly depends …

Creepy, Invasive, And Exploitative Algorithms: A Cpm Analysis Of Users' Privacy Breakdowns And Recalibration Practices With Social Media Algorithms, Matthew J. A. Craig, Jeffrey T. Child

Human-Machine Communication

Social media content filtering algorithms can both provide desired personalized content and ads for users. However, sometimes these recommendations can resemble individual private information. How might users navigate these experiences to best manage their private information? The present exploratory study utilizes the rules- and systems-based framework of communication privacy management (CPM) theory to explore social media users’ experiences of privacy breakdowns with social media algorithms and investigates what users do in response to said breakdowns. These responses were refined using content analysis and divided into different categories of privacy breakdowns and recalibration strategies. Implications for future research surrounding human-machine communication …

The Hai-Io Model: A Framework For Understanding The Human-Ai Communication Process, Rae Francis Quilantang

Human-Machine Communication

The increasing integration of artificial intelligence (AI) into daily life calls for new theoretical frameworks that capture human-AI interaction’s dynamic, feedback-driven nature. Traditional models treat AI as a passive medium, overlooking its adaptive capabilities. This paper proposes the Human-AI Interaction Outcomes (HAI-IO) model, an interdisciplinary framework synthesizing human-machine communication, social exchange theory, dialogue systems, and computational feedback models like cybernetics and reinforcement learning. The HAI-IO model frames interaction as iterative and bidirectional—AI adapts through predictive processing while users adjust based on AI feedback. This mutual adaptation shapes trust, engagement, and system optimization. The model informs AI system design, user education, …

Casa Renovations: Examining Social Responses To An Anthropomorphic Media Representative That Is Separate From The Core Technology Being Represented, Rabindra Ratan, Dayeoun Jang, Taenyun Kim, Kelsey Earle, Gabriel E. Hales, Yiming Skylar Lei, Chaeyun Lim, Andrew Gambino

Human-Machine Communication

To extend our understanding of CASA and account for changes in technological complexity and configuration, this research asks: Do increasing levels of social cues in a media representative that is separate from the core technology it represents lead to increased perceived anthropomorphism of the core technology? And does such perceived anthropomorphism predict positive treatment of the core technology? We conducted two complementary studies observing e-scooter use and treatment, an online experiment focused on responses to video stimuli and a field experiment focused on actual user behavior. Findings suggest that, indeed, social cues in a media representative (i.e., an e-scooter rental …

Machine Learning-Enhanced Optimization For The Real-Time, Secure, And Efficient Operation Of The Energy-Water-Hydrogen Nexus, Mostafa Goodarzi

Graduate Thesis and Dissertation post-2024

Given the escalating urgency of climate change, our research addresses the imperative need to curb carbon emissions, particularly from the electricity sector, which accounts for a quarter of total emissions in the U.S. We propose integrated systems, such as the energy-water nexus (EWN) and an innovative concept known as the energy-water-hydrogen (EWH) nexus, which integrates renewable energy sources (RESs) with green hydrogen production through water electrolysis. These concepts align seamlessly with the nation’s commitment to global climate agreements, including the Paris Agreement. Furthermore, green hydrogen—a clean and efficient energy source produced via water electrolysis powered by renewable energy—emerges as a …

Gradient-Based Optimization And Control Of Systems With Two Decision-Makers And Delayed Information, Mohammad Safayet Hossain

Graduate Thesis and Dissertation post-2024

In today’s world systems are complex in nature, often involving two or more decision-makers (DMs) each attempting to optimize the performance of the system using an objective function that reflects its own preferences and is usually different from the other. Such systems are very common in power, microeconomics, and other systems. The focus of this research is how such systems are optimized and how the results of optimization are implemented in practice. Without loss of generality, we consider systems with two DMs, each attempting to optimize the system’s by minimizing its own objective function. The solution of these types of …

Continuously Variable Series Reactor Modeling And Application, Mohammadali Hayerikhiyavi

Graduate Thesis and Dissertation post-2024

In today's rapidly evolving energy landscape, the Continuously Variable Series Reactor (CVSR) emerges as a vital tool for enhancing the stability and efficiency of power systems. This proposal presents a detailed investigation into the operational characteristics and performance optimization of CVSR through advanced modeling techniques. The Continuously Variable Series Reactor (CVSR) offers a unique capability to regulate the reactance of an AC circuit through the magnetizing characteristic of its ferromagnetic core. By utilizing both AC and DC windings, the CVSR effectively controls power flow, dampens oscillations, and balancing the voltage within the power grid. To effectively integrate CVSR into grid …

Tail-Imbalance Diffusion Equalizer For Class-Balanced Generation, Chinmay Dhanraj Nehate

Graduate Thesis and Dissertation post-2024

Real-world image corpora are often imbalanced. On long-tailed datasets, the head class can outnumber the tail by a large margin, and a vanilla diffusion probabilistic model (DPM) consequently loses fidelity and diversity on rare categories, undermining controllable generation. To address this gap, we present Tail Imbalance Diffusion Equalizer (TIDE), a training framework that restores class balance without discarding data or adding extra training stages. Our contribution is threefold: (i) score field rebalancing, where we embed class-prior knowledge into a mixing matrix that routes gradients toward minority classes, with this class-prior knowledge consisting of the empirical label frequencies in the training …

A Forgotten Kairos: Rhetorical Memory In 16th Century Europe, Phillip Martin

Graduate Thesis and Dissertation post-2024

In 16^th century Europe the nearly 2000-year-old discipline of rhetoric, still a pillar of education, underwent significant change. One of the more curious changes occurred in rhetoric’s subdivision known as memory, memoria, that is, memory’s “artificial” cultivation. During the 16^th century rhetorical memory was paradoxically both maximized and marginalized as a discipline. And by the early 17^th century memoria was all but extinct as a branch of rhetoric. The causal mechanisms of this transformation remain underexamined, particularly by rhetoric and composition scholars, and most explanations remain unclear and uncompelling. Accordingly, this thesis attempts a new approach …

Fit Index Criteria For Multisample Analysis In Sem With Ordinal Data: A Monte Carlo Simulation Study, Suat Babayigit

Graduate Thesis and Dissertation post-2024

Measurement invariance is crucial for valid group comparisons in structural equation modeling (SEM), yet testing invariance becomes challenging when using ordinal data. This dissertation evaluates the adequacy of widely used fit index difference criteria (ΔCFI, ΔRMSEA, ΔSRMR) for multisample invariance testing with ordinal indicators. Traditional Δ fit cutoff thresholds were established based on continuous, normally distributed data, but ordinal measures can distort invariance conclusions. Monte Carlo simulations address this issue by manipulating a comprehensive set of conditions: sample sizes (200, 400, 1000), group size ratios (equal vs. unequal), model complexity (low vs. high), underlying distributions (normal vs. nonnormal), Likert scale …

Advancing Anomaly Detection With Robust And Graph-Based Learning Methods: From Support Vector Data Description To Graph Neural Networks., Emil Agbemade

Graduate Thesis and Dissertation post-2024

Anomaly detection is crucial across various domains, particularly in handling highly skewed datasets where only normal operating conditions are available for training. To effectively identify abnormal events, specialized one-class classifiers have been developed. This dissertation explores robust and scalable anomaly detection methods, focusing on enhancing support vector techniques to accommodate complex data structures like graphs. The first study introduces the Robust Support Vector Data Description (RSVDD) model, which improves standard SVDD by incorporating a rescaled hinge loss function, making it more resistant to outliers. Using a half-quadratic optimization method, RSVDD dynamically adjusts the influence of each data point, leading to …

Supervised Topic Modeling For Scientific Texts: Bayesian Shrinkage Methods And Domain-Specific Applications In Forensics And Online Aggression Sciences, Amir Alipour Yengejeh

Graduate Thesis and Dissertation post-2024

Topic modeling has become a powerful tool for analyzing large-scale textual corpora across scientific domains. However, traditional unsupervised approaches like Latent Dirichlet Allocation (LDA) often produce redundant or weakly informative topics, limiting their interpretability and predictive utility. This dissertation presents a dual-track study that advances the field of interpretable topic modeling and domain-specific text analysis by (1) quantitatively analyzing research trends in Cyber Aggression and Abuse (CAA), and (2) developing a supervised topic selection framework for forensic science literature.

In the first part, we apply LDA to a corpus of 2,309 journal abstracts on CAA sourced from the Web of …

Achievement In Performing Arts At Title I Schools In Florida: A Case Study On The Impact Of Administrative Leadership And Financial Decisions On Student Performance, Mario L. Ford

Graduate Thesis and Dissertation post-2024

Researchers have established that the leadership strategies of a principal can directly impact student achievement, though it has not been clearly stated how that occurs (Cox & Mullen, 2023). This single-case instrumental study investigates the financial and administrative priorities of a principal at a Title I high school in Florida and the impact they have on the Music Performance Assessment (MPA) ratings of band, chorus, and orchestra programs at the district level. Additionally, the purpose of this case study is to provide insight into how the principal at a Title I high school whose performing arts programs have a trend …

Numerical Evaluation Of A Novel Transpiration Cooling Scheme For Use In Dynamic Control Of Atmospheric Entry System, Caroline J. Anderson

Graduate Thesis and Dissertation post-2024

Future space exploration missions require sustainable, efficient thermal protection systems for atmospheric entry, instead of disposable ablative shields. Transpiration cooling has interest as a long studied method but never used for missions to date, prompting further study to address concerns of flow transition downstream. In consideration of additional need for aerodynamic maneuvering for greater landing accuracy, a novel asymmetric scheme of transpiration cooling is presented as a method of using onboard coolant to meet entry trajectory maneuvering requirements. This numerical study creates a coupled model of vehicle-scale flow domain and material-scale thermal response to converge on accurate vehicle surface values.

Accurate And Efficient Orbit Probability Approximation Framework For Space Situational Awareness, Pugazhenthi Sivasankar

Graduate Thesis and Dissertation post-2024

Uncertainty Propagation in astrodynamics has gained importance in space situational awareness (SSA) problems such as space debris tracking, collision avoidance, and Cislunar operations. In this dissertation, the technique of Orbit Probability Approximation (OPA) is developed. OPA propagates orbital uncertainty using Liouville’s theorem with different functional approximations. First, OPA is formulated with Chebyshev polynomials to propagate the uncertainty on a geocentric planar orbit problem and then validated using two sources of satellite data: GRACE navigation data from the Jet Propulsion Laboratory (JPL) database, and FireOPAL ground-based observer provided by Lockheed Martin. In this validation process, OPA propagates uncertainty without using any …

Preparing For The Habitable Worlds Observatory: Spectropolarimetric Radiative Transfer Models Of Terrestrial (Exo)Planets, Kenneth E. Goodis Gordon

Graduate Thesis and Dissertation post-2024

The next major step for the exoplanet community lies in the characterization of terrestrial exoplanets, especially when it comes to identifying biosignatures and determining the habitability of these worlds. In response to this, NASA has proposed the Habitable Worlds Observatory (HWO), with the primary goal of searching for and characterizing Earth-sized planets in the habitable zones (HZ) of their stars. However, current characterization strategies that only rely on the unpolarized flux from the planets lose some of the informational content of the observed light and therefore suffer from degeneracies in the calculated planetary parameters. The sensitivity of polarization to the …

Insurance With Investments, Jingxi Liao

Graduate Thesis and Dissertation post-2024

Insurance is that insurer collects premiums from insured and reimburses claims. If the premiums collected are not enough to pay claims, insurance company will go bankrupt. Therefore, insurer may need to consider ruin probability of insurance to avoid bankruptcy. In this article, we assume that insurer invests premiums in both risky and risk-free assets with some allocated restrictions. We will try to find the optimal investment proportion by maximizing expected general utility function of surplus process corresponding to the ruin probability. We will prove that the optimal investment proportion has ”bang bang” characteristic under some conditions.

Moreover, in traditional term …

The Hilbert Series Of Paths, Cycles, And Related Graphs, Tiffany Nielander

Graduate Thesis and Dissertation post-2024

The Hilbert Series of a finitely-generated graded R-module, M, is a series which is often given in the form a rational function in the variable, t. This series encodes a great many invariant properties of the module M. In this dissertation, I study the Hilbert series and related invariants of the graph rings for paths and cycles. By utilizing a result of Kyle Trainor, I am able to examine the Hilbert series and the related invariants of these graph rings recursively through second and higher-order difference equations. This technique allows me to extract information about the …

Phase Retrieval And Fidelity Preserving Quantum Channels, Kai Liu

Graduate Thesis and Dissertation post-2024

This thesis investigates the preservation of fundamental quantum properties under noisy quantum processes. Motivated by the challenges of maintaining coherence and information integrity in realistic quantum systems, we study three distinct structural properties: phase retrievability, orbit injectivity, and fidelity preservation. Each property reflects a unique facet of how quantum information can be robustly encoded, distinguished, or compared in the presence of environmental interactions.

In the first part of the thesis, we analyze phase retrievable quantum channels which allow recovery of a quantum state up to a global phase. We provide structural characterizations using the Kraus representation and the joint spectrum …

Analysis Of Numerical Methods For Spdes, And Applications Of Free Boundary Problems In Financial Mathematics, Corey A. Prachniak

Graduate Thesis and Dissertation post-2024

This dissertation studies the key properties of a fully discrete finite element method for a class of stochastic moving boundary problems. Then, free boundary problems and their applications in finance are explored.

The first part of this thesis studies the properties of a proposed fully discrete finite element method scheme with an interpolation operator for stochastic Cahn-Hilliard equations with functional-type noise. These solutions cannot be differentiated in time, so we provide Holder continuity results to aid in the scheme’s error analysis. We further derive the uniform boundedness of higher-order L²-norm moment for use in the analysis. Then nearly …

Examining Educational Inequalities And Driving Science’S Next Generation Forward: Geobus Program Theory Evaluation, Elise M. Lorenzo

Graduate Thesis and Dissertation post-2024

This study presents a theory-driven evaluation of GeoBus, a mobile science lab designed to deliver hands-on, “fun learning experiences” via a solar powered, technology-rich learning environment brought directly to K-12 students in Central Florida Title 1 schools. While STEM education is widely recognized as essential for promoting equity, many schools, particularly in under-resourced communities, continue to face barriers to accessing meaningful and inclusive science learning opportunities. GeoBus was created in response to this need, offering students an informal learning environment with exposure to geospatial technologies and interactive science content.

Drawing on mixed-methods data, including content from the GeoBus website and …

Multiclass Cyberbullying Detection Using Advanced Neural Network Architectures: A Comparative Study Amidst The Covid-19 Pandemic, Mahyar Alinejad

Data Science and Data Mining

Amidst the COVID-19 pandemic, the digital communication landscape has seen an unprecedented rise in cyberbullying incidents. Addressing this critical issue, our study develops and evaluates a novel multiclass cyberbullying detection framework employing several advanced neural network architectures—namely Neural Networks (NN), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU). Utilizing a balanced dataset created through Dynamic Query Expansion, this research benchmarks the performance of these models in accurately classifying cyberbullying according to specific victim attributes such as age, ethnicity, gender, and religion. Our results demonstrate that LSTM and GRU models, in particular, exhibit superior performance …

Multiclass Cyberbullying Detection Using Advanced Neural Network Architectures: A Comparative Study Amidst The Covid-19 Pandemic, Mahyar Alinejad

Data Science and Data Mining

Amidst the COVID-19 pandemic, the digital communication landscape has seen an unprecedented rise in cyberbullying incidents. Addressing this critical issue, our study develops and evaluates a novel multiclass cyberbullying detection framework employing several advanced neural network architectures—namely Neural Networks (NN), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU). Utilizing a balanced dataset created through Dynamic Query Expansion, this research benchmarks the performance of these models in accurately classifying cyberbullying according to specific victim attributes such as age, ethnicity, gender, and religion. Our results demonstrate that LSTM and GRU models, in particular, exhibit superior performance …

Comparative Analysis Of Matrix Factorization And Neural Collaborative Filtering For Movie Recommendation Systems, Mahyar Alinejad

Data Science and Data Mining

This paper presents a comparative study of two recommendation system approaches for predicting movie ratings: Matrix Factorization with Stochastic Gradient Descent (SGD) optimization and Neural Collaborative Filtering (NCF) using Tensor Flow. The study aims to evaluate the effectiveness of these methods in recommending movies to users based on the MovieLens 100K dataset. The Matrix Factorization approach utilizes latent features to model user preferences and item characteristics, optimizing parameters through SGD. On the other hand, NCF integrates traditional collaborative filtering with neural networks to capture complex user-item interactions. Experimental results demonstrate the performance of both models in terms of Root Mean …

Detecting Physical Activity Using Wearable Sensor Data, Dipok Deb

Data Science and Data Mining

This study focuses on detecting physical activity using wearable sensor data, specifically distinguishing between walking and running. A dataset comprising accelerometer and gyroscope readings is used to train and evaluate various machine learning models, including logistic regression, random forest, k-nearest neighbors, naïve Bayes, and XGBoost. Extensive preprocessing, such as creating lag features and rolling statistics, is performed to enhance temporal data representation. The models are evaluated using metrics like accuracy, precision, recall, and F1 score. Incorporating lag and rolling features significantly improves model performance, with logistic regression achieving perfect scores across all metrics. These findings demonstrate the effectiveness of enhanced …

Performance Of Lasso And Ridge Regression For Variable Selection In Genome-Wide Association Studies Of Maize Flowering Time, Dipok Deb

Data Science and Data Mining

Genome-Wide Association Studies (GWAS) are instrumental in identifying genetic variants linked to complex traits, providing valuable insights into trait heritability and biological mechanisms. This study applies GWAS to investigate flowering time in maize, a critical adaptive trait, using a diverse dataset of 5,000 recombinant inbred lines across eight environments. Traditional GWAS methods often encounter challenges in high-dimensional datasets due to the presence of multiple small-effect genetic loci. To address this, we compared two penalized regression methods—LASSO and Ridge regression—to perform variable selection and regression analysis within a GWAS framework. LASSO effectively reduced the number of predictors by selecting the most …

Data Science Job Salary Prediction Using Linear Regression, Dipok Deb

Data Science and Data Mining

In the evolving landscape of data science, accurate salary prediction plays a crucial role in shaping career expectations, informing educational strategies, and guiding organizational hiring decisions. This study investigates the key factors influencing entry-level data science salaries in the United States by applying a multiple linear regression model to a recent dataset spanning from 2020 to 2024. Through data preprocessing, transformation, and diagnostic evaluation, we identify how job roles, experience levels, employment types, work arrangements, residency status, and company size impact compensation. Despite challenges such as outliers, heteroscedasticity, and non-normal residuals, model refinements like the Box-Cox transformation and variable selection …

Handwritten Digit Recognition Using Machine Learning, Dipok Deb

Data Science and Data Mining

Handwritten Digit Recognition (HDR) remains a fundamental benchmark in pattern recognition and machine learning due to its practical applications and inherent classification challenges posed by diverse handwriting styles. This study investigates and compares two classical statistical classifiers—Gaussian Naive Bayes (GNB) and Linear Discriminant Analysis (LDA)—to recognize the digits from the MNIST dataset. Both models assume underlying normality in feature distributions and offer computational efficiency, making them suitable for high-dimensional input such as image pixels. Using 60,000 training and 10,000 test samples, we evaluate model performance through accuracy, precision, recall, F1 score, and confusion matrices. The results reveal that while GNB …

Clustering Dataset Using K-Mean Clustering, Dipok Deb

Data Science and Data Mining

Clustering is a fundamental technique in unsupervised machine learning, widely applied in various domains such as pattern recognition, data segmentation, and anomaly detection. This study evaluates the performance of the K-Means clustering algorithm on multiple benchmark datasets, including low-dimensional, high-dimensional, and imbalanced datasets. The clustering results are assessed using four key evaluation metrics: Mean Squared Error (MSE), Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and Silhouette Score. Experimental results demonstrate that K-Means performs effectively on datasets with well-separated clusters, particularly in high-dimensional spaces, where it achieves near-perfect clustering accuracy. However, its performance deteriorates in datasets with overlapping clusters and …