Digital Commons Network^™

Emote - A Modular Action Figure For Childhood Emotional Growth, Terrence Li

Theses

Emotional regulation and communication are one of the most important skills that we can learn. This skill allows us not only to recognize and effectively communicate our feelings to others but also allows us to recognize them in others. Although learning and recognizing these emotions may be a pursuit in which progress varies from person to person, this skill is especially invaluable to young children. Beginning as early as the age of 3, many children begin to show early awareness of their own emotions, such as reacting to discomfort or comfort, or starting to use words for feelings. This learning …

Towards Reliable And Trustworthy Deep Learning Through Explainability And Interpretability, Dipkamal Bhusal

Theses

Deep neural networks achieve state-of-the-art performance across many domains, yet their deployment in high-stakes settings is constrained by two challenges: opaque decision-making and vulnerability to adversarial manipulation. This thesis investigates explainability and interpretability as principled mechanisms for improving the reliability and trustworthiness of deep learning models. First, we develop new post-hoc explanation methods that improve feature attribution and concept-based explanations. These methods provide faithful decision cues by modeling meaningful feature interactions and extracting faithful coherent concepts, enabling more reliable understanding of why a model predicts a given label. Second, we show that explanation quality is not solely a property of …

How Policing Data Visualizations Affect Comprehension, Decision Confidence, And Perceptions Of Racial Disparities, Abeer Mustafa

Theses

Police departments use public dashboards to share use-of-force data for policymaking and public awareness, but it remains unclear how visualization formats affect how people interpret this information. This between-subjects study with 64 participants compares absolute use-of-force incident counts (Totals) and population-adjusted rates (Rates) across four United States cities. The research included a quantitative analysis of graph comprehension, policy prioritization, confidence ratings, and attitude change, as well as a qualitative examination of open-ended responses. Results showed a strong framing effect: those who viewed absolute numbers prioritized Aurora, Colorado (highest incidents) for policy intervention, often disregarding population baselines, while those viewing per-capita …

Leveraging Machine Learning For Traffic Congestion Management In Smart Cities, Omar Alhasai

Theses

Traffic congestion continues to be a major urban issue, leading to traffic delays, higher fuel costs, and air pollution problems. Traffic management systems currently function in reactive mode because their algorithms only operate following congestion development rather than preventing it. Smart cities need predictive systems based on data analytics and machine learning to actively control urban traffic movements because traffic continues to rise as a result of urbanization and population growth. The proposed research designs a machine learning–driven traffic congestion prediction system that uses genuine data obtained from Aarhus, Denmark, and METR-LA, Los Angeles. The study will analyze fundamental traffic …

Wegmans School Of Health And Nutrition’S Culinary Kitchen Cart Manual Development, Neena Bhala

Theses

To facilitate the initiation of culinary medicine at RIT, a manual to guide the use of a mobile kitchen cart was developed and evaluated. This manual was developed to support faculty, staff, and students’ use of a Mobile Kitchen Cart to be able to support culinary medicine and nutrition education activities. The Manual was directed to RIT faculty, staff, and students who have experience with the cart or intend to have future use with the cart. A qualitative evaluation study was conducted with ten participants including RIT faculty (n=2), students (n=4), and staff (n=4). Feedback on the manual was obtained …

A Comparative Study Of Inference-Time Scaling Strategies For Large Language Models, Oluwamayowa Owolabi

Theses

Large language models (LLMs) have demonstrated strong performance on a range of reasoning tasks, however, their reliability often depends not only on model size or training data, but also on inference-time strategies. However, existing inference-time methods are typically evaluated in isolation and under differing experimental assumptions, making it difficult to draw systematic conclusions about their relative effectiveness. This thesis proposes a controlled empirical study of inference-time scaling strategies for large language models under fixed inference-time compute budgets. The findings reveal that no single strategy dominates uniformly. PRM guided selection with the IBM Granite verifier achieves the highest absolute accuracy across …

Software Vulnerability Recidivism In Open-Source Projects, Brandon Keller

Theses

Software vulnerabilities present a major threat to businesses and individuals alike and it is therefore critical that a culture exists among software engineers to encourage the discovery and patching of security flaws. Vulnerability counts are a common way of evaluating a project’s security. However, this metric can run counter to building a developer culture of fault recognition if more vulnerabilities is always seen as a bad thing. While these counts can present a rough idea of a project’s history with security, they provide no insight into how the development team improves and learns as a result of a vulnerability. A …

Under The Surface: A Scalable Experiential Framework For Accessible Design Archives, Lo Fasano

Theses

Preserving design history is usually done by hiding it away. While institutions like the Vignelli Center for Design Studies house over 750,000 artifacts, the vast majority remain in restricted storage, with minimal space dedicated to displaying process materials alongside canonical final works. Digital archives document portions of these collections, but typically present them as static image galleries, leaving the evolutionary logic of a design, and the human labor behind it, invisible to the public. Under the Surface is a scalable, interactive framework designed to bridge archival preservation and public discovery. By transforming preserved artifacts into real-time digital experiences, the project …

Optimizing Urban Commute Quality Through Traffic Congestion Analysis And Predictive Modeling, Obaid Almansoori

Theses

Urban traffic congestion imposes significant economic, environmental, and social costs on rapidly growing cities worldwide. This research investigates how predictive analytics and machine  learning can be leveraged to classify and forecast traffic congestion severity in real time,  enabling data-driven decision-making for transportation planning, signal optimization, and  congestion management. A real-world traffic monitoring dataset comprising 5,952 observations collected over two months via  computer vision sensors at an urban intersection was analysed under the CRISP-DM frame- work. The  dataset records counts of four vehicle classes including cars, bikes, buses, and trucks at  15-minute intervals, alongside temporal variables such as time of day, …

Adapting Specpt For Hst Grism Spectroscopy Via Transfer Learning, Clive Kalathoor Binu

Theses

This thesis demonstrates the successful application of transfer learning to bridge ground-based and space-based spectroscopic analysis through adapting SpecPT (Spectroscopy Pre-trained Transformer) for Hubble Space Telescope WFC3 grism data for redshift prediction. Originally trained on high-resolution DESI spectra, SpecPT initially failed when applied directly to low-resolution, noisy HST WFC3 grism observations (Normalized Median Absolute Deviation (NMAD) = 0.2095, catastrophic outlier fraction ($\eta$) = 47.97\%). Through transfer learning on 8,530 high-quality 3D-HST spectra with emission-line SNR > 2.5 and z < 1.7, the model achieved substantial improvement (NMAD = 0.0724, $\eta$ = 26.69\%), representing a 65\% reduction in typical redshift error and 46\% decrease in catastrophic failures. The research addresses two primary objectives: establishing transfer learning effectiveness for cross-domain spectroscopic analysis and investigating whether supplementing grism spectra with broadband photometric data enhances performance. Counterintuitively, integrating comprehensive multi-wavelength photometric data from CANDELS significantly degraded performance (NMAD = 0.1641, $\eta$ = 36.51\%), challenging conventional astronomical assumptions about multi-modal data fusion and revealing critical failure modes in astronomical machine learning. This work establishes a unified framework for automated analysis of both ground-based and space-based spectroscopic surveys, with important implications for JWST, Euclid, and the Nancy Grace Roman Space Telescope. The demonstrated capability to adapt models across instrumental domains provides a scalable approach for processing large data volumes from next-generation missions, validating foundational model approaches that can be developed once and efficiently adapted across diverse observational contexts.

In-Context Retrieval For Molecules And Chemical Synthesis Pathways, Abhisek Dey

Theses

Contrastive learning methods require well-defined positive pairs, limiting their applicability to domains where complete, high-fidelity pairings are available. In practice, large-scale scientific corpora --including patents, publications, and web-scale data -- contain vast quantities of contextually relevant but incompletely paired samples that are discarded under standard training paradigms. In this work, we demonstrate that hard negative mining can be leveraged to construct pseudo-positive supervision signals from unpaired or partially paired data, enabling contrastive learning to exploit the full breadth of available corpora without sacrificing representational quality. Using a large-scale chemical drug patent corpus as a testbed, we train a cross-modal contrastive …

Toward Reliable Computational Social Science: Inconsistency-Aware Methods For Human Annotation And Ai Inference, Sujan Dutta

Theses

As artificial intelligence (AI) becomes increasingly common in computational social science, \textit{inconsistency} has emerged as a key challenge. AI models often contradict themselves when given equivalent inputs, disagree with other models on the same data, and diverge from human judgments in seemingly opaque ways. Human annotators exhibit their own inconsistencies, both within individuals and across groups shaped by differing values and identities. Rather than treating these inconsistencies simply as noise, this dissertation argues that they contain meaningful signals that can be leveraged to improve learning efficiency, strengthen evaluation, and increase the reliability of large-scale social measurement. To study this phenomenon, …

Toward A Unified Framework For Open World Visual Learning, Yuansheng Zhu

Theses

Artificial intelligence systems have achieved remarkable performance across a wide range of visual tasks. However, most existing models operate under the unrealistic closed-world assumption, where training and test data are drawn from the same distribution. In real-world applications such as anomaly detection, autonomous driving, and medical diagnosis, learning systems frequently encounter novel or out-of-distribution scenarios. These settings require models that can recognize unknown inputs, adapt to new information over time, and maintain reliable performance under evolving conditions. This dissertation studies the problem of Open World Visual Learning, a paradigm that enables visual learning systems to operate robustly in dynamic and …

Using Deep Learning To Increase Eye-Tracking Robustness, Accuracy, And Precision In Virtual Reality, Kevin Barkevich

Theses

Algorithms for the estimation of gaze direction from mobile and videobased eye trackers typically involve tracking a feature of the eye that moves through the eye camera image in a way that covaries with the shifting gaze direction, such as the center or boundaries of the pupil. Tracking these features using traditional computer vision techniques can be difficult due to partial occlusion and environmental reflections. Although recent efforts to use machine learning (ML) for pupil tracking have demonstrated superior results when evaluated using standard measures of segmentation performance, little is known of how these networks may affect the quality of …

Medical Detection Dogs: A Visual Exploration Of Canine Olfactory Anatomy & Volatile Organic Compounds As Applied To Non-Invasive Biomedical Detection, Kirsten Santiago

Theses

Medical detection dogs provide a non-invasive early method of disease and medical alert detection that is underrepresented in the medical visualization sector. The domesticated dog, or Canis familiaris, is known for its loving nature and has become an important member of many households, but they are also incredibly intelligent with a capacity for specialized training. Dogs have a specialized olfactory system, allowing them to detect scents with high acuity (Guest & Otto, 20). Volatile organic compounds or VOCs are molecular substances associated with metabolic processes that are often a result or byproduct of certain diseases and can be influenced by …

Horizontal Gene Transfer In Neisseria, Wen Ting Dong

Theses

Antibiotics are a special category of drugs that help treat bacterial infections by killing bacteria or hindering their growth and replication. Antibiotics are greatly significant in modern medical practices, providing an effective treatment for bacterial infections as well as enabling organ transplants, open surgeries, and chemotherapy to be possible. Unfortunately, cases of antibiotic resistance were observed shortly after the introduction of antibiotics. The appearance and spread of these resistant strains poses a significant threat to public health safety. The Centers for Disease Control (CDC) estimates there are 2,868,700 antibiotic-resistant bacterial and fungal infection cases per year in the United States, …

What Do Future Nutrition Professionals Think About Wic Jobs? A Study Of Nutrition Career Perceptions, Madison Degenfelder

Theses

Background: The Special Supplemental Nutrition Program for Women, Infants and Children (WIC) program faces a critical workforce shortage, yet little is known about the knowledge, attitudes, and beliefs of nutrition students and recent graduates regarding WIC career pathways, requirements, and compensation. Understanding these factors is essential to design strategies to strengthen recruitment and build a sustainable WIC workforce. Objective: To identify and describe knowledge, attitudes and beliefs on entering the WIC workforce among college nutrition and dietetics students and recent graduates. Design: A mixed-methods, sequential study design which included a cross-sectional survey and two focus groups was employed to ascertain …

The Gospel Of Glamour: A Christian Woman's Style Blueprint, Osaromwenyeke Osemwota

Theses

The Gospel of Glamour: The Christian Woman’s Style Blueprint was a luxury-branded, sustainability-focused, faith-based style guide that addressed a gap in fashion, theology, and sustainability scholarship. Existing literature had either systematized fashion, explored the spiritual significance of dress, or examined sustainable and luxury practices—but none had integrated these dimensions into a prescriptive, actionable guide for Christian women. Drawing on Scripture as the foundational authority, including passages from Genesis, Exodus, Psalms, and Proverbs, the guide framed clothing as both practical provision and visible expression of covenantal identity. The style guide synthesized secular fashion methodology, modest-fashion scholarship, and sustainable luxury principles to …

The Disney Adult: A Consumer That Creates Free Marketing, Emily Elizabeth Kempf

Theses

This research looks at the evolving complex relationship between the Walt Disney Company, The Disney Adult content creators and the fan base. It focuses on how user-generated content (UGC) and social media can play a large role in contemporary marketing strategies. As the target audience has expanded to include Disney Adult, this new audience has become a central part in the company’s marketing strategy encouraging fan engagement across all social media platforms. This research is a qualitative case study on the Disney Adult and how social media content can be used as a core marketing strategy. The Disney Adult influencer …

Artemisia Gentileschi's Judith And Her Maidservant Serves As A Message Of Hope, Holly Kyseth

Theses

This thesis argues that through iconography and specific artistic techniques, Artemisia Gentileschi’s Judith and Her Maidservant utilizes the biblical story of Judith and Holofernes to relay a distinctly female-centered narrative, reflective of the ideals of her seventeenth century Italian community. This piece stands apart from other representations of this narrative in that Judith was commonly shown as either dainty yet violent in the act of decapitating Holofernes, or as a saintly victor proudly displaying his head to her people. With Judith and Her Maidservant, Artemisia displays an obvious sororal alliance between Judith and her servant Abra, while cleverly implementing Caravaggesque …

Mediators Of The Dead: Funerary Strategies And Material Practice In The Final Jōmon Period, Celeste Marie Tran

Theses

Shakōki-dogū are prehistoric clay figurines from Japan’s Final Jōmon period, characterized by enlarged, goggle-eyed features. In both scholarship writing and museum display texts, generalized thematic labels or iconographic readings sometimes reduce the corpus’s internal variability. This study examines a focused set of Final Jōmon shakōki-dogū from northern Honshū (Tōhoku) and Hokkaidō (Figures 2–6) through their formal and material evidence—shape, surface treatment, and condition—read alongside published excavation reports and catalogue records. It asks what these figurines can be shown to do, as prehistoric artworks, within death-related practice when interpretation is anchored to observable features and documented context, rather than to a …

Art Therapy In The Art Classroom, Sherry Williams

Theses

The integration of art therapy practices within the art classroom provides a transformative framework for supporting the emotional and psychological well-being of students. Research into these strategies demonstrates that creative practices allow students to manage stress and anxiety while fostering mindfulness, reflection, and self-expression. Art educators can create safe and supportive environments by incorporating therapeutic activities such as drawing mandalas, doodling, and engaging in collaborative projects. These methods prioritize the creative process over technical achievement, allowing students to communicate complex feelings that may be difficult to articulate verbally. 

While art teachers are not licensed therapists, they can effectively implement therapeutic …

Software Engineering In The Age Of Coding Agents: Failure Modes And Rejection Patterns, Mahd Mohd Hindi

Theses

This thesis investigates the real-world behavior of LLM-driven coding agents that generate code changes and submit pull requests (PRs) to public software repositories. As these tools evolve from autocomplete-style assistants into more autonomous agents, their contributions increasingly interact with socio-technical review processes (human reviewers, bots, CI/CD gates, and project norms). The thesis focuses on understanding why agent-generated PRs are accepted or rejected and what these outcomes reveal about current agent limitations in practical development workflows.

The main objective of this thesis is to systematically characterize rejection patterns and failure modes of agent-generated pull requests in real repositories. Specifically, the thesis …

Engineering Human Microphysiological Models To Investigate Bacterial Extracellular Vesicle–Driven Endothelial And Blood–Brain Barrier Dysfunction, Louis P. Widom

Theses

Pathogenic bacterial extracellular vesicles (BEVs) are nanoscale particles derived from bacteria that contain pro-inflammatory cargo. During bacterial infections, BEVs provoke the host inflammatory response and may cause widespread damage. Furthermore, antibiotic treatment can boost BEV production and thereby increase the number of toxic signals traveling through the circulatory system. This is especially dangerous in brain blood vessels since evidence suggests that BEVs may destabilize the protective blood–brain barrier (BBB), resulting in neuroinflammation associated with cognitive decline and development of neurological disorders. Our understanding of BEV interactions with the host remains limited, necessitating the development of in vitro models to better …

Optimizing Traffic Signal Timings Using Real Time Data Analytics, Hamad Abdulla Binsultan

Theses

City traffic jams have become a major challenge for contemporary cities, causing delays, increased fuel consumption, and other environmental impacts. Conventional traffic signal controlling systems usually depend on fixed or preset signal plans that are incompetent to adjust themselves with changing traffic conditions. Traffic signal timings are optimized across the intersection health of the system by processing real-time data analytics. Through real-time data analytics, traffic flow efficiency is improved while waiting time at signalized intersections is reduced. Using traffic real-time data like vehicle counts, vehicle types, time of day and day of the week to see howtraffic behaves under different …

The Heart Seat™: An In Home Cardiovascular Monitoring Device, Archana Venkataramani

Theses

Heart failure (HF) is one of the most common disorders, affecting an estimated 64 million people globally. Although heart failure incidence is stabilizing in some countries, prevalence is rising because of an aging population, improved post-myocardial infarction survival, and advances in treatment. High mortality, morbidity, reduced quality of life, and significant healthcare costs are results of heart failure (Shahim et al., 2023). Recurrent heart failure episodes are associated with an increased risk of death. Heart monitoring systems exist in several forms, including implantable cardiac devices, wearable and portable monitors, and remote patient monitoring systems. Each type differs in monitoring needs, …

Security Evaluation Of Post-Quantum Ml-Dsa Implementations Against Software-Induced Fault Attacks, Alexis Korensky

Theses

Quantum computing is a form of computation that uses the principles of quantum mechanics to perform mathematical computations at a faster rate than classical computers. Although quantum computing is currently still in its early stages, if a general-purpose, large-scale, and fault-tolerant quantum computer were to be built, it would jeopardize the security of modern public-key cryptosystems. If these cryptosystems were broken, secure connections could not be authenticated, enabling Man-in-the-Middle (MitM) attacks, and digital messages could not be signed. All data sent over secured HTTPS and/or TLS connections would be vulnerable and potentially malicious since its origin and integrity could not …

The Usability And Influence Of Comprehensive Sports Nutrition Handouts For Adolescent-Aged Female Athletes, Chloe Brassie

Theses

Objective: This study aimed to examine the usability and influence of comprehensive sports nutrition handouts for adolescent-aged female athletes. Design: Cross-sectional online survey incorporating post retrospective-pre self-assessment Participants: Adolescent female athletes between 13 and 17 years of age Methods: Participants received a series of digital nutrition handouts every day for seven days and completed an online survey. Variables: Age, sport, engagement, knowledge, behavior, features of interest, and experience with nutrition education. Analysis: Quantitative data were analyzed with descriptive statistics and qualitative data were examined using thematic analysis. A Wilcoxon signed-rank test assessed the change in responses for both knowledge and …

Prediction Of Methane Gas By Using Hyperspectral Imageries And Remote Sensing Techniques, Aysha Ali Alhajeri

Theses

Greenhouse gases is important for sustaining life on earth as well as in mitigating climate change. Methane (CH4) is considered as one of the most important critical gases for the global climate change and have significant influences on our life. Accordingly, the prediction of this greenhouse gas emissions is very important for avoiding the climate change effects and to maintain environmental sustainability. The objective of this study is to explore the potential applications for remote sensing to predict methane levels in the Earth’s atmosphere with a combination of local ground data and data from hyperspectral satellite imagery. By using hyperspectral …

Optimizing Motor Insurance Premiums In The Uae Using Predictive Analytics, Samar Abdelsalam

Theses

The car insurance industry in the UAE and GCC faces growing challenges including premium inflation, fraudulent claims, inconsistent pricing models, and lagging innovation in underwriting. This study investigates how predictive analytics and machine learning can enhance motor insurance premium optimization, with a focus on incorporating socio-demographic, behavioral, environmental, and vehicle-specific risk factors. Using a publicly available dataset of over 125,000 insurance policy records, this research applies a CRISP-DM framework to develop and evaluate supervised learning models including Linear Regression, Decision Trees, and Random Forests. Results highlight Linear Regression as the most effective model (RMSE = 193.62, R² = 0.9016), enabling …