Digital Commons Network^™

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 …

Alternative Color Mode Design: Supporting Mobile App Creators With Evidence-Based Guidelines, Sarah Andrew

Theses

Alternative color modes, such as light, dark, dim, and high contrast modes, in mobile apps can improve accessibility for people with vision impairments and usability for people without vision impairments across situational contexts. However, current mobile apps exhibit inconsistent color implementations for UI elements (e.g., background, text, buttons, images, and non-selectable icons), leaving users with limited accessible options. My dissertation addresses a central question in human-computer interaction and accessibility: How can mobile app designers be supported to implement alternative color modes that meet the accessibility and usability needs of people with and without vision impairments? Through an eight-study mixed-methods investigation, …

Optimizing Virtual Scrolling Performance In Angular: A Comparative Study Of Cdk And Custom Implementation, Guri Sokoli

Theses

Modern web applications often display large datasets with tens of thousands of items, such as e-commerce catalogs, data tables, and social media feeds. Rendering all items in the Document Object Model (DOM) at once causes browser freezing, high memory use, and slow interfaces. Virtual scrolling solves this problem. It is widely adopted but rarely studied through direct performance comparison. Few empirical studies measure how different implementations behave under varying dataset sizes, devices, or browsers. This research conducts a comparative analysis of Angular CDK Virtual Scroll as an industry-standard baseline and develops an optimized implementation incorporating framework-specific enhancements: OnPush change detection …

Maintenance Insights For Power Transformers In Energy Networks, Saleh Hassan Al-Ali

Theses

This thesis examines machine learning approaches for predicting failures in electrical power distribution transformers, with the goal of helping utility operators intervene before outages occur. The dataset covers 16,000 distribution transformers operated by Compa ˜n´ıa Energ ´etica de Occidente (CEO), a Colombian utility serving 42 municipalities in the Cauca Department. Each transformer record includes geographic location, rated power capacity, self-protection features, ceramic insulation criticality levels, removable connector configurations, customer categories, user counts, estimated un-supplied energy, installation types, network topology, and secondary line lengths. Failure event histories were also included, which allowed the problem to be framed as a supervised binary …

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 …

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 …

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 …

Defining Her2 Associated Proteogenomic Features In Breast Cancer And Extending To Gynecologic Cancers, Maya Anand

Theses

HER2 amplification is a well-established driver of breast cancer and serves as the primary basis for clinical classification and treatment selection. However, this framework assumes that HER2-driven tumor biology is defined solely by ERBB2 amplification or overexpression. The goal of this study was to evaluate whether HER2-associated signaling is represented as a pathway-level activation state and whether this framework could help identify tumors with clinically relevant HER2 activity beyond current routine classification methods. HER2-associated transcriptional programs were identified across three independent breast cancer cohorts, resulting in conserved gene sets (P76 and P25). Amplification-independent HER2 activation was assessed using the HER2 …

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 …

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 …

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 …

Hardware Integrity Checking On An Fpga Through Power Side-Channel Analysis, Ethan Vuong

Theses

FPGAs have seen extensive usage in applications such as cloud-computing, hardware acceleration, mobile devices, and military alike. While the reconfigurability of these devices allow them to be as adaptable as they are fast, it raises concerns of adversaries modifying not mere software, but hardware itself. Moreover, designers face an IP trust issue where they cannot be sure that a third-party IP was not modified in transaction, programming, or even post-programming. Cloud computing centers are hesitant to rent fabric on multi-tenant FPGAs due to the plethora of vulnerabilities and uncertainties that come with allowing users to reconfigure hardware. This thesis aims …

Evaluating Expected Real-World Tactile Experience From Virtual Fabric Perception, Julianna Gross

Theses

Most people have experienced the disappointment of ordering products online and receiving a product completely different to what was expected. For example, a sweater may appear that it is made out of extremely soft blue cotton, yet when seen in person is an itchy purple polyester blend. The current research seeks to ameliorate this confusion by evaluating various visual conditions that have the potential to lead to disparities between real-world feel and online images. Lighting and material characteristics are two major indicators of fabric, specifically real life look and feel. The angle of lighting and which light source is chosen …

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 …

Leveraging Volatile Ecram Dynamics For Short-Term Plasticity In Neuromorphic Circuits, Sean Borkholder

Theses

Short Term Plasticity (STP) is fundamental for information processing and computational efficiency within biological neural systems. STP has previously been implemented at the circuit level using complex designs with high power and area overheads, resulting in designs that are not scalable in neuromorphic systems. Electrochemical random-access memory (ECRAM) devices naturally exhibit STP behavior through volatile ion dynamics, creating transient conductance modulation.  In previous ECRAM implementations, this behavior was seen as an undesirable artifact of device programming when implemented as a Compute in Memory (CIM) device; however, this thesis proposes leveraging the volatile  behavior to instead act as a computational resource. …

Efficiency Evaluation Of Water Pumping Stations Using Data Envelopment Analysis (Dea), Rowdha Abdullah Alblooshi

Theses

Water pumping stations are a critical component of water transmission systems, ensuring reliable delivery of potable water while maintaining operational requirements and international standards. With the increasing focus on sustainability and energy optimization, improving the efficiency of pumping stations has become a key priority. However, there is a lack of structured benchmarking approaches to evaluate the relative performance of pumping stations across multiple operational factors. This study addresses this gap by applying a Data Envelopment Analysis (DEA) framework to evaluate the efficiency of water pumping stations. A multi-model approach is adopted, including the CCR and BCC models, along with the …

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, …

A Data-Driven Machine-Learning Framework For Intermittent Demand Classification And Forecasting Of Electrical Spare Parts In Dubai’S Water Pumping Stations, Ayesha Khamis

Theses

Irregularity in spare parts demand has been a recurring problem in many critical industries. The same problem is found in Dubai's water pumping stations, where demand is highly intermittent, with long periods of no usage followed by sudden increases. These irregularities are usually caused by maintenance activities or equipment failures. Forecasting such demand is challenging, as irregular patterns can lead to stockouts or overstocking. In this research, a machine learning (ML) forecasting framework is developed to handle intermittent demand for electrical spare parts in Dubai's pumping stations. The framework includes demand classification and prioritization, as well as the application of …

Imaging Performance Analysis Of Euv Mask Stacks For Sub-24 Nm Features At High And Hyper Numerical Aperture, Abdusame Omran N. Berish

Theses

As extreme ultraviolet (EUV) lithography advances toward high numerical apertures (NAs) of 0.55 and 0.75, mask-3D effects degrade image fidelity, limiting resolution and the process window. This work investigates the performance of alternative EUV absorber materials for sub-12 nm half-pitch patterning under high-NA and hyper-NA conditions. The lithographic simulations were conducted using the PROLITH Maxwell rigorous model under dipole illumination optimized for sub-24 nm resolution dense line/space features. The EUV mask stacks were derived from five fabricated and tested absorbers: TaBN, Ru/Ta, Pt2W, Ni/CrN, and TaSi2.  The absorber thickness was recalibrated to an optimal value, ensuring a maximum normalized image …

Sampling And Counting Graph Structures With Triangle Motifs, Sherry Robinson

Theses

Understanding the structure of real-world networks often relies on identifying significant (i.e., occurring significantly more frequently than random) subgraph patterns, or motifs, such as triangles. To assess their significance, null models generate random samples from a constrained distribution of graphs, preserving selected properties while randomizing others. These models may either generate random graphs or sample structures from a fixed input graph. This thesis focuses on the latter, specifically the problem of sampling and counting graph structures that incorporate triangle motifs. While efficient algorithms exist for sampling classical structures such as matchings, extending these methods to higher-order motifs remains an important …

Full-Field Experimental Analysis Of 3d Printed Open-Hole Plates Reinforced With Isotropic And Concentric Carbon Fibers, Mozah Alyammahi

Theses

Additive Manufacturing technology (AM) has become an attractive process for innovative products in recent decades. The applications of AM materials widen to include composite materials. The fabrication of composite materials via AM makes the process more cost-effective than traditional fabrication methods. Nowadays, there are 3D printers that can produce composite materials with different strategies which results in different mechanical properties. This study examines the impact of carbon fiber reinforcement, both isotropic and concentric, on 3D printed open-hole plates utilizing full-field experimental analysis.  Problem Statement  The research on investigating different fiber reinforcement is increasing rapidly. However, most of the studies are …

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 …

Predicting Teacher Burnout Across Cultures: A Machine Learning Approach Using Talis 2018 Data, Fatma Fraishan Abdulla Hassan Alkhzaimi

Theses

Teacher burnout is a persistent global challenge with significant consequences for educator wellbeing, instructional quality, and school climate. Despite extensive research, most studies rely on small local samples, predefined burnout scales, and limited analytical techniques, leaving gaps in understanding the latent structure of burnout and the factors that predict it across diverse educational systems. This study addresses these gaps by applying a hybrid machine learning framework to the OECD TALIS 2018 teacher dataset (N = 38,081) to discover latent burnout profiles and build predictive models capable of identifying teachers at risk. Unsupervised k-means clustering was used to uncover naturally occurring …

Cancer Detection System Using Binary Neural Network On Dna Based Architecture, Antar Narayan Chowdhury

Theses

Deoxyribonucleic acid (DNA) is among the most durable chemical storage media, capable of encoding the fundamental instructions for protein synthesis (the central dogma). Each human cell contains a unique DNA sequence characterized by identifiable markers that facilitate pattern recognition. These molecular features offer significant potential for personalized drug development and disease identification. Recent advancements in DNA based research have demonstrated that fundamental arithmetic operations can be executed directly through molecular interactions, bypassing the need for silicon-based computational assistance. These biochemical applications can be further scaled to support Binary Neural Network (BNN) models, which are particularly well-suited for mitigating stochastic noise …

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.

Phrases, Crystal Ching-Lam Tam

Theses

Motivational posters are widely used in educational environments to encourage perseverance, build confidence, and promote positive thinking. However, many of these visuals rely on cliché imagery, generic language, and overly decorative styles, making them feel inauthentic and easy to ignore. In visually saturated campus environments, they often fade into the background, acting as noise rather than as meaningful communication. How can motivation be communicated in a more engaging, intentional, and relevant way for college students? This thesis introduces Phrases, a visual communication system that reimagines motivational design through abstraction, clarity, and restraint. Rooted in Swiss design principles and Gestalt theory, …

Same Day Discharge Can Be Performed Safely After Atrial Fibrillation Catheter Ablation Using A Wide-Footprint Lattice-Tip Dual-Energy System., Tyler L Taigen, Devi G Nair, Dinesh Sharma, Erich L Kiehl, Jose Osorio, Petr Neuzil, Josef Kautzner, Stavros E Mountantonakis, Andrea Natale, John D Hummel, Shephal K Doshi, Anish K Amin, Usman R Siddiqui, Kelly A Van Bragt, Jeffrey Cerkvenik, Khaldoun G Tarakji, Vivek Y Reddy, Moussa Mansour, Elad Anter

Heart and Vascular Articles

BACKGROUND: Same-day discharge (SDD) after atrial fibrillation (AF) ablation is generally considered safe, since most complications are identified during (or immediately) after the procedure.

OBJECTIVE: This study aimed to evaluate SDD in the SPHERE Persistent-Atrial Fibrillation trial.

METHODS: Patients with persistent AF were randomized to the dual-energy lattice-tip mapping and ablation catheter (investigational) vs a conventional contact-force radiofrequency ablation system (control). SDD and timing of 30-day procedure- or device-related serious adverse events (SAEs) were assessed in the full cohort. In a subset of centers that performed SDD in the trial, predictors of SDD, 30-day readmissions, and mortality rates were assessed. …

Determinants Of Evaluation Demand In The United States Federal Government: A Sequential Mixed Methods Study With Hierarchical Linear Modeling, Ruqayyah Abu-Obaid

Dissertations

This dissertation examines the factors that influence demand for evaluation in the federal government of the United States. Although evaluation has become a central element of evidence-based policymaking, limited empirical research explains what drives variation in evaluation activity across federal agencies. The study addresses this gap through a sequential exploratory mixed-methods design that integrates qualitative document analysis with quantitative hierarchical (panel) data modeling. Hierarchical linear modeling (HLM) was employed to analyze longitudinal data from 23 federal agencies (2008–2024), accounting for both within- and between-agency variation over time and providing more accurate estimates of factors influencing evaluation demand.

In the qualitative …

Cultural Memory And Colonial Legacies: The Pandemic Era In France, Kathryn (Kate) R. Cross

Graduate Program in International Studies Theses & Dissertations

In the twenty-first century, how are former colonial powers addressing colonial legacies? Amidst the massive social movements of the pandemic years and greater representations of a new generation of politicians in major elections, the global zeitgeist was increasingly characterized by representation of disenfranchised and previously silenced memories and narratives. These shifts, especially in France, are a sharp contrast to the unofficial historic policy of amnesia maintained regarding traumatic legacies and build on movements that began at the turn of the century. The environment has facilitated an increased ability for cultural products to create, shape, and sustain cultural memories of colonial …

Personality Predictors Of Cybersecurity Vulnerability: Insights From Self-Reports And Stimulated Threat Scenarios, Saroja Roy Grandhi

Psychology Theses & Dissertations

In this cyber dependent and enabled era, understanding the role of human factors in digital security is essential. This study investigates the relationship between Big-Five personality traits and cybersecurity behaviors by examining both self-reported and stimulated behaviors in security threat scenarios. Participants completed validated questionnaires to report their personality traits, cybersecurity practices and engage in task-based stimulations to capture behaviors such as phishing detection, password creation, and response to security alerts. The study tested whether higher conscientiousness, openness, and agreeableness would be associated with stronger cybersecurity practices and smaller discrepancies between self-reported and observed behaviors. And, whether greater extraversion and …