Digital Commons Network^™

Transformers In Time-Series Forecasting: Enhancing Robustness Via Dynamic Attention Mechanisms, Kush Patel

Master's Projects

Transformer architectures have emerged as powerful tools for time series forecasting, excelling at capturing complex temporal dependencies across multivariate inputs. However, these models are highly susceptible to adversarial attacks such as the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM), which can significantly degrade predictive performance through small, targeted perturbations. This work integrates dynamic attention mechanisms, adaptive masking modules that introduce controlled variability into attention pathways, into a transformer forecasting model to enhance robustness against such attacks. Using two distinct datasets, we compare the performance of a standard transformer and a dynamic attention-enhanced transformer under both clean and …

Transformer Integration, Fine-Tuning And Zero-Shot Learning For State Of Health Estimation In Li-Ion Batteries Using Large Language Models, Chinmay Nilesh Mahagaonkar

Master's Projects

In this thesis, we present a comparative analysis of the use of transformerbased and Large Language Model (LLM) models for State of Health (SoH) and Remaining Useful Life (RUL) prediction of lithium-ion batteries. With electric cars and renewable energy systems based on batteries at the forefront, the need to predict degradation accurately in order to enhance the performance and reduce maintenance costs has become imperative. Most traditional prediction methods lag the complex and non-linear characteristics of degradation in batteries, and hence the usage of sophisticated methods becomes a necessity. The research employs the CALCE dataset, which includes long-horizon cycling data …

Medilightrag: A System For Medical Query Response Using Fine-Tuned Llms And Graph Based Retrieval, Rajiv Karthik Reddy Kodimala

Master's Projects

The exponential increase in medical data has created a greater demand for precise and efficient information retrieval systems. Existing Large Language Models (LLMs) face domain-specific difficulties such as sophisticated medical jargon, situational comprehension, and the continual advancement of healthcare knowledge. To tackle these challenges, we present MediLightRAG, an innovative two-stage system which integrates parameter-efficient fine-tuning of Large Language models with LightRAG’s graph-based retrieval. The first stage focuses on enabling accurate resource-efficient model adaptation for the medical domain through QLoRA fine-tuning. In the second stage, LightRAG’s two-tiered retrieval architecture that combines graph-based indexing with dynamic knowledge retrieval is employed to enhance …

Energy Considerations For Large Pre-Trained Neural Networks, Leo Mei

Master's Projects

In recent years, neural network models have achieved phenomenal performance due to the increasing parameters and complexity of model architectures. However, these advancements come with high environmental costs as they require massive computational resources and consume substantial amounts of electricity, leading to high carbon emissions. Previous studies have demonstrated that substantial redundancies exist in large pre-trained models, and reducing these redundancies through compression would not compromise model performance. While these studies focused on retaining comparable model performance, the direct impact of compression on energy consumption when training models appears to have received little attention. By quantifying the energy usage associated …

Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula

Master's Projects

Traditional physiotherapy methods tend to be non-interactive and provide little to no personalized instruction, even though physiotherapy is critical to stroke recovery. This thesis explores a fully adaptive, sensor-based, feedback architecture intended for stroke patients which remotely supervises movement and personalizes exercises enabled by multimodal sensors. The system uses filtering and windowed segmentation of accelerometer and skeletal data to compute features like jerk, speed, and joint movement angular range. A game engine applies accelerometer and skeletal features together with optimized, lightweight ML models to drive adaptive feedback, scoring, and difficulty adjustment. The architecture supports responsive continuous sensor streaming within the …

Link Failure Localization In Hierarchical, Multidomain Optical Networks, Martin Mihailov Bojinov

Master's Projects

Optical networks transport data encoded on light signals over optical fiber cables. Individual optical networks are managed by domain administrators, such as service providers, vendors, or regional bodies. Multidomain optical networking explores the possibility of enabling seamless data transmission across domain boundaries. In this project, we explore how we can perform network monitoring in a hierarchical multidomain optical network while preserving domain security and autonomy. This is done by allocating dedicated monitoring trails across various broker abstractions of our network topologies. We propose three heuristic functions that expedite trail selection. Of the three heuristics, our least monitored algorithm performs the …

Suicidal Ideation Detection On Reddit Using Llm-Annotated Data And Graph Neural Networks, Ikbal Singh Gurdev Singh Dhanjal

Master's Projects

Suicide is the fourth leading cause of death among people aged 15-29. More than 720, 000 people commit suicide every year. During the COVID-19 pandemic, we saw an increase in people seeking out mental health support on anonymous forums like Reddit. These anonymous forums allow people to express their suicidal ideation without judgment and give them a support structure that not everyone has. The aim of this project is to detect suicidal ideation using Reddit. In this work, we propose SIRGEL (Suicidal Ideation on Reddit using Graph Embeddings and LLMs), a dual-pipeline approach that combines large language model (LLM)- based …

Strengthening The Preservation Ecosystem For Below-Market-Rate Housing In Santa Cruz, Bennett Williamson

Master's Projects

In Santa Cruz, California, policies and political movements of the last fifty years have both prevented sprawl and stymied the construction of new infill housing. As a result, today Santa Cruz is one of the most expensive housing markets in the nation, and the lack of available affordable housing has led to incredibly high rent burdens for the majority of the population, putting residents at risk of displacement through landlord action or market forces. This report assesses the efficacy of preserving existing affordable housing as an anti-displacement strategy in Santa Cruz. It uses key stakeholder interviews with local housing practitioners …

Advanced Knowledge Extraction With Biomedical Data Using Llms, Akshat Krishna

Master's Projects

The rapid growth of biomedical research has led to an overwhelming volume of unstructured textual data in the scientific literature. This has necessitated the development of an automated approach for knowledge extraction and integration. In

this project, we present a comprehensive pipeline for constructing a unified biomed- ical knowledge graph by combining two well-known datasets: CHEMPROT [1],

which captures chemical–protein interactions, and EU-ADR [2], which annotates drug–gene–disease relationships. In order to identify important biomedical entities and interactions from CHEMPROT dataset, we perform Named Entity Recognition (NER) and relation Extraction (RE) using state-of-the-art biomedical models like BioBERT [3], BioGPT [4] and …

Coral Vision – Crustose Coralline Algae Detection With Computer Vision, Ryan Tseng

Master's Projects

Crustose coralline algae (CCA) are a group of red algae that are vital contributors to the health of coral reef ecosystems. Monitoring CCA abundance can serve as an indicator for coral reef health and improve reef conservation efforts. Autonomous Reef Monitoring Structures (ARMS) are artificial structures that can be deployed into coral reef ecosystems and retrieved to gather ecological data without harming reef structures. Traditional methods of calculating CCA abundance require manual analysis and are labor-intensive. Recent developments in computer vision and deep learning technology have provided an avenue to fully automate this task. This research aims to train a …

Secured Data Storage Management With Deduplication In Cloud Computing And Local Gpt Integration, Pavan Myana

Master's Projects

Exponential growth in cloud computing has brought enormous changes in data storage and processing, but also raised several questions on the security, privacy, and efficient storage of data. This report provides a dual-focused approach toward solving these challenges. First, we try to build an application securely and efficiently using data deduplication and Proxy Re-Encryption for optimization of storage and enabling secure data sharing. Deduplication ensures that redundant data is removed before encryption for maximum efficiency in storage, while PRE enables the safe sharing of encrypted data by re-encrypting the keys for specified recipients without the leakage of sensitive information. We …

Rift - Reddit Information Falsity Tagger, Parth Joshi

Master's Projects

Social media platforms such as Reddit are widely used for sharing and consuming information. User-generated content poses a great risk for misinformation creation and dissemination on these platforms. “Fake news”, as it is commonly referred to, has far-reaching social implications, swaying public perception, making political viewpoints more radical, and adversely impacting health decisions. The covariable features that come with fake news make it even harder to detect because it is presented in the form of text, images, videos, and even social interactions. This paper describes a novel method for detecting fake news on Reddit: RIFT, short for Reddit Information Falsity …

Reinforcement Learning-Based End-To-End Monitoring Path Selection In Multi-Domain Optical Networks, Soham Choudhury

Master's Projects

This paper presents a novel approach for optimizing network monitoring in optical communication systems using Reinforcement Learning (RL). Assuming a multi-domain architecture with limited domain visibility, we simulate multiple optical connections using an optical communications simulation software, GNPy, obtaining key network metrics to model the system. We developed two RL agents: the first agent selects near-optimal monitoring paths based on network states, and the second agent dynamically adapts its selected paths in response to state changes, such as fiber failures or issues with ROADMs. This adaptive approach allows for continuous improvement of network monitoring, ensuring resilience and efficient fault detection. …

Nontraditional Students Enrolling In Online Education: Assessing Outcomes And Barriers To Success, Kua Vang

Master's Projects

Online education has significantly reshaped how colleges and universities define and evaluate academic achievement. In March 2020 during the height of the Covid-19 pandemic, the U.S. government enforced social distancing regulations that affected operational continuity for higher education institutions. The need to transition from traditional classroom instruction to online was unavoidable. Institutions were not prepared, however, and their shortcomings in providing high-quality online learning for students were exposed. The inadequacy can be due to higher education institutions decade-long mindset of seeing online education as a less credible alternative. As a result, institutions are more eager than ever before to invest …

Managing Compliance: The Role Of Leadership Styles In Information Security Practices In Nigerian Public University Libraries” Insights From North-East Nigeria, Musa Giade Ya'u Musagiade, Gaberial O. Alegbeleye, Madukoma Ezinwanyi

Library Philosophy and Practice (e-journal)

Background: Information security compliance comprises adhering to established policies, protocols and to ensure absolute security. Despite the importance of security compliance in academic libraries, evidence from previous studies reported a low level of information security compliance among personnel in public university libraries in North-East Nigeria. However, proper leadership styles influence information security compliance leading to absolute protection of information systems. So, this study explored the influences of leadership styles in security compliance in public university libraries in North-East, Nigeria.

Method: This study adopted a survey research design. The population consisted of 618 library personnel from the 14 public university libraries …

Working With Similarities And Differences: Relational Processes In Transdisciplinary Qualitative Research With Diverse Teams, Michael S. Dao, Soma De Bourbon, Melissa Mcclure Fuller, Miranda Worthen

Faculty Research, Scholarly, and Creative Activity

Transdisciplinary research and research teams are becoming increasingly valued in academic spaces. The potential of transdisciplinary research is that the diversity of thought and experience can create robust research processes from project inception, methodological protocol, data collection, data analysis and reporting output. Yet, transdisciplinary research teams can also bring about complications pertaining to conflicting epistemological perspectives, areas of expertise, and objectives for applied impact. In noting the benefits and downsides of transdisciplinary research, this article aims to detail how a transdisciplinary research team navigated a qualitative and participatory longitudinal research project. Drawing from a larger community-based participatory research project, the …

Layover Start Timing Predicts Layover Sleep Quantity And Timing On Long-Range And Ultra-Long-Range Trips, Michael J. Rempe, Ian Rasmussen, Kevin Gregory, Cheyenne Johnson, Matthew Hsin, Erin Flynn-Evans, Amanda Lamp, Cassie J. Hilditch

Faculty Research, Scholarly, and Creative Activity

Study Objectives: Airline transport pilot sleep during layover is an important factor for alertness on subsequent flights. Assessing pilots' sleep on layover is an important first step in helping them obtain the most recuperative sleep possible on layover. Here, we investigate the quantity and timing of sleep during layovers and determine predictors for layover sleep. Methods: Sleep was assessed in 256 pilots flying a total of 473 long-range (LR; flight time 12-16 hours) or ultra-long-range (ULR; flight time > 16 hours) trips. Sleep was assessed using actigraphy. We employed linear mixed-effects models with layover sleep characteristics as the outcomes. The predictor …

Cross-Species Real-Time Detection Of Trends In Pupil Size Fluctuation, Sharif I. Kronemer, Victoria E. Gobo, Catherine R. Walsh, Joshua B. Teves, Diana C. Burk, Somayeh Shahsavarani, Javier Gonzalez-Castillo, Peter A. Bandettini

Faculty Research, Scholarly, and Creative Activity

Pupillometry is a popular method because pupil size is easily measured and sensitive to central neural activity linked to behavior, cognition, emotion, and perception. Currently, there is no method for online monitoring phases of pupil size fluctuation. We introduce rtPupilPhase—an open-source software that automatically detects trends in pupil size in real time. This tool enables novel applications of real-time pupillometry for achieving numerous research and translational goals. We validated the performance of rtPupilPhase on human, rodent, and monkey pupil data, and we propose future implementations of real-time pupillometry.

Automated Sponge Segmentation With Mask R-Cnn On Arms Plate Images For Reef Monitoring, Mrunali Abhijit Thokadiwala

Master's Projects

Climate change-driven ocean warming and acidification are disrupting the ecological balance of coral reefs. Notably, these oceanic conditions are undermining coral health and accelerating their decline which is favoring some sponge species in outcompeting them for dominance. Although functional, these altered reefs destabilize the reef architecture, hinder nutrient cycling, and support fewer marine species. Thus, monitoring the growth and abundance of various sponges and understanding their roles at different stages of ecological succession in coral reefs is vital. Autonomous reef monitoring structures (ARMS) are often used for this purpose, but manual taxonomic analysis using their images is time-consuming, inconsistent and …

Evorgcn: Harnessing Esm-2 Evolutionary Embeddings With Relational Gcns For High-Fidelity Protein-Protein Interaction Prediction, Mohit Kunder

Master's Projects

Accurately predicting protein-protein interactions (PPIs) is essential for understanding cellular function and advancing biomedical discovery. We model PPIs as graphs, where nodes represent proteins and edges denote interactions. Using interaction data from the STRING database, we use two samples of it, namely the benchmark datasets—SH27K and SH148K—filtered by confidence score and annotated by interaction mode (multiple relations). In this project, we present EvoRGCN, a graph-based machine learning framework for PPI prediction that integrates both sequence-level (ESM-2 embeddings) and network-level information. We incorporate various Graph Neural Network architectures, including Graph Convolutional Networks, Graph Attention Networks, and Relational Graph Convolutional Networks. Our …

Synthetic Malware Generation Using Generative Ai, Phanidhar Sai Sravan Chandana

Master's Projects

Malware grows in numbers and complexity, evading conventional signature-and anomaly-based defenses and worsening extreme data sparsity and class imbalance problems for machine learning based detection. Generative models, specifically GANs conditioned on contextual embeddings like BERT have proved effective augmenting training corpora to improve classifier accuracy, but these approaches have largely produced family-specific samples In this paper, we propose a generalized augmentation scheme for generating robust malware embeddings for various families. We begin by extracting opcode sequences from 13 malware families and encoding them into three embedding methods: CountVectorizer, TF-IDF, and BERT’s ‘[CLS]‘ vectors. We therefore train standard GANs and Wasserstein …

Collaborative Governance In Practice: Evaluating Intergovernmental Relations Through The Santa Clara County Healthy Cities Initiative, Astrid J. Robles

Master's Projects

In the United States, intergovernmental relations (IGR) is rooted in the principles of American federalism, which focuses on the constitutional power dynamics between national, state, and local governments. While federalism outlines the structural framework, IGR specifically focuses on how federal, state, and local governments interact administratively, financially, and politically within the federal system. Beginning in the 1960’s, governments across the U.S. began to rely on non-governmental and private sector organizations for program implementation (Boyd & Fauntroy, 2000). This shift gave rise to the concept of collaborative governance, which expanded the scope of IGR by incorporating traditionally excluded groups from the …

Application Of Root Cause Analysis For Fall Prevention: A Quality Improvement Initiative For Older Adults In A Skilled Nursing And Long-Term Care Facility, Terrence Ranjo

Doctoral Projects

Falls in older adults are common and often have severe outcomes. They are the leading cause of fatal and non-fatal injuries among people aged 65 and older and continue to increase. Half of residents in nursing facilities fall annually, and one in every ten falls will lead to a severe injury. Federal regulations like the Centers for Medicare and Medicaid Services require long-term care (LTC) facilities to incorporate quality assurance performance improvement initiatives to address identified quality concerns in LTC facilities. Root cause analysis (RCA) can help clinicians identify several root causes of falls and guide clinicians in developing personalized …

Advocating For The Field Of Occupational Therapy Among High School Students, Linda Crabtree

Doctoral Projects

The field of occupational therapy (OT) is a healthcare field that is impactful on the lives of many individuals however there is currently a lack of awareness of the field of OT due to a lack of knowledge of and interest in OT among healthcare providers and the public (Richards and Valleé, 2020). The Aim of Research. The purpose of this project is to advocate for the field of OT and assess if an introductory presentation and hands-on lab about OT’s scope can improve the interest in OT among high school students in both rural and urban environments. Methods. The …

Collective Liberation Through Critical Pedagogy, Robert A. Marx, Anne E. Dufault, Miriam R. Arbeit

Faculty Research, Scholarly, and Creative Activity

The collective liberation of queer and trans people—which is inherently tied to the liberation of those oppressed by white supremacy and racism, ableism, classism, ageism, and other asymmetrical power distributions—is possible when we develop critical consciousness through critical pedagogy. As young people develop an awareness of the contradictions and falsehoods they are taught (e.g., the myth of meritocracy), they begin to work to change their environments and offer resistance to the structures that inequitably shape their world. The vast majority of research on queer and trans adolescents, though, has been shaped by dominant, deficit-based views of queerness that presuppose that …

Separation And Surface Examination Of Spacecraft Cabin Particulates, Grace A. Belancik

Master's Theses

Particulate matter in the atmosphere is a known detriment to human health, but several factors affect any particulate’s particular toxicity including particle size, surface area, and chemical composition. Extensive studies have been conducted to examine particulate hazards on Earth, but in crewed spacecraft, like the International Space Station (ISS), the particulate environment is wholly unique due to both the controlled environment and lack of gravity. Atmospheric sampling studies are underway, but another convenient source of airborne particulates for analysis is the contents of the vacuum bags used by the astronauts to clean the air vents. However, no method currently exists …

Study Finds Pelvic Floor Dysfunction In Women With Multiple Sclerosis Is An Unmet Need Requiring Nursing Action, Nicole Zhang

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

Game Theory: Using Play To Talk With My Children About The Unpredictable Road To Recovery, Miranda Worthen

Faculty Research, Scholarly, and Creative Activity

In this narrative, a mother with an unexpected medical setback creates a board game with her children to talk with them about what happened and share their emotions about the experience.

Moral Intensity Dimensions Of Information Security Policy Compliance: Perspectives Of Construal Level Theory And Ethical Theories, Dailin Zheng, Zhiping Walter

Faculty Research, Scholarly, and Creative Activity

Sanctions are often ineffective in promoting employee compliance with information systems security policies (ISSPs) and may lead to undesired outcomes. We establish that ISSP compliance is an ethical decision and examine it through the lens of ethical decision-making using scenario-based surveys. Guided by normative ethical theories and the construal level theory, we find that both the opinions of co-workers and perceived negative consequences of noncompliance to the organization influence employee ISSP compliance intention. Additionally, perceived social distance affects employees' assessment of when damages to the organization can occur. Both the assessed timing of damage and the perceived social distance from …

Reliability And Severity Levels Of The Voice-Related Experiences Of Nonbinary Individuals, Grace Shefcik, Pei Tzu Tsai, Satveer Kler

Faculty Research, Scholarly, and Creative Activity

Objectives: Clinicians providing gender-affirming communication services to nonbinary individuals often utilize client questionnaires. The Voice-related Experiences of Nonbinary Individuals (VENI) is the only published questionnaire exclusively for nonbinary clients. This questionnaire consists of 17 items that gain insight into the client's self-perception of voice and voice-related concerns. According to Shefcik and Tsai (2023), the VENI's content validity is good to excellent. This study evaluated the measure's reliability through internal consistency and test-retest reliability analyses and created impact ratings to support interpretation of scores. Study Design: This study utilized an online survey-based design with test-retest administration. The initial survey was administered …