Digital Commons Network^™

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 …

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 …

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 …

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 …

Why We Need To Fill In Our Mixed-Use Communities’ Business Vacancies And How We Can Do That, Victor Huynh

Master's Projects

This planning report provides an in-depth exploration as to why so many mixed use communities, including transit-oriented developments, in metropolitan regions across the country are facing challenges with filling in their retail business vacancies at the ground floor. It also provides a current snapshot of best practices to remedy this widespread barrier to cities functioning more effectively and sustainably. This report provides a combination of findings from a literature review which identifies the most common reasons and remedies for business vacancies, and a research design which identifies which urban planning or urban design characteristics correlate most frequently with case sites …

Enhancing Code Review Automation With Large Language Models Using Qlora Fine-Tuning And Rags, Sumukh Naveen Aradhya

Master's Projects

In this technological era where Artificial Intelligence and Machine Learning are revolutionizing various domains, Large Language Models (LLMs) are emerging as a very powerful tool. In the software development lifecycle, it is extremely important to have reliable code reviews to ensure security and maintain code quality. This project aims to survey various existing methodologies to aid creation of efficient code review automation agents and also research on ways to make this process more efficient. Parameter Efficient Fine-Tuning (PEFT) methodologies such as LoRA and QLoRA have been explored with an additional focus on a hybrid model that combines adaptive QLoRA with …

Equity In Public Budgeting: Community Engagement In Morgan Hill, Ariana Perez

Master's Projects

Through this research, I will examine what citizen participation strategies have been implemented and their role and effectiveness in addressing wealth inequities. My research will explore these topics and examine what programs and policies the City of Morgan Hill can implement to increase residents' sense of belonging and ultimately push for greater social equity. My primary research question is, what are the programs and policies that the City of Morgan Hill can implement to engage immigrant communities, specifically Spanish-speaking residents, and low-income residents in shaping decisions around public investments and funding? This project will serve as a needs assessment to …

Characterizing Somatic Variants In Nanopore Data With Machine Learning, Shwethal Sayeeram Trikannad

Master's Projects

Oxford Nanopore Technology (ONT) is a popular long-read sequencer in genomics. However, its high base-calling error rate produces several sequencing artifacts. Detection of somatic variants in ONT sequenced tumor-normal samples remains challenging due to low frequencies. In this study, machine learning was applied to a dataset created by benchmarking ClairS output against HCC1395 and colo829 truth sets to classify variants and artifacts. Relevant features were engineered from sequence context and variant site characteristics to model artifact profiles. HistGradientBoostingClassifier achieved 0.876950 accuracy, outperforming all other models. Variant quality was the top predictor with an aggregate accuracy of over 85%. This work …

Retrieval-Augmented Generation (Rag) Chatbots: A Comparative Study Of Claude, Gpt-4o, Deepseek, And Llama, Kalindi Vijesh Parekh

Master's Projects

The use of Retrieval-augmented generation (RAG) in chatbot platforms has transformed academic spaces by significantly improving information accessibility. RAG has become a viable approach to upgrading Large Language Models (LLMs) with external knowledge access in real time. With the growing availability of advanced LLMs such as GPT, DeepSeek, Claude, Gemini, and Llama, there is a growing need to compare RAG systems based on different LLMs. This study compares the responses of four different RAG chatbots using popular LLMs against a uniquely designed evaluation dataset. Specifically, the study compares the responses and performance of closed-source (GPT-4o and Claude) and open-source models …

Social Engineering Scenario Generation For Awareness-Based Attack Resilience, Jade Webb

Master's Projects

Social engineering is found in a strong majority of cyberattacks today, as it is a powerful manipulation tactic that does not require the technical skills of hacking. Calculated social engineers utilize simple communication to deceive and exploit their victims, all by capitalizing on the vulnerabilities of human nature: trust and fear. When successful, this inconspicuous technique can lead to millions of dollars in losses. Social engineering is not a one-dimensional technique; criminals often leverage a combination of strategies to craft a robust yet subtle attack. In addition, offenders are continually evolving their methods in efforts to surpass preventive measures. A …

Machine Learning Based Network Traffic Classification With Cosine-Similarity Based Out-Of-Distribution Detection, Prabhat Edupuganti

Master's Projects

The changes occurring in the amount of encrypted network traffic is growing at an alarming rate. This development has created intricate problems in traffic classification which is vital for effective cybersecurity. Moreover, most frameworks seem to ignore OOD detection, model calibration and novel pattern detection as cornerstone problem areas. The due analysis is presented as a machine learning approach aimed at resolving encrypted traffic classification issues and focuses on novel OOD detection and calibration issues. Primary contributions comprise detection of out-of-distribution states using softmax scaled cosine similarity, advanced variance-based feature elimination, and lowering ECE using stringent NNs. This work demonstrates …

Landslide Prediction Using Time-Series Decomposition, Reinforcement Learning-Based Feature Selection And Ml Models, Mohith Sai Venkat Ankem

Master's Projects

Landslides pose significant risks to human life, the community, and the environment, yet their prediction remains a complex and unexplored challenge. Existing prediction models often rely on surface measurements and satellite images, neglecting the critical role, in providing deeper insights into landslide analysis. The literature review highlights a lack of research in time series decomposition techniques, despite their potential to improve prediction accuracy. Similarly, feature selection methods that enhance model robustness and precision have not been adequately addressed. This study presents a novel approach to predicting landslide displacement by combining feature selection through reinforcement learning techniques with advanced time-series machine …

Adaptive Cobot Interaction Via Smartwatch Data Fusion For Car Assembly Automation, Riddhik Tilawat

Master's Projects

In modern car manufacturing, collaborative robots (cobots) work with human operators during shared workcell interactions to maximize production speed and flexibility. Collaboration between humans and robots is safe and effective only when operator intent recognition via a single wrist-worn inertial measurement unit (IMU) is accurate and low-latency. This thesis develops an IMU-only intent recognition pipeline, and is evaluated on three datasets: the public OPPORTUNITY dataset, the Sony Smartwatch Gesture dataset and a custom Samsung Galaxy watch 6 dataset. The proposed framework leverages five step sequence-to-label problems which are stepwise posed as data streams transforming raw IMU data into trainable tensors. …

Real-Time Adaptive Framework For Topic Modeling In Social Engineering Attacks, Manav Bhasin

Master's Projects

Detecting social engineering attempts is crucial for security, as these threats are becoming more frequent and increasingly exploit human vulnerabilities. This research focuses on topic modeling using conversational data from Kevin Mitnick’s ”The Art of Deception” with dialogues that illustrate various social engineering strategies. The dataset comprises manually extracted and synthetically augmented conversations to ensure natural dialogue flow. Two methodologies are presented for utterance-level and global topic extraction: prompt engineering leveraging OpenAI’s GPT-4o-mini, characterized by few-shot learning and chain-of-thought prompting, and Quantized Low Rank Adaptation (QLoRA) utilizing Mistral’s 7B instruct model for efficient fine-tuning. Through experimentation and evaluation, this study …

Living In The Hyphen: Creating Puentes Through Encuentros And Pláticas With Latinx Cross-Cultural Kids, Jennifer Jane Daby

Dissertations

Latinx cross-cultural kids, or children of immigrants, are often faced with a feeling of not belonging to mainstream American culture nor their parents’ home culture(s) of origin. As such, they are viewed through a deficit lens and forced to negotiate their sense of identity and belonging through language and culture to fit in. By embracing the concept of educación, using culturally responsive methods of encuentros and pláticas with cross-cultural kids and their families, this study intends to find ways to foster a sense of belonging for this population in schools through participants’ testimonies. It aims to find ways to bridge …

Toward Strategy Identification And Subtask Decomposition In Task Exploration, Tom Odem

Master's Projects

This research builds on work in anticipatory human-machine interaction, a subfield of human-machine interaction where machines can facilitate advantageous interactions by anticipating a user’s future state. The aim of this research is to further a machine’s understanding of user knowledge, skill, and behavior in pursuit of implicit coordination. A task explorer pipeline was developed that uses clustering techniques, paired with factor analysis and string edit distance, to automatically identify key global and local strategies that are used to complete tasks. Global strategies identify generalized sets of actions used to complete tasks, while local strategies identify sequences that used those sets …

Comparative Analysis Of Embedding Techniques With Clustering Algorithms For Malware Opcodes, Ayush Koul

Master's Projects

Malware detection and classification remain critical challenges in cybersecurity, especially as malicious software becomes increasingly sophisticated and prevalent. While much of the work involving embeddings has traditionally relied on supervised learning approaches, there is significant potential in leveraging unsupervised learning techniques to discern hidden structures in malware data. By employing embedding techniques to convert malware samples into high-dimensional vector representations, we can capture the subtle and complex patterns inherent in malicious code without relying on pre-labeled data. This unsupervised approach helps categorize malware into predefined malware families, greatly aiding in developing cybersecurity solutions. In contrast to traditional supervised models that …

Gen Ai For Malicious Network Data, Aneesh Maturu

Master's Projects

Though botnet attacks are on the rise, they also have become sophisticated and difficult to detect. Such a rising threat demands more and more sophisticated cybersecurity that leverages machine learning technology. Nevertheless, one of the biggest bottlenecks remains the unavailability of large and well-balanced datasets, particularly for malicious traffic, which hampers the efficacy of detection models. In an attempt to address this issue, our research utilizes Generative Adversarial Networks (GANs) to produce synthetic samples of botnet traffic from the CTU-13 dataset. While the majority of generative models have been targeting image data, we use GANs for a new application: generating …

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 …

The Inequitable Access: Black And Latinx Students In Ap Courses, Jean C. Woolcock

Dissertations

This study posits that in American high schools, the preparation of Black and Latinx students, starting from the freshman year, can be effective in helping to increase their participation in Advanced Placement (AP) courses. Structural inequities, in how schools are resourced, affect the ways in which Black and Latinx students are denied equal opportunities. On the contrary, members of their peers do obtain this kind of preparation. As a framework, Critical Race Theory (CRT) contends that race, the law, and power are deeply ingrained in maintaining established bastions of wealth and dominance. Gaps in opportunity and achievement spill over into …

Educator Perspectives Of High School Long-Term English Learners, Martin Gutierrez Jr.

Dissertations

This qualitative study was twofold. The study began by exploring how educators perceive students who are Long Term English Learners (LTELs) and then moved to an autoethnographic examination of the researcher’s own secondary school experiences as an LTEL. The research reviewed teacher and administrator perceptions by focusing on three high school teachers who taught Long Term English Learners during the study period to understand the reclassification process. The researcher interviewed a district-level administrator to understand institutional policies and procedures that determine LTEL student identification and reclassification processes. The researcher presented their autoethnographic narrative to explore how holistic measures support multilingual …

Comparing E-Mosaics Predictions Of High-Redshift Proto-Globular Clusters With Jwst Observations In Lensed Galaxies, Joel Pfeffer, Duncan A. Forbes, Aaron J. Romanowsky, Nate Bastian, Robert A. Crain, J. M.Diederik Kruijssen, Kenji Bekki, Jean P. Brodie, Mélanie Chevance, Warrick J. Couch, Jonah S. Gannon

Faculty Research, Scholarly, and Creative Activity

High-resolution imaging and strong gravitational lensing of high-redshift galaxies have enabled the detection of compact sources with properties similar to nearby massive star clusters. Often found to be very young, these sources may be globular clusters detected in their earliest stages. In this work, we compare predictions of high-redshift (-10) star cluster properties from the E-MOSAICS simulation of galaxy and star cluster formation with those of the star cluster candidates in strongly lensed galaxies from JWST and Hubble Space Telescope (HST) imaging. We select galaxies in the simulation that match the luminosities of the majority of lensed galaxies with star …

A Privacy-Preserving Federated Learning Approach For Airline Upgrade Optimization, Sien Chen, Yinghua Huang

Faculty Research, Scholarly, and Creative Activity

Purpose: A key issue of making upgrade decisions is to match the most relevant upgrade offers to the right customers at the right time. To optimize upgrade strategies and profitability, companies seek to break “data silos” between themselves and other business partners for a more holistic view of customers' consumption experiences. However, multi-source data fusion may lead to potential privacy leakage. To overcome these two challenges in data silos and privacy protection, this study introduced a privacy-preserving federated learning (FL) approach and explained the process of using FL in modeling airline passengers’ willingness to pay for upgrade offers. Design/methodology/approach: Federated …

Generating Synthetic Malware Samples Using Generative Ai, Tiffany Bao, Kylie Trousil, Quang Duy Tran, Fabio Di Troia, Younghee Park

Faculty Research, Scholarly, and Creative Activity

Malware attacks have a significant negative impact on organizations of varied scales in the field of cybersecurity. Recently, malware researchers have increasingly turned to machine learning techniques to combat sophisticated obfuscation methods used in malware. However, collecting a diverse set of malware samples with various obfuscation techniques is challenging and often takes years, especially for newly developed malware. This issue is further compounded by a well-known limitation of machine learning models: their poor performance when training data is scarce. In this paper, we propose a new system for generating synthetic malware samples to augment imbalanced malware dataset. Our approach decomposes …

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 …

Using A Sober Curious Framework To Explore Barriers And Facilitators To Helping Sexual Minority Women Reduce Alcohol-Related Harms: Protocol For A Descriptive Study, Tonda L. Hughes, Lauren Bochicchio, Laurie A. Drabble, Belinda Lunnay, David Whiteley, Jillian R. Scheer, Beth Meadows, Paul Ward, Carol Emslie

Faculty Research, Scholarly, and Creative Activity

Background: Globally, women consume less alcohol than men, but alcohol consumption among women has declined less in recent years than among men. Drinking rates and alcohol-related harms vary substantially across population groups of women, and sexual minority women (eg, lesbian, bisexual, and queer) are at notably high risk. An emerging body of literature suggests that in addition to minority stress (eg, stigma, discrimination), drinking norms and drinking cultures likely influence sexual minority women’s drinking. Almost no research has explored these factors as possible targets of interventions. Sober curiosity is a rapidly growing wellness movement that may be particularly salient for …

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 …

Stream Response To A Dam Removal: Mill Creek, Davenport, Ca, Jessica J. Williamson

Master's Theses

Dams have many effects on stream morphology and ecology, including a loss of sediment supply downstream, reduced access to the entire stream for aquatic fauna, and a flooded area upstream of the dam. Dams are a hurdle for migrating organisms such as anadromous fish, which is a group of concern. An over 100-year-old dam on Mill Creek, a stream draining a coastal watershed in central California, was removed in the fall of 2021. The site is in the Santa Cruz Mountains, where endangered salmonids such as steelhead, Oncorhynchus mykiss, have historically lived. To study the change in the stream from …

A Review Of Social, Behavioral, And Ethical Considerations For Smart Life Engineering, John Murray

Faculty Research, Scholarly, and Creative Activity

Smart Life (SL) systems do not exist in a technological vacuum; they are embedded in a social ecosystem and have rich and complex interactions with their surroundings. It is important to recognize that their design and implementation will have an impact on the individuals and communities around them. This chapter examines the social, behavioral, and ethical issues to be considered in a holistic systems approach to SL engineering, to help practitioners and stakeholders to design the strategies needed for successful SL deployments. It addresses these topics from several disciplinary viewpoints and provides a comprehensive set of references for those requiring …

Improving Equity, Diversity, And Inclusion In Lis Education: Insights For Curriculum Development And Professional Preparation, Lili Luo

Faculty Research, Scholarly, and Creative Activity

Objective – To explore how the graduate-level Library and Information Studies (LIS) curriculum can be redesigned to be more socially justice focused and thus better prepare graduates to address equity, diversity, and inclusion (EDI) issues in their workplace. Design – A cross-sectional, descriptive online survey study. Setting – MA/PG Diploma program in LIS at University College, London (UCL). Subjects – 59 recent graduates from the MA/PG Diploma program in LIS at University College, London (UCL). Methods – Using the descriptive survey methodology, a 13-item online questionnaire was sent to a purposive sample of 733 alumni from the MA/PG Diploma program …