Digital Commons Network^™

A Guide To Falls In A Skilled Nursing Facility, Sarah Ghione

Doctoral Projects

Approximately one in four older adults experiences a fall each year in the United States (Centers for Disease Control and Prevention [CDC], 2024b; Kakara et al., 2023). Among those residing in nursing homes for either short-term rehabilitation or long-term care, the risk of falls is doubled (Acker & Janssen, 2017; Shao et al., 2023). The purpose of this doctoral capstone project was to develop an educational resource aimed at enhancing staff education and fall prevention efforts within a single nursing home, supporting the facility’s goal of reducing fall rates. Methods included a needs assessment, observational experiences, resource development, and the …

Visionmate: Ai-Powered Image Captioning Web Application, Sai Anoushka Kokku

Master's Projects

VisionMate is a web application that generates captions for camera-captured images. It is designed
to assist users with visual impairments by converting visual input into spoken and written text. The
application uses the GIT-base model from Hugging Face, which processes the image and returns a
descriptive caption. Users can take a picture using the device camera—either via webcam on
desktop or the native camera interface on mobile. The app provides audio output using the
SpeechSynthesis API and uses full-screen tap interaction to simplify accessibility.

The frontend is implemented in React.js, and the backend is built with FastAPI. The backend calls …

Multimodal Deception Detection Via Audio-Text Fusion With Deep Learning And Asr, Xiangyi Li

Master's Projects

Emotion detection plays a crucial role in human-computer interaction, enabling machines to recognize and respond appropriately to human emotional states. This project explores a two-stage approach to emotion detection using multimodal data, first predicting dimensional values (Arousal, Valence, Dominance) from textual and audio inputs, then mapping these representations to discrete emotion categories. We compare this approach with direct categorical classification using transformer-based language models like BERT, RoBERTa, and DeBERTa for text processing, alongside various audio feature extraction methods, including MFCCs and spectrograms. Using the IEMOCAP dataset, we evaluate both approaches across text-only, audio-only, and multimodal configurations. Our findings reveal that …

A Unifying Double-Reference Approach To Semantic Paradoxes: From The White-Horse-Not-Horse Paradox And The Ultimate-Unspeakable Paradox To The Liar Paradox In View Of The Principle Of Noncontradiction, Bo Mou

Faculty Research, Scholarly, and Creative Activity

The purpose of this study is to suggest and explain an engaging approach to three distinct types of (alleged or genuine) semantic paradoxes, the White-Horse-Not-Horse Paradox, the Ultimate-Unspeakable Paradox, and the Liar Paradox, in a unifying way that is sensitive to distinct features of them. Although the three types of semantic paradoxes address distinct types of objects, and although their seemingly paradoxical features are different (alleged or genuine), their distinct structures and contents can be understood and treated on the same common ground, which is jointly conceived in people's pretheoretic understandings of truth and of the double-reference feature of people's …

Multilingual Sentiment Analysis Using Ensemble Learning, Farhan Ansari

Master's Projects

The widespread use of multiple social media platforms has amplified the expression of public opinions over the Internet in languages such as English, Hindi and Spanish. With the aid of technological advancements in machine learning, we can analyze opinions posted on the Internet and gauge public sentiments. There are organizations and businesses that are interested in the evaluation of these sentiments as these type of data can generally be used to obtain the opinion of a product, restaurant, a candidate, etc. In this study, we perform a comparative analysis of three popular ensemble learning methodologies (Boosting, Bagging and Stacking) based …

Phishing Detection Using Continual Learning And Large Language Models, Gopi Prajeev Battula

Master's Projects

Adaptive phishing detection remains crucial as the nature of cyber-attacks changes over time, which renders static models obsolete. This project extends phishing detection through the implementation of continual learning approaches, namely Elastic Weight Consolidation (EWC) and Learning Without Forgetting (LWF) with RoBERTa, a Large Language Model (LLM) and compares the results of these approaches against GPT-4o-mini, another LLM. Our approach begins with fine-tuning RoBERTa on multiple phishing datasets to establish an effective baseline. EWC is then implemented to preserve vital model parameters based on their importance measured by the Fisher Information Matrix, while LWF uses knowledge distillation to retain prior …

Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel

Master's Projects

The widespread adoption of Large Language Models (LLMs) has revolutionized text generation and heightened concerns over misinformation and the erosion of journalistic integrity. Detecting AI-generated text is critical to addressing these challenges, yet current detection methods face adaptability, scalability, and accuracy limitations. This research paper uses machine-learning techniques to explore the classification of human and AI-generated articles, including a mix of human and AI-written content. The primary focus is on evaluating the effectiveness of clustering algorithms (K-Means and Agglomerative Clustering), auto-encoders, and Part-Of- Speech Tag Transition Matrix Log-Likelihood for distinguishing between AI-generated and human-written texts. Our findings reveal that while …

Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database, Jyothi Vaidyanathan

Master's Projects

Healthcare is one of the most important fields that benefits from advancements in Artificial Intelligence (AI). From classic models like linear regression to cuttingedge transformers, AI is applied across various healthcare subdomains, such as drug discovery, predictive analytics, and personalized medicine, to name a few. These techniques enable medical practitioners to make more informed decisions, significantly improving both the speed and accuracy of diagnoses and treatments. Machine learning has played a transformative role in oncology, especially in areas like early detection, diagnosis, treatment planning, and patient monitoring, by analyzing medical images, clinical information, genomic data, sensor information. Our research aims …

Image-To-Text Transcription: Analyzing And Describing Visual Content, Zixiao Fan

Master's Projects

Image captioning, which provides a textual understanding of visual content, is the fundamental support for the advancement of Human-A.I. Interaction technology. In the hope of exploring the application of such technology, this project focuses on two specific goals. One is to directly explore the application of the image informationretrieving abilities, and the other is to dive into the specifics of the pipeline and components of image captioning models. As a result, this project presents a working app that exploits the text retrieval functionalities to enable image storage with functions like tagging and transcription. It also supports search functionality with a …

Extraction Of A Knowledge Graph Of Biomedical Relationships, Brian Tran

Master's Projects

Rapid release in biomedical literature poses a challenge in linking information. This thesis aims to extract data from expanding datasets to identify and form meaningful relationships between biomedical entities. Large language models (LLMs) enable us to learn at a rapid pace. Creation of LLms from scratch are impractical. This thesis aims to collect a small dataset, containing biomedical papers, and use it to train large language models (LLMs) to extract entities from the text and learn the relationships between these entities. The experiment will be divided into two stages and utilize EU-ADR and ChemProt dataset. Starting with named entity recognition …

Maple: Malware Analysis Through Projection Of Low-Dimensional Embeddings, Quang Duy Tran

Master's Projects

Machine learning has become a popular and powerful tool for malware analysis and detection. With the rise in popularity of natural language processing (NLP) techniques, researchers can now extract contextual embeddings from malware opcode sequences, enabling the capability to analyze hidden malware patterns and advanced code obfuscation strategies. However, unlike malware binaries, which can be directly visualized as images, these embeddings exist in high-dimensional spaces, making it difficult to observe their global patterns or spatial structures. In this paper, we propose a framework for visualizing malware embeddings in lower-dimensional space using various dimensionality reduction techniques. Our approach converts malware binaries …

Human Vs. Llm: Interpreting Emotion From Neutral Faces In Contextually Charged Backgrounds, Sai Mounika Peteti

Master's Projects

In this study, we investigate the intersection of cultural context, visual perception, and implicit bias in interpreting neutral human expressions. Specifically, we explore how emotionally charged backgrounds can shape viewers interpretations of neutral facial expressions. Using an experimental setup, participants are shown neutral human portraits paired with varying background types ranging from emotionally neutral or pleasant to evocative scenes. Participants are tasked with selecting which background best matches the emotional state of the person depicted in the portraits. Eye-tracking data is collected to analyze visual attention patterns and cognitive processing. In parallel, we also evaluate how large language models (LLMs) …

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 …

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 …

Ai Powered Legal Decision Support System, Alisha Rath

Master's Projects

The large volume of legal cases presented by judicial professionals has made it
challenging to study and predict results. With advances in research methods and
technology, predicting law cases in a more accurate manner has become an important
trend. Prediction tools based on AI may help manage a large number of legislative
texts and documents that cannot possibly be fully read, reduce the number of cases
to be seen, and give accurate outcomes of how cases may turn out. Now, when
we look into the current AI legal prediction tools in this domain, they mostly lack
efficiency and interpretability, the …

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 …

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 …

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 …

"Acoustical Bedlam": A Contextualization Of Beethoven's Christus, Leanna R. York

The Beethoven Journal

The premiere of Beethoven’s Christus am Ölberge at the Theater an der Wien on April 5, 1803, marked the composer’s first and only contribution to the complex tradition of the German oratorio. Since the genre’s inception at the turn of the eighteenth century, it reflected artistic, literary, and theological controversies of its day. Beethoven’s chosen theme, the passion of Christ, came with its own set of ever-shifting compositional and textual expectations, influenced by older traditions of sepolcri and staged oratorios (common in Vienna and at the court of the elector of Cologne after mid-century) and lyric works such as Graun’s …

Second Thoughts? Czerny, Beethoven, And The Ninth Symphony’S Choral Finale, Mark Evan Bonds

The Beethoven Journal

Sometime after the premiere of the Ninth Symphony, according to Carl Czerny, Beethoven told a small circle of friends that he had made a “wrong choice” in writing a choral finale, that he was resolved to write a purely instrumental finale in its place, and that he already had an idea in his head for the new finale. Czerny never committed this report to writing, but he did transmit it orally to three others who did: Leopold von Sonnleithner, Otto Jahn, and Gustav Nottebohm. Anton Schindler would later deny the report, but the available evidence strongly supports the veracity of …

Innovative Sonorities: Atypical Pitch Orders In Beethoven's Piano Trios, Yoko Maruyama

The Beethoven Journal

In his chamber music with keyboard, Beethoven famously emancipated the cello from its bass function to make it equal with the other instruments, as has been repeatedly noted. Some studies have also highlighted, partly in relation to this emancipation, the sonorous balance between strings and piano in Beethoven’s chamber works. The pitch relationship between the cello and violin in his piano trios has been less discussed, yet attending to both parts reveals a tendency to use an atypical order in which the cello’s pitch is set above the violin or between the violin’s chord tones. This tendency increases from Beethoven’s …

Conference Report: 200th Anniversary Of Beethoven's 1824 Academy Concerts (Beethoven-Haus, Bonn, 2024), Mary Kirchdorfer, Cheston Humphries

The Beethoven Journal

No abstract provided.

Understanding Mobility-Related Challenges For Aapi Older Adults: A Preliminary Study In Southern California, Yongping Zhang, Priscila Salgado Inzunza, Carol Kachadoorian, Wen Cheng, Calvin Wong

Mineta Transportation Institute

The Federal Highway Administration (FHWA) and state Departments of Transportation (DOTs) Nationwide, the Asian American Pacific Islander (AAPI) community is projected to constitute 11 percent of people 65 years and older in the United States by 2050 (He et al., 2005). The challenges limiting the transportation and mobility of AAPI older adults include, but are not limited to, language barriers, cultural barriers, anti-Asian hate, accessibility to public transit, traffic safety and public security concerns, and changes to mobility due to the COVID-19 pandemic. This project conducted an extensive literature review and a preliminary multi-language survey in Southern California to better …

Science, Physiology, And Nutrition For The Nonscientist, Judi S. Morrill

Open Educational Resources

A wonderful blend of physiology, nutrition, biochemistry, genetics, biology, evolution, chemistry--what we all need to know as informed citizens. A basic knowledge of the life sciences and how our bodies work--to promote our own good health, especially as we're bombarded with misleading advertisements, soundbites, and the like. DNA fingerprinting, calorie requirements, dietary advice, genetic engineering (including gene editing with CRISPR cas9)--all in an easy-to understand book.

Carbohydrate Counting: Traditional Filipino Food Lists For Management And Prevention Of Diabetes Mellitus, Ashwini Wagle

Open Educational Resources

The Carbohydrate Counting tool was created to address the specific dietary needs of the Filipino population. It is an original intellectual work by Ashwini Wagle, Ed.D., MS, RD, Chair of the Department of Nutrition, Food Science, and Packaging at San Jose State University (SJSU).

The development of this tool was made possible through the collaboration and support of several contributors. Serena Starr, MS; Justine Martinez Cuaresma, MS, RD; Nazli Goudarzi, MS; Leah Olaivar; and Joe Johnston—a graduate student in the Department—played integral roles in the project. Their contributions included verifying the authenticity of traditional foods, designing culturally appropriate meal plans, …

Realities Of Nutrition, Judi S. Morrill

Open Educational Resources

What Is Nutrition Reality? Is it in the advertising claims that a food is natural or supplies instant energy? Is it in the myriad of dietary supplements? Is it in the diet plans for instant weight loss and glowing health? The only way to find out is to learn the basic principles of nutrition science, so you can be your own nutrition expert. Here is the true introduction to nutrition that you will read with pleasure and real understanding. It will free you from dependence on popular sources of information - often misinformation - so you can distinguish illusion from …