Digital Commons Network^™

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 …

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 …

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 …

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 …

Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems, Mrudula Patteparapu

Master's Projects

Big Data infrastructure growth has produced overwhelming metadata volumes which create extensive problems for spatial indexing and both system scalability and database queries. Traditional solutions consisting of R-trees and conventional Bloom filters manage to provide either range query support or approximate membership testing, yet they face performance issues when used at large-scale metadata management. This research proposes Hierarchical Bloom Filter Tree (HBFT) as an improved framework that integrates hierarchical spatial partitioning with partitioned, scalable, cuckoo, and striped Bloom filter variants based on existing studies in hierarchical and probabilistic indexing. The complete evaluation process shows that HBFT outperforms PostGIS (an industry-standard …

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 …

Edurag: Improving Ai Teaching Assistants With Retrieval-Augmented Generation, Geethika Vadlamudi

Master's Projects

This paper is based on the emerging need for AI-driven teaching assistants to deliver personalized, effective, and responsive educational assistance. Earlier proposals for educational technology such as rule-based systems and adaptive learning platforms have been limited by the lack of flexibility, domain accuracy, and slow, non-interactive support. With the launch of large language models (LLMs) like GPT-3, GPT-4, they have demonstrated that they can generate the kind of human-responsive text. When it comes to more substantive education matters, though, such models suffer from domain accuracy, in addition to whether accurate information can be imparted. And that is where the aspect …

Semanticgraphrec: Lightweight Hybrid Recommendations Powered By Semantic Item Representations And Graph Collaborative Filtering, Devi Surya Kumari Akula

Master's Projects

Graph neural networks (GNNs) have emerged as a powerful paradigm for collaborative filtering. However, they often fall short in fully leveraging side textual content, resulting in suboptimal recommendations. To address this limitation, we explore the synergy between GNNs and deep contextual embeddings of item descriptions, aiming to enhance recommendation quality on the Amazon-Books dataset. We propose SemanticGraphRec, which combines GNNs with Large Language Models (LLMs) to leverage both collaborative filtering and textual item content. Experimental results demonstrate that incorporating semantic item embeddings produced by fine-tuning LLMs consistently improves performance. Our approach enhances recommendation relevance in sparse data scenarios by leveraging …

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 …

Galora: A Lightweight Graph-Aware Llm Framework For Node Classification On Text-Attributed Graphs, Mayur Choudhary

Master's Projects

With the exponential rise of language models (LMs) and their potential to understand semantic relationships, large LMs are being used across a wide range of applications. Text-attributed graphs (TAGs) are one notable example where LLMs can be combined with Graph Neural Networks (GNNs) to enhance node classification results. TAGs associate textual content with each node and are commonly seen in various domains such as social networks, citation graphs, recommendation systems, etc. Effectively modeling TAGs would enable deeper insights into different aspects of the graph and improve decision-making in relevant domains. We present GaLoRA, a parameter-efficient framework to integrate structural information …

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 …

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan

Master's Projects

Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …

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 …

Bot Detection In Social Media Using Graphsage And Bert, Abhishek Deshmukh

Master's Projects

This project details a novel bot detection system developed to battle the ever- changing challenge of disinformation, misinformation, and other bot-generated content.

The methodology employed in this project combines the text-based analytical strength of BERT (Bidirectional Encoder Representations from Transformers) with the strength of GraphSage (Graph Sample and Aggregation) for analyzing network structures. The project concatenates BERT and GraphSage vectors to create an 896-size feature embedding with a rich blend of network and text features. This project employs a Support Vector Machine to process the concatenated embeddings, as SVM works well with high-dimensional data. This project was evaluated on two …

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 …

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 …

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 …

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 …

Ai-Generated Text Detection And Source Identification, Anjana Priyatham Tatavarthi, Faranak Abri, Nada Attar

Faculty Research, Scholarly, and Creative Activity

The use of advanced machine learning techniques to detect AI-generated text is a very practical application. The ability to distinguish human-written content from machine-generated text while identifying the source generative model helps address growing concerns about authenticity and accountability in digital communication. The differentiation of human-generated and AI-generated text is highly relevant to several applications, from news media to academic integrity, and is key to ensuring transparency and trust in content-driven environments. However, existing models are often insufficient to accurately detect AI-generated text and determine the specific AI source due to the complex nature of machine-generated content. To address this, …

Self-Disclosure And Social Support In A Web-Based Opioid Recovery Community: Machine Learning Analysis, Yu Chi, Huai Yu Chen, Khushboo Thaker

Faculty Research, Scholarly, and Creative Activity

Background: The opioid crisis remains a critical public health challenge, with opioid use disorder (OUD) imposing significant societal and health care burdens. Web-based communities, such as the Reddit community r/OpiatesRecovery, provide an anonymous and accessible platform for individuals in recovery. Despite the increasing use of Reddit for substance use research, limited studies have explored the content and interactions of self-disclosure and social support within these communities. Objective: This study aims to address the following research questions: (1) What content do users disclose in the community?; (2) What types of social support do users receive?; and (3) How does the content …

“They're Like Slash”: Multimodality And Embodied Agency In Students' Critical Engagements With Texts, María José Aragón, Meghan Corella, Nora W. Lang

Faculty Research, Scholarly, and Creative Activity

Despite recent calls to more fully incorporate multimodal perspectives into literacies research, there is still limited scholarship examining how students critically engage in reading activities by drawing on embodied practices. Racially and linguistically minoritized students are particularly disadvantaged by dominant logocentric and developmentalist approaches, which privilege oral and written discourse and often position these students as less capable of performing complex literacy practices. Drawing from three independent ethnographic studies, our multimodal interactional analysis examines how students of a range of ages and raciolinguistic backgrounds use embodied actions and other semiotic resources to agentively navigate text, task, and ideological constraints in …

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 …

Empowering Customer Service With Generative Ai: Enhancing Agent Performance While Navigating Challenges, Charles Costa, Souvick Ghosh

Faculty Research, Scholarly, and Creative Activity

Introduction. As large language models (LLMs), such as GPTs, become more intelligent, a key area of exploration is how these technologies can improve the customer experience. Contrary to common belief, many consumers, including Gen Z, prefer human-provided customer service, illustrating the importance of human-AI collaboration in the space. Method. By leveraging the author’s real-world knowledge of enterprise knowledge management and customer service delivery, we reviewed numerous literature about AI, knowledge management, and service design and synthesised practical insights for industry professionals to build a successful AI strategy. Analysis. We examined the gap between academic research on generative AI and how …

Investigating Changes In Connected Speech In Nonfluent/Agrammatic Primary Progressive Aphasia Following Script Training, Stephanie M. Grasso, Karinne Berstis, Kristin Schaffer Mendez, Willa R. Keegan-Rodewald, Lisa D. Wauters, Eduardo Europa, H. Isabel Hubbard, Heather R. Dial, J. Gregory Hixon, Maria Luisa Gorno-Tempini, Adam Vogel, Maya L. Henry

Faculty Research, Scholarly, and Creative Activity

Script training is a speech-language intervention designed to promote fluent connected speech via repeated rehearsal of functional content. This type of treatment has proven beneficial for individuals with aphasia and apraxia of speech caused by stroke and, more recently, for individuals with primary progressive aphasia (PPA). In the largest study to-date evaluating the efficacy of script training in individuals with nonfluent/agrammatic primary progressive aphasia (nfvPPA; Henry et al., 2018), robust treatment effects were observed, with maintenance of gains up to one year post-treatment. However, outcomes were constrained to measures of script accuracy, intelligibility, and grammaticality, providing a limited view of …

Book Review: Creating Inclusive Libraries By Applying Universal Design: A Guide By Carli Spina, Katrina Williams

School of Information Student Research Journal

Creating Inclusive Libraries by Applying Universal Design: A Guide is a practical and useful resource for improving libraries so that they are accessible to the broadest range of people possible. The format is well organized, and the case studies illustrate principles in engaging and recognizable ways. A reader who starts with no knowledge of Universal Design would acquire a working knowledge and be able to assess their own spaces with the guidance provided through examples and checklists. Rather than focus on resources for disabled patrons, Spina encourages readers to adopt a reflective practice for considering the needs of all library …

Enhancing Cross-Cultural Communication In Low-Resource Language Conversational Agents, Hardi V. Trivedi

Master's Theses

Recent advancements in natural language processing (NLP) and large language models (LLMs) have facilitated the development of systems capable of generating human-like responses across a wide range of tasks. However, the majority of research has focused predominantly on English, overlooking the vast linguistic diversity globally. For true global inclusivity, extending research to other languages is crucial, particularly as it can significantly benefit various sectors such as business, healthcare, government, and education. A major challenge in this expansion is the scarcity of digital data available and the limited number of pre-trained models for low-resource languages. Our research specifically addresses these challenges …

Swim Bladder Morphology Influences The Responses Of Nearshore Rockfishes To Barotrauma, Molly K. Alvino

Master's Theses

Rockfishes (Sebastes spp.) are ecologically and economically important fishes in the continental shelf and slope regions of the Eastern Pacific Ocean. All species within the Sebastes genus have a buoyancy organ, the swim bladder, which is sensitive to rapid changes in pressure that occur when fish are caught and brought up to the surface. Although all rockfishes have swim bladders, pressure-related injuries (barotrauma) affect rockfish species differently. I determined whether swim bladder morphology can explain differences in barotrauma among semi-pelagic (Blue and Olive rockfish) and benthic (Gopher and Vermilion rockfish) species that occupy different habitat zones. Seven different swim bladder …

An Exploration Into Why There Is An Overrepresentation Of Bame People In Missing Person Cases, Phoebe Sleigh-Johnson, Fiona Gabbert Dr., Adrian J. Scott

International Journal of Missing Persons

The overrepresentation of Black, Asian, and Minority Ethnic (BAME) people in missing person cases is an issue that is not understood within society and has largely been neglected within academic literature to date. This study, therefore, aims to explore why this overrepresentation might exist by obtaining the views and opinions of 24 professionals (including police officers) working within the field of missing people. The study used a qualitative, exploratory method and an anonymous online survey containing a series of open-ended questions. Thematic analysis was performed on the data, and three main themes were identified; the first related to the idea …

Racial Bias In Risk Allocation And Resource Utilisation: A Contributing Factor To Ethnic Minority Overrepresentation In Missing Person Investigations?, Amy Van Langeraad, Fiona Gabbert, Adrian J. Scott

International Journal of Missing Persons

Statistics from the National Crime Agency reveal that people from ethnic minority backgrounds are over-represented within the missing population in the United Kingdom, and previous archival studies have shown there is a discrepancy in the recovery of ethnic minority missing persons compared to White missing persons. A wider body of research shows that racial bias is prevalent in the criminal justice system and law enforcement, resulting in different outcomes for victims depending on their racial background. Such outcomes have yet to be examined in the context of missing persons. Therefore, this study tries to address this gap in the literature …