Digital Commons Network^™

Intelligent Caching In Named Data Networking, Deep Pradipbhai Shah

Master's Projects

Named Data Networking (NDN) has a built-in caching capability that is enabled with the help of its Content Store. Caching in NDN has several benefits, such as reducing overhead on the producer side, avoiding a single point of failure, and reducing network load. The primary caching policy of the NDN architecture is to leave copies everywhere. However, this scheme induces significant cache redundancy. Existing advanced cache techniques either periodically share the entire list of cached content at a node or make a caching decision without knowing the cached content at other nodes in the network. We propose an intelligent cache …

Multimodal Retrieval-Augmented Generation: Design And Application, Charul Rathore

Master's Projects

The rapid advancement in generative AI and large language models have forever revolutionized how we synthesize data. This project explores and experiments with the potential of a multimodal Retrieval-Augmented Generation (RAG) framework for processing text, tabular and image data. Starting with prompt engineering techniques, we address their limitations in dynamic and domain-specific real world applications by building a multimodal RAG pipeline and evaluating it against human-generated ground truth. The project culminates in BrightMind.ai, a full-stack educational platform featuring novel personalized AI companions for context-aware and adaptive response generation. Its innovative capabilities extend to music, video, and code generation, setting it …

Comparison Of Protein Structures Predicted By Genai Tools In A Zero-Shot Manner, Kruthi Shankar Rao

Master's Projects

Generative AI models have vast applications and one such critical application explored in this study is protein structure prediction. The 3D structures of proteins determine their function. Our study mainly focuses on using generative AI models such as ESMFold and ColabFold to predict and examine naturally occurring and mutated sequences. The workflow begins with collecting antimicrobial resistance (AMR) and toxin-antitoxin (TA) protein data. The sequences are applied over pretrained AI models to predict protein structures. Following this, models are fine-tuned with original and mutated target datasets. A comparison of models’ performances is done using metrics such as root mean square …

Leveraging Large Language Models For Transforming Student Information Into Actionable Data, Sree Hari Karri

Master's Projects

Admission season places significant demands on university committees, necessitating the review of vast arrays of documents to assess students’ competence. This project advances the development of an automated system designed to streamline this process by evaluating application materials such as Letters of Recommendation (LoRs), Statements of Purpose (SoPs), and resumes. Utilizing a variety of advanced Natural Language Processing (NLP) techniques, the system compares the performance of several Large Language Model (LLM) approaches. It also experiments with different data handling strategies, including the use of vector stores versus traditional context-based processing, to optimize model efficiency and accuracy. Special attention is given …

Implicit Personality Detection From User Behaviour In Recommendation Systems, Uzma Zubair Shaikh

Master's Projects

Recommendation systems are an integral part of any business, and a crucial factor in determining their success as these systems help businesses in marketing their products to the right kind of audience. Conventional methods of building recommendation systems such as collaborative filtering and content-based recommendation, although effective, suffer from limitations such as cold start and the data sparsity problems. Moreover, these methods aim at finding similar products as user’s past interactions rather than personalizing the recommendations. The upsurge in use of social media, over-the-top content (OTT), and e-commerce platforms has made the task of personalizing recommendations imperative, leading to the …

Artifacts In Low-Pass Whole Genome Sequencing, Nguyen Mai Anh Do

Master's Projects

Low-pass whole genome sequencing (LP-WGS) provides a cost-effective way to achieve broad genomic coverage, but it comes with the challenge of sequencing artifacts that can complicate accurate variant detection. To address this, we developed a bioinformatics pipeline using Nextflow. Starting with raw sequencing data, the pipeline performed variant calling using VarDict, with Genome in a Bottle (GIAB) high-confidence variants serving as the benchmark for variant validation. We explored machine learning approaches, testing classifiers such as AdaBoost, ExtraTrees, and RandomForest, to evaluate variant classification. Twenty-two features generated by VarDict were fed into Machine Learning pipeline, with AdaBoost standing out for its …

Exploring The Use And Misuse Of Large Language Models (Llms), Hezekiah Paul D. Valdez

Master's Projects

Large Language Models (LLMs) have quickly gone from simple rule-based systems to complex knowledge bases capable of tackling many different tasks across a variety of fields. What began as an exercise in human-computer interaction has become the basis for artificial intelligence in a variety of mediums. When attached to larger systems, LLMs become generative assistants that can perform highly on human proficiency assessments and other benchmark skill assessments. This increase in proficiency has led these systems to be deployed in fields such as cybersecurity, business, and programming to help improve productivity and efficiency. However, such a wide availability has allowed …

Knowledge Graph-Based Multiple-Choice Question Generation, Durga Muralidharan

Master's Projects

Knowledge-based tests are widely used to assess knowledge on a specific subject and have many applications in education and professional certifications. These tests usually consist of Multiple Choice Questions (MCQs), where a question with a few possible answers is given. Along with the correct answer, three or more incorrect answers are provided, which are called distractors. MCQs are a popular method for these tests because they are easy to grade. These tests can check different levels of comprehension ranging from beginners to advanced by creating distractors that may confuse unprepared test takers. This project proposes the Knowledge Graph Multiple Choice …

Code Quality Enhancement: Evaluating Ai Code Generation With Software Metrics, Sirisha Krishna Murthy

Master's Projects

With the advancements in the stream of AI in the recent time and the evolution of Generative AI, it is a given that there is a need to effectively integrate AI into daily tasks, including Coding. When talking about Generative AI, one important thing to consider is prompting, which is that way to talk to the AI. Depending on specific needs and tasks the way we need to prompt AI can vary. With rapid development in the field, there are a lot of new benchmarks that evaluate the AI coders on correctness, but to effectively adapt AI into actual coding …

Enhancing Qwen2.5-Coder: A Deep Dive Into Fine-Tuning Using Peft For Superior Code Outputs, Lohith Nagaraja

Master's Projects

The main objective of this research is to improve the quality of software code that is produced by the Qwen2.5-Coder model specifically in terms of maintainability, complexity, and reliability. Our approach is going to be a more specific one that will involve the Parameter-Efficient Fine Tuning (PEFT) framework combined with quantization through Low-Rank Adaption (LoRA). This approach involves fine-tuning only some of the parameters of a model to make it suitable for software programming with the general structure of the model largely intact. In this paper, SonarQube is used as a tool to help quantify the improvements made to the …

Facial Expression Mood Classification Using Machine Learning, Tiantong Li

Master's Projects

Facial expression classification is a powerful tool for understanding human emotions, with applications spanning human-computer interaction, healthcare, and entertainment. By analyzing facial cues, systems can interpret emotional states and adapt their responses, creating more personalized and emotionally aware experiences. One emerging application of facial expression classification is in music recommendation systems, where user emotions are integrated to suggest music that aligns with their current mood. While prior research has primarily classified facial expressions into four emotion categories, this study broadens the scope to seven emotions: angry, disgust, fear, happy, neutral, sad, and surprise. The project evaluates four machine learning techniques—CNN, …

Detecting Crustose Coralline Algae (Cca) In Marine Photos Using Mask R-Cnn, Vrushali Harshwardhan Deshpande

Master's Projects

Coral reefs, made up of thousands of polyps - tiny sac-like marine invertebrates sea anemones and jellyfish, are important to marine ecosystems and prevent loss of life by acting as a natural barrier against storms, floods, and waves. These reefs support a wide range of species, many of which are underexplored and new species being discovered regularly. Crustose coralline algae (CCA) is one of the vital algal species that provides reef structure. Studying the abundance of CCA is important in helping marine biologists analyze coral reef health while understanding the impact of climate change on the marine lifeforms. This study …

Cluster Analysis For Concept Drift Detection In Malware, Aniket Mishra

Master's Projects

The rapid evolution of malware presents significant challenges for detection systems. This is due to malware families adapting through feature manipulation and obfuscation, which causes concept drift. A clustering based approach is used to detect and adapt to these shifts. The KronoDroid dataset is segmented into batch sizes of 50 and analyzed with MiniBatch K-Means clustering. The silhouette coefficient is used to evaluate clustering quality, and help identify drift by detecting significant changes in cluster patterns. Concept drift will cause retraining of supervised classifiers, including Linear SVM, RF, MLP, and XGBoost. Three scenarios are used: static models, periodic retraining, and …

Extending A Graphical User Interface For Evidential Reasoning, Vaidehi Sanjay Joshi

Master's Projects

Systems like Capri are used for large-scale graph modeling and integration and PyGrapher aims to do that in a simplified manner. This project is an extension of PyGrapher which was a tool created by previous students at the university. The enhancements include adding customizable default parameters for nodes and edges, automating JSON conversion, and enabling real-time highlighting. These features specifically aim to improve usability, streamline workflows, and provide interactive feedback for the users. The enhancement of the project also added additional and rigorous testing of the platform's compatibility and user interaction. It demonstrates significant improvements in functionality and user experience. …

Enhanced Inter-Satellite Routing With Multi-Path Selection And Congestion Modeling, Jaesung Yoo

Master's Projects

Satellite networks play a crucial role in global connectivity today and making efficient routing algorithms is crucial for optimal performance. While existing routing algorithms have made significant progress using machine learning techniques, they often overlook network congestion and multiple path availability. This report introduces an enhanced routing framework that builds upon LSTM-based predictive routing using dynamic congestion modeling and multi-path selection. Our approach introduces a busy state metric that tracks satellite memory utilization, allowing for adaptive path selection based on both distance and current network load. Through simulations using a constellation of 20 satellites, our enhanced algorithm demonstrates significant improvements …

Llamatalk: Empowering Conversations With Retrieval-Augmented Generation, Aravind Rokkam

Master's Projects

This research report talks about the implementation and a comparative study of Llama 7B model’s fine-tuning technique and Retrieval Augmented Generation (RAG) capabilities in the context of creating a reliable AI therapist. This study focuses on training these models using diverse datasets consisting of doctor-patient conversations predominantly addressing general health issues. Using a technique like fine-tuning within the Llama 7B model, the project focuses on training the model with a diverse dataset comprising doctor-patient interactions primarily addressing general health concerns. Additionally, carefully organized mental health dataset from HOPE dataset, ensuring the bot's responsiveness to mental health inquiries. Through integration with …

Teaching Children Programming Concepts Through Video Games, Kayla Musleh

Master's Projects

Children have a tendency to lose focus when they are presented with something that does not entertain them or tailor to their personal interests; such as studying [1], [3], [10]. The research performed for this project focuses on studying how much more children can comprehend and focus on learning educational material if they are learning through playing a video game rather than being taught information directly in a typical classroom manner. For this study I created a computer game designed to introduce educational subjects such as mathematics and programming concepts to the child playing the game. By completing the tasks …

Exploring Fluctuations In Working Memory Load Through Pupillometry Using Gabor Image Deletion Tasks, Neenu Antony

Master's Projects

Van der Wel & Van Steenbergen mention that there has been a surge in pupillometry research in the past two decades, particularly in the area of task-evoked pupil dilation in the context of cognitive control tasks. The goal of most of these studies has been focused on finding a link between pupil dilation and effort exerted by an individual [10]. The review by authors Van der Wel & Van Steenbergen, aimed to assess the potential of pupil dilation as an indicator of effort rather than task complexity. Their analysis revealed that heightened task demands in domains such as updating, switching, …

Wall-E: An Autonomous Ai Rover For Precision Agriculture, Simar Ghumman

Master's Projects

Unmanned Ground Vehicles (UGVs) are emerging as a crucial tool in the world of precision agriculture. By working with UGVs equipped with machine learning, we can find solutions to a range of complex agricultural problems. My project, titled “Wall-E: Artificial Intelligence Robot for Precision Agriculture,” focuses on developing a UGV capable of navigating through agriculture fields autonomously while capturing data. Using machine learning, computer vision, and other sensor technologies, Wall-E is capable of estimating the total yield of crops, self-localization, mapping its environment in real time, and avoiding obstacles along its route. The purpose of this project is to automate …

Improving The Diversity And Fairness In Job Recommendations Using The Stable Matching Algorithm, Jatin Unecha

Master's Projects

In today’s competitive job market, recommendation systems are essential for con- necting job seekers with suitable opportunities. However, traditional recommendation

models, such as collaborative and content-based filtering face challenges in ensuring diversity and fairness. This results in low diversity and high congestion around popular job listings which refers to the problem where a few jobs are recommended to large numbers of users as well as imbalanced job exposure. These drawbacks lower the satisfaction for job applicants and reduce the employers’ ability to reach a broader pool of candidates. In this research we address these challenges by introducing the Gale-Shapley algorithm …

Malware Detection Using Qr And Aztec Code Representations, Atharva Khadilkar

Master's Projects

In recent years, the use of image-based techniques for malware detection has gained prominence, with numerous studies demonstrating the efficacy of deep learning approaches such as convolutional neural networks (CNNs) in classifying images derived from executable files. In this paper, we consider an innovative method that relies on an image conversion process that consists of transforming executable files into QR and Aztec codes. These codes capture structural patterns in a format that may enhance the learning capabilities of CNNs. We design and implement CNN architectures tailored to the unique properties of these codes and apply them to a comprehensive analysis …

Investigating Uncertainty In Gaussian Process Models, Wilson Strasilla

Master's Projects

N. A.

Enhancing Medical Chatbots With Image Diagnosis, Swatisri Chavali

Master's Projects

Medical chatbots, at the conjunction of artificial intelligence and healthcare, are the very cornerstone of a transformative force in diagnostic capabilities and communication channels for healthcare professionals. The history of this journey, from early chatbot models to sophisticated systems, is born out of a relentless pursuit of accuracy and contextual understanding. This proposal acknowledges the critical role played by NLTK in raising the interpretability and communicative capabilities of intelligent systems, meeting challenges that arise from varying writing styles and accommodating the standards of the medical field. The integration of NLTK is a linchpin, bridging the gap between sophisticated technological architectures …

Multimodal Emotion Detection In Conversations And Dialogues: A Fusion Model Approach, Abhinay Jatoth

Master's Projects

Emotion recognition is gaining traction due to its wide range of potential applications across different fields. With the rise of social media, chat platforms, and voice assistants, there is a vast increase in data through which humans implicitly and explicitly carry emotional cues. With new algorithms being developed for understanding the nuances of human language and emotion, businesses can tailor more personalized and empathetic service. Sentiment analysis, expresses a positive, negative, or neutral viewpoint laid the foundation of Emotion classification. Emotion classification in conversations represents the most advanced stage of classification. It is also challenging due to the existence and …

Building Lean Standalone Web Servers, And Routing Engine, Ajita Shrivastava

Master's Projects

As a result of advancement in technology the web and email servers have greatly expanded in size.
This has created a need for miniaturization, and people are trying to minimize technology whilst
making it fast and efficient. This report discusses the development of a set of servers aligned with
the miniaturization trend: Atto servers. These are simple to use single file PHP servers created for
moderate usages including web traffic and email tasks. The purpose of this project is to develop
small server solutions which could act as working counterparts of products like Apache or Nginx.
It makes the server …

Leading From The Middle: Arts Administrators' Beliefs About The Impact Of Arts Education On High-Needs Students, Sofia Fojas

Dissertations

Research on arts education notes the positive impact of arts education on educational outcomes for students, particularly students of color, students in poverty, and students needing additional academic support. Students living in low-income communities receive different educational opportunities than those in high-wealth districts. This disparity is especially true for highly mobile and high-needs students of color. Arts education has made positive differences in student outcomes. This qualitative study examined the beliefs of county arts leads, leaders in the middle, situated between the California Department of Education and their local school districts, and how they can be empowered to be change …

"Who The Hell Am I? What Am I Bringing In Here?": A Phenomenographic Exploration Of Antiracist Mentor Development, Kelly Lynn Mack

Dissertations

Teacher attitudes toward racial diversity profoundly impact students, especially when compounded by teacher and student demographic disparities. Despite the purported goal of addressing biases and deficit thinking that limit teachers' ability to support all learners, antiracist pedagogy in teacher credentialing is poorly understood. Also included in the credentialing process, newly qualified teachers (NQTs) in California engage in a two-year induction program, collaborating closely with an induction mentor to attain their clear credential. Employing a qualitative, phenomenographic approach, this study explored how mentors within a longstanding induction program understood and implemented antiracist pedagogy. The research uncovered significant insights into (a) dimensions …

Queer A.F.: Queer Educators In Affinity Family Personal-Professional Development, James Egisto Aguirre

Dissertations

Scholarship shows that affinity groups are spaces of support, learning, and healthy career development that are responsive to the needs of a particular marginalized community. The concept of heteroprofessionalism– the implicit or explicit pressure/s queer educators feel to fit within the proverbial cisgender/heterosexual (cishet) box–is but one of a myriad issues and pressures affect queer educators across the nation. This study brings queer educators from one school district together to unpack their experiences through the lens of heteroprofessionalism. Via queer theory, qualitative, written response data was collected and subsequently analyzed in three, half-day sessions offered in spring 2024. Thirty participants …

The Anticipation Is Killing Me! - Examining The Mediating And Moderating Relationships Between Mistreatment Anticipation And Work Engagement, Mia N. Fagan

Master's Theses

The mistreatment of workers by customers in the service industry is widespread and well-known. Although customer mistreatment is associated with negative consequences for employees, there is little knowledge about the potential consequences of the expectation of future mistreatment. The present study examined the relationship between mistreatment anticipation and work engagement and hypothesized that mistreatment anxiety would mediate the relationship between the two and that perceived organizational support (POS) would moderate the relationship between mistreatment anticipation and mistreatment anxiety. Data were collected from a sample of 183 individuals via an online survey. Results showed a significant and negative relationship between mistreatment …

Whose Vote Is It Anyway? A Geospatial Analysis Of The California Voter's Choice Act Ballot Drop Box Criteria In Santa Cruz County, Judith Heher

Master's Projects

The way Californians vote is changing. While some of the laws surrounding these changes are close to a decade old, the events of 2020 surrounding the November Presidential Election and the COVID-19 pandemic both accelerated and expanded the adoption of contactless voting approaches such as vote-by-mail and the ballot drop box. Neither tool is new to California voters. Vote-by-mail was introduced to the state in 1962 (CA Secretary of State, unknown) and ballot drop boxes were first used in 2014 (Sherman, 2020). In 2016 California Senate Bill 450 (SB-450, 2016) was signed into law. Also known as the Voters Choice …