Digital Commons Network^™

Zookeeper Through The Ages: A Comparative Performance Analysis Of Evolutionary Versions, Ashish Khanchandani

Master's Projects

The rise in the need for scalable, fault-tolerant, and high-performance systems is the primary factor driving the developments in distributed computing. However, the proliferation of distributed computing creates significant difficulties. In an internet- scale setting where errors and network delays are frequent, coordinating distributed applications presents difficulties that ZooKeeper attempts to solve. It ensures several crucial characteristics that guarantee reliability and consistency. However, performance and latency play a huge part when it comes to dealing with systems built for handling very high loads. ZooKeeper has very low latency for read-heavy workloads, making it suitable for real-time applications. These factors contribute …

A Censorship Resistant News Website Using The Ethereum Blockchain, Hoang Lai

Master's Projects

Misleading information, false claims, and fabricated news articles not only misguide readers but also undermine the trustworthiness of the news platforms themselves. The blockchain provides decentralized, immutable data storage and offers a promising solution to prevent censorship on news websites. Compared to traditional news websites, a decentralized application (dApp) offers benefits such as greater stability and resistance to information manipulation. A decentralized web app is harder to attack than centralized servers since the database is stored across a blockchain network. Moreover, blockchain prevents censorship by letting readers check data across all blocks in the Blockchain, which is good for a …

A Two-Stage Machine Learning Approach For Fake News Detection And News Article Categorization, Snegdha Adusumilli

Master's Projects

This project employs machine learning techniques to develop a sequential model for detecting and categorizing fake news, aiming to mitigate its proliferation in today's digital landscape. The model operates in two phases: in the first phase, the classification algorithms like Naïve Bayes, XGBoost and Random Forest are used to distinguish between true and false news stories and in the second phase the capabilities of Naïve Bayes, XGBoost, Random Forest, and the Transformer-based BERT (Bidirectional Encoder Representations from Transformers) model are leveraged to further categorize the news into specific topics.

The methodology encompasses several key steps: data acquisition, preprocessing, feature extraction, …

Echo: A Browser Extension That Runs Experimental Javascript, Prayuj Pillai

Master's Projects

Narcissus is a JavaScript interpreter written in JavaScript. While it is a good engine for experimenting with JavaScript’s design, it does not integrate easily into the browser. This project introduces ‘‘Echo’’, a browser add-on designed to execute Narcissus JavaScript files and scripts within web browsers. The project explores the performance of the Narcissus interpreter against native browser JavaScript engines and benchmarks the results, showcasing the trade-offs in running an experimental engine—the Narcissus interpreter—on the browser versus native JavaScript. Additionally, as a proof of concept, we implement taint tracking, a capability meant to boost security by preventing sensitive data from being …

Secured Data Storage Management With Deduplication In Cloud Computing, Ganesh Regoti

Master's Projects

In the cloud era, cloud storage has become a major service and the security of data and user privacy algorithms are becoming of great importance. This way, we make sure that the encrypted data is kept in the cloud storage. But, the challenges follow: First, storing encrypted data may result in ineffective utilization of cloud resources as in the provision of encrypted data, redundancy cannot be provided. Access control to the encrypted data is difficult as the underlying data is hidden and there is no metric with which the decision to share among users can be easily taken. Deduplication is …

Enhancing Environmental Health And Safety: Fine-Tuning Large Language Models For Domain-Specific Applications, Mohammad Adil Ansari

Master's Projects

This study aims to simplify Environmental Health and Safety (EHS) by leveraging the power of Large Language Models (LLMs). In this research, we focus on fine-tuning three LLMs — LLaMA, Mistral, and Falcon — using PEFT techniques such as QLoRA and SFT, to address domain-specific needs such as safety compliance, incident reporting, and knowledge dissemination. Our research methodology involves fine-tuning each LLM model on a custom dataset compiled from various regulatory agencies, supplemented by targeted web scraping and manual collection of questionnaires to capture and enrich the models with the latest regulations and guidelines. This study aims to compare the …

Resume Content Generation Using Llama 2 With Adapters, Navaneeth Sai Nidadavolu

Master's Projects

The primary objective of this project is to optimize the Llama language model to generate customized resumes containing domain-specific job descriptions and maintain the linguistic capabilities of the large language model. Building upon the prior research by Sumed Kale on Job Tailored Resume content generation using GPT-2, where he employed full fine-tuning of the model and demonstrated the capability of LLMs to generate resume content, it is evident that while effective, full fine-tuning has its limitations. Primarily, it is computationally expensive, which can pose constraints, especially for large models. Additionally, during the fine-tuning process, there is a risk of losing …

Instagram Data Analysis Using Machine Learning, Lakshmi Prasanna Gorrepati

Master's Projects

With enormous amount of social media content, we can draw valuable insights. In this paper, we apply different Machine Learning and Deep Learning techniques on Instagram data to determine the techniques that work well to discover the engagement class of a social media post. Out of all the social media platforms, Instagram is growing rapidly not just in the number of users but also in terms of Advertisement and marketing surpassing YouTube’s advertisement revenue. The end goal of this paper is to propose a technique to predict the engagement class. We applied Random Forest (RF), Stacking Classifier, Extreme Gradient Boost …

Parallel Powerplay: Optimizing Performance With Mapreduce And Kubernetes Fusion, Shradha Chaturvedi

Master's Projects

The combination of MapReduce (MR) & Kubernetes (K8s) strengths is not explored, and this study leverages the synergy between the two frameworks to meet the growing demands of data-intensive applications. First, this report elaborates on the existing literature work to understand the pros and cons of using MR and K8s, in what use cases these frameworks come to use, and investigates the effectiveness of research studies that explore the combination. This study aims to research the efficacy of the fusion of MR and K8s, considering these factors - application use case, infrastructure design, resource allocation, load balancing, and hypertuning parameters …

Satellite Handover Optimization Using Predicted Satellite-To-Base-Station Proximity, Pranathi Kunadi

Master's Projects

Modern telecommunications heavily rely on Satellite communication networks to provide global coverage, especially in remote areas which link the whole world in a loop. Conventional handover algorithms methods rely on fixed and predefined rules and thresholds predefined statically to make a handover decision. However, these static handover algorithms may become inefficient under the changing conditions of the network. Therefore, it would be useful to measure the proximity order of satellites to the specific base station. Consequently, the assessed relative proximity helps in optimizing the handovers proactively inside related coverage areas. This results in the service quality and the delays in …

Birdsong Classification Using Deep Learning And Mixit, Sasanka Kosuru

Master's Projects

The identification of bird species using deep learning techniques presents a novel approach in bioacoustics, by significantly advancing our understanding and enhancing our capabilities in bird species recognition from audio recordings. The value of audio over visual data for monitoring ecological patterns in birds can be highlighted with the deployment of automated recording devices in remote wildlife sensing, offering a more cost-effective, non-invasive, and practical solution. However, the methods of processing and classifying the audio remain challenging due to the complexity of bird audio, characterized by diverse vocalizations and imminent environmental noise, which poses difficult challenges to perform effective classification. …

Novel Approach To Music Analysis Using Apache Spark, Nidhi Zare

Master's Projects

Music is one of the most common source of entertainment. Every user has their own taste of music and prefer to listen music that adheres to their taste and mood. There are various categories, called as music genres in which music can be classified. This research project addresses the challenge in music genre classification by using various deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Very Deep Convolutional Networks (VGGNet), ResNet and others. The primary objective of this research is to enhance the accuracy of music genre classification using a distributed computing framework Apache Spark. …

Optimization Of Inter-Satellite Routing Using Lstm-Based Path Prediction Model, Yash Bhamare

Master's Projects

Satellite networks are one of the most important components that fulfill the world’s need for connectivity. To ensure that communication is efficient and reliable, robust routing algorithms are a must. Because, although it is true that certain routing characteristics may not be permanently and continuously flawless, a routing technique must effectively adapt to modifications in such network characteristics. The new routing method uses a Long Short-Term Memory (LSTM) model to manage dynamic metrics for Low Earth Orbit satellite networks. This LSTM model is aimed at predicting the optimal routing direction on the premise that a satellite is soon to be, …

Emulating Human Personality With Large Language Models Through Contextual Prompts And Fine-Tuning, Mrunal Zambre

Master's Projects

The quest for AI systems that can mirror the intricate aspects of human emotion and personality is crucial for enhancing their performance. This project delves into the capabilities of Large Language Models (LLMs) to mimic the Big Five personality traits in human-written essays by utilizing contextual prompts and fine-tuning methods. Diverging from traditional research in this domain, this project explores smaller, open-source LLMs, including LLaMA 2 7B chat, LLaMA 2 13B chat, and Vicuna v.15 13B, to assess their potential in personality prediction tasks, thereby making high-level personality emulation more accessible and practical for application integration. Through meticulous prompt engineering, …

Explaining The Maliciousness Of Urls Using Shap And Lime, Ayush Nair

Master's Projects

No system has ever reached the levels of proliferation that the Internet now enjoys. It stands as the most widely spread distributed system across the globe; yet this evolution has given rise to an ever-growing wave of malintent that challenges every user and entity on the vast expanse of cyberspace. Malicious URLs loom large as vulnerabilities leaving users naked as they traverse online landscapes, but cybersecurity experts craft models with esoteric algorithms in a bid to stem this tide and shield users from cybercrime. However, peering into the decision-making corridors of these models holds key importance, it’s through understanding such …

Scalable Container Caching Optimization With Action Masking For Serverless Edge Computing, Manikanta Sanjay Veera

Master's Projects

Serverless edge computing is an emerging technology that realizes the low latency and resource-efficient function calls for responsive computing. In cloud-based serverless computing, it is a common practice to cache sufficiently many function containers for future reuse to reduce the overhead of container initiation. In contrast, the capacity limitation of edge nodes poses a complex problem to the caching strategy in serverless edge computing of selecting an appropriate set of container caches based on the request distribution. Deep Reinforcement Learning (DRL) can play a crucial role in optimizing the caching decisions under dynamic request arrivals. In this paper, we propose …

In Their Words: Voices Of High School Graduates Who Were Labeled English Learners, Yolanda K. Jimenez

Dissertations

This research captures the experiences of high school graduates who were labeled English Learners while they were enrolled in a public high school in the greater Sacramento area of California. The study examined what these bilingual high school graduates report as beneficial and detrimental experiences faced while completing the course requirements for high school graduation. This study builds on research that includes secondary classroom teachers and their perceptions of English Learners, classroom teaching and learning, and experiences of emergent bilingual students. The study expands the body of research and informs praxis pertaining to supporting emergent bilingual students as they attempt …

Addressing Equity Gaps In Stem: A Self-Study, Prabhjeet Kaur Brar

Dissertations

The Hispanic/Latinx population is increasing nationwide and in the state of California; however, the Hispanic/Latinx population remains underrepresented in STEM professions contributing to a lack of diversity in STEM. To increase the academic success of the Hispanic/Latinx student population in STEM majors, high leverage research-based teaching strategies through the lens of historically responsive literacy were applied and examined throughout this research. The purpose of this mixed-methods self-study was to examine the impact of research-based effective strategies/high leverage practices on Latinx students’ success rates in my introductory biology classrooms. The study focused on two biology course sections with a laboratory. Each …

How Can We Do Better? Understanding Student Experiences And Support Needs During And After The Covid-19 Pandemic, Kevina M. Brown

Dissertations

This dissertation study aimed to provide opportunities for students at San José State University (SJSU), to share about their experiences during the Covid-19 pandemic and to provide feedback and recommendations regarding resources and culturally engaging supports on campus. The research inquiry involved survey and interview methodology to gain insights into student experiences pertaining to financial and mental health challenges and academic disruption and social isolation during the beginning, middle and end of the pandemic, as well as their engagement with resources and supports provided on campus. Recruitment was done by flyer distribution throughout campus and via email distribution to students …

Examining The Impact Of Registered Nurse Engagement In Medical Assistant Clinical Competency Development And Team Effectiveness In Ambulatory Care, Colleen Burckin

Doctoral Projects

Background

As ambulatory care grows in complexity, there is a growing need to ensure that staff possess a high level of competence and team effectiveness to provide safe, efficient, and cost-effective care. Leveraging the expertise and leadership skills of Registered Nurses (RN) to support the team of unlicensed, Medical Assistants (MA) will be incentivized. With limited knowledge of MA preparation to meet the rapidly changing demands, managers will struggle to build effective teams and look to the impact of RN- supported MA onboarding to bridge skill gaps.

Purpose

To examine this issue, a quasi-experimental, quality improvement study was done to …

A Framework For Scientific Data Indexing, Searching And Sharing, Apoorva Mohite

Master's Projects

Scientific data continues to grow. Wildfire simulation experiments performed by the WIRC team at SJSU have generated over 138 TB of data so far and it is expected to keep growing. It becomes difficult for researchers to search through that data to find the data of their interest. This data is stored on an HPC cluster that external users do not have access to. The WIRC team also conducts experiments and publishes their research, but the size of data makes it difficult to share these datasets. This project introduces a novel solution to indexing scientific data, searching through the data …

Credit Score-Based Lending System On The Ethereum Platform, Mayuri Shimpi

Master's Projects

Traditional banking systems act as intermediaries, assessing risks and profiting from interest rate differentials. Credit scores, provided by trusted bureaus, are commonly used to evaluate the creditworthiness of borrowers. Cryptocurrencies have emerged as a significant and innovative medium due to their decentralized nature, operating without reliance on a central authority, such as a government.

This report describes a project to implement the Autonomous Lending system on the Ethereum Platform (ALOE), as proposed in [1], aiming to seamlessly integrate traditional credit scoring methodologies for evaluating a borrower's risk of default. The objective of this project report is to establish a robust …

Ml-Based User Identification Through Mouse Dynamics, Rakshit Gupta

Master's Projects

User authentication and identification plays a crucial role in ensuring the security and integrity of digital systems. Traditional authentication methods, such as passwords and biometrics, have inherent limitations that can compromise system security. This research proposes a novel approach to user authentication by leveraging machine learning techniques and behavioral biometrics, specifically mouse dynamics. The primary objective is to develop a sophisticated framework that can accurately identify individuals based on their unique mouse behavior patterns. The study explores and compares multiple deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Transformer models, to generate embeddings from …

Analysis And Application Of Adaptive Ml Algorithms For Malware Classification, Rashmi Boddukuri

Master's Projects

Malware classification is the process of distinguishing malware samples into categories of malware families that it is associated with and remains a critical step in the process of mitigating malware-related threats. In recent years, machine learning techniques have emerged as a powerful tool for such malware classification tasks. In this study, we explore the application of adaptive machine learning models to malware classification in order to analyze and determine how they compare in performance to similar but non-adaptive algorithms. The results achieved in this study share insight into the strengths and limitations of adaptive learning models when applied towards malware …

Distinguishing Chatbot From Human, Gauri Anil Godghase

Master's Projects

There have been many recent advances in the field of Generative Artificial Intelligence and Large Language Models, with GPT 3 or ChatGPT model being one of the frontrunners in this field. These large language models have become so powerful that it has become difficult to differentiate between text written by humans and machine-generated text. This paper proposes a solution to the problem of classification of the origin of data (human or chatbot) by using Machine Learning. In addition, the proposed solution also helps us analyze the text generated by such Language Models and understand the underlying patterns present in the …

Adaptive Metric-Driven Load Balancing For Specialized Clusters Using Nginx, Juhi Raju Malkani

Master's Projects

Adaptive Metric-Driven Load Balancer is an innovative two-tier load-balancing system that uses NGINX and Prometheus to optimize resource allocation in specialized cloud clusters. This framework is built to give great performance and flexibility and runs on Google Kubernetes Engine (GKE), but it may also be deployed on local cloud environments for added security. The first tier of our system uses an NGINX-based load balancer to route incoming requests based on content type, sending traffic to hardware-optimized clusters for processing requests through specialized hardware. In our algorithm, the second tier dynamically modifies load distribution throughout each cluster by calculating pod weights …

Embracing Mindfulness-Based Professional Development In Early Childhood Education, Drew Giles

Dissertations

There is a growing need for school districts to identify additional strategies to support the work-related stress and emotional labor ingrained in an early educator’s career. By orienting early childhood education (ECE) staff to mindfulness practices, education leaders may identify new approaches to assist early childhood educators’ well-being, building healthy work atmospheres, and activities to shift the culture in early childhood settings. This study employs a qualitative exploratory research design, specifically video documentary methodology to better understand how early childhood educators experience a mindfulness-based professional development (MBPD) program designed for ECE staff, explore what they say education leaders should know …

(Miss)Educated Latino Males: Academic Preparedness, Navigation, And Retention At 2-Year Colleges, Nannette Stephanie Regua

Dissertations

This study centers Latino males, first generation, first year students at four 2-year colleges in the Silicon Valley by understanding their experiences in transferable English 1A Writing Composition course and looking at the acts of instilling a sense of belonging and mattering by institutional agents and support services that are invaluable to student retention and success. The purpose of this study is to determine Latino male students’ perceptions of academic preparation, navigation, and success in higher education after the implementation of California Assembly Bill 705. My findings revealed important themes that map onto conceptual frameworks, most notably the Community Cultural …

Optimizing Field-Linked Simulations Of Dry Season Uptake And Monsoon Infiltration Within An Aspen-Mixed Conifer Forest, Raymond J. Hess

Master's Theses

Climate models forecast that headwater catchments in the western U.S. will undergo a reduction in snowpack, early season snowmelt, and increases in evapotranspiration. The resulting extended dry season will stress vegetation in mountainous watersheds throughout the Upper Colorado River Basin. We investigate infiltration patterns and root water uptake in response to dry season disturbances within the East River watershed in Colorado. To do this, we collected soil cores, measured matric potential and sap flow, and monitored tree xylem and soil for stable isotopes of water (2H, 18O) in two soil profiles to 90 cm depth (with three Engelmann spruce and …

Roundtable On Warhol V. Goldsmith: An Introduction To Warhol V. Goldsmith, Amy Adler, Lionel Bently, Susan Bielstein, Johanna Burton, Martha Buskirk, Noam M. Elcott, Jane Ginsburg, Branden W. Joseph, Joan Kee, Liz Linden, Rebecca Tushnet, Graeme Williams, Winnie Wong

Faculty Research, Scholarly, and Creative Activity

No abstract provided.