Computer Engineering Commons^™

J-Cag: Java Comment Analysis & Generation - A Sublime Text Plugin Powered By Gpt, Linh Le

Master's Projects

Developers are notoriously disinterested in writing and maintaining comments for their code. The J-CAG plugin automates code analysis and comment generation to save developer time and improve code quality. The project utilizes the Generative Pretrained Transformer model to analyze and provide users with constructive feedback on JavaDoc comments and functions. Embedded within Sublime Text, J-CAG is designed to provide developers with useful advice. It can also generate JavaDoc comments based on the function itself, reducing the time developers spend on writing documentation. The plugin integrates seamlessly with Sublime Text, offering an intuitive interface. With positive results in comment analysis 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. …

Deep-Learning Approaches To Predict Remaining Useful Life Of Hard Disks, Rohan Mohapatra

Master's Projects

On a daily basis, data centers process huge volumes of data using inexpensive hard disks. Data stored in these disks serve a range of critical functional needs from financial, and healthcare to aerospace. As such, premature disk failure and consequent loss of data can be catastrophic. To mitigate the risk of failures, cloud storage providers perform condition-based monitoring and replace hard disks before they fail. By estimating the remaining useful life (RUL) of hard disk drives, one can predict the time-to-failure of a particular device and replace it at the right time, ensuring maximum utilization whilst reducing operational costs. We …

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 …

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 …

Adversarial Attacks And Defense Mechanisms In Multivariate Time-Series Forecasting For Applications In Smart And Connected Infrastructures, Pooja Krishan

Master's Projects

The advent of deep learning models has revolutionized the industry over the past decade, leading to the widespread proliferation of smart devices and infrastructures. They play a crucial role in safety-critical applications like self-driving cars and medical image analysis, sustainable technologies like power consumption prediction, and in health monitoring tools to replace industrial equipment like hard disk drives, semiconductor chips, and lithium-ion batteries. But these indispensable deep learning models can be easily fooled to give incorrect predictions with utmost conviction, leading to catastrophic failures in applications where safety is of utmost importance, and resulting in the wastage of resources in …

Fine-Tuning Large Language Models For Folder Structure Generation, Likhith Nemani

Master's Projects

Starting a new project is a significant challenge in the software development world. Building a new project skeleton and configurations will require vast amounts of time and effort. This project aims to overcome the difficulty presented by this challenge using advanced large language models, specifically fine-tuning LLMs. Our initial focus with the implementation is to use the powerful capabilities of advanced modern models to simplify and accelerate the complicated process of getting new projects started. The solution process begins with a user posting a README file to a predetermined repository. This README file then is used as a source for …

Domain Expert Bot, Amrutha Dondemadahalli Ramegowda

Master's Projects

The fast growth of artificial intelligence in human-computer interaction has been aided significantly by the introduction of conversational AI systems. This project presents a Domain Expert Bot, a multi-domain conversational bot built with advanced NLP techniques incorporated through Sentence-BERT and MapReduce to allow the bot to analyze and comprehend challenging user queries on various topics. The bot can converse on different subjects ranging from technology topics to healthcare, environment, politics, and casual discussions. It excels in understanding deep language contexts and efficiently processes large datasets, ensuring prompt and accurate responses. Furthermore, it uses advanced ranking algorithms to perform real- time …

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 …

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, …

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 …

Ranking-Based Hashtag Recommendation With Collaborative And Content-Based Filtering, Fei Pan

Master's Projects

The purpose of this project is recommending relevant hashtags for users using both Collaborative Filtering (CF) and Content-based filtering with Twitter dataset. The Twitter dataset was collected by leveraging Twitter API v2. After data preprocessing, 40,806 tweets posted by 278 users with 3,107 hashtags from 01/01/2022 to 04/30/2022 are used for model training and testing. For CF models, we will mainly focus on generating embeddings to learn about user and hashtag latent factors and finally predict a probability for unseen hashtags with most possibility will be ranked as topK items for corresponding users. In this project, Matrix Factorization (MF), Neural …

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 …

Domain Switch On Sentiment Analysis Using Gradient Reversal Layer, Hemish Veeraboina

Master's Projects

Switching domains in sentiment analysis presents the challenge of transferring learned knowledge from one context to another without the need to label data. Traditional methods often struggle when dealing with differences in data distribution a problem known as the domain shift issue. To tackle this using Gradient Reversal Layers (GRL) has emerged as a solution for adapting to different domains in an unsupervised learning setting. This study introduces an enhancement to the standard GRL approach by incorporating a sigmoid function that gradually adjusts how intensely domain adaptation occurs during training. This upgraded GRL technique ensures controlled learning outcomes making it …

Ai-Driven Credit Card Fraud Detection With Enhanced User Interaction, Toshi Bhat

Master's Projects

This thesis describes the development and testing of a unique system for detecting credit card fraud. The system employs graph neural networks (GNNs) and a real-time user interaction platform. The primary goal of this study is to use advanced machine learning methods and interactive technologies to improve fraud detection accuracy and the speed with which users can receive assistance. GraphSAGE, a type of GNN, was trained on a simulated set of credit card transactions, allowing the system to detect and predict fraud very accurately. Simulating a real-world transaction scenario is an important aspect of the project. In this case, the …

Optimized Community Detection Across Distributed Heterogeneous Servers, Akash Narang

Master's Projects

The exploration of community detection is crucial across various fields, including marketing, and biological research. This area has evolved from non-overlapping communities to recognize nodes as part of multiple overlapping communities. Current research continues to uncover these dynamics. The main challenge is identifying overlapping communities in graphs with billions of nodes and edges. This paper aims to enhance methodologies for community detection in parallel for unprecedentedly large and complex networks. We introduce the HeteroNodesAdapter algorithm, which supports heterogeneous worker nodes and optimized load distribution in graph stream processing. Additionally, we propose the TailBalancedCommunitySize algorithm to find an optimum community size, …

Adaptive Bounded-Confidence Model For Opinion Maximization, Jacob Ortiz

Master's Projects

Social networks have become a significant source of information due to their easy accessibility, low cost, and ability to spread information quickly. Opinions are crucial in shaping our communication and decision-making processes, and our social connections significantly influence them. We model opinions as continuous values from 0 to 1, i.e., 0 means strong disagree and 1 means strong agree. Each agent has an initial opinion as well as a confidence about her opinion and through interactions with the other agents both are updated. Opinion maximization has gained popularity due to social media’s growing impact on our daily lives, where we …

Influence Maximization Using Triadic Closures, Communities, And Quotas, Matthew Fu

Master's Projects

Online social networks have exploded in popularity in the last decade. In addition, traditional advertising methods such as television advertising have greatly decreased. This allows companies to utilize viral marketing more effectively. With viral marketing, companies can spread information on a product to a social network by reaching out to a small group of early adopters, who will go on to inform the people around them of the product. The problem is selecting the early adopters that can maximize the spread of influence. The Influence Maximization (IM) problem is finding a social network’s most influential (early adopters) starting nodes, called …

Regional Sea Level Rise Prediction In Monterey Bay With Lstms And Vertical Land Motion, Branden Lopez

Master's Projects

Earth system data is vast in volume and variety, and is used to forecast weather,

hurricanes, floods, and sea level. Sea Level Rise (SLR) impacts various sectors, espe- cially ecosystems, food production, industry, population, health, and the availability of

clean water. Because of its broad impact, describing the behavior and forecasting SLR is an important topic. Traditional Machine Learning (ML) models vary in use, but many are not capable of capturing all the non-linear spatial and temporal properties of SLR factors. Deep learning models efficaciously handle complex time series data, noise, and high dimensional spaces, making them a focus of …

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 …

Unveiling Gender Bias: An Eye-Tracking Analysis Of Scene Perception, Naga Srija Gopisetty

Master's Projects

Gender bias deeply affects how we perceive and interact with the world around us. This study examined how gender bias affects scene perception by using eye-tracking technology. The relation between gender bias and pupil dilation is examined utilizing art as a stimuli and also studied the underlying cognitive processes. This experiment combines image presentations along with input from participants, drawing on previous research in gender bias, cognitive psychology, and eye-tracking methodologies. The experiment design showcases a series of images to participants, subtly replacing one image during the trial, and then asks participants whether the

replacement took place. This study uses …

Exploring Gender Bias In Large Language Models: Cross-Linguistic Comparisons And Evaluation Letters Analysis, Athira Kumar

Master's Projects

Large language models (LLMs) play a significant role in modern human-computer interaction. They have exploded in popularity recently, becoming widely used for various tasks. However, concerns persist regarding potential biases within these models. This project investigates gender bias in the popular LLMs - GPT-3.5, GPT-4, Gemini, and LLAMA. The first part of our study focuses on analyzing biases using ambiguous sentences across three languages - English, Malayalam, and Tamil. We evaluate the LLMs to see if they associate occupations with commonly held gender stereotypes, by using specific professions within our test sentences. Through the use of two low-resource languages, this …

Deception Detection Models From Speech, Tien Nguyen

Master's Projects

Recently, researchers have shown an increased interest in automatically detecting deceptive actions. The attention given to this area can be attributed to the many potential applications of deception detection, especially in the field of criminology. To contribute to the deception detection research, this project investigates textual and audio data extracted from spoken and written words. We evaluated and compared the traditional linguistic models with advanced Large Language Models (LLMs) while using Natural Language Processing (NLP) techniques. Additionally, various feature selection techniques were applied to assess the importance of linguistic features. We conducted extensive experiments to evaluate the effectiveness of both …

Hindi Image Captioning Using Indictrans2 And Encoder-Decoder Architecture, Anahita Vayalombrone Dinesh

Master's Projects

One of the most prominent tasks that lie on the conjunction of Natural Language Processing (NLP) and computer vision, is image captioning. Image captioning is the generative task of achieving textual descriptions from images. Its application finds use in many real-world scenarios like aiding the visually impaired, editing applications, recommendation systems, and medical imaging. This research focus lies in Hindi image captioning, the official language of India, as it has not been explored as far as its need. Several challenges such as the lack of substantial Hindi text data for training models, the need for human annotators to verify the …

Lexigen: Lexical-Driven Image Generation, Sangram Prashant Chincholkar

Master's Projects

This research project proposes a novel approach to user-driven image editing via natural language descriptions. The aim is an accurate change of certain features of an image with respect to the descriptive text while maintaining, with equal concern, the integrity of the remaining parts of the image not affected by the description. The task is particularly relevant for fields like content creation, personalized design, and automated image editing that require both coherence of a visual scene and textual description. We propose a generative model, LexiGen, which perfectly integrates natural language descriptions with their corresponding visual changes within an image. The …

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 …

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 …

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 …

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 …