Digital Commons Network^™

An Attributed And Diverse Encoder-Decoder Processing Technique For Anomaly Detection., Kenneth Antony John

Master's Projects

Attributed graphs are graphs that contain extra information about the attributes of nodes and edges. They can be used to model a plethora of real-world scenarios like social networks, bank transactions, and even academic citation data. Anomalies in such graphs can be irregularities or unusual patterns that are observed in the attributes or the structure of the graph. Anomaly detection in attributed networks is a crucial task, aiming to identify such anomalies. Existing methodologies use various deep learning techniques using graph neural networks, graph encoder-decoder architectures, and multi-layer perceptions. This study proposes a new approach to improve the existing methods …

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 …

Optimizing Web Design Code Generation: A Comparative Study Of Finetuning, Pretrained Models, And Rag (Retrieval Augmented Generation), Srinivas Rao Chavan

Master's Projects

Website Creation is revolutionized by automated code generation, reducing the development effort, speeding the production process, and ensuring consistency in design. Automated web design code generation has emerged as a transformative tool bridging the gap between design and development. In this research, a website design tool is developed and used to create visual layouts, exporting them as JSON designs. These JSON outputs were then transformed into textual prompts, optimized using established HCI principles and UI/UX rules to ensure consistency, visual hierarchy, aesthetics and minimalistic design, accessibility, user-friendly navigation and flexibility. These generated prompts were fed into large language models for …

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, …

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 …

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 …

Assumptions, Resources, And Inputs To Case Management: Implications For California’S Regional Center System, Jonathan Flint

Master's Projects

This project adds to knowledge of case management assumptions, resources, and inputs for California’s Regional Center system by surveying members of the Service Access and Equity working group, formed by the Department of Developmental Services (DDS). It recommends development of a logic model to evaluate case management activities because their intended societal impacts are difficult to directly measure. Additionally, it adds to the debate on health equity and racial disparities in Medicaid long-term services and supports (LTSS). In 1969, passage of the Lanterman Developmental Disabilities Services Act (The Lanterman Act) led to the first and still only entitlement to community-based …

Social Media Bot Detection Using Dropout-Gan, Anant Shukla

Master's Projects

Bot activity on social media platforms is a pervasive problem, undermining the credibility of online discourse and potentially leading to cybercrime. We propose an approach to bot detection using Generative Adversarial Networks (GAN). We discuss how we overcome the issue of mode collapse by utilizing multiple discriminators to train against one generator, while decoupling the discriminator to perform social media bot detection and utilizing the generator for data augmentation. We demonstrate that our approach outperforms---in terms of accuracy---the state-of-the-art techniques in this field. We also show how the generator in the GAN can be used to evade such a classification …

Suburban Bay Area City Approaches To Diversity, Equity, And Inclusion (Dei), Arianna Bush

Master's Projects

The ultimate goal of government is to serve the community for the greater good. Creating an inclusive and representative environment for those working in government and for the population they serve will improve many aspects of public service. In recent decades, Diversity, Equity, and Inclusion (DEI) have been increasingly prioritized as America has become increasingly diverse. However, this effort intensified in 2020 after the murder of George Floyd and the subsequent Black Lives Matter (BLM) protests. “Three years after Floyd's death and the movement hit the streets, 74% of Black executives said they saw positive change in hiring, retention, and …

The Impact Of Daylight Saving Time Transitions On Domestic Violence Call Volume, Quynh-Nhu Pham

Master's Projects

Daylight Saving Time (DST) is a longstanding practice in many countries, involving the seasonal adjustment of clocks by one hour forward in the spring, and one hour backward in the fall. Although DST was initially introduced to promote energy conservation and maximize daylight hours, it has become a subject of debate, given its impact on physical and mental health, cognitive performance, and criminal behavior (Kountouris & Remoundou, 2014).

In 2022, Colorado enacted a law adopting year-round DST, contingent upon a federal law enabling states to maintain DST throughout the year as opposed to ST, like Hawaii and Arizona (Chasan, 2024). …

How Are Mcps Doing On Achieving Assessed Calaim Requirements? A Comparative Analysis Of Selected Not-For-Profit, Publicly Governed Health Plans In California, Junell Chen

Master's Projects

At the heart of a larger societal movement, the imperative to foster diversity, equity, and inclusion (DEI) is a resounding call to action across all sectors. This pressing concern underscores the need for proactive and equity-centric solutions, including within the American healthcare system. The U.S. Department of Health and Human Services has been instrumental in shaping policies and initiatives, such as Healthy People, aimed at promoting health equity and reducing health disparities. Similarly, the Centers for Medicare and Medicaid Services (CMS) has instituted supplementary compliance requirements to hold healthcare stakeholders accountable for implementing equitable programs designed to eliminate health disparities …

Integrating Chatgpt With A-Frame For User-Driven 3d Modeling, Ivan Hernandez

Master's Projects

ChatGPT is a large language model that is capable of creating conversational text and functional code that can be integrated into various technologies, including computer graphics software. Currently, 3D modeling applications can be relatively difficult for novices to learn and understand due to the overwhelming amount of graphical user interfaces. However, we can remedy this issue by leveraging ChatGPT’s conversational language capabilities. Our project described in this report integrates ChatGPT with A-Frame, an online framework for developing virtual reality experiences, to create an immersive and user-friendly 3D modeling environment where users can create and modify 3D models through natural language …

Noteblocklib: A Library For Physics- And Animation-Driven Virtual Midi Instruments For Use In Video Games, Kevin Rotunni

Master's Projects

To better capture the relationship between the performer of a piece of music and the music itself in a video game context, I have designed NoteBlockLib, a system by which MIDI instructions are generated and processed in real time based on the motion and collision data of in-game objects. Ultimately, the movement of instruments made using this system would be driven by the animations of a character in the game. This system would thus allow the player character to interact with the performer in the game without sacrificing the relationship between the performer’s actions and the resulting music or to …

Gradual Typing For Information Flow Control In Typescript Using Es Lint, Ashish Agarwal

Master's Projects

Current state-of-the-art systems tackle data security threats by incorporating information flow control (IFC) to ensure that a piece of information reaches only its intended recipient. However, most IFC implementations introduce a custom language built on top of a well-known language. Adaptations of such languages are limited due to limited support and updates, along with difficulty in learning new syntaxes. Implementations without a custom language offer incomplete IFC support. We present a comprehensive framework by leveraging Typescript, in conjunction with ESLint and NodeJS, aiming to resolve some of the limitations of IFC and intending to facilitate acceptance by a wide range …

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 …

Comparing Balancing Techniques For Malware Classification, Ranjit John

Master's Projects

There have been many breakthroughs over the years in the field of Machine Learning to detect and classify malware threats. However, training a holistic machine learning model to effectively classify malware has been an ongoing topic of research. Datasets represent some malware types disproportionately, which can affect the performance of machine learning classifiers. Without ample data, less common but highly dangerous malware can go undetected by classifiers, leading to devastating outcomes. Data balancing techniques have proven to be effective in representing minority classes better and lessening the bias towards the majority class. Also, recent research showed that generative modeling effectively …

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 …

Mitigating The Risk Of Reentrancy Attack In Smart Contract Development, Eric Ngo

Master's Projects

Smart contracts, while revolutionizing the blockchain with their immutable nature, are prone to attacks such the reentrancy attack. This attack allows malicious adversaries to repeately enter a contract before previous executions are completed. SpartanScript, a custom dialect of Scheme, is a way for developers to write and develop contracts in an experimental blockchain environment like SpartanGold. Compared to cryptocurrencies that use a virtual machine to run on the blockchain, SpartanScript utilizes a simplified interpreter for rapid prototyping. However, SpartanScript does not have a way to detect and warn developers of reentrancy vulnerabilities. Hence, there is a need to implement reliable …

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 …

Enhancing Restaurant Sales Prediction: The Dynamic Forecasting Engine, Rahul Sanjay Morishetti

Master's Projects

This project introduces a "dynamic forecasting engine," designed to transform the way restaurants predict sales. The engine dynamically handles seasonal ARIMA_HoltWinter hybrid model, XGBoost, and LSTM algorithms to dynamically select the best forecasting method based on data volume, variety, and customer taste preferences delving upon the spice level categorical sales. This guide differs from traditional crystal ball approaches because it has the ability to improve over time as new data comes in terms of spice levels. It emphasizes the importance of dataset size in the selection of machine learning algorithms through complexity for large datasets and simplicity for smaller ones …

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. …