Digital Commons Network^™

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 …

Nft Price Prediction Using Machine Learning, Akhil Patil Bagili

Master's Projects

In the evolving cryptocurrency marketplace, Non Fungible Tokens (NFT) pose a unique challenge when it comes to predicting the prices due to their high volatility and fluctuating nature. This project aims to create a model that utilizes deep learning techniques to accurately forecast NFT prices. Based on the real time data and the transaction history, the model uses Convolutional Neural Networks (CNNs) and Long Term Short Memory Networks (LSTM) to analyze and make effective predictions about future prices. The methodology involves gathering data from two online marketplaces, Dune and Opensea to create a dataset that enhances the model’s predictive capabilities. …

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, …

Detecting Fake Reviews Using Aspect Based Sentiment Analysis And Graph Convolutional Networks, Prathana Phukon

Master's Projects

Online reviews significantly influence consumer behavior and business reputa- tions. Detecting fake reviews is important for maintaining trust and integrity in

these platforms. In this project, an application of Aspect-Based Sentiment Analy- sis (ABSA) called FakeDetectionGCN is introduced to distinguish genuine feedback

from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph Convolutional Networks (GCNs) are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated as …

Emotion Detection Using Ensemble Learning, Priya Harika Yerapothu

Master's Projects

Emotion detection is gaining exponential necessity in today’s technological age. This research seeks to delve into ways conversational AI could be enhanced by integrating emotional intelligence using an ensemble learning approach. Traditional machine learning along with advanced neural network architectures are implemented to improve the understanding and intricacies of emotion detection from textual data. The dataset we use is GoEmotions dataset, annotated with 27 emotional labels, to conduct a detailed analysis of emotion recognition. Various machine learning models, such as HistGradientBoosting, LightGBM, CatBoost, and MLP, will be evaluated side by side with advanced models of Bidirectional Long Short-Term Memory (BiLSTM) …

Multirelational Twitter Bot Detection Using Graph Neural Networks, Ketan Jadhav

Master's Projects

Social media is a key resource in modern human communication as well as for information. Ease of access and global reach is a primary factor to the popularity of several social media platforms like Twitter. Social Media Bots are automated programs which are developed for social engagement. These bots, however, are being used with malicious intent as well, to spread fake news and manipulate the masses. Identification of social media bot accounts has become crucial since social media has become one of the primary sources of news and information for a lot of people. This project aims to propose Multirelation …

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 …

Community Detection Using Deep Learning: Variational Graph Autoencoder Enhanced With Leiden And K-Truss Techniques, Jyotika Hariom Patil

Master's Projects

Community detection in networks is essential for understanding the complex structures of connected systems. Traditional deep learning (DL) methods such as Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs) have shown promised results in supervised tasks, like classification, but often fail in unsupervised tasks like community detection because of the lack of labels. Self- supervised approaches where we integrate crucial community information offer a solution. This project seeks to explore DL methods for community detection, focusing specifically on using Graph Variational Autoencoders (VGAEs). While classical approaches can efficiently handle small to medium-sized networks, they typically struggle with larger-sized structures. …

Frame Rate Enhancement Using Gans: A Deep Learning Approach, Shanmukah Sri Harsha Anivilla

Master's Projects

Videos are sequences of frames that are displayed continuously within a time frame, which creates the illusion. FPS is defined as the number of frames per second, and is crucial to determine the smoothness of motion or scene changes in the video. To improve the appearance of the videos, we can a technique called Frame Rate Enhancement. This is an approach to augment generated frames between pairs of frames using Generative Adversarial Networks. There are a few traditional techniques using Convolution Neural Networks and Optical Flow based methods, but they create unwanted artifacts such as blurring or ghosting and might …

Gesture Recognition Dynamics: Unveiling Video Patterns With Deep Learning, Nithish Reddy Agumamidi

Master's Projects

This paper, Gesture Recognition Dynamics: Revealing Video Patterns with Deep Learning, explores the combination of Long Short-Term Memory(LSTM) with Convolutional Neural Network(CNN) in the identification of convoluted human activities. The study assesses LSTM’s capability to capture temporal dependencies and CNN’s potential to apprehend and extract spatial characteristics to detect the gestures from UCF50. It further evaluates the architecture linkage of LSTM and CNN, which will improve the analytical capacity to interpret and validate dynamic gesture trends. The paper utilizes Mediapipe, an open-source framework created by Google specifically designed for extracting poses. The Mediapipe tool is well-designed to track important body …

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 …

Understanding Distracted Driving And Gaze Patterns In A Driving Simulator: Simple Vs. Complex Challenges, Rishi Prabhat Narayana Sannala

Master's Projects

Distracted driving has grown in criticality over the recent years, given the numerous distractions that drivers face today, and which have further been magnified by the proliferation of in-vehicle technologies and mobile devices. Such distractions can seriously compromise a driver's ability to be fully focused on the road and to carry out timely responses and informed decisions that are key in minimizing the risks of a crash and maximizing road safety. The general aim of the research is to observe how simple versus complex distractors affect driving performance and gaze patterns. The eyetracker used is the Tobii Pro Fusion, synchronized …

Gender Classification Through Postural Analysis: A Comparative Study Of 2d Images And 3d Reconstructions., Prathamesh Dixit

Master's Projects

In computer vision, gender classification has become a vital task having applications in human-computer interaction, healthcare, and surveillance. In this study, we look at a two-step approach based on human joint information for gender classification. In this research, we use convolutional neural networks (CNNs).

With Leeds Sports Pose (LSP) dataset, we use a C5 pre-trained model to map and extract joint information from 2D RGB images and after pre-processing and background removal, we use PiFUHD to transform these 2D images into 3D representations. Next, we train our models on RGB images and joint images for both 2D and 3D representations. …

Deciphering Speech Through Vision: A Deep Learning Lip Reading System, Srujith Rao Ambati

Master's Projects

Lip-reading, a ubiquitous field between computer vision and speech processing, focuses on identifying what spoken words a person generates depending on their uttering lip movements. This paper presents a streamlined lip-reading solution that employs machine learning and deep learning. First, Our work utilizes the Multi-Task Cascaded Convo- lutional Networks to detect facial “landmarks,” including the face and lips region, and the aligns the face. The aligned faces are segmented to get the lip images. Lip images are preprocessed using the Real-Enhanced Super Resolution Generative Adversarial Network to enhance image resolution to identify subtle lip movement in video images: a critical …

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 …

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 …

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 …

Multi-Platform Cyberbullying Detection Using Nlp And Machine Learning, Chinmayi Lokeshwar Hegde

Master's Projects

The issue of cyberbullying is growing due to the online anonymity and due to online platforms having less repercussions. This research proposes for proactive measures to detect and prevent such behavior before it reaches the victim. By using data from various social media platforms and employing machine learning techniques, this research proposes an innovative system aimed at identifying and thwarting cyberbullying incidents preemptively. While existing methods have primarily focused on prediction and detection of cyberbullying incidents, there remains a significant gap in research regarding prevention strategies. This project aims to address this gap by leveraging machine learning, natural language processing …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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, …