Digital Commons Network^™

Investigating The Misinformation Effect And Physical Activity In An Online Study, Victoria C. Paoloni

Master's Theses

The purpose of this study was to explore the relationship between physical activity and susceptibility to false memory. Among the many benefits of physical activity are brain changes and cognitive improvements. However, few studies have examined the effects of physical activity on false memory, and none have investigated the relationship between physical activity and susceptibility to false memories generated via the misinformation effect. This study was conducted to address that gap and was also among the first to use a misinformation paradigm in an online study. It was hypothesized that greater amounts of weekly physical activity among young adults would …

Effects Of Hypoxia On Anti-Predator Behavior Of English Sole (Parophrys Vetulus) And Speckled Sanddabs (Citharichthys Stigmaeus), Alexandra Beatrix Stella

Master's Theses

Predator detection and successful escape behaviors are critical to the survival of juvenile fish. However, rising temperatures and increased nutrient pollution result in more frequent hypoxic events (i.e., low dissolved oxygen levels), which may impact fish health and survival. This research examined the effects of hypoxia on two flatfish species commonly found in the Elkhorn Slough, an important nursery habitat in central California. English sole and speckled sanddabs were exposed to five DO treatments ranging from 8.0 mg/L DO (normoxic) to 1.0 mg/L DO (severely hypoxic). A startle response trial was used to examine the impact of increasingly hypoxic conditions …

An Exploration Of The Low Prevalence Effect During Phising Detection, Sherry J. Wei

Master's Theses

Phishing attacks are attempts to obtain individual credentials or other private information through deception, usually in email format. As the Internet becomes increasingly intertwined with everyday lives, such attacks are on the rise, threatening individuals and businesses alike. Existing anti-phishing training measures fail to address possible prevalence effects on detection performance: in tasks where targets appear rarely, participants have heightened miss rates. This low prevalence effect could be present in phishing detection because phishing emails are observed much less frequently than legitimate emails. Emerging research has reported observing heightened miss rates as a function of phishing email rarity. This study …

Prediction Of 2024 Indian Pm Election Results Using Sentiment Analysis On Twitter Data, Surabhi Gupta

Master's Projects

This sentiments analysis study presents a methodical approach to predict the 2024 Indian Prime Minister Election. Data collected spanning from 2020 to 2023 from Twitter using hashtags such as IndianPMElection2024 and on topics such as the revocation of the special status of Jammu and Kashmir, the Farm Bill, and the Digital India initiative, form the core of this research. We utilized a combination of sentiment extraction tools-namely, the NLP Town's Bidirectional Encoder Representations from Transformers (BERT)-based multilingual uncased sentiment model, Valance Aware Dictionary for Sentiment Reasoning (VADER), and TextBlob. Additionally, we used a well-established machine learning model Naive Bayes, deep …

Context Aware Neural Machine Translation Using Graph Encoders, Saurabh Kale

Master's Projects

Machine translation presents its root in the domain of textual processing that focuses on the usage of computer software for the purpose of translation of sentences. Neural machine translation follows the same idea and integrates machine learning with the help of neural networks.Various techniques are being explored by researchers and are famously used by Google Translate, Bing Microsoft Translator, Deep Translator, etc. However, these neural machine translation techniques do not incorporate the context of the sentences and are only determined by the phrasesor sentence structure. This report explores the neural machine translation technique dedicated to context-aware translations. It also provides …

The New Student: The Enhancement Of An Ebook To Support Emotional Connection, Rebecca Zumaeta

Master's Projects

EBooks are a form of multimedia applications that encourage cognitive learning. Multimedia can also influence readers to have a deeper connection to the story. Understanding the influence of a static picture book versus an animated and audio guided eBook can prove valuable in developing learning media and other forms of content. In this research we take a published children's book and apply the content into a multimedia eBook. The purpose of the creation of the eBook is to compare the interest of a reader on a story when static format, when some multimedia is added and when the story is …

Video Sign Language Recognition Using Pose Extraction And Deep Learning Models, Shayla Luong

Master's Projects

Sign language recognition (SLR) has long been a studied subject and research field within the Computer Vision domain. Appearance-based and pose-based approaches are two ways to tackle SLR tasks. Various models from traditional to current state-of-the-art including HOG-based features, Convolutional Neural Network, Recurrent Neural Network, Transformer, and Graph Convolutional Network have been utilized to tackle the area of SLR. While classifying alphabet letters in sign language has shown high accuracy rates, recognizing words presents its set of difficulties including the large vocabulary size, the subtleties in body motions and hand orientations, and regional dialects and variations. The emergence of deep …

Ml-Based User Authentication Through Mouse Dynamics, Sai Kiran Davuluri

Master's Projects

Increasing reliance on digital services and the limitations of traditional authentication methods have necessitated the development of more advanced and secure user authentication methods. For user authentication and intrusion detection, mouse dynamics, a form of behavioral biometrics, offers a promising and non-invasive method. This paper presents a comprehensive study on ML-Based User Authentication Through Mouse Dynamics.

This project proposes a novel framework integrating sophisticated techniques such as embeddings extraction using Transformer models with cutting-edge machine learning algorithms such as Recurrent Neural Networks (RNN). The project aims to accurately identify users based on their distinct mouse behavior and detect unauthorized access …

Classifying World War Ii Era Ciphers With Machine Learning, Brooke Dalton

Master's Projects

We examine whether machine learning and deep learning techniques can classify World War II era ciphers when only ciphertext is provided. Among the ciphers considered are Enigma, M-209, Sigaba, Purple, and Typex. For our machine learning models, we test a variety of features including the raw ciphertext letter sequence, histograms, and n-grams. The classification is approached in two scenarios. The first scenario considers fixed plaintext encrypted with fixed keys and the second scenario considers random plaintext encrypted with fixed keys. The results show that histograms are the best feature and classic machine learning methods are more appropriate for this kind …

Spam Comments Detection In Youtube Videos, Priyusha Kotta

Master's Projects

This paper suggests an innovative way for finding spam or ham comments on the video- sharing website YouTube. Comments that are contextually irrelevant for a particular video or have a commercial motive constitute as spam. In the past few years, with the advent of advertisements spreading to new arenas such as the social media has created a lucrative platform for many. Today, it is being widely used by everyone. But this innovation comes with its own impediments. We can see how malicious users have taken over these platforms with the aid of automated bots that can deploy a well-coordinated spam …

Cabg Vs. Pci: Greater Outcomes For Severe Coronary Artery Disease, Haejin Cho

Master's Projects

Coronary artery disease is the leading cause of mortality in the United States. The lifetime probability of developing coronary artery disease after 40 is 40% in men and 32% in women. Cardiac revascularization for patients with complex coronary artery disease is performed commonly. Two common procedures for cardiac revascularization are coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI). The purpose of this systemic review is to compare long-term outcomes of CABG and PCI in patients with severe artery disease. This review concluded that PCI was associated with higher rate of repeat revascularization compared to CABG, but the incidence …

Comparing Enacted And Perceived Parental Communication As Predictors Of Adolescents’ Emotion Regulation In Families With Harmful Versus Non-Harmful Parental Alcohol Use, Marie C. Haverfield, Jennifer A. Theiss

Faculty Research, Scholarly, and Creative Activity

Emotion regulation is an important skill that adolescents typically learn through early interactions with their primary caregivers. Associations between parental communication and adolescent emotion regulation are well-documented however, it is unclear whether the parent’s actual communication behavior or adolescents’ perceptions of the parent’s behavior is a more robust predictor of emotion regulation outcomes. This study used Baumrind’s parenting styles typology as a theoretical foundation for examining parents’ enacted responsiveness and control and adolescents’ perceptions of their parent’s responsiveness and control during conversation as competing predictors of adolescents’ self-reported emotion regulation during two parent-child interactions. Sixty parent-adolescent dyads participated in an …

Establishing A Framework For The Ethical And Legal Use Of Web Scrapers By Cybercrime And Cybersecurity Researchers: Learnings From A Systematic Review Of Australian Research, Katie Logos, Russell Brewer, Colette Langos, Bryce Westlake

Faculty Research, Scholarly, and Creative Activity

The Internet has become an increasingly attractive location for collecting data about cyber threats, driven by the abundance of quality data available and accessible online. As such, researchers and practitioners have turned to automated data collection technologies (ADCT), including ‘web crawlers’ and ‘web scrapers’, to study these threats. The rapid proliferation of ADCT has meant directions for their ethical and legal operation have been slow to adapt, with no clear guidelines regulating their use for research. This article identifies the relevant ethical and legal frameworks guiding the deployment of ADCT in Australia for cybersecurity research. This is accomplished through a …

Intersections Of Disproportion: A Critical Quantitative Examination Of Dis/Ability And Gender In Black Students’ School Discipline Outcomes, Ceema Samimi, Noah Jefferson, Shelby Flanagan, Yolanda Anyon

Faculty Research, Scholarly, and Creative Activity

This study uses critical race quantitative intersectionality to examine the impacts of gender and dis/ability type on Black students’ school discipline outcomes. We use multilevel logistic regression models to analyze data from a large urban school district, considering the intersectional impact of gender and dis/ability type on school discipline outcomes among Black students (suspension, restorative justice, referral to law enforcement). We found that Black students identified as male, labeled with emotional dis/abilities, or identified as having ADHD were more likely to experience school discipline consequences than those who were not. These findings suggest that gender and dis/ability status are significant …

Restorative Practices And Exclusionary School Discipline: An Integrative Review, Ceema Samimi, Tyler M. Han, Amy Navvab, Jennifer A. Sedivy, Yolanda Anyon

Faculty Research, Scholarly, and Creative Activity

This study used integrative review methodology to synthesize research on the relationship between school-based restorative practices and exclusionary discipline outcomes in the United States. Exclusionary discipline outcomes were defined as out-of-school suspensions, expulsions, and referrals to law enforcement. A literature search produced 5,764 publications, and 11 studies were included in the final sample. Peacemaking circles were the most common restorative practice implemented, and secondary analysis of school records was the most common method utilized. Findings indicated that restorative practices are associated with reduced suspension rates, which suggest that school-based restorative practices are a promising approach to reducing exclusionary discipline outcomes.

Investigating The Factor Structure And Measurement Invariance Of The Eating Disorder Examination-Questionnaire (Ede-Q) In A Community Sample Of Gender Minority Adults From The United States, Jason M. Nagata, Emilio J. Compte, F. Hunter Mcguire, Tiffany A. Brown, Jason M. Lavender, Stuart B. Murray, Matthew R. Capriotti, Annesa Flentje, Micah E. Lubensky, Mitchell R. Lunn, Juno Obedin-Maliver

Faculty Research, Scholarly, and Creative Activity

Objective: The Eating Disorder Examination-Questionnaire (EDE-Q) is one of the most widely used self-report assessments of eating disorder symptoms. However, evidence indicates potential problems with its original factor structure and associated psychometric properties in a variety of populations, including gender minority populations. The aim of the current investigation was to explore several previously published EDE-Q factor structures and to examine internal consistency and measurement invariance of the best-fitting EDE-Q model in a large community sample of gender minority adults. Methods: Data were drawn from 1567 adults (337 transgender men, 180 transgender women, and 1050 gender-expansive individuals) who participated in The …

Edible Learning: School Food Service Programming And Experiential Education That Promote Sustainable Agriculture And Better Nutritional Health, Kristina Grasty

Dissertations

The typical American diet, which involves the consumption of processed foods high in saturated fats, sodium, and refined sugar, is known to be unhealthy. Millions of students in the U.S. suffer from symptoms of malnutrition, making them susceptible to compromising health conditions such as type 2 diabetes that may persist into adulthood. Well-designed school nutrition programs and action learning that promotes sustainable agriculture have the potential to enhance students’ nutritional well-being as well as their understanding of the ecological systems needed to support good nutrition. This dissertation uses documentary film as an exploratory, qualitative research method to investigate approaches used …

Detecting Botnets Using Hidden Markov Model, Profile Hidden Markov Model And Network Flow Analysis, Rucha Mannikar

Master's Projects

Botnet is a network of infected computer systems called bots managed remotely by an attacker using bot controllers. Using distributed systems, botnets can be used for large-scale cyber attacks to execute unauthorized actions on the targeted system like phishing, distributed denial of service (DDoS), data theft, and crashing of servers. Common internet protocols used by normal systems for regular communication like hypertext transfer (HTTP) and internet relay chat (IRC) are also used by botnets. Thus, distinguishing botnet activity from normal activity can be challenging. To address this issue, this project proposes an approach to detect botnets using peculiar traits in …

Hate Speech Detection In Hindi, Pranjali Prakash Bansod

Master's Projects

Social media is a great place to share one’s thoughts and to express oneself. Very often the same social media platforms become a means for spewing hatred.The large amount of data being shared on these platforms make it difficult to moderate the content shared by users. In a diverse country like India hate is present on social media in all regional languages, making it even more difficult to detect hate because of a lack of enough data to train deep/ machine learning models to make them understand regional languages.This work is our attempt at tackling hate speech in Hindi. We …

Personalized Tweet Recommendation Using Users’ Image Preferences, Shashwat Avinash Kadam

Master's Projects

In the era of information explosion, the vast amount of data on social media platforms can overwhelm users. Not only does this information explosion contain irrelevant content, but also intentionally fabricated articles and images. As a result, personalized recommendation systems have become increasingly important to help users navigate and make sense of this data. We propose a novel technique to use users’ image preferences to recommend tweets. We extract vital information by analyzing images liked by users and use it to recommend tweets from Twitter. As many images online have no descriptive metadata associated with them, in this framework, we …

Application Of Knowledge Graph Techniques On Textbooks, Yutong Yao

Master's Projects

Textbooks are written and organized in a way that facilitates learning and understanding. Sections like glossary terms at the end of a textbook provide guidance on the topic of interest. However, it takes manual effort to create the index terms in the glossary that highlight the key referenced terminologies and related terms. Knowledge graphs, which have been used to represent and even reason over data and knowledge, can potentially capture textbook’s important terms, concepts, and their relations. Popular since the initial introduction by Google Knowledge Graphs (KGs), they combine graph and data to capture and model enormous amounts of relational …

The Bias Report: An Automated News Aggregator For Political Bias Classification And News Summarization, Anant Joshi

Master's Projects

Political polarization is on the rise in the US, driven in large part by divisive news that goes viral on the Internet. Specifically, many media outlets use slanted language and publish misinformation in order to drive user traffic and engagement. Almost 80% of US citizens get their news from online sources, but there is a lack of public safeguards against biased news. A large amount of news is published online every day by media organizations, and it is impossible to manually analyze this amount of data. There is a clear need for automated, public-facing solutions in the current political climate …

Wikipedia Web Table Interpretation, Keyword-Based Search, And Ranking, Kartikee Dabir

Master's Projects

Information retrieval and data interpretation on the web, for the purpose of gaining knowledgeable insights, has been a widely researched topic from the onset of the world wide web or what is today popularly known as the internet. Web tables are structured tabular data present amidst unstructured, heterogenous data on the web. This makes web tables a rich source of information for a variety of tasks like data analysis, data interpretation, and information retrieval pertaining to extracting knowledge from information present on the web. Wikipedia tables which are a subset of web tables hold a huge amount of useful data, …

Comparative Analysis Of Transformer-Based Models For Text-To-Speech Normalization, Pankti Dholakia

Master's Projects

Text-to-Speech (TTS) normalization is an essential component of natural language processing (NLP) that plays a crucial role in the production of natural-sounding synthesized speech. However, there are limitations to the TTS normalization procedure. Lengthy input sequences and variations in spoken language can present difficulties. The motivation behind this research is to address the challenges associated with TTS normalization by evaluating and comparing the performance of various models. The aim is to determine their effectiveness in handling language variations. The models include LSTM-GRU, Transformer, GCN-Transformer, GCNN-Transformer, Reformer, and a BERT language model that has been pre-trained. The research evaluates the performance …

Sign Language Recognition Using A Hybrid Machine Learning Model, Peeyusha Shivayogi

Master's Projects

Sign Language is a visual language used by millions of people around the world. American Sign Language (ASL) is one of the most popular sign languages and the third most popular language in the United States. Automatic recognition of ASL signs can help bridge the communication gap between deaf and hearing individuals. In this project, we explore the use of deep learning models for ASL sign recognition, using the MNIST dataset as a benchmark. We preprocessed the data by reshaping the images to the input layer size of the models and normalized the pixel values. We evaluated five popular deep-learning …

Document-Level Machine Translation With Hierarchical Attention, Yu-Tang Shen

Master's Projects

Machine translation (MT) aims to translate texts with minimal human involvement, and the utilization of machine learning methods is pivotal to its success. Sentence-level and paragraph-level translations were well-explored in the past decade, such as the Transformer and its variations, but less research was done on the document level. From reading a piece of news in a different language to trying to understand foreign research, document-level translation can be helpful.

This project utilizes a hierarchical attention (HAN) mechanism to abstract context information making document-level translation possible. It further utilizes the Big Bird attention mask in the hope of reducing memory …

Malware Classification Using Api Call Information And Word Embeddings, Sahil Aggarwal

Master's Projects

Malware classification is the process of classifying malware into recognizable categories and is an integral part of implementing computer security. In recent times, machine learning has emerged as one of the most suitable techniques to perform this task. Models can be trained on various malware features such as opcodes, and API calls among many others to deduce information that would be helpful in the classification.

Word embeddings are a key part of natural language processing and can be seen as a representation of text wherein similar words will have closer representations. These embeddings can be used to discover a quantifiable …

Leveraging Tweets For Rapid Disaster Response Using Bert-Bilstm-Cnn Model, Satya Pranavi Manthena

Master's Projects

Digital networking sites such as Twitter give a global platform for users to discuss and express their own experiences with others. People frequently use social media to share their daily experiences, local news, and activities with others. Many rescue services and agencies frequently monitor this sort of data to identify crises and limit the danger of loss of life. During a natural catastrophe, many tweets are made in reference to the tragedy, making it a hot topic on Twitter. Tweets containing natural disaster phrases but do not discuss the event itself are not informational and should be labeled as non-disaster …

Keystroke Dynamics And User Identification, Atharva Sharma

Master's Projects

We consider the potential of keystroke dynamics for user identification and authentication. We work with a fixed-text dataset, and focus on clustering users based on the difficulty of distinguishing their typing characteristics. After obtaining a confusion matrix, we cluster users into different levels of classification difficulty based on their typing patterns. Our goal is to create meaningful clusters that enable us to apply appropriate authentication methods to specific user clusters, resulting in an optimized balance between security and efficiency. We use a novel feature engineering method that generates image-like features from keystrokes and employ multiclass Convolutional Neural Networks (CNNs) to …

Efficacy Of Gabapentin Versus Benzodiazepines In The Management Of Alcohol Withdrawal Syndrome, Douglas R. Fredona

Master's Projects

Alcohol abuse, complicated by a dependency relationship, is the third leading modifiable cause of death in the United States. In patients with chronic alcohol use disorder who experience a sudden cessation or significant decrease in alcohol consumption, half will experience symptoms of alcohol withdrawal syndrome. Benzodiazepines are the typical first line treatment for alcohol withdrawal. However, benzodiazepines carry with them significant risks and side effects. As a result, ongoing research has taken place to find either an alternative treatment or a method of reducing total benzodiazepine dosage during treatment. Gabapentin is a medication that is primarily used for seizure control …