Digital Commons Network^™

Demoralizing Elite Fraud, Zephyr Teachout

Faculty Scholarship

The Supreme Court keeps striking down wire fraud convictions. Why?

As I show in this piece, from the 1970s to this year, the Court has repeatedly attempted to shift the framework of fraud cases from moral language to morally neutral language. As a result, the Court ends up making a hash of statutory interpretation, because it then has to interpret statutes that it has effectively rewritten. For instance, much of the current fraud jurisprudence revolves around defining what property means. The Mail Fraud statute does not have the language of property in it, and the legislative history does not involve …

A Deal With The Devil: Pragmatic Mission And Early American Methodism’S Complicity With Slavery, William Payne

The Asbury Journal

Early American Methodism inherited a staunch abolitionist position from John Wesley. Bishops Francis Asbury and Thomas Coke strongly opposed slavery. Under their leadership, the early minutes and disciplines included a series of rules that required preachers to free their slaves and ameliorate the effects of slavery. They also waged an ongoing “war” with the various state legislatures that allowed slavery. After a strong backlash threatened Methodism’s ability to minister to slaves, enter plantations, and work in the South, the church prioritized the evangelistic mandate over the cultural mandate. The compromise mitigated social hostility and allowed Methodism to become the largest …

Exploring Etymology Assignment Description, David Wolff

Open Educational Resources - Teaching and Learning

The English language is a borrowed language, a blend of words from many languages from around the world. We see this in the various ways sounds are represented by letters and letter combinations. In transparent or shallow orthographies, there is high predictability and consistent letter-sound correspondence whereas in opaque or deep orthographies, there are many ways to spell the same sound as well as there are many sounds for the same spellings (Burkins & Yates, 2021; Moats, 2020). This assignment description is a guided inquiry for preservice teachers to explore the concept of etymology by watching and reflecting on six …

Home Of The Brave Book Study Assignment Description, David Wolff

Open Educational Resources - Teaching and Learning

Individuals lead storied lives, and everyone has a story to tell. Our stories can be shared orally and documented in print. Often, learners are exposed to stories through novels and other trade books. Teacher educators may benefit from using the stories in novels and trade books as case studies in preservice teacher preparation course. This assignment description outlines how to use the novel, Home of the Brave by Katherine Applegate, as a case study to contextualize and understand the lived story of an individual learning a second language and living in a new country. Through the novel, preservice teachers experience …

Fish In A Tree Book Study Assignment Description, David Wolff

Open Educational Resources - Teaching and Learning

Individuals lead storied lives, and everyone has a story to tell. Our stories can be shared orally and documented in print. Often, learners are exposed to stories through novels and other trade books. Teacher educators may benefit from using the stories in novels and trade books as case studies in preservice teacher preparation course. This assignment description outlines how to use the novel, Fish in a Tree by Lynda Mullaly Hunt, as a case study to contextualize and understand the lived story of an individual living and learning with dyslexia. Through the novel, preservice teachers experience the dilemmas faced and …

Language Learning Simulation Using Duolingo Assignment Description, David Wolff

Open Educational Resources - Teaching and Learning

It is likely that preservice teachers will work with students learning English as their second (third, fourth, etc.) language. For preservice teachers to better understand the language learning process, Duolingo was used to simulate the learning experience. The assignment description outlines how preservice teachers can reflection pre-simulation and post-simulation about what they learned about language learning.

Bulgarian Clitics Are Sensitive To Number Attraction, Tanya Ivanova-Sullivan, Irina A. Sekerina, Sol Lago

Publications and Research

Previous research has shown that the computation of subject-verb number agreement can be derailed by the presence of syntactically illicit nouns, a phenomenon called agreement attraction. By contrast, the incidence of agreement attraction with anaphoric dependencies is less clear: Previous work has mostly focused on reflexives and strong pronouns, which sometimes show attraction and other times do not. Meanwhile, research on clitics – a different class of pronominal anaphora – is scarcer. To expand the empirical record, we examined clitic pronouns in an under-researched language, Bulgarian. The results of a large sample eye-tracking study showed clear agreement attraction effects in …

Snap-Ed New Mexico Impact Evaluation Report 2024, Belinda E. Vicuña, Robert Mccreary, Mahtab Soleimani, Lucinda V. Denatale, Camile R. Velarde, Yvonne C. Vitanzos, Theresa H. Cruz

Reports & Documents

No abstract provided.

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 …

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 …

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 …

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 …

Knowledge Graph-Based Multiple-Choice Question Generation, Durga Muralidharan

Master's Projects

Knowledge-based tests are widely used to assess knowledge on a specific subject and have many applications in education and professional certifications. These tests usually consist of Multiple Choice Questions (MCQs), where a question with a few possible answers is given. Along with the correct answer, three or more incorrect answers are provided, which are called distractors. MCQs are a popular method for these tests because they are easy to grade. These tests can check different levels of comprehension ranging from beginners to advanced by creating distractors that may confuse unprepared test takers. This project proposes the Knowledge Graph Multiple Choice …

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, …

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 …

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, …

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 …

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, …

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 …

Vincentian Heritage 37:2

Vincentian Studies

No abstract provided.

What Are The Key Factors Influencing The Successful Integration Of Ai In Healthcare?, Gabe Herman

Graduate Research Projects

No abstract provided.

Understanding Early Learning Educator Perspectives On Retention In Western Washington State: A Qualitative Inquiry, Nouanechanh Kitnikone, Susan Lee, Laura Walker

Educational and Organizational Learning and Leadership Dissertations

There is a significant issue surrounding the retention of educators within the early childhood education field. The COVID-19 pandemic exacerbated the fragile system that was already malfunctioning, and there is hyperfocus on determining how to increase early educator retention and reduce attrition levels. This inquiry utilized participatory action research (PAR) through a collaborative partnership with an early learning center in Western Washington State. Researchers used a transformative worldview with an adaptive leadership focus as the theoretical framework with an appreciative inquiry lens. The study aimed to identify factors and motivations that contribute to the retention of educators in the Early …

Closing The Culture Gaps: Policies And Codesign In Remote Western Australia. A Long-Time Story Of A Community-Based Kimberley Aboriginal Organisation, Arjati Schipf

Theses

This research is the result of a collaboration with the Kimberley Aboriginal Law and Cultural Centre (KALACC), a remote community-based Aboriginal Community Controlled Organisation (ACCO) located in Fitzroy Crossing, Western Australia. The research investigates the organisation’s strategies to overcome systemic barriers to support remote Aboriginal communities to achieve Closing the Gap (CTG) socioeconomic targets in improving education, employment, and health. KALACC believes that without culture at the centre of CTG strategies, the targets will not be achieved. The key question is how will diverse understandings of culture be achieved through codesign and engagement with Aboriginal people and their community-based organisations …