Digital Commons Network^™

Impact Ipsa Loquitur: A Reverse Hand Rule For Consumer Finance, Susan Block-Lieb, Edward J. Janger

Faculty Scholarship

Algorithms in consumer finance can entrench, exacerbate, and conceal bias. This article considers the increased importance, in the age of algorithmic lending, of disparate impact analysis as a tool to combat predation and discrimination in consumer finance transactions. It explores the existing legal landscape and finds gaps in the relationship between discrimination and predatory lending doctrines. It then situates both disparate impact analysis and UDAAP doctrines within the broader context of balancing and burden-shifting rules in tort law and considers how this analysis might be tailored for the new algorithmic lending environment. We conclude that predatory discrimination permits consideration of …

Defamation, Presumed Damages, And Reputational Injury: A Legal And Philosophical Inquiry, Benjamin C. Zipursky

Faculty Scholarship

No abstract provided.

The Implications Of Section 230 For Black Communities, Spencer Overton, Catherine Powell

Faculty Scholarship

Section 230 of the Communications Decency Act generally immunizes online platforms such as Facebook, YouTube, Amazon, and Uber from liability for third-party user content (for example, posts, comments, and videos) and for moderation of that content. This Article addresses an important issue overlooked by both defenders and critics of Section 230: the implications of the law and proposed reforms for Black communities in particular. By relieving tech platforms of most legal liability for third-party content, Section 230 helps facilitate Black social activism, entrepreneurship, and artistic creativity. Section 230 also relieves platforms of most legal liability for content moderation, which boosts …

A Plain Account Of Christian Purity: Berlin Walls, Dean G. Blevins, Marie Gregg

The Asbury Journal

While theological definitions of holiness incorporate purity terminology among several metaphors, the challenges resident in using this language may well impede opportunities of engaging difference and reconciliation. Wesleyans need a “Plain Account” of Christian purity to guide both ecclesial discussions that stress not only strengths, but also limits, in purity thinking. Using an example involving the Church of the Nazarene and Pentecostalism, the writing reveals how purity thinking risks creating “Berlin walls” when engaging differences. The analysis argues that a moral fear of degradation, rather than an acknowledgment of difference, often pushes purity thinkers to oppose certain issues.

Book Reviews, Glen O'Brien, Zachariah S. Motts, Roderick T. Leupp, Jonathan Tysick, Duane F. Watson, William B. Bowes

The Asbury Journal

No abstract provided.

An Approach To The History Of Missions In The Church Of The Nazarene In Cuba, Mirelys Correoso Calzadilla

The Asbury Journal

The history of a church is impossible to tell if the chronicles of its missions are ignored. It is the latter that give it life and continuity. This article aims to present a brief historical review of the development of the missions in the Church of the Nazarene in Cuba. We begin with Miss Leona Gardner, a North American missionary, who arrived on the island in 1902. She preached the gospel in the area of Casilda, Trinidad, in the former province of Santi Espíritus, for 25 years. The merit lies in the fact that although she did not manage to …

De Los Archivos: E. Stanley Jones- ¿El Cristo Del Camino Andino?, Robert A. Danielson

The Asbury Journal

No abstract provided.

Salmonella Detection In Food Using A Hek-Htlr5 Reported Cell-Based Sensor, Esma Eser, Victoria A. Felton, Rishi Drolia, Arun K. Bhunia

Biological Sciences Faculty Publications

The development of a rapid, sensitive, specific method for detecting foodborne pathogens is paramount for supplying safe food to enhance public health safety. Despite the significant improvement in pathogen detection methods, key issues are still associated with rapid methods, such as distinguishing living cells from dead, the pathogenic potential or health risk of the analyte at the time of consumption, the detection limit, and the sample-to-result. Mammalian cell-based assays analyze pathogens’ interaction with host cells and are responsive only to live pathogens or active toxins. In this study, a human embryonic kidney (HEK293) cell line expressing Toll-Like Receptor 5 (TLR-5) …

Natural Enemies Of Varroa Destructor Identified From Eastern North America Honey Bee Colonies: A Biological Survey Of Candidates For Mite Control From Maryland, Usa, Francisco Posada-Flórez, Daniel Sonenshine, Jay Evans, Dawn Boncristiani, Monica Pava-Ripoll, Steven Cook

Biological Sciences Faculty Publications

A survey was conducted of microbes and arthropods associated with Varroa destructor (Acari: Varroidae) mites (hereafter, Varroa) collected from honey bee colonies in Maryland, USA. Live Varroa were challenged with samples of both microbes and arthropods and assessed for their potential as biocontrol agents against the mites. To our knowledge, this is the first survey of natural enemies of Varroa conducted in North American honey bee colonies. The survey revealed 21 fungi, 25 bacteria, one nematode, and 8 arthropod species associated with Varroa in honey bee colonies, and from challenge assays using 11 of these, including 4 species of …

Postgraduate Supervision In A Chatgpt World: What’S Next?, Angela Wright

Conference Materials

This study explores the considerable challenges for research supervisors of postgraduate students in Higher Education. For experienced academics, the responsibility of assessing theses and dissertations has never been more critical. With the advent of ample search engine opportunities, Generative Artificial Intelligence (GenAI) technology, and adhering to proper academic integrity processes, how can supervisors navigate these complexities, unsupported by solid procedures, due to the rapidly changing nature of AI? If a supervisor is concerned as to the integrity of the presented work during the supervisory process, what options are available to the supervisor to address concerns with the student in an …

Paddlesport Safety Culture In Ireland: An Exploratory Study, John Pierce, Kevin O'Callaghan

Publications

Anecdotally, paddlesports (canoeing, kayaking, stand-up paddleboarding, and rafting) are seen as safe sports, though prior to this report little evidence was available to support this view in an Irish context. This report provides a summary of a research project exploring safety practices in Irish paddlesports. It includes the aim, objectives, research methodology, findings, and recommendations. Discussions early in the research process about how to investigate safety practices in Irish paddlesports brought a number of objectives into focus. It was agreed to compile empirical evidence on current safety practices across Irish paddlesports and to determine what, if any, additional supports may …

Modelled Effectiveness Of Nbs In Reducing Disaster Risk: Evidence From The Operandum Project, Paul Bowyer, Silvia Maria Alfieri, Bidroha Basu, Emilie Cremin, Sisay Debele, Prashant Kumar, Veronika Lechner, Michael Loupis, Massimo Menenti, Slobodan Mickovski, Jan Pfeiffer, Francesco Pilla, Beatrice Pulvirenti, Paolo Ruggieri, Arunima Sarkar Basu, Christos Spyrou, Silvia Unguendoli, Thomas Zieher, Silviana Di Sabatino

Publications

The use of nature-based solutions (NbS) to address the risks posed by hydro-meteorological hazards have not yet become part of the mainstream policy response, and one of the main reasons cited for this, is the lack of evidence that they can effectively reduce disaster risk. This paper addresses this issue, by providing model-based evidence from five European case studies which demonstrate the effectiveness of five different NbS in reducing the magnitude of the hazard and thus risk, in present-day and possible future climates. In OAL-Austria, the hazard is a deep-seated landslide, and the NbS analysed is afforestation. Modelling results …

Bioconcentration Of Per- And Polyfluoroalkyl Substances And Precursors In Fathead Minnow Tissues Environmentally Exposed To Aqueous Film-Forming Foam–Contaminated Waters, Nicholas I. Hill, Jitka Becanova, Simon Vojta, Larry A. Barber, Denis R. Leblanc, Alan M. Vajda, Heidi M. Pickard, Rainer Lohmann

Graduate School of Oceanography Faculty Publications

Exposure to per- and polyfluoroalkyl substances (PFAS) has been associated with toxicity in wildlife and negative health effects in humans. Decades of fire training activity at Joint Base Cape Cod (MA, USA) incorporated the use of aqueous film-forming foam (AFFF), which resulted in long-term PFAS contamination of sediments, groundwater, and hydrologically connected surface waters. To explore the bioconcentration potential of PFAS in complex environmental mixtures, a mobile laboratory was established to evaluate the bioconcentration of PFAS from AFFF-impacted groundwater by flow-through design. Fathead minnows (n = 24) were exposed to PFAS in groundwater over a 21-day period and tissue-specific …

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 …