Digital Commons Network^™

A Biblical Framework For Understanding And Responding To Ai, David J. Mulder

Faculty Work Comprehensive List

This chapter is about developing a biblically informed, theologically sound perspective on what it means to be human in a world where artificial intelligence (AI) seems to be omnipresent and nearly omnipotent. Along the way, we will consider the history of AI, the current state of AI, and a biblical view of AI. However, to land in a place where we have some solid understanding of a biblically informed perspective on the role of technology, we need to begin with our own imaginations.

Trading Cloud Computing Stocks Using Sma, Xianrong Zheng, Lingyu Li

Information Technology & Decision Sciences Faculty Publications

As cloud computing adoption becomes mainstream, the cloud services market offers vast profits. Moreover, serverless computing, the next stage of cloud computing, comes with huge economic potential. To capitalize on this trend, investors are interested in trading cloud stocks. As high-growth technology stocks, investing in cloud stocks is both rewarding and challenging. The research question here is how a trading strategy will perform on cloud stocks. As a result, this paper employs an effective method—Simple Moving Average (SMA)—to trade cloud stocks. To evaluate its performance, we conducted extensive experiments with real market data that spans over 23 years. Results show …

Query Augmentation For Information Retrieval (Ir) Using Large Language Model (Llm), Anish Bhusal

Theses

In the context of exponential data growth, the efficient retrieval of information remains a known challenge. One key problem lies in bridging the gap between the search query and the information available. This thesis introduces a framework for information retrieval with the help of large language models (LLMs) along with query augmentation. Given a query, its sub-queries are created, using a fine tuned Seq2Seq (sequence to sequence) model through a technique called knowledge distillation. Different prompting methods are applied to produce an efficient query graph. The graph generated is then fed through a retrieval augmented generation (RAG) pipeline to respond …

Multilabel Defect Classification Using Graph Neural Networks For Autonomous Visual Inspections, Md Sazzad Hossen

Theses

Visual inspections of safety-critical systems are crucial in reducing the risk of equipment failures, downtime, and loss of life. This nondestructive testing (NDT) method uses a portable borescope or camera along with other sensors directly or mounted on robotic platforms to inspect difficult-to-access areas with ease, minimum time, and cost. Although state-of-the-art visual inspection platforms are equipped with sensors from multiple modalities, the inspection tasks still require human subject matter experts to identify defects and analyze them. This jeopardizes human safety in a hazardous work environment in energy industries, as well as the extended time for inspection and human error. …

Digital Resurrection Of Historical Figures: A Case Study On Mary Sibley Through Customized Chatgpt, James Hutson, Paul Huffman, Jeremiah Ratican

Faculty Scholarship

This study investigates the emerging realm of digital resurrection, focusing on Mary Sibley (1800–1878), the esteemed founder of Lindenwood University. The core objective was to demonstrate the capability of advanced artificial intelligence, specifically a customized version of ChatGPT, in revitalizing historical figures for educational and engagement purposes. By integrating comprehensive diaries from Sibley with Claude 2.0, the research utilized a substantial autobiographical dataset to develop a GPT beta version that replicates her distinct voice and tone. The incorporation of her official portrait and diaries into the GPT Builder was pivotal, creating an interactive platform that accurately reflects her perspectives on …

Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart

Theses and Dissertations

Enabling machines to learn measures of human activity from bioelectric signals has many applications in human-machine interaction and healthcare. However, labeled activity recognition datasets are costly to collect and highly varied, which challenges machine learning techniques that rely on large datasets. Furthermore, activity recognition in practice needs to account for user trust - models are motivated to enable interpretability, usability, and information privacy. The objective of this dissertation is to improve adaptability and trustworthiness of machine learning models for human activity recognition from bioelectric signals. We improve adaptability by developing pretraining techniques that initialize models for later specialization to unseen …

Enhancing Sentiment Analysis In Niche Domains: Introducing Diverse Datasets And Evaluating Model Performance In Car Dealership And Board Game Reviews, Kimon Andreou

CCAC Theses and Dissertations

The field of Natural Language Processing (NLP) has witnessed significant advancements in recent decades, with text classification emerging as a critical task, particularly in sentiment analysis applications. However, a constant challenge within sentiment analysis research is the scarcity of diverse and specialized labeled datasets. The present dissertation addresses this gap by developing two novel, labeled textual datasets sourced from niche areas: BoardGameGeek.com's top 250 board game reviews and TrustPilot.com's car dealership reviews

The main goal of this dissertation is to enrich sentiment analysis methodologies by providing unique datasets and insights into the performance of current models within specialized domains. By …

Trust In Human-Agent Teams: A Multilevel Perspective And Future Research Agenda, Jessica L. Wildman, Daneil Nguyen, Amanda L. Thayer, Valerie T. Robbins-Roth, Meredith Carroll, Kendall Carmody, Cherrise Ficke, Mohammed Akib, Arianna Addis

Psychology Faculty Publications

As technology continues to advance, interest in how humans perceive and interact with autonomous agents has increased, spurring ample research within human-agent teams (HATs). However, the word team is a bit of a misnomer, in that much of this research has examined one human in relation to one agent. We extend the HAT literature by applying a multilevel lens to develop future research questions regarding trust in heterogeneous HATs. First, we assert that trust is an attitude that is multireferent, suggesting research should explore not just humans’ trust in agents, but many other perspectives such as agents’ trust in humans. …

Natural Language Processing And Neurosymbolic Ai: The Role Of Neural Networks With Knowledge-Guided Symbolic Approaches, Emily Barnes, James Hutson

Faculty Scholarship

Neurosymbolic AI (NeSy AI) represents a groundbreaking approach in the realm of Natural Language Processing (NLP), merging the pattern recognition of neural networks with the structured reasoning of symbolic AI to address the complexities of human language. This study investigates the effectiveness of neurosymbolic AI in providing nuanced understanding and contextually relevant responses, driven by the need to overcome the limitations of existing models in handling complex linguistic tasks and abstract reasoning. Employing a hybrid methodology that combines multimodal contextual modeling with rule-governed inferences and memory activations, the research delves into specific applications like Named Entity Recognition (NER), where architectures …

Digitalization Of Railway Transportation Through Ai-Powered Services: Digital Twin Trains, Salih Sarp, Murat Kuzlu, Vukica Jovanovic, Zekeriya Polat, Ozgur Guler

Engineering Technology Faculty Publications

Digitalization is a key concept that transformed the various industries through technologies like Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twin (DT). Although innovations provided by the advancement of digitalization have paved the way for more efficient operations and products for transportation, the rail transportation sector struggles to keep up with the rest of the transportation industry, since trains are designed to last for decades, and the insufficient infrastructure investment leads to multiple railroad derailments across the globe. Therefore, the primary aim is to transform current railway systems into human-centric, adaptable, sustainable and future-proof networks, aligning with Industry …

A Brief Exploration Of Artificial Intelligence In Dental Healthcare: A Narrative Review, Prakrati Kamath, Prathvi Kamath, Sharon J.R. Saldanha, Thilak B. Shetty

Open Access archive

Artificial intelligence is a computer system which can replicate human behavior and largely supports human actions and interpretation, but not replace human responses. Over the past few decades, the field of artificial intelligence (AI) has experienced phenomenal development and expansion. We are surrounded by several instances of AI. AI has applications in different branches of Dentistry. A total of 59 papers from an electronic search using Google Scholar and PubMed were used to create this narrative review. Artificial intelligence can be utilised for diagnosis, decision-making, treatment planning, early detection and prevention of oral disease, and finally result prediction by utilising …

Chatting With Ai: Deciphering Developer Conversations With Chatgpt, Esteban Parra Rodriguez, Suad Mohamed, Abdullah Parvin

Funded Scholarship

Large Language Models (LLMs) have been widely adopted and are becoming ubiquitous and integral to software development. However, we have little knowledge as to how these tools are being used by software developers beyond anecdotal evidence and word-of-mouth reports. In this work, we present a study toward understanding how developers engage with and utilize LLMs by reporting the results of an empirical study identifying patterns in the conversation that developers have with LLMs. We identified a total of 19 topics describing the purpose of the developers in their conversations with LLMs. Our findings reveal that developers use LLMs to facilitate …

Locating Liability For Medical Ai, W. Nicholson Price Ii, I. Glenn Cohen

Articles

When medical AI systems fail, who should be responsible, and how? We argue that various features of medical AI complicate the application of existing tort doctrines and render them ineffective at creating incentives for the safe and effective use of medical AI. In addition to complexity and opacity, the problem of contextual bias, where medical AI systems vary substantially in performance from place to place, hampers traditional doctrines. We suggest instead the application of enterprise liability to hospitals—making them broadly liable for negligent injuries occurring within the hospital system—with an important caveat: hospitals must have access to the information needed …

Ai As A Tool For Education Equity: A Review Of Recent Literature, Jace F. Peterkin-Rawls, J.D.

Journal of Race, Gender, and Ethnicity

This article addresses the challenges faced by one in five children in the United States who have learning or thinking differences, disabilities, or disorders. Despite existing resources like Individualized Education Plans (IEPs) and 504 plans, many students with learning disabilities struggle due to insufficient personalization, limited teacher support, and inadequate funding. The article explores the potential of Artificial Intelligence (AI) to enhance current educational resources and better support these students. It provides an overview of common learning disabilities and existing support mechanisms, followed by a discussion of the ethical implications of AI in education from four scholars. The article then …

A Point Of Singularity For Technology And Engineering Education, Philip A. Reed

Educational Leadership & Workforce Development Faculty Publications

[First paragraph] I attended graduate school at Virginia Tech in the late 1990's and some of my fondest memories are from the side conversations with the faculty. The faculty at that time consisted of my mentor and program leader, Jim LaPorte, and other leaders in the field: Allen Bame, Sharon Brusic, Tom Jeffries, and Mark Sanders. Bill Dugger had recently retired from the university but maintained an office in Blacksburg to work full time on the Technology for All Americans Project (TfAAP, ITEEA, 2024) and he was very gracious about hosting students at the TfAAP office …

The Right To A Glass Box: Rethinking The Use Of Artificial Intelligence In Criminal Justice, Brandon L. Garrett, Cynthia Rudin

Faculty Scholarship

Artificial intelligence (“AI”) increasingly is used to make important decisions that affect individuals and society. As governments and corporations use AI more pervasively, one of the most troubling trends is that developers so often design it to be a “black box.” Designers create AI models too complex for people to understand or they conceal how AI functions. Policymakers and the public increasingly sound alarms about black box AI. A particularly pressing area of concern has been criminal cases, in which a person’s life, liberty, and public safety can be at stake. In the United States and globally, despite concerns that …

Integrating Generative Artificial Intelligence With Systems Architecting Diagram Creation: Advancement, Challenges, Opportunities And Future Perspectives, Cansu Yalim, Holly H. Handley

Engineering Management & Systems Engineering Faculty Publications

Generative AI (GenAI) serves as a powerful tool that can create a wide range of content, including but not limited to text, speech, images, code, videos, and 3D models. ChatGPT stands out as a particularly appealing Generative Pretrained Transformer (GPT) model that offers supplementary capabilities through GPTs and plugins. These extensions enable users to engage with the chatbot and improve its functionality, surpassing mere content generation. Our study delves into the potential of ChatGPT, specifically GPT-4, to expedite the creation of diagrams to support the system architecting process. To this end, we explored the use of ChatGPT's Diagrams Show Me …

Differences In Student-Ai Interaction Process On A Drawing Task: Focusing On Students' Attitude Towards Ai And The Level Of Drawing Skills, Jinhee Kim, Yoonhee Ham, Sang-Soog Lee

STEMPS Faculty Publications

Recent advances and applications of artificial intelligence (AI) have increased the opportunities for students to interact with AI in their learning tasks. Although various fields of scholarly research have investigated human-AI collaboration, the underlying processes of how students collaborate with AI in a student-AI teaming scenario have been scarcely investigated. To develop effective AI applications in education, it is necessary to understand differences in the student-AI interaction (SAI) process depending on students' characteristics. The present study attempts to fill this gap by exploring the differences in the SAI process amongst students with varying drawing proficiencies and attitudes towards AI in …

The Role Of Shopping Orientations And Intrinsic Experiential Value In Consumer's Willingness To Follow Embodied-Ai's Advice In Fashion Shoe Stores, Christina Soyoung Song, Ji Young Lee, Dooyoung Choi

STEMPS Faculty Publications

This study employs a synthesis of Intrinsic Motivation Theory with three shopping orientations, namely “adventure,” “idea,” and “personalized” shopping, in order to examine their potential influence on individuals' motivation towards shopping. We proposed that consumers’ experiential value of intrinsic enjoyment is an indispensable mediator that affects their willingness to follow EAI’s advice. The study offers novel insights into the way that consumers’ characteristics of influencing others’ clothing consumption affect their shopping motivations to find adventure and stimulation, keep up with new fashion trends and products information, and their preference to patronize stores and interact with store staff on a personal …

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …

‘I Know It When I See It’– Developing Quality Schedules Considering Subjective Or Unspecified Criteria, Douglas L. Moody

Publications and Research

Most timetabling problems have a given objective function to measure the quality of a solution. However, users may have a “I know it when I see it” recognition of a quality schedule, without specifying the complete basis for their judgment. In this situation, the objective function cannot be exclusively used as a solution quality measurement. This work presents an AI based approach to aid in categorizing the solution’s quality when the users have not explicitly defined all factors used in their criteria.

The Flesh And Silicon Mesh: A New Materialist Conception Of Advanced Digital Machines, Joshua Nieubuurt

English Faculty Publications

Digital machines, including artificial intelligence (AI), have long been held as potential agents of social change. From their binary role in cultural productions to the real-world implementation of such, digital machines have typically been considered within the Heideggerian “readiness-to-hand” category of Being. This paper seeks to alter conceptually this dynamic through the use of new materialism and Morton’s concept of the “mesh” to provide a new understanding of the social and pragmatic roles advanced digital machines play in ecologies in which they dwell and continue to emerge. Through briefly tracing the complex intermingling of advanced digital machines in the realms …

Machine-Learning-Enabled Diagnostics With Improved Visualization Of Disease Lesions In Chest X-Ray Images, Md. Fashiar Rahman, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Eric Walser, Scott Moen, Alex Vo, Johnny C. Ho

Mathematics & Statistics Faculty Publications

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network’s determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region …

The Promise And Pitfalls Of Genai In Education, Helen Crompton

STEMPS Faculty Publications

Since ChatGPT was made available to the public, artificial intelligence (AI) has had accelerated growth in the field of education at an unprecedented rate. This study examined the misuses and limitations of ChatGPT and similar generative AI as well as the affordances of these tools for teaching and learning. Using PRISMA protocol, this thematic systematic review examined extant literature from November 2022 to June 2024. The findings reveal five misuses and limitations trends in the literature of cheating, inaccuracies, plagiarism, bias, and data privacy. Affordances highlighted six areas of content creation, personalization, engagement, student coaching and assistance, assessment, and task …

Types Of Teacher-Ai Collaboration In K-12 Classroom Instruction: Chinese Teachers' Perspective, Jinhee Kim

STEMPS Faculty Publications

The advancing power and capabilities of artificial intelligence (AI) have expanded the roles of AI in education and have created the possibility for teachers to collaborate with AI in classroom instruction. However, the potential types of teacher-AI collaboration (TAC) in classroom instruction and the benefits and challenges of implementing TAC are still elusive. This study, therefore, aimed to explore different types of TAC and the potential benefits and obstacles of TAC through Focus Group Interviews with 30 Chinese teachers. The study found that teachers anticipated six types of TAC, which are thematized as One Teach, One Observe; One Teach, One …

An Exacerbated Power Imbalance: The Danger In Allowing Ai To Render Arbitral Awards In Employment Arbitration, Elizabeth G. Stein

Mitchell Hamline Law Review

No abstract provided.

Establishing A Future-Proof Framework For Ai Regulation: Balancing Ethics, Transparency, And Innovation, Marcia Narine Weldon, Gabrielle Thomas, Lauren Skidmore

Transactions: The Tennessee Journal of Business Law

No abstract provided.

The Educational Affordances And Challenges Of Chatgpt: State Of The Field, Helen Crompton, Diane Burke

STEMPS Faculty Publications

ChatGPT was released to the public in November 30, 2022. This study examines how ChatGPT can be used by educators and students to promote learning and what are the challenges and limitations. This study is unique in providing one of the first systematic reviews using peer review studies to provide an early examination of the field. Using PRISMA principles, 44 articles were selected for review. Grounded coding was then used to reveal trends in the data. The findings show that educators can use ChatGPT for teaching support, task automation, and professional development. These were further delineated further by axial sub …

A Generative Approach For Document Enhancement With Small Unpaired Data, Mohammad Shahab Uddin, Wael Khallouli, Andres Sousa-Poza, Samuel Kovacic, Jiang Li

Engineering Management & Systems Engineering Faculty Publications

Shipbuilding drawings, crafted manually before the digital era, are vital for historical reference and technical insight. However, their digital versions, stored as scanned PDFs, often contain significant noise, making them unsuitable for use in modern CAD software like AutoCAD. Traditional denoising techniques struggle with the diverse and intense noise found in these documents, which also does not adhere to standard noise models. In this paper, we propose an innovative generative approach tailored for document enhancement, particularly focusing on shipbuilding drawings. For a small, unpaired dataset of clean and noisy shipbuilding drawing documents, we first learn to generate the noise in …

An Ai-Based Conceptual Framework To Improve Program Management Of Complex Systems, Michael D. Parrish, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

With evolving technologies, changing requirements, and limited budgets, governments and industries need to consider new methodologies to help streamline program lifecycle management, from cradle to grave, to ensure projects are delivered on time, on budget, and to the expected performance standards. Traditional approaches fail to adequately address the added complexities of System of Systems programs such as integration, interoperability, and variable lifecycle of subcomponents. The objective of this study is to assess and address the research question - can a new acquisition approach be designed to address and improve program lifecycle management of complex systems? A comparison study, using the …