Physical Sciences and Mathematics Commons^™

Exploring Hedonic And Utilitarian Aspects Through Perceived Warmth In Human-Designed Vs. Ai-Generated Fashion, Dooyoung Choi, Ha Kyung Lee

Educational Leadership & Workforce Development Faculty Publications

Among various ways in which artificial intelligence (AI) is used in the fashion industry, its utilization in design has sparked public discussion about the potential replacement of human designers by AI. Along with this critical question, it is imminent to examine how consumers would respond to designs by AI. The purpose of this study is to explore consumers’ perceptions toward a fashion product labeled as generated by an AI system, comparing it to the same product labeled as designed by a human designer. Specifically, drawing from existing literature, we examine if the design source affects consumers’ perceptions of a product …

Artificial Intelligence For The Electron Ion Collider (Ai4eic), C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, M. Battaglieri, J. Bernauer, M. Bondì, N. Branson, T. Britton, A. Butter, I. Chahrour, P. Chatagnon, E. Cisbani, E. W. Cline, S. Dash, C. Dean, W. Deconinck, A. Deshpande, M. Diefenthaler, R. Ent, C. Fanelli, M. Finger, M. Finger Jr., E. Fol, S. Furletov, Y. Gao, J. Giroux, N. C. Gunawardhana Waduge, O. Hassan, P. L. Hegde, R. J. Hernandez-Pinto, A. Hiller Blin, T. Horn, J. Huang, A. Jalotra, D. Jayakodige, B. Joo, M. Junaid, N. Kalantarians, P. Karande, B. Kriesten, R. Kunnawalkam Elayavalli, Y. Li, M. Lin, F. Liu, S. Liuti, G. Matousek, M. Mceneaney, D. Mcspadden, T. Menzo, T. Miceli, V. Mikuni, R. Montgomery, B. Nachman, R. R. Nair, J. Niestroy, S. A. Ochoa Oregon, J. Oleniacz, J. D. Osborn, C. Paudel, C. Pecar, C. Peng, G. N. Perdue, W. Phelps, M. L. Purschke, H. Rajendran, K. Rajput, Y. Ren, D. F. Renteria-Estrada, D. Richford, B. J. Roy, D. Roy, A. Saini, N. Sato, T. Satogata, G. Sborlini, M. Schram, D. Shih, J. Singh, R. Singh, A. Siodmok, J. Stevens, P. Stone, L. Suarez, K. Suresh, A. -N. Tawfik, F. Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, R. Tyson, S. Volkova, A. Vossen, E. Walter, D. Whiteson, M. Williams, S. Wu, N. Zachariou, P. Zurita

Computer Science Faculty Publications

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. …

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 …

Artificial Intelligence For Post Secondary Accounting Students, Sarah Rahim

Honours Bachelor of Business Administration

No abstract provided.

Crafting Effective Prompts: Leveraging Generative Ai In Libraries, April Sheppard, Kristin Flachsbart

Staff and Faculty Scholarship

Discover how strategic prompt design can help you harness the power of generative artificial intelligence (AI) in your library. Through a series of examples, the presenters will demonstrate the impact that well-crafted prompts can have on the quality and relevance of AI-generated outputs.

Artificial Intelligence And News Consumption: A Study Of Trust, Credibility And Transparency In Automated Journalism, Julia Lobo Paes

Dissertations and Theses

According to Gallup poll (2023), over the last 50 years there has been a decline in how much Americans trust mass media. While in the 1970’s 72% of the population responded that they trust the media a 'great deal/fair amount', this number dropped to 34% in 2023. Given the decreasing public trust in news, this thesis focused particularly on analyzing trust in the organization, trust in the news story and perceived credibility in AI generative content. In addition to articles created by AI, this study also aimed to analyze how the public perceives information that has been personalized and distributed …

Embracing Ai In English Composition: Insights And Innovations In Hybrid Pedagogical Practices, James Hutson, Daniel Plate, Kadence Berry

Faculty Scholarship

In the rapidly evolving landscape of English composition education, the integration of AI writing tools like ChatGPT and Claude 2.0 has marked a significant shift in pedagogical practices. A mixed-method study conducted in Fall 2023 across three sections, including one English Composition I and two English Composition II courses, provides insightful revelations. The study, comprising 28 student respondents, delved into the impact of AI tools through surveys, analysis of writing artifacts, and a best practices guide developed by an honors student. Initially, the study observed a notable anxiety and mistrust among students regarding the use of AI in writing. However, …

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 …

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 …

Infusing Machine Learning And Computational Linguistics Into Clinical Notes, Funke V. Alabi, Onyeka Omose, Omotomilola Jegede

Mathematics & Statistics Faculty Publications

Entering free-form text notes into Electronic Health Records (EHR) systems takes a lot of time from clinicians. A large portion of this paper work is viewed as a burden, which cuts into the amount of time doctors spend with patients and increases the risk of burnout. We will see how machine learning and computational linguistics can be infused in the processing of taking clinical notes. We are presenting a new language modeling task that predicts the content of notes conditioned on historical data from a patient's medical record, such as patient demographics, lab results, medications, and previous notes, with the …

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 …

Autonomous Strike Uavs In Support Of Homeland Security Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming crucial tools in modern homeland security applications, primarily because of their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. This study focuses on the use of autonomous UAVs to conduct, as part of homeland security applications, strike missions against high-value terrorist targets. Owing to developments in ledger technology, smart contracts, and machine learning, activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of the challenges and preliminary solutions for the successful implementation of an autonomous UAV mission. Specifically, we …

Integrating Art And Ai: Evaluating The Educational Impact Of Ai Tools In Digital Art History Learning, James Hutson

Faculty Scholarship

This study delves into the burgeoning intersection of Artificial Intelligence (AI) and art history education, an area that has been relatively unexplored. The research focuses on how AI art generators impact learning outcomes in art history for both undergraduate and graduate students enrolled in Ancient Art courses, covering eras from ancient Mesopotamia to the fall of Rome. Utilizing a mixed-methods approach, the study analyzes AI-generated artworks, reflective essays, and survey responses to assess how these generative tools influence students’ comprehension, engagement, and creative interpretation of historical artworks. The study reveals that the use of AI tools in art history not …

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 …

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 …

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 …

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 …

Malware Detection With Artificial Intelligence: A Systematic Literature Review, Matthew G. Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI-powered malware-detection model: malware sophistication, analysis techniques, malware repositories, feature selection, and machine learning vs. deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool. Static analysis is fast but is …

A Technical Perspective On Integrating Artificial Intelligence To Solid-State Welding, Sambath Yaknesh, Natarajan Rajamurugu, Prakash K. Babu, Saravanakumar Subramaniyan, Sher A. Khan, C. Ahamed Saleel, Mohammad Nur-E-Alam, Manzoore E. M. Soudagar

Research outputs 2022 to 2026

The implementation of artificial intelligence (AI) techniques in industrial applications, especially solid-state welding (SSW), has transformed modeling, optimization, forecasting, and controlling sophisticated systems. SSW is a better method for joining due to the least melting of material thus maintaining Nugget region integrity. This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). Studies on Diffusion Bonding reveal that ANN and Generic Algorithms can predict outcomes …

Diagnostic Performance Of Ai-Based Models Versus Physicians Among Patients With Hepatocellular Carcinoma: A Systematic Review And Meta-Analysis, Feras Al-Obeidat, Wael Hafez, Muneir Gador, Nesma Ahmed, Marwa Muhammed Abdeljawad, Antesh Yadav, Asrar Rashed

All Works

Background: Hepatocellular carcinoma (HCC) is a common primary liver cancer that requires early diagnosis due to its poor prognosis. Recent advances in artificial intelligence (AI) have facilitated hepatocellular carcinoma detection using multiple AI models; however, their performance is still uncertain. Aim: This meta-analysis aimed to compare the diagnostic performance of different AI models with that of clinicians in the detection of hepatocellular carcinoma. Methods: We searched the PubMed, Scopus, Cochrane Library, and Web of Science databases for eligible studies. The R package was used to synthesize the results. The outcomes of various studies were aggregated using fixed-effect and random-effects models. …

A Smart Energy-Efficient Hybrid Gait Monitoring System, Elsa Joy Harris

CGU Theses & Dissertations

Triboelectric nanogenerators are devices that harvest mechanical energy from the environment and turn it into electricity. By coupling the effect of contact electrification and electrostatic induction between two materials that come into contact and then separate they can convert the irregular, low frequency, waste biomechanical energy of human motion into useful electrical energy to run small body-worn electronics. This has shown promising results in multiple applications such as self-powered motion and haptic sensing, self-charging micro-storage devices, neuromorphic computing, and designing batteryless circuits to power small wearables. This work will investigate a smart energy-efficient hybrid gait monitoring system that is powered …

The Great Scrape: The Clash Between Scraping And Privacy, Daniel J. Solove, Woodrow Hartzog

Faculty Scholarship

Artificial intelligence (AI) systems depend on massive quantities of data, often gathered by “scraping” – the automated extraction of large amounts of data from the internet. A great deal of scraped data is about people. This personal data provides the grist for AI tools such as facial recognition, deep fakes, and generative AI. Although scraping enables web searching, archival, and meaningful scientific research, scraping for AI can also be objectionable or even harmful to individuals and society.

Organizations are scraping at an escalating pace and scale, even though many privacy laws are seemingly incongruous with the practice. In this Article, …

Research And Development Of Simulation Training Platform For Multi-Agent Collaborative Decision-Making, Cheng Cheng, Zhijie Chen, Ziming Guo, Ni Li

Journal of System Simulation

Abstract: Reinforcement learning simulation platform can be an interactive and training environment for reinforcement learning. In order to make the simulation platform compatible with the multi-agent reinforcement learning algorithms and meet the needs of simulation in military field, the similar processes in multi-agent reinforcement learning algorithms are refined and a unified interface is designed to embed and verify different types of deep reinforcement learning algorithms on the simulation platform and to optimize the back-end service of the simulation platform to accelerate the training process of the algorithm model. The experimental results show that, by unifing the interface, the simulation platform …

Guilty Machines: On Ab-Sens In The Age Of Ai, Dylan Lackey, Katherine Weinschenk

Critical Humanities

For Lacan, guilt arises in the sublimation of ab-sens (non-sense) into the symbolic comprehension of sen-absexe (sense without sex, sense in the deficiency of sexual relation), or in the maturation of language to sensibility through the effacement of sex. Though, as Slavoj Žižek himself points out in a recent article regarding ChatGPT, the split subject always misapprehends the true reason for guilt’s manifestation, such guilt at best provides a sort of evidence for the inclusion of the subject in the order of language, acting as a necessary, even enjoyable mark of the subject’s coherence (or, more importantly, the subject’s separation …

Role Of Authentication Factors In Fin-Tech Mobile Transaction Security, Habib Ullah Khan, Muhammad Sohail, Shah Nazir, Tariq Hussain, Babar Shah, Farman Ali

All Works

Fin-Tech is the merging of finance and technology, to be considered a key term for technology-based financial operations and money transactions as far as Fin-Tech is concerned. In the massive field of business, mobile money transaction security is a great challenge for researchers. The user authentication schemes restrict the ability to enforce the authentication before the account can access and operate. Although authentication factors provide greater security than a simple static password, financial transactions have potential drawbacks because cybercrime expands the opportunities for fraudsters. The most common enterprise challenge is mobile-based user authentication during transactions, which addresses the security issues …

Development Of An Explainable Artificial Intelligence Model For Asian Vascular Wound Images, Zhiwen Joseph Lo, Malcolm Han Wen Mak, Shanying Liang, Yam Meng Chan, Cheng Cheng Goh, Tina Peiting Lai, Audrey Hui Min Tan, Patrick Thng, Patrick Thng, Tillman Weyde, Sylvia Smit

Research Collection School Of Computing and Information Systems

Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into …

Leveraging Generative Agents: Autonomous Ai With Simulated Personas For Interactive Simulacra And Collaborative Research, James Hutson, Jay Ratican

Faculty Scholarship

The advent of large language models (LLMs) and AI learning have fundamentally reshaped the research landscape, paving the way for novel problem-solving approaches. This paper introduces a unique framework that leverages the capabilities of autonomous AI agents with simulated personas to drive collaborative research in groundbreaking ways. Inspired by a recent study of autonomous agents mirroring human behavior, this concept encourages the use of a cadre of AI agents, each possessing specialized expertise for collective endeavors. By replicating human diversity in teamwork, this approach targets complex and hitherto unsolvable issues. The key to this strategy is persona and emotional simulation, …

Leveraging Artificial Intelligence For Team Cognition In Human-Ai Teams, Beau Schelble

All Dissertations

Advances in artificial intelligence (AI) technologies have enabled AI to be applied across a wide variety of new fields like cryptography, art, and data analysis. Several of these fields are social in nature, including decision-making and teaming, which introduces a new set of challenges for AI research. While each of these fields has its unique challenges, the area of human-AI teaming is beset with many that center around the expectations and abilities of AI teammates. One such challenge is understanding team cognition in these human-AI teams and AI teammates' ability to contribute towards, support, and encourage it. Team cognition is …

Use Of Ai To Recreate And Repatriate Lost, Destroyed Or Stolen Paintings: The 1785 Parisian Salon Case Study, Charles E. O'Brien, James Hutson, Trent Olsen, Jay Ratican

Faculty Scholarship

This study investigates the efficacy of artificial intelligence (AI) in the field of artwork restoration, focusing on lost, stolen, or destroyed artworks. Employing a dual approach that combines traditional manual restoration techniques with advanced generative AI tools, the research centers on a case study of the 1785 Parisian Salon. It specifically examines the reconstitution of Antoine François Callet's painting, Achilles Dragging the Body of Hector, unveiled alongside Jacques-Louis David's Oath of the Horatii. The study utilizes Easy Diffusion and Stable Diffusion 2.1 technologies for inpainting and colorization processes. These AI tools are employed in concert with manual restoration practices to …

Essence As Algorithm: Public Perceptions Of Ai-Powered Avatars Of Real People, James Hutson, Jay Ratican, Colleen Biri

Faculty Scholarship

This paper investigates the intersection of generative AI, Large Language Models (LLM), and robotics. Exemplified by systems like ChatGPT and technological marvels such as Ameca the Robot, the combination of technologies will allow humans to transcend the limitations of death. Through digital necromancy, a practice encompassing the technological resurrection of deceased individuals, the ability to not only passively see recordings of loved ones but to interact with them is made possible, leading to ethical and psychological considerations. Therefore, examining these trends extends into the motives underlying engagement with both incorporeal and corporeal reproductions of individuals, with reasons ranging from memory …