Computer Sciences Commons^™

Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker

Research outputs 2022 to 2026

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and …

Accuracy Of Machine Learning To Predict The Outcomes Of Shoulder Arthroplasty: A Systematic Review, Amir H. Karimi, Joshua Langberg, Ajith Malige, Omar Rahman, Joseph A. Abboud, Michael A. Stone

Department of Orthopaedic Surgery Faculty Papers

BACKGROUND: Artificial intelligence (AI) uses computer systems to simulate cognitive capacities to accomplish goals like problem-solving and decision-making. Machine learning (ML), a branch of AI, makes algorithms find connections between preset variables, thereby producing prediction models. ML can aid shoulder surgeons in determining which patients may be susceptible to worse outcomes and complications following shoulder arthroplasty (SA) and align patient expectations following SA. However, limited literature is available on ML utilization in total shoulder arthroplasty (TSA) and reverse TSA.

METHODS: A systematic literature review in accordance with PRISMA guidelines was performed to identify primary research articles evaluating ML's ability to …

Can Ai Become An Information Literacy Ally? A Survey Of Library Instructor Perspectives On Chatgpt, Melissa S. Del Castillo, Hope Y. Kelly

Works of the FIU Libraries

Libraries can play a role in navigating the AI era by integrating these tools into information literacy (IL) programs. To implement generative AI tools like ChatGPT effectively, it is important to understand the attitudes of library professionals involved in IL instruction toward this tool and their intention to use it for instruction. This study explored perceptions of ChatGPT using survey data that included acceptance factors and potential uses derived from the emerging literature. While some librarians saw potential, others found it too unreliable to be useful; yet the vast majority imagined utilizing the tool in the future.

On The Feasibility Of Simple Transformer For Dynamic Graph Modeling, Yuxia Wu, Yuan Fang, Lizi Liao

Research Collection School Of Computing and Information Systems

Dynamic graph modeling is crucial for understanding complex structures in web graphs, spanning applications in social networks, recommender systems, and more. Most existing methods primarily emphasize structural dependencies and their temporal changes. However, these approaches often overlook detailed temporal aspects or struggle with long-term dependencies. Furthermore, many solutions overly complicate the process by emphasizing intricate module designs to capture dynamic evolutions. In this work, we harness the strength of the Transformer’s self-attention mechanism, known for adeptly handling long-range dependencies in sequence modeling. Our approach offers a simple Transformer model, called SimpleDyG, tailored for dynamic graph modeling without complex modifications. We …

Academic Literature Review In Age Of Ai And Large Language Models​, Aaron Tay

Research Collection Library

Explore the evolving landscape of academic research with a focus on open data and AI advancements, particularly in natural language processing. Join us for a practical presentation on leveraging emerging tools for literature review. Discover platforms like Connected Papers, ResearchRabbit, and Litmaps, offering paper exploration and recommendations based on initial 'seed papers.' Dive into AI-enhanced search engines like Elicit, Scispace, Semantic Scholar, and Scite.ai, powered by Large Language Models such as BERT and GPT. Learn about the latest developments, strengths, and weaknesses of these tools, and how they reshape literature review methods, from tool selection to query input techniques.

Ai And Advocacy: Maximizing Potential, Minimizing Risk, Matthew Salzano, Nicholas Fung, Ada Lin, Sofia Marchetta, Faith Colombo, Kaylah Davis, John Flynn, Carlos Fuentes, Fion Li, Malar Paavi Muthukumaran, Angelica Paramoshin, Chrisanne Pearce, Vianney Ramos, Charles St. Hilaire, Xi Zheng, Wei Zhuang

School of Communication and Journalism Faculty Publications

New Generative AI tools are revolutionizing writing and communication. This report focuses on AI and advocacy, the act of influencing public policy and resource allocation decisions within political, economic, and social systems and institutions. This report identifies three major opportunities and accompanying risks, plus one strong recommendation for advocates considering using AI. We argue that AI can be useful for advocates, but they must be careful to center human judgment and avoid risks that could distract from their important work or even contribute to societal harms.

Simulacra And Historical Fidelity In Digital Recreation Of Lost Cultural Heritage: Reconstituting Period Materialities For The Period Eye, Trent Olsen, James Hutson, Charles O'Brien, Jeremiah Ratican

Faculty Scholarship

The advancement of digital technologies in art history has opened avenues for reconstructing lost or damaged cultural heritage, a need highlighted by the deteriorated state of many artworks from the 1785 Salon. Grounded in the concept of the “Period Eye” by art historian Michael Baxandall, which emphasizes understanding artworks within their original historical and cultural contexts, this study proposes a subfield focused on Reconstituting Period Materialities for the Period Eye. This methodology bridges comprehensive historical research with generative visual artificial intelligence (AI) technologies, facilitating the creation and immersive virtual reality viewing of artworks. Beyond mere visual replication, the approach aims …

Advancing Sentiment Analysis Through Emotionally-Agnostic Text Mining In Large Language Models (Llms), Jay Ratican, James Hutson

Faculty Scholarship

The conventional methodology for sentiment analysis within large language models (LLMs) has predominantly drawn upon human emotional frameworks, incorporating physiological cues that are inherently absent in text-only communication. This research proposes a paradigm shift towards an emotionallyagnostic approach to sentiment analysis in LLMs, which concentrates on purely textual expressions of sentiment, circumventing the confounding effects of human physiological responses. The aim is to refine sentiment analysis algorithms to discern and generate emotionally congruent responses strictly from text-based cues. This study presents a comprehensive framework for an emotionally-agnostic sentiment analysis model that systematically excludes physiological indicators whilst maintaining the analytical depth …

Vr Circuit Simulation With Advanced Visualization For Enhancing Comprehension In Electrical Engineering, Elliott Wolbach

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

As technology advances, the field of electrical and computer engineering continuously demands innovative tools and methodologies to facilitate effective learning and comprehension of fundamental concepts. Through a comprehensive literature review, it was discovered that there was a gap in the current research on using VR technology to effectively visualize and comprehend non-observable electrical characteristics of electronic circuits. This thesis explores the integration of Virtual Reality (VR) technology and real-time electronic circuit simulation with enhanced visualization of non-observable concepts such as voltage distribution and current flow within these circuits. The primary objective is to develop an immersive educational platform that makes …

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …

Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang

Research Collection School Of Computing and Information Systems

Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. …

The Role Of Student Motivation In Integrating Ai Into Web Design Education: A Longitudinal Study, Jason Lively, James Hutson

Faculty Scholarship

Amidst the current wave studies of artificial intelligence (AI) in education, this longitudinal case study, spanning Spring 2023 to Spring 2024, delves into the integration of AI in the UI/UX web design classroom. By introducing both text-based and image-based AI tools to students with varying levels of skill in introductory web design and user experience (UX) courses, the study observed a significant enhancement in student creative capabilities and project outcomes. The utilization of text-based generators markedly improved writing efficiency and coding, while image-based tools facilitated better ideation and color selection. These findings underscore the potential to augment traditional educational methods, …

Sliding Markov Decision Processes For Dynamic Task Planning On Uncrewed Aerial Vehicles, Trent Wiens

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

Mission and flight planning problems for uncrewed aircraft systems (UASs) are typically large and complex in space and computational requirements. With enough time and computing resources, some of these problems may be solvable offline and then executed during flight. In dynamic or uncertain environments, however, the mission may require online adaptation and replanning. In this work, we will discuss methods of creating MDPs for online applications, and a method of using a sliding resolution and receding horizon approach to build and solve Markov Decision Processes (MDPs) in practical planing applications for UASs. In this strategy, called a Sliding Markov Decision …

Learning Adversarial Semantic Embeddings For Zero-Shot Recognition In Open Worlds, Tianqi Li, Guansong Pang, Xiao Bai, Jin Zheng, Lei Zhou, Xin Ning

Research Collection School Of Computing and Information Systems

Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-world scenarios where there are test samples of unknown classes for which neither samples (e.g., images) nor their side semantic information is known during training. Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes. To tackle this combined ZSL and OSR problem, we consider the case of “Zero-Shot Open-Set Recognition” (ZS-OSR), where a model is trained under the ZSL …

Enhancing Visual Grounding In Vision-Language Pre-Training With Position-Guided Text Prompts, Alex Jinpeng Wang, Pan Zhou, Mike Zheng Shou, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Vision-Language Pre-Training (VLP) has demonstrated remarkable potential in aligning image and text pairs, paving the way for a wide range of cross-modal learning tasks. Nevertheless, we have observed that VLP models often fall short in terms of visual grounding and localization capabilities, which are crucial for many downstream tasks, such as visual reasoning. In response, we introduce a novel Position-guided Text Prompt ( PTP ) paradigm to bolster the visual grounding abilities of cross-modal models trained with VLP. In the VLP phase, PTP divides an image into N x N blocks and employs a widely-used object detector to identify objects …

Artificial Intelligence And Film: A Journey In Public Perception From 1960 To The Present Day, Kayla Anderson, Andrew Roggeman, Joseph Fuller

Celebrating Scholarship and Creativity Day (2018-)

An analysis of accomplishments in film from the 1960s-2020s that feature Artificial Intelligence to give a full picture of how public perception has changed towards these technologies over time, supplemented by historical and technological context.

Using Ai Chatbots As Ideation Machines, Brett Hawley, Naomi Hollans

Student Works

The team analyzed 3 popular chatbots and found that none of them could consistently produce idea-centered essay help responses. The team approached them with 3 separate prompts, one from each of three academic subjects. The team analyzed how each chatbot adapted to the addition of personal information from the “student” and to the phrase, “what are some ideas that could help me get started?” The goal with each interaction was to receive a response in which the chatbot did not produce any pre-written content. Overall, the team’s research did not suggest that AI is fully reliable as an ideation tool.

The Borderline Between Beneficial And Dishonest Ai: A Technical Report, Seth Richards, Katherine Shell, Seth Wright

Student Works

Artificial Intelligence (AI) has been used since 1950 but it was largely overlooked by the public until 2022. Current discussions about AI center around academic integrity. This report seeks to understand if AI can be handled, used, or accepted in Lipscomb’s academic environment as a beneficial aid to writing and research, without actively doing these tasks for an individual. Generative AI is a neural network, which enables it to receive input, gather information from a database of existing content, and create new content [2]. Due to the nature of generative AI, its beneficial contributions to academia are extremely limited.

Individualized Learning As An Ai Tool: A Technical Report, Petsimnan Blessing Dayit, Kasen Holt, Nuala Roper

Student Works

The purpose of the report’s research is to test and analyze whether Artificial Intelligence (AI) platforms can be used as beneficial tools for individualized learning at Lipscomb University without violating the Academic Integrity Policy. The methods section evaluates AI on the scopes of accuracy, analytical thinking, and adaptability. The results demonstrated how each platform responded to the prompts within the lines of the scope. The answers they gave were accurate, detailed, and contained various adaptations to make explanations clearer for the user. The team concluded that AI can be used at Lipscomb as a beneficial tool for students in their …

Immersive Japanese Language Learning Web Application Using Spaced Repetition, Active Recall, And An Artificial Intelligent Conversational Chat Agent Both In Voice And In Text, Marc Butler

MS in Computer Science Project Reports

In the last two decades various human language learning applications, spaced repetition software, online dictionaries, and artificial intelligent chat agents have been developed. However, there is no solution to cohesively combine these technologies into a comprehensive language learning application including skills such as speaking, typing, listening, and reading. Our contribution is to provide an immersive language learning web application to the end user which combines spaced repetition, a study technique used to review information at systematic intervals, and active recall, the process of purposely retrieving information from memory during a review session, with an artificial intelligent conversational chat agent both …

Artificial Intelligence Could Probably Write This Essay Better Than Me, Claire Martino

Augustana Center for the Study of Ethics Essay Contest

No abstract provided.

Cardiogpt: An Ecg Interpretation Generation Model, Guohua Fu, Jianwei Zheng, Islam Abudayyeh, Chizobam Ani, Cyril Rakovski, Louis Ehwerhemuepha, Hongxia Lu, Yongjuan Guo, Shenglin Liu, Huimin Chu, Bing Yang

Mathematics, Physics, and Computer Science Faculty Articles and Research

Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai …

A Computer Vision Solution To Cross-Cultural Food Image Classification And Nutrition Logging​, Rohan Sethi, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

The US is a culturally and ethnically diverse country, and with this diversity comes a myriad of cuisines and eating habits that expand well beyond that of western culture. Each of these meals have their own good and bad effects when it comes to the nutritional value and its potential impact on human health. Thus, there is a greater need for people to be able to access the nutritional profile of their diverse daily meals and better manage their health. A revolutionary solution to democratize food image classification and nutritional logging is using deep learning to extract that information from …

Visualizing Routes With Ai-Discovered Street-View Patterns, Tsung Heng Wu, Md Amiruzzaman, Ye Zhao, Deepshikha Bhati, Jing Yang

Computer Science Faculty Publications

Street-level visual appearances play an important role in studying social systems, such as understanding the built environment, driving routes, and associated social and economic factors. It has not been integrated into a typical geographical visualization interface (e.g., map services) for planning driving routes. In this article, we study this new visualization task with several new contributions. First, we experiment with a set of AI techniques and propose a solution of using semantic latent vectors for quantifying visual appearance features. Second, we calculate image similarities among a large set of street-view images and then discover spatial imagery patterns. Third, we integrate …

Artificial General Intelligence And The Mind-Body Problem: Exploring The Computability Of Simulated Human Intelligence In Light Of The Immaterial Mind, Caleb Parks

Senior Honors Theses

In this thesis I explore whether achieving artificial general intelligence (AGI) through simulating the human brain is theoretically possible. Because of the scientific community’s predominantly physicalist outlook on the mind-body problem, AGI research may be limited by erroneous foundational presuppositions. Arguments from linguistics and mathematics demonstrate that the human intellect is partially immaterial, opening the door for novel analysis of the mind’s simulability. I categorize mind-body problem philosophies in a manner relevant to computer science based upon state transitions, and determine their ramifications on mind-simulation. Finally, I demonstrate how classical architectures cannot resolve so-called Gödel statements, discuss why this inability …

Smu Libraries – An Enabling Partner In Ai Information Literacy, Samantha Seah, Zhe Benedict Yeo, Lukas Tschopp

Research Collection Library

SMU Libraries plays a pivotal role in advancing AI information literacy within the larger need for digital literacy skills in the SMU community. In this presentation, participants will get an overview of SMU Libraries' engagement and partnerships with the academic community and will showcase initiatives and resources supporting AI literacy. This includes a discussion of insights from the scholarly literature, research findings and critical perspectives to inform teaching and learning practices related to AI. Speakers will share SMU Libraries’ contributions towards awareness and adoption of AI through a portfolio of successful collaborations and initiatives with partners and stakeholders within and …

Rethinking Plagiarism In The Era Of Generative Ai, James Hutson

Faculty Scholarship

The emergence of generative artificial intelligence (AI) technologies, such as large language models (LLMs) like ChatGPT, has precipitated a paradigm shift in the realms of academic writing, plagiarism, and intellectual property. This article explores the evolving landscape of English composition courses, traditionally designed to develop critical thinking through writing. As AI becomes increasingly integrated into the academic sphere, it necessitates a reevaluation of originality in writing, the purpose of learning research and writing, and the frameworks governing intellectual property (IP) and plagiarism. The paper commences with a statistical analysis contrasting the actual use of LLMs in academic dishonesty with educator …

A New Canvas Of Learning: Enhancing Formal Analysis Skills In Ap Art History Through Ai-Generated Islamic Art, Krista Carpino, James Hutson

Faculty Scholarship

This study explores the use of AI art generators to enhance formal analysis skills in AP Art History students, with a focus on Islamic Art and Architecture. Students, often entering the course with high academic achievements, find the unique challenge of articulating detailed visual descriptions of artworks. The study’s approach involves using AI image-generation websites, like wepik.com, where students create AI images resembling Islamic artworks studied in class. This method aims to refine their descriptive skills, focusing on visual evidence rather than relying on identifying details. The choice of Islamic Art, markedly different from other historical periods covered in the …

Navigating The Maze: The Role Of Pre-Enrollment Socio-Cultural And Institutional Factors In Higher Education In The Age Of Ai, Emily Barnes, James Hutson

Faculty Scholarship

This article explores the complex interplay between pre-enrollment socio-cultural and institutional factors and their impact on the higher education landscape. It challenges traditional metrics of academic achievement, presenting a nuanced perspective on student success that emphasizes the importance of socio-economic backgrounds, cultural capital, and K-12 education quality. The analysis extends to the significant role of institutional attributes in shaping student readiness and decision-making processes. The study advocates for the integration of artificial intelligence (AI)-driven assessments by higher education institutions to cater to the diverse needs of the student body, promoting an inclusive and supportive learning environment. Anchored in an extensive …

Multi-Aspect Rule-Based Ai: Methods, Taxonomy, Challenges And Directions Towards Automation, Intelligence And Transparent Cybersecurity Modeling For Critical Infrastructures, Iqbal H. Sarker, Helge Janicke, Mohamed A. Ferrag, Alsharif Abuadbba

Research outputs 2022 to 2026

Critical infrastructure (CI) typically refers to the essential physical and virtual systems, assets, and services that are vital for the functioning and well-being of a society, economy, or nation. However, the rapid proliferation and dynamism of today's cyber threats in digital environments may disrupt CI functionalities, which would have a debilitating impact on public safety, economic stability, and national security. This has led to much interest in effective cybersecurity solutions regarding automation and intelligent decision-making, where AI-based modeling is potentially significant. In this paper, we take into account “Rule-based AI” rather than other black-box solutions since model transparency, i.e., human …