A Point Of Singularity For Technology And Engineering Education,
2024
Old Dominion University
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 Promise And Pitfalls Of Genai In Education,
2024
Old Dominion University
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,
2024
Old Dominion University
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 …
Ai-Designed Clothing And Perceived Values: What Can Move Consumers' Minds With The Ai-Designed Clothing?,
2024
Old Dominion University
Ai-Designed Clothing And Perceived Values: What Can Move Consumers' Minds With The Ai-Designed Clothing?, Dooyoung Choi, Ha Kyung Lee
STEMPS Faculty Publications
This study investigates the perceived values of AI-designed clothing (quality, emotion, ease) and their impact on willingness to pay (WTP) and word-of-mouth (WOM), with the moderating effect of gender differences. A total of 314 respondents completed the survey via MTurk. Participants watched a video clip demonstrating how an AI system creates various clothing designs by altering garment elements (e.g., style, size). After watching the video clip, they were asked to answer a series of questions about the AI-designed clothing and themselves. The collected data were analyzed using AMOS 26.0. Results showed that, for male and female consumers, the quality value …
Language Models For Rare Disease Information Extraction: Empirical Insights And Model Comparisons,
2024
University of Kentucky
Language Models For Rare Disease Information Extraction: Empirical Insights And Model Comparisons, Shashank Gupta
Theses and Dissertations--Computer Science
End-to-end relation extraction (E2ERE) is a crucial task in natural language processing (NLP) that involves identifying and classifying semantic relationships between entities in text. This thesis compares three paradigms for end-to-end relation extraction (E2ERE) in biomedicine, focusing on rare diseases with discontinuous and nested entities. We evaluate Named Entity Recognition (NER) to Relation Extraction (RE) pipelines, sequence-to-sequence models, and generative pre-trained transformer (GPT) models using the RareDis information extraction dataset. Our findings indicate that pipeline models are the most effective, followed closely by sequence-to-sequence models. GPT models, despite having eight times as many parameters, perform worse than sequence-to-sequence models and …
Transfer Learning-Enhanced Transformer For Virtual Sensing Applications In Resistance Spot Welding,
2024
University of Kentucky
Transfer Learning-Enhanced Transformer For Virtual Sensing Applications In Resistance Spot Welding, Ethan York
Theses and Dissertations--Mechanical and Aerospace Engineering
Resistance spot welding is a crucial manufacturing process used across a wide range of industries for permanently joining metal components. Characterized by its applications in the automotive industry, resistance spot welding is valued for its speed, efficiency, and relatively low cost to set up and maintain. The process involves running a pulse of electrical current through two metal sheets to liquify the material and create a permanent bond. The process complexity necessitates precise control over various parameters to ensure acceptable results, emphasizing the importance of quality control. Because there are no low-cost and non-invasive techniques to inspect welds, strategies utilizing …
Artificial Intelligence Enabled Machinery Fault Detection And Diagnosis Using Vibro-Acoustic Signals,
2024
University of Kentucky
Artificial Intelligence Enabled Machinery Fault Detection And Diagnosis Using Vibro-Acoustic Signals, Srinivasa Rao Ippili
Theses and Dissertations--Mechanical and Aerospace Engineering
In various industries, the early detection of faults in rotating machinery is crucial to prevent system failures and ensure customer satisfaction. Typically, vibration measurement and diagnosis are employed for fault detection, but this process faces challenges in automation due to the complexity of installing and maintaining accelerometers, particularly in end-of-line quality control or pre-installed machinery health assessments. Acoustic signals, as a form of mechanical wave, offer an alternative for monitoring machinery while in operation. Unlike accelerometers, acoustic transducers are non-contact and easy to set up, enabling real-time data collection without interrupting equipment operation. However, utilizing acoustic signals in manufacturing poses …
Fostering Trust In Artificial Intelligence In Commercial Aviation: An Exploratory Study,
2024
Embry-Riddle Aeronautical University
Fostering Trust In Artificial Intelligence In Commercial Aviation: An Exploratory Study, Leila Halawi, Mark Miller, Sam Holley
Publications
Artificial intelligence (AI) is a transformative force, compelling industries to adapt their operations, management systems, and workforce capabilities. The aviation sector finds itself at the forefront of this transformation, confronted with the imperative to navigate the complex dynamics of trust amidst AI's integration. Through a comprehensive survey involving 310 professionals from across the US commercial aviation sector, the research aims to shed light on the trust construct. The exploratory study provides critical insights for strategic AI adoption within the industry. A crosstabulation explored how employee trust in AI for decision-making differed across various demographic groups. In addition, a onesample T-test …
Understanding Data Through The Lens Of Topology,
2024
Thayer School of Engineering
Understanding Data Through The Lens Of Topology, Quang Truong
Dartmouth College Master’s Theses
Machine learning depends on the ability to learn insightful representations from data. Topology of data offers a rich source of information for constructing such representations, yet its potential remains under-explored by the broader machine learning community. This work investigates the power of applied topology through two complementary projects: Topological Message Passing with Path Complexes and Persistent Homology for Anomaly Detection. In the first project, we extend the topological message passing framework by introducing a novel approach centered on path complexes, where paths form the fundamental building blocks. Our theoretical analysis demonstrates that this model generalizes existing topological deep learning and …
Locating Liability For Medical Ai,
2024
University of Michigan Law School
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 …
Use Of Artificial Intelligence In Drug Development,
2024
University of Michigan Law School
Use Of Artificial Intelligence In Drug Development, Louise C. Druedahl, Nicholson Price, Timo Minssen, Dipl Jur, Ameet Sarpatwari
Articles
Considerable focus has been placed on the health care applications of artificial intelligence (AI). Already, machine learning, a subset of AI that involves “the use of data and algorithms to imitate the way that humans learn” has been used to predict diseases, while AI-powered smartphone apps have been developed to promote mental health and weight loss. Owing in part to such successes, the market for AI in health care has been forecasted to increase more than 1000% between 2022 and 2029, from $13.8 billion to $164.1 billion. One area of substantial promise is drug development, which is poised to benefit …
Implementing Unmanned Aerial Vehicles To Collect Human Gait Data At Distance And Altitude For Identification And Re-Identification,
2024
West Virginia University
Implementing Unmanned Aerial Vehicles To Collect Human Gait Data At Distance And Altitude For Identification And Re-Identification, Donn E. Bartram
Graduate Theses, Dissertations, and Problem Reports (ETD)
Gait patterns are a class of biometric information pertaining to the way a person moves and poses. Gait information is unique to each person and can be used to identify and reidentify people. Historically, this task has been achieved through the use of multiple ground-based imaging sensors. However, as Unmanned Aerial Vehicles (UAVs) advance, they present the opportunity to evolve the process of persons identification and re-identification. Collecting human gait data using UAVs at distances ranging from 20m to 500m and altitudes ranging from 0m to 120m is a challenging task. The current biometric data collection methods, primarily designed for …
The Integration Of Neuromorphic Computing In Autonomous Robotic Systems,
2024
Michigan Technological University
The Integration Of Neuromorphic Computing In Autonomous Robotic Systems, Md Abu Bakr Siddique
Dissertations, Master's Theses and Master's Reports
Deep Neural Networks (DNNs) have come a long way in many cognitive tasks by training on large, labeled datasets. However, this method has problems in places with limited data and energy, like when planetary robots are used or when edge computing is used [1]. In contrast to this data-heavy approach, animals demonstrate an innate ability to learn by communicating with their environment and forming associative memories among events and entities, a process known as associative learning [2-4]. For instance, rats in a T-maze learn to associate different stimuli with outcomes through exploration without needing labeled data [5]. This learning paradigm …
Wave Energy Converter Wave Force Prediction Using A Neural Network,
2024
Michigan Technological University
Wave Energy Converter Wave Force Prediction Using A Neural Network, Morgan Kline
Dissertations, Master's Theses and Master's Reports
Due to the unpredictable nature of large bodies of water, wave energy can be a difficult renewable resource to rely on. One way to make Wave Energy Converters (WECs) more efficient is to apply a control strategy. In many control solutions, it is assumed that the wave excitation force is known into the future. In many instances, especially with complex waveforms, this is simply not the case. Simulation studies have shown the promise of wave force prediction using neural networks. This study demonstrates this experimentally and aims to characterize the important factors when designing such a network. Several wave elevation …
Programming By Voice,
2024
Michigan Technological University
Programming By Voice, Sadia Nowrin
Dissertations, Master's Theses and Master's Reports
Programmers typically rely on a keyboard and mouse for input, which poses significant challenges for individuals with motor impairments, limiting their ability
to effectively input programs. Voice-based programming offers a promising alternative,
enabling a more inclusive and accessible programming environment. Insights from interviews with motor-impaired programmers revealed that memorizing unnatural commands in existing voice-based programming systems led to frustration. In this work, we explore how programmers naturally speak a single line of code and present a comprehensive methodology for a voice programming system aimed at making programming more accessible for diverse users. To achieve this, we adopted a two-step pipeline. …
Tension Control And Interproximation Techniques Forshape Design And Rgb-Depth Segmentation Reconstruction And Modeling,
2024
University of Kentucky
Tension Control And Interproximation Techniques Forshape Design And Rgb-Depth Segmentation Reconstruction And Modeling, Anastasia Kazadi
Theses and Dissertations--Computer Science
Human eyes possess remarkable capabilities to perceive and interpret a wealth of information about our environment; from discerning colors and depths to identifying object boundaries and navigating obstacles, our eyes serve as invaluable guides in our daily lives. Ongoing research in the fields of computer vision and computer graphics continuously explore the ways to replicate extraordinary human vision abilities in order to develop systems and frameworks which would enable computers to capture, analyze, and act upon discerned information. In this context, this dissertation seeks to investigate and automate various shape control and data processing techniques for 3D modeling and shape …
Finding Hierarchies To Improve Learning In Hierarchical Reinforcement Learning,
2024
University of Kentucky
Finding Hierarchies To Improve Learning In Hierarchical Reinforcement Learning, Roy Mobley
Theses and Dissertations--Computer Science
Reinforcement Learning (RL) is an approach to allowing computer agents to try and learn how to solve problems by learning what actions are best to take in a given situation. RL is effective for learning what to do in an environment, but as the problem grows larger, the amount of information needed grows exponentially, making RL less effective on complex problems. A big challenge, often called the curse of dimensionality, is that the number of states and possible number of actions in an environment can grow too large to sufficiently test every possible combination of state and action. One method …
Integrating Art And Ai: Evaluating The Educational Impact Of Ai Tools In Digital Art History Learning,
2024
Lindenwood University
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 …
Intelligent Capabilities Of Traditional Knowledge Organization Methods,
2024
1.School of Information Management, Nanjing University, Nanjing 210023
2.Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023
Intelligent Capabilities Of Traditional Knowledge Organization Methods, Xinning Su
Journal of Scientific Information Research
[Purpose/significance]By analyzing the system and rules of traditional knowledge organization methods, the intelligent capabilities of traditional knowledge organization methods are refined and integrated into artificial intelligence（AI） technology, to enhance the precision and efficiency of AI in information processing. [Method/process]This paper reviews the development of knowledge organization and analyses the inherit structure and mechanisms of traditional knowledge organization methods. [Result/conclusion]Research suggests that over centuries of development and evolution, knowledge organization has gained the ability to reflect knowledge systems and disciplinary systems across different disciplines from diverse perspectives, establish semantic relations from diverse knowledge associations, and associate and integrate knowledge of different …
Cyclegan-Gradient Penalty For Enhancing Android Adversarial Malware Detection In Gray Box Setting,
2024
University of Dschang
Cyclegan-Gradient Penalty For Enhancing Android Adversarial Malware Detection In Gray Box Setting, Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua
VMASC Publications
Adversarial attacks pose significant threats to Android malware detection by undermining the effectiveness of machine learning-based systems. The rapid increase in Android apps complicates the management of malicious software that can compromise user defense solutions. Many current Android defense techniques rely on deep learning methods. Malicious users exploit GAN-based attacks to achieve adversarial attack transferability and deceive target models by crafting adversarial examples based on known models. We propose a new model based on a Cycle Generative Adversarial Network (CycleGAN) to detect GAN-based attacks. This model incorporates a gradient penalty to enhance the detection rate of the target model. Our …
