Hiccups And Hallucinations: Critically Engaging Ai In The Design Classroom,
2024
Clemson University
Hiccups And Hallucinations: Critically Engaging Ai In The Design Classroom, Drew Sisk
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
Ghostwriter To Co-Author: Helping Students Leverage Ai In The Classroom,
2024
Clemson University
Ghostwriter To Co-Author: Helping Students Leverage Ai In The Classroom, Ishani Banerji
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
Navigating Anxiety And Activity: Generative Ai And Writing Support,
2024
Clemson University
Navigating Anxiety And Activity: Generative Ai And Writing Support, Chelsea J. Murdock
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
Academic Ethics In Ai-Assisted Writing: A Writing Center-Informed Approach,
2024
Clemson University
Academic Ethics In Ai-Assisted Writing: A Writing Center-Informed Approach, John Falter
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
Using Generative Artificial Intelligence For Engaged Student Learning,
2024
Clemson University
Using Generative Artificial Intelligence For Engaged Student Learning, Janice G. Lanham, Charlotte Branyon
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
Optimizing Ideation And Digital Prepress Workflows With Ai Integration,
2024
Clemson University
Optimizing Ideation And Digital Prepress Workflows With Ai Integration, Carl N. Blue
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
The Use And Misuse Of Generative Ai For Photos And Imagery,
2024
Clemson University
The Use And Misuse Of Generative Ai For Photos And Imagery, Erica B. Walker
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
Requiring Students To Integrate Chatgptinto Course Assignments,
2024
Clemson University
Requiring Students To Integrate Chatgptinto Course Assignments, Mark Small, Venera Balidemaj
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
Ai-Enhanced Education: Fostering Creativity, Efficiency, And Future-Ready Skills,
2024
Clemson University
Ai-Enhanced Education: Fostering Creativity, Efficiency, And Future-Ready Skills, Rodger Eugene Bishop
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
Using Ai In The Teacher Preparation Programs And Social Studies Classrooms,
2024
Clemson University
Using Ai In The Teacher Preparation Programs And Social Studies Classrooms, Brandon Beck
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
Language Portraits: A Space To Explore Identities In A Graduate Course,
2024
Clemson University
Language Portraits: A Space To Explore Identities In A Graduate Course, Hazel Vega
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
Critical Ai Engagement: Crafting Assignments That Encourage Productive Engagement With Ai,
2024
Clemson University
Critical Ai Engagement: Crafting Assignments That Encourage Productive Engagement With Ai, Carl Ehrett
Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI
No abstract provided.
Challenging Others When Posting Misinformation: A Uk Vs. Arab Cross-Cultural Comparison On The Perception Of Negative Consequences And Injunctive Norms,
2024
Hamad Bin Khalifa University, College of Science and Engineering
Challenging Others When Posting Misinformation: A Uk Vs. Arab Cross-Cultural Comparison On The Perception Of Negative Consequences And Injunctive Norms, Muaadh Noman, Selin Gurgun, Keith Phalp, Preslav Nakov, Raian Ali
Natural Language Processing Faculty Publications
This study investigates the factors influencing the willingness to challenge misinformation on social media across two cultural contexts, the United Kingdom (UK) and Arab countries. A total of 462 participants completed an online survey (250 UK, 212 Arabs). The analysis revealed that three types of negative consequences (relationship cost, negative impact on the person being challenged, futility) and also injunctive norms influence the willingness to challenge misinformation. Cross-cultural comparisons using t-tests showed significant differences between the UK and the Arab countries in all factors except the injunctive norms. Multiple regression analyses identified differences between the UK and Arab participants concerning …
Poster, Performed: Understanding Public Opinions Of Authorship In Generative Artificial Intelligence Models Via Analogy,
2024
Dartmouth College
Poster, Performed: Understanding Public Opinions Of Authorship In Generative Artificial Intelligence Models Via Analogy, Wylie Z. Kasai
Dartmouth College Master’s Theses
Over the last decade, generative artificial intelligence models have advanced significantly and provided the public with several tools to create new works of art. However, the true authorship of these works has been debated due to their training on web-scraped data. Serving as an analogy to these larger models, Poster, Performed is an interactive artificial intelligence exhibition project that uses image assets submitted by the public to create poster compositions with custom image processing algorithms. During the course of a four-day exhibition, visitors were asked to identify the exhibition’s primary artist from five options: (1) participants who submitted image assets, …
Automatic Classification Of Activities In Classroom Videos,
2024
University at Albany, State University of New York
Automatic Classification Of Activities In Classroom Videos, Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton
VMASC Publications
Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed …
Nonuniform Sampling-Based Breast Cancer Classification,
2024
University of Kentucky
Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso
Theses and Dissertations--Electrical and Computer Engineering
The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …
Conversational Localization: Indoor Human Localization Through Intelligent Conversation,
2024
Singapore Management University
Conversational Localization: Indoor Human Localization Through Intelligent Conversation, Sheshadri Smitha, Kotaro Hara
Research Collection School Of Computing and Information Systems
We propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that …
Active Discovering New Slots For Task-Oriented Conversation,
2024
Singapore Management University
Active Discovering New Slots For Task-Oriented Conversation, Yuxia Wu, Tianhao Dai, Zhedong Zheng, Lizi Liao
Research Collection School Of Computing and Information Systems
Existing task-oriented conversational systems heavily rely on domain ontologies with pre-defined slots and candidate values. In practical settings, these prerequisites are hard to meet, due to the emerging new user requirements and ever-changing scenarios. To mitigate these issues for better interaction performance, there are efforts working towards detecting out-of-vocabulary values or discovering new slots under unsupervised or semi-supervised learning paradigms. However, overemphasizing on the conversation data patterns alone induces these methods to yield noisy and arbitrary slot results. To facilitate the pragmatic utility, real-world systems tend to provide a stringent amount of human labeling quota, which offers an authoritative way …
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 …
Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network,
2024
University of Virginia
Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall
Civil & Environmental Engineering Faculty Publications
This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. …
