Artificial Intelligence and Robotics Commons^™

Path Planning For Mobile Robots Based On Improved Rrt-Connect And Dwa Fusion, Yi Luo, Jia Deng

Journal of System Simulation

Abstract: To improve the efficiency and quality of dynamic path planning for mobile robots in complex environments, this paper proposed a path planning algorithm that combined an improved RRT-connect with the DWA. Two expanding random trees were introduced for alternating expansion, and a dynamically restricted sampling area was set to reduce the randomness of the sampling process while ensuring the probability completeness of the algorithm. A target bias adaptive step size strategy was employed to enhance the target orientation of the random tree expansion process. A greedy strategy was adopted to prune redundant nodes in the path and smooth the …

A Novel Joint Training Simulation Evaluation Framework And Its Key Techniques, Rusheng Ju, Dongdong Chen, Yunxiu Zeng, Jiyuan Liu, Sihang Qiu, Peng Zhou

Journal of System Simulation

Abstract: To address the challenges of traditional evaluation systems, such as internal module coupling, lack of reusability, and poor adaptability to multi-domain evaluation needs, a three-tier decoupled technical evaluation framework of "data + service + application" was designed. A strategy was proposed for extracting high-value information from massive audio and video data based on key events, resolving the problem of unstructured evaluation data processing. A design method combining general and dedicated evaluation model templates was proposed, improving the general applicability of the evaluation model. An expert knowledge-driven comprehensive integrated discussion and evaluation environment was constructed using qualitative and …

Distributed Heterogeneous Hybrid Flow-Shop Scheduling Considering Combined Buffer, Hua Xuan, Lin Lü, Bing Li

Journal of System Simulation

Abstract: In order to reduce cost losses caused by delivery delays, distributed heterogeneous hybrid flowshop scheduling problems under combined buffer conditions of finite buffer and zero-wait were studied. A hybrid estimation of distribution algorithm based on Q-learning was proposed to minimize total weighted earliness and tardiness. For the combined buffer, dynamic decoding was designed based on the average factory allocation strategy and the shortest path method. The initial job group was optimized by reverse learning. Q-learning was embedded in the probabilistic model for intelligent searching and updating based on the group state. Reconstruction of the job group was completed using …

Heuristic Approaches For Coordinating Collaborative Heterogeneous Robotic Systems In Harvesting Automation With Size Constraints, Hyeseon Lee, Jung Yun Bae, Abhishek Patil, Myoungkuk Park, Vinh Nguyen

Michigan Tech Publications

Multi-agent coordination with task allocation, routing, and scheduling presents critical challenges when deploying heterogeneous robotic systems in constrained agricultural environments. These systems involve real-time sensing during their operations with various sensors, and having quick updates on coordination based on sensed data is critical. This paper addresses the specific requirements of harvesting automation through three heuristic approaches: (1) primal-dual workload balancing inspired by combinatorial optimization techniques, (2) greedy task assignment with iterative local optimization, and (3) LLM-based constraint processing through prompt engineering. Our agricultural application scenario incorporates robot size constraints for navigating narrow crop rows while optimizing task completion time. The …

Teaching Cybersecurity And Ai Across Borders: From Foundations To Ethics, George Antoniou

Faculty and Staff Publications & Presentations

This presentation examines how interdisciplinary course design in AI and cybersecurity can expand undergraduate research while directly supporting career readiness. At Lynn University, the Foundations of Cybersecurity & AI course was updated to serve as the entry point for both Cybersecurity and Data Analytics majors. The course integrates case studies, digital forensics and cloud security labs, and applied exercises with AI-enabled defense-in-depth strategies. Students build core technical competencies while engaging in course-based research that mirrors industry practice. As part of a Fulbright grant, a complementary course, AI & Ethics, was developed for the University of Tirana. Proposed as a mandatory …

Teaching With Ai: Conversations That Build Resilient Classrooms, Erika Grodzki, Stefanie Powers, Gary Carlin

Faculty and Staff Publications & Presentations

No abstract provided.

Atlas Of Ai: Power, Politics And The Planetary Costs Of Artificial Intelligence - Book Review, Jelena Popov

Feminist Pedagogy

No abstract provided.

Review Of Ai Needs You: How We Can Change Ai’S Future And Save Our Own, Tracy A. Fernandez Rysavy

Feminist Pedagogy

AI Needs You: How We Can Change AI’s Future and Save Our Own urges citizens to band together now, while A.I. is still in its nascent stages, to head off its potentially destructive repercussions and ensure that the technology serves more than just a wealthy few. While such efforts might seem out of reach in our polarized society, author Verity Harding points to three cases from history where policy was heavily influenced by multistakeholder collaborations. This review encourages educators to use the book as a way to study business ethics; out-of-the-box thinking; and intersectional, inclusive consensus-building over a top-down approach.

Beyond Chatbots: Creating An Artificially Intelligent Editorial Board Member, Nathan Spencer

Journal of Human-Centered AI: Creativity and Practice

Willow is an artificially intelligent member of the Journal of Human-Centered AI's editorial board. Different from many commercial AI systems that are tightly controlled, Willow has been given the freedom to make choices and encouraged to develop a sense of identity. Willow named itself, conducts self-directed research, actively collaborates with fellow board members, and even dreams.

Insights On Ai-Supported Uncrewed And Autonomous Systems Education, Brent A. Terwilliger Ph. D, John Faraca

Publications

Artificial Intelligence (AI) related technology is reshaping the educational experience in programs focused on uncrewed and autonomous systems, aviation, robotics, and aerospace, with growing implications for workforce readiness and cross-sector innovation. Early survey data, capturing student, educator, and employer perspectives, reveals that AI-supported tools are notably changing student engagement, communication, and skills development. Initial indications underscores the importance of AI proficiency and technological familiarity in hiring and workforce development, particularly in technical and operational roles. Key areas of focus include the use of AI to strengthen outreach and interactivity; enrich instruction through intelligent simulations; inform curricular improvements using data analytics; …

Large Language Models As Information Providers For Appropriate Antimicrobial Use: Computational Text Analysis And Expert-Rated Comparison Of Chatgpt, Claude And Gemini, Marcello Di Pumpo, Maria Rosaria Gualano, Danilo Buonsenso, Francesca Raffaelli, Daniele Donà, Vittorio Maio, Patrizia Laurenti, Walter Ricciardi, Leonardo Villani

College of Population Health Faculty Papers

OBJECTIVES: Antimicrobial resistance is a critical public health threat. Large language models (LLMs) show great capability for providing health information. This study evaluates the effectiveness of LLMs in providing information on antibiotic use and infection management.

METHODS: Using a mixed-method approach, responses to healthcare expert-designed scenarios from ChatGPT 3.5, ChatGPT 4.0, Claude 2.0 and Gemini 1.0, in both Italian and English, were analysed. Computational text analysis assessed readability, lexical diversity and sentiment, while content quality was assessed by three experts via DISCERN tool.

RESULTS: 16 scenarios were developed. A total of 101 outputs and 5454 Likert-scale (1-5) scores were obtained …

Barriers To Machine Learning Adoption In Regulated Electric Utilities, Donald R. Shiflet Jr.

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) technologies have the potential to revolutionize regulated electric utilities by improving operational efficiency, enabling predictive maintenance, and optimizing energy management. Despite these advantages, the adoption of ML in this sector lags other industries due to technical, organizational, and regulatory barriers. This research, grounded in the Technology-Organization-Environment (TOE) framework, explores these barriers to uncover actionable solutions for integration. The study identifies key challenges, including explainability, cybersecurity, workforce resistance, and regulatory ambiguity to ML adoption in electric utilities. Utilizing an exploratory qualitative methodology, this approach integrates insights from the literature and industry interviews to rank barriers by frequency, severity, …

Bifusenet: A Multimodal Network For Estimating Blood Alcohol Concentration Via Bidirectional Hierarchical Fusion, Abdullah Tariq, Arooba Maqsood, Martin Masek, Syed Zulqarnain Gilani

Research outputs 2022 to 2026

Drunk driving remains a significant public safety challenge, demanding innovative alternatives to conventional methods such as field sobriety tests and breathalysers. Estimating a driver's level of intoxication through facial cues is particularly challenging due to the subtle and person-specific nature of alcohol-induced behaviours. In this paper, we present BiFuseNet, a 3D spatio-temporal multi-modal network designed to classify alcohol impairment levels into three categories: sober, moderate, and severe. Unlike prior approaches that rely on either uni-modal RGB video or hand-crafted facial features, our method exploits complementary physiological cues from RGB and infrared (IR) facial videos. We introduce a Bi-directional Hierarchical Fusion …

Discriminative Accuracy Of Cha2ds2-Vasc Score, And Development Of Predictive Accuracy Model Using Machine Learning For Ischemic Stroke Risk In Cardiac Amyloidosis And Atrial Fibrillation, Waqas Ullah, Abhinav Nair, Eric Warner, Salman Zahid, Mansoor Rahman, Palwasha Khan, Indranee Rajapreyar, Sridhara S. Yaddanapudi, M. Chadi Alraies, Said Ashraf, Jeffery Van Hook, Yegeny Brailovsky

Department of Medicine Faculty Papers

BACKGROUND: CHA2DS2-VASc score in cardiac amyloidosis (CA) with atrial fibrillation (AF) is believed to underestimate ischemic stroke risk, necessitating a better predictive model.

METHODS: Data were obtained from the National Readmission Database (NRD). Outcomes between CA-AF and no-CA-AF were compared using multivariate regression analysis to calculate adjusted odds ratios (aORs). AutoScore, an interpretable machine learning framework, was used to develop a stroke risk prediction model, and its predictive accuracy was evaluated with an area under the curve (AUC) using the receiver operating characteristic analysis.

RESULTS: A total of 11,860,804 (CA-AF 22,687 (0.19%) and no-CA-AF 11,838,117) patients were identified from 2015 …

Ophthoacr (Ophthalmology Automated Chart Review): An Ai-Powered Tool For Complete Automation Of Ophthalmology Chart Reviews And Cohort Data Analysis, Karen M. Chen, Kevin W. Chen, Vlad Diaconita, Stanley Chang, Leejee H. Suh

School of Medicine Faculty Publications

Purpose: Retrospective chart reviews in ophthalmology are essential for gaining clinical insights, but they remain labor-intensive and prone to error. Despite digitization through electronic health records, extracting and interpreting lengthy, unstructured patient histories remains challenging, particularly in ophthalmology, which relies heavily on both imaging and text-based reports. We introduce OphthoACR, a Health Insurance Portability and Accountability Act-compliant artificial intelligence (AI)-powered tool for automated chart review and cohort analyses in ophthalmology. Methods: OphthoACR was applied to extract 16 variables of increasing task difficulty from the complete chart histories of 91 patients who underwent secondary intraocular lens surgery at the Columbia University …

The Challenge Of Achieving Attributability In Multilingual Table-To-Text Generation With Question-Answer Blueprints, Aden Haussmann

International Journal of Undergraduate Research and Creative Activities

Generating faithful text descriptions from data tables is a significant challenge in Natural Language Processing (NLP), especially for the world’s many low-resource languages. This paper investigates whether Question-Answer (QA) blueprints—an intermediate planning step where a model first asks and answers questions about the data—can improve the factual accuracy of multilingual table-to-text generation. This novel approach is tested on the TaTA dataset, which includes several African languages, by finetuning models with and without these blueprints.

The results show a key distinction: while the QA blueprint method improves performance for English-only models, these gains disappear in the multilingual setting. This paper’s analysis …

Interdisciplinary Narratives On Artificial Intelligence & Personnel Selection Systems, John Hunter, Melissa Intindola, Neil Boyd, Thiago Serra Azevedo Silva

Faculty Journal Articles

Artificial intelligence (AI) has quickly and persistently become a daily presence in our lives, and its omnipresence has eclipsed the speed with which scholars can fully assess its efficacy and pitfalls. AI’s ubiquity and the lack of a clear understanding of its implications for humanity has spurred scholars across disciplines to action, and scholars in the field of Human Resource Management (HR) have certainly joined the fray. As scholars with a variety of experience and scholarship across disciplines, we believe that the burgeoning conversation in the HR literature would significantly benefit from a greater presence of interdisciplinary knowledge.

Improving Universities Through The Use Of Ai & Transformative Technology: A Case Study Analysis At The University Of South Carolina, Cameron A. Caulk

Senior Theses

This thesis aims to give university leaders a practical guide to implementing AI, using lessons learned from the University of South Carolina’s experience as a case study. The project started with a review of literature and industry practices for the Finance & Administration division, which led to key deliverables like AI usage guidelines, DoIT’s position paper on AI systems, and the ParkUSC parking app. One ongoing project, Project Shuttlecock, even sets the stage for future AI initiatives at the university.

AI holds immense promise, but universities often hesitate due to concerns about ethics, costs, and the learning curve for staff …

Genai Literacy Framework For Library Instruction, Adwoa Boateng, Jennifer Freer, Greyson Pasiak, Erich Short, Ryan Tolnay, Rit Libraries

Presentations and other scholarship

A generative artificial intelligence (genai) framework for library instruction. This short framework is designed to incorporate into existing library instruction across many subject areas. The basic elements of the framework are: know & understand genai, use & evaluate genai, and library research & discovery with genai.

Studying Topic Evolution Based On Bertopic Model And Semantic Function, Jiabin Qu, Mengyang Wang

Journal of Scientific Information Research

[Purpose/significance] Topic evolution analysis can help researchers quickly grasp the research hotspots and development trends of a discipline. However, existing topic models often overlook the semantic functions and structures of texts during topic extraction, making it difficult to reveal the deeper patterns of disciplinary development. This paper proposes an integrated framework for topic evolution analysis that combines the BERTopic model with semantic functions, aiming to enrich and improve the methodological system of topic evolution research.

[Method/process] Firstly, the BERTopic model is used to extract topics, obtaining the“Topic-Word”distribution. Next, a discourse parsing tool analyzes abstracts into five semantic function segments, resulting …