Artificial Intelligence and Robotics Commons^™

Dynamic Supernetwork Modeling Of Command Information System Based On Task Timing, Xuehuan Qiu, Zhiming Dong, Liang Li, Zhuoli Liu

Journal of System Simulation

Abstract: Due to the difficulty in reflecting the various information activities and interactions within the command information system using general modeling methods for complex system structure, the advantages of supernetwork in characterizing node heterogeneity and link multiplicity of the system were utilized. Based on the research on the mapping mechanism of the command information system across three domains, the dynamic and multifunctional properties of the functional network structure were analyzed. A dynamic supernetwork model based on task timing was constructed considering task requirements, providing model support for further research on complex interaction relationships in the command information system. The dynamic …

Research On Time Sequence Design Method Of Dynamic Simulation Scene For Starlight Navigation, Xiaoting Su, Xiaowei Zhang, Yi Tian, Qi Li, Shuaihao Wang

Journal of System Simulation

Abstract: To solve the problem of misidentification of star maps due to time sequence mismatch in the hardware-in-the-loop simulation system of star navigation, where star trackers with different shutter types (global shutter and rolling shutter) and star simulators with varying refresh display methods (whole frame refresh and line sweep refresh) operated without synchronization, a time sequence design method of the dynamic simulation scene for starlight navigation without the need of external synchronization signals was proposed. The method could design the refresh frequency and duty cycle of the corresponding star simulators according to the detector integration time of the tested star …

Robot Path Planning Optimization Based On Fusion Of Improved Ant Colony Algorithm And A* Algorithm, Lanying Yang, Chao Li, Haifeng Zou, Jiangtao Wan, Renqiang Zhang, Hui Liu, Hong Lu

Journal of System Simulation

Abstract: To improve slow search efficiency and achieve real-time obstacle avoidance in traditional ant colony algorithms, an adaptive ant colony algorithm was proposed. A guidance direction mechanism was introduced to shorten the time of node selection. The A* algorithm's path-finding mechanism was introduced into the heuristic function to reduce the length and number of circles of the optimal path solution. The route planned by the traditional A* algorithm was used as the initial iteration data of the ant colony algorithm in global path planning, so as to solve the problem of slow initial convergence of the ant colony algorithm. The …

Quality Assessment Of Pathology Board-Exam-Style Mcqs Produced By Chatgpt3.5: A Comparative Study, Arianna B. Morton, Zunaira Naeem, Allison F. Goldberg, Alexis R. Peedin, Joanna Chan

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Residents preparing for pathology board exams frequently use multiple-choice questions (MCQs) from question banks (QBs) like PathDojo and PathPrimer, which can be costly. ChatGPT, a free tool, has been used to generate MCQs for other tests like the SAT. This study compared the quality of pathology MCQs created by ChatGPT versus commercially available study questions for the American Board of Pathology’s (ABPath) certifying exams. A rubric adapted from the National Board of Medical Examiners’ (NBME) question writing guide was validated by two pathologists using commercially available pathology board exam questions. This rubric was then used to evaluate MCQs from commercially …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …

Quantum Readiness In Cybersecurity Education: A Framework For Preparing The Next Generation In The Post-Quantum Era, George Antoniou

Faculty and Staff Publications & Presentations

The rapid advancement of quantum computing represents both a revolutionary opportunity and an existential threat to contemporary cybersecurity infrastructure. While quantum computers promise unprecedented computational capabilities, they simultaneously pose a critical risk to current cryptographic protocols that protect sensitive data, financial systems, and national security frameworks. Post-quantum cryptography (PQC) standards, recently formalized by NIST in 2024, provide a roadmap for quantum-resistant encryption. However, a significant gap exists between technological advancement and educational preparedness, with most cybersecurity curricula failing to adequately prepare students for the quantum era. This paper addresses the urgent need for comprehensive quantum readiness in cybersecurity education across …

Chronosort: Revealing Hidden Dynamics In Alphafold3 Structure Predictions, Matthew J. Argyle, William P. Heaps, Corbyn Kubalek, Spencer Gardiner, Bradley C. Bundy, Dennis Della Corte

Faculty Publications

Protein function emerges from dynamic conformational changes, yet structure prediction methods provide only static snapshots. While AlphaFold3 (AF3) predicts protein structures, the potential for extracting dynamic information from its ensemble predictions has remained underexplored. Here, we demonstrate that AF3 structural ensembles contain substantial dynamic information that correlates remarkably well with molecular dynamics simulations (MD). We developed ChronoSort, a novel algorithm that organizes static structure predictions into temporally coherent trajectories by minimizing structural differences between neighboring frames. Through systematic analysis of four diverse protein targets, we show that root-mean-square fluctuations derived from AF3 ensembles can correlate strongly with those from MD …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi T. Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …

Artificial Intelligence (Ai) And The Anthropic Economic Index In The Mountain West, 2025, Cason Noll, Olivia K. Cheche, William E. Brown Jr.

Economic Development & Workforce

This fact sheet presents 2025 data on the state of artificial intelligence (AI) adoption among the five Mountain West states of Arizona, Colorado, Nevada, New Mexico, and Utah. The data are sourced from the “Anthropic Economic Index,” which provides data on Claude.ai (an AI large language model) and its adoption across all 50 U.S. states and Washington, D.C. This fact sheet focuses on Claude.ai usage, the most common topic Claude.ai has been used for, and augmentation and automation shares for each Mountain West state.

Correcting Class Imbalance Through Synthetic Training Data And 3d Modeling For Carabid Pitfall Trap Sampling, Blair Mirka

Geography ETDs

Crowdsourced biodiversity data provide an accessible foundation for large-scale ecological monitoring, but class imbalance limits automated species identification, particularly for rare taxa. This research explores the use of synthetic training data generated from 3D models of carabid beetle museum specimens to improve detection and classification performance for underrepresented species in crowdsourced datasets. High-resolution 3D models were created to simulate variation in lighting, orientation, and background. These synthetic images were incorporated into convolutional neural network training datasets at varying synthetic-to-real ratios to assess their impact on classification accuracy. Models were evaluated using controlled pitfall-trap imagery to examine the influence of scene …

Detecting Burned Vegetation Areas By Merging Spectral And Texture Features In A Resnet Deep Learning Architecture, Jiahui Fan, Yunjun Yao, Yajie Li, Xueyi Zhang, Jiquan Chen, Joshua B. Fisher, Xiaotong Zhang, Bo Jiang, Lu Liu, Zijing Xie, Luna Zhang, Fei Qiu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Timely and accurate detection of burned areas is crucial for assessing fire damage and contributing to ecosystem recovery efforts. In this study, we propose a framework for detecting fire-affected vegetation anomalies on the basis of a ResNet deep learning (DL) algorithm by merging spectral and textural features (ResNet-IST) and the vegetation abnormal spectral texture index (VASTI). To train the ResNet-IST, a vegetation anomaly dataset was constructed on high-resolution 30 m fire-affected remote sensing images selected from the Global Fire Atlas (GFA) to extract the spectral and textural features. We tested the model to detect fire-affected vegetation in ten study areas …

Engaging With Ai In A Technical Writing Course: A Collaboration Between A Writing Instructor And A Librarian, Isabel Baca, Joy Urbina

HIIT 2025

After describing our collaboration (a Technical Writing Instructor and a Librarian) on teaching students how to use artificial intelligence (AI) to strengthen their writing, we will engage attendees by having them reflect and practice with AI. For our workshop presentation, attendees will:

• Learn how a librarian and a writing instructor collaborated to teach students to use AI effectively and ethically in their writing.
• Reflect on how they can incorporate AI in their classroom or workplace.
• Learn how a librarian can help them incorporate AI into their courses.
• Practice using AI and developing their prompt engineering skills.

Our workshop presentation will …

Preparing Tomorrow’S Professionals: Industry-Informed Ai Integration, Brent A. Terwilliger Ph.D, John Faraca

Publications

As AI reshapes operations across aviation and aerospace, organizations are investing in ways to preserve data integrity, safeguard proprietary knowledge, and uphold critical professional competencies. This presentation shares emerging findings from a study that surveys and interviews industry professionals about their use of AI tools, their concerns about misuse, and the importance of secure, enterprise-controlled “walled garden” environments. The work explores how employers define appropriate, effective, and innovative AI adoption, particularly in roles requiring high-stakes decision-making, compliance, and technical acumen.

By analyzing organizational expectations around AI-related knowledge, skills, and abilities (KSAs), this research offers practical guidance for academic programs seeking …

Student Perspectives On Ai-Enabled Tools For Adaptive Learning, John Faraca

Publications

Artificial Intelligence (AI) is increasingly influencing the delivery of higher education, especially in aviation technical disciplines. From AI-assisted gimbals and video production tools to generative AI platforms, these technologies are helping learners to engage with course material, accomplish objectives, and connect academic concepts with professional applications. By offering pathways for personalization, streamlining resource access, and supporting interactive instruction, AI tools expand opportunities for effective learning. This work builds on a current collaborative research project with a faculty researcher to explore the student perspective in the active review and application of these tools to highlight their potential to improve usability, address …

Advancing Power System Reliability And Security With Efficient And Resilient Graph Neural Network Frameworks, Seyed Hamed Haghshenas

USF Tampa Graduate Theses and Dissertations

Enhancing the reliability and security of smart grids is critical for ensuring their seamless operation and resilience against disruptions. The increasing integration of distributed energy resources, advanced measurement devices, and cyber-physical elements introduces both opportunities and challenges for grid management. While these advancements provide enhanced visibility and operational control, they also expose the grid to vulnerabilities from cyber-physical stresses, such as cyber-attacks, equipment failures, and fluctuating power demands. Traditional methods for reliability assessment and threat detection often rely on model-based approaches that struggle to adapt to the complexity and dynamic nature of modern smart grids. These limitations necessitate novel data-driven …

Composition Pedagogy As Ai‑Native Coding: From Design Kit To Scholarly Framework, Daniel Plate, James Hutson

Faculty Scholarship

This article advances a field-ready framework that reconceives first-year composition as AI-native coding, translating a complete “design kit” into scholarly method, evaluative protocol, and curriculum architecture. Background: Contemporary composition pedagogy emphasizes process, genre awareness, and collaborative revision; meanwhile, modern software practice operationalizes iteration through version control, test-driven development, and continuous integration. The uploaded kit demonstrates that these cultures are isomorphic: writing stages align with SDLC phases, and automated pipelines can lint prose, execute argument “tests,” and publish artifacts with auditable histories. Approach: The study systematizes that kit into (1) a conceptual map that recasts authorship as orchestration and verification, (2) …

Navigating Equity In The Digital Era : Addressing Challenges And Advancing Rights Of Female Seafarers Through Policy Reform, And Artificial Intelligence, Margaret Dixon

World Maritime University Dissertations

No abstract provided.

From Prohibition To Preparation: Reframing Academic Integrity In The Age Of Ai, James Hutson

Faculty Scholarship

This study analyzes how U.S. universities reconfigure academic integrity during the 2024–2025 cycle in response to widespread generative AI adoption. The analysis foregrounds three loci: student ignorance and metacognitive blind spots; the expanded remit of Academic Integrity Officers prioritizing education over punishment; and deliberate AI-enabled misconduct that exposes the evidentiary limits of detection technologies. A mixed-methods design integrates a multi-site review at Arizona State University, Montclair State University, and Cornell University with synthesis of surveys, policies, and faculty development guidance. Findings show that detector outputs function as conversational prompts rather than adjudicative proof, necessitating dialogic resolution standards, process evidence, and …

Ai Companions And The Lessons Of Family Law, Clare Huntington

Faculty Scholarship

Virtual friends and lovers powered by artificial intelligence are rapidly moving to the center of our emotional and social lives. Millions of people turn to AI companions every day for conversation, romance, sexual intimacy, therapy, and education. AI companionship holds promise, potentially reducing loneliness, supporting people without access to mental health treatment, helping students learn, and offering a judgment-free space for sensitive conversations. But AI companionship also raises significant concerns. The technology's addictiveness may exacerbate loneliness and can undermine human relationships. Therapy bots may prove more harmful than helpful. AI companions can be emotionally abusive. And their access to the …