Artificial Intelligence and Robotics Commons^™

Cybersecurity Center For Offshore Wind Energy (Final Project Round), Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models …

Drafting Policy To Address Academic Integrity: The Other Ai In Education, Elizabeth Burns

STEMPS Faculty Publications

The article focuses on the role of school librarians in drafting policies to address academic integrity amid the increasing use of artificial intelligence (AI) in K-12 education. It defines AI, including generative AI (GenAI), and discusses both the challenges and opportunities AI presents for teaching and learning, emphasizing the need for clear guidelines on ethical use. The article outlines three common AI policy models—permissive, hybrid, and restrictive—and stresses the importance of collaboration between librarians and teachers to establish consistent expectations, promote critical thinking, and ensure proper attribution of AI-generated content. School librarians are positioned as key facilitators in guiding students …

Students-Generative Ai Interaction Patterns And Its Impact On Academic Writing, Jinhee Kim, Sang-Soog Lee, Rita Detrick, Jialin Wang, Na Li

STEMPS Faculty Publications

Considering both the transformative opportunities and challenges presented by generative AI (GenAI) in academic writing, effectively integrating GenAI into the academic setting becomes a significant need requiring prioritization. Yet, there is limited understanding regarding the nature of interactions between different types of students, what behavioral patterns students exhibit during a student-GenAI interaction (SAI) on a given task, and how these different SAI patterns relate to the actual writing task performance. This study, therefore, aimed to identify SAI patterns of academic writing tasks depending on students’ level of AI literacy and examine the differences in academic writing performance between the identified …

A Comparison Of Conversational Chatbots And The Internet For Consumer Information Search, Wondwesen Tafesse, Yoseph Mamo

STEMPS Faculty Publications

This study compares consumer perceptions of conversational chatbots and the internet for information search. While the internet is a mature platform, conversational chatbots represent an emerging technology, and insight into how consumers view them in relation to the internet for information search is lacking. Drawing on the information source utility perspective, the study builds a comparative model based on four key dimensions: information currency, information customisation, information trustworthiness, and media richness. Additionally, the study investigates consumers’ prior experience with conversational chatbots as a moderating factor. Data was collected from 191 respondents recruited through MTurk. Paired sample t-tests assessed mean differences …

Crossing The Theory Threshold: The Pedagogical Potential Of Generative Artificial Intelligence In Educational Research, Amanda Burbage, Jennifer L. Styron

EVMS School of Health Professions Faculty Publications

Purpose

This paper presents findings from an educational research graduate course in which generative artificial intelligence (AI) was incorporated to strengthen learners' understanding of threshold concepts related to theoretical frameworks. Medical and health professionals often struggle with the transition from a clinical role into the educational research role.

Methods

The study posits that the use of generative AI will help learners understand and apply theoretical frameworks beyond a superficial level, furthering their understanding, constructing new knowledge, and strengthening their ability to develop sound educational research studies. Journal and AI transcripts were analyzed for 37 participants.

Results

Open-ended codes were grouped …

Automation And Autonomy In The Ivf Laboratory: Concepts And Implications For Embryologists, Jacques Cohen, Gerardo Mendizabal-Ruiz, Giles Anthony Palmer, Giuseppe Silvestri, Mina Alikani

EVMS School of Health Professions Faculty Publications

Automation is regarded as the next phase in the evolution of laboratory IVF. Despite technological advances, most laboratory procedures remain operator-dependent, contributing to variability in performance and outcomes. While automation has improved reproducibility and efficiency in many areas of medicine, its adoption in IVF has been slow. This review examines how automation may be integrated into IVF laboratories through a set of conceptual distinctions. Automation refers to the execution of procedural steps by machine-controlled systems, whereas autonomy describes the degree to which such systems can operate without human intervention. This review distinguishes between static automation, which maintains or monitors the …

The Lawyer Of The Future: Ethics And Identity In The Age Of Ai, David Yosifon, Michael Santoro, Isaac Nikssarian

American University Law Review

The dawn of artificial intelligence (AI) poses a fundamental challenge to the legal profession’s practical function and social identity. We argue that this challenge should be met as an opportunity to reclaim the profession’s core ethical intentions. We approach the emergence of AI as an occasion to define and safeguard what is irreducibly human in legal work. We ground our approach in the ethical imperatives set out in the American Bar Association’s Model Rules of Professional Conduct, while extending them into an invigorated framework for the AI era.

We survey the uses and ethical challenges associated with cutting-edge deployment of …

Ai-Driven Penetration Testing For Arm Systems: A Comprehensive Framework With Experimental Validation, Matthew Ragsdale

College of Graduate Studies: Theses & Dissertations

The convergence of artificial intelligence and cybersecurity presents new opportunities for automated penetration testing capable of discovering, prioritizing, and remediating vulnerabilities at machine speed. However, deployment on resource-constrained ARM platforms remains unexplored despite ARM’s dominance in mobile, IoT, and edge computing with over 280 billion chips deployed globally. This thesis presents systematic experimental evaluation of AI-driven penetration testing across four paradigms—traditional machine learning, deep learning, large language models, and reinforcement learning—on three ARM platform tiers: Raspberry Pi 5 (8GB, Cortex-A76), Radxa ROCK 5B Plus (16GB LPDDR5 with NPU), and NVIDIA Jetson Nano (4GB with Maxwell GPU). The experimental framework generates …

Network-Aware Airline-Specific Flight Delay Prediction Using Tree-Based Ensemble Models, Mary Dufie Afrane

College of Graduate Studies: Theses & Dissertations

Flight delays pose persistent challenges to the efficiency and reliability of air transportation systems, affecting airlines, airports, regulators, and passengers alike. As traffic demand grows and operational environments become increasingly interconnected, accurately predicting both departure and arrival delays has become crucial for effective planning and mitigation. This study presents a network-aware, airline-specific framework for predicting flight delays in U.S. domestic air transportation systems using tree-based ensemble machine learning models. A large-scale dataset of 1.98 million flights, enriched with weather information, is used to develop predictive models for both departure and arrival delays. To capture the structural and operational complexity of …

Pixel-To-World Mapping For Multi-Camera Warehouse Robot Localization, Mariam Faruque Sharif

College of Graduate Studies: Theses & Dissertations

This thesis presents a comprehensive framework for camera calibration and pixel-to-world coordinate mapping for multi-camera robot localization in a structured warehouse environment. The study is conducted in the APRN-ROWS laboratory, where four overhead cameras observe a planar grid of known barcode locations used as the reference coordinate system.

The proposed approach combines geometric modeling and optimization-based techniques to estimate camera parameters. Initially, camera extrinsic parameters, including position and orientation, are derived using physical measurements and geometric relationships. Principal point locations are estimated through a zoom-based alignment method, ensuring accurate correspondence between the optical axis and the world coordinate system. Intrinsic …

Real-Time Isolated Asl Recognition: Evaluating Spatial-Temporal Networks And Multimodal Llms, Raga Mouni Batchu

West Chester University Graduate Theses, Dissertations, and Final Projects

This thesis investigates the deployment of high-accuracy Isolated ASL Recognition (ISLR) in resource-constrained edge environments. We train a lightweight Spatio-Temporal Attention Network (SSTAN,∼2.7 M parameters,∼10 MB) on the WLASL-100 benchmark, achieving 75.25% Top-1 and 88.24% Top-5 accuracy with 139 ms CPU-only inference. A systematic comparison against frontier multimodal LLMs (Gemini 3 Flash, Gemini 3.1 Pro, Qwen 3 VL) shows SSTAN outperforms the best LLM baseline by∼1.85×in accuracy while being 22–230×faster and up to 40×cheaper annually. The LLMs’ core limitation is a lack of fine-grained temporal perception; they impose English-language semantic priors rather than learning the articulatory distinctions that define ASL …

Efficient Dynamic And Momentum Aperture Optimization For Lattice Design Using Multipoint Bayesian Algorithm Execution, Z. Zhang, I. Agapov, S. Gasiorowski, T. Hellert, W. Neiswanger, X. Huang, D. Ratner

Physics Faculty Publications

We demonstrate that multipoint Bayesian algorithm execution (multipointBAX) can overcome fundamental computational challenges in storage ring design optimization. Dynamic aperture and momentum aperture optimization is a multipoint, multiobjective design task for storage rings, ultimately informing the flux of x-ray sources and luminosity of colliders. Current production black-box optimization methods require extensive particle-tracking simulations for each trial configuration; the high computational cost restricts the extent of the search to ~ 10³ configurations and therefore limits the quality of the final design. We remove this bottleneck using multipointBAX, which selects, simulates, and models each trial configuration at the single-particle level. We demonstrate …

Psag: Projection-Based Stabilized Attribution Guidance For Online Continual Learning, Hang Yu, Kun Hu, Zhuqiang Lu, Steven Qiang Lu, Zhiyong Wang, Fengxiang He

Research outputs 2022 to 2026

Online Continual Learning (OCL) aims to incrementally learn from non-stationary data streams in a one-pass setting, facing the dual challenges of catastrophic forgetting and insufficient training. These challenges intensify the stability-plasticity dilemma, where pre-serving old knowledge conflicts with acquiring new information. In this paper, we propose Projection-based Stabilized Attribution Guidance (PSAG), a modular framework that leverages gradient-based attributions as active guidance signals to selectively preserve task-relevant representations. Our framework consists of three complementary mechanisms: (1) Attribution-Guided Feature Modulation (AGFM) that anchors critical features in the representation space; (2) Importance-Aware Loss Reweighting (IALR) that prioritizes informative samples at the loss level; …

Sparse Gradient Training For Recommender Systems, Yunke Qu, Liang Qu, Tong Chen, Xiangyu Zhao, Jianxin Li, Hongzhi Yin

Research outputs 2022 to 2026

Recommender systems are widely applied in numerous online platforms such as shopping and social media platforms. They typically utilize large embedding tables that map users and items to dense vectors of uniform sizes. As the number of users and items continues to grow, this design leads to significant memory consumption and computational inefficiencies. This challenge is particularly pronounced in scenarios such as federated learning, where model parameters are updated locally on edge devices with limited computational resources before being transmitted to a central server for aggregation. Numerous approaches have been proposed to address this issue, among which embedding pruning methods …

ℵ-Ipomdp: Mitigating Deception In A Cognitive Hierarchy With Off-Policy Counterfactual Anomaly Detection, Nitay Alon, Joseph M. Barnby, Stefan Sarkadi, Lion Schulz, Jeffrey S. Rosenschein, Peter Dayan

Research outputs 2022 to 2026

Social agents with finitely nested opponent models are vulnerable to manipulation by agents with deeper recursive capabilities. This imbalance, rooted in logic and the theory of recursive modelling frameworks, cannot be solved directly. We propose a computational framework called ℵ-IPOMDP, which augments the Bayesian inference of model-based RL agents with an anomaly detection algorithm and an out-of-belief policy. Our mechanism allows agents to realize that they are being deceived, even if they cannot understand how, and to deter opponents via a credible threat. We test this framework in both a mixed-motive and a zero-sum game. Our results demonstrate the ℵ-mechanism’s …

Protomix: Unified Prototype Learning For Semantic Change Detection In Remote Sensing, Hengzhi Chen, Kun Hu, Patrick Filippi, Wei Xiang, Thomas Francis Bishop, Zhiyong Wang

Research outputs 2022 to 2026

As an emerging topic, semantic change detection (SCD) in remote sensing aims to both identify the locations of changes before and after events and determine the land cover (LC) classes of changed regions. While deep learning has demonstrated its great potential for SCD, the following two fundamental challenges remain: large variations among the same LC class as well as close similarity between different LC classes, and the severe underutilization of massive unchanged regions, which dominate remote sensing scenes but lack detailed semantic annotations. To address these issues, we propose a novel prototype learning framework for SCD, namely ProtoMix, by leveraging …

Rrto: A High-Performance Transparent Offloading System For Model Inference In Mobile Edge Computing, Zekai Sun, Xiuxian Guan, Zheng Lin, Yuhao Qing, Haoze Song, Zihan Fang, Zhe Chen, Fangming Liu, Heming Cui, Wei Ni, Jun Luo

Research outputs 2022 to 2026

Deploying Machine Learning (ML) applications on resource-constrained mobile devices remains challenging due to limited computational resources and poor platform compatibility. While Mobile Edge Computing (MEC) offers an offloading-based inference paradigm using GPU servers, existing approaches can be divided into non-transparent and transparent methods, with the former necessitating source-code modifications. Non-transparent offloading achieves high performance but requires intrusive code changes, limiting compatibility with diverse applications. Transparent offloading, in contrast, offers wide compatibility but introduces two challenges: performance degradation under MEC's low and fluctuating bandwidth, where per-operator remote procedure calls (RPCs) inflate end-to-end latency and energy consumption; and black-box logic reconstruction, which …

Artificial Intelligence And Machine Learning In Smart Vaginal Formulation Development, Deborah A. Ogundemuren, Vivek Agrahari, Andrew P. Wong, Carolina Herrera, Margaret O. Ilomuanya, Gustavo F. Doncel

CONRAD Publications

Vaginal drug delivery in women's health remains underutilized and insufficiently studied, largely due to the complexity and dynamic nature of the vaginal microenvironment. Variations in vaginal pH, hormonal levels, and microbiota composition introduce significant biological variability, complicating formulation design and contributing to inconsistent therapeutic outcomes and poor patient adherence. Conventional vaginal formulations often fail to account for these individual differences, highlighting the need for more adaptive and predictive approaches. Emerging advances in artificial intelligence (AI) and machine learning (ML) offer promising strategies to address these challenges by enabling multi-parameter, data-driven formulation development that explicitly considers biological variability. Despite their transformative …

Large Language Model-Assisted Research Question Development In Public Health: A Case Study In The Special Supplemental Nutrition Program For Women, Infants, And Children (Wic), Qi Zhang, Bidusha Neupane, Priyanka Patel, Futun N. Alkhalifah, Yi He, Leslie Hodges

Health Behavior, Policy & Management Faculty Publications

Objective:

To assess the feasibility of using large language models (LLMs) to develop research questions about changes to the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) food packages.

Design:

We conducted a controlled experiment using ChatGPT-4 and its plugin, MixerBox Scholarly, to generate research questions based on a section of the USDA summary of the final public comments on the WIC revision. Five questions weekly for three weeks were generated using LLMs under two conditions: fed with or without relevant literature. The experiment generated 90 questions, which were evaluated using the FINER criteria (Feasibility, Innovation, Novelty, Ethics, …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …