Artificial Intelligence and Robotics Commons^™

Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi

School of Cybersecurity Faculty Publications

With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the …

Deepsecure: A Novel Deep Learning Model For Effective Detection Of Attacks On Big Data In Internet Of Urban Things, Laiba Sabir, Nadeem Javaid, Mariam Akbar, Nabil Alrajeh, Safdar Hussain Bouk, Abdulaziz Aldegheishem

School of Cybersecurity Faculty Publications

The Internet of Urban Things (IoUTs) regularly generates large amounts of data, making it a focus of cyberthreats such as denial-of-service attacks and malware bot networks. Traditional intrusion detection systems struggle to detect intricate attack patterns, handle class imbalance, capture temporal dependencies, and exhibit transparency. To address these limitations, we introduce a novel deep machine learning model, DeepSecure, a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction and Deep Neural Networks for attack classification. DBN is used for feature selection through unsupervised learning to extract hierarchical representations in the IoUTs network data. We assess the random …

Qos-Aware Link Adaptation For Beyond 5g Networks: A Deep Reinforcement Learning Approach, Ali Parsa, Neda Moghim, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Modern wireless communication systems face increasingly complex challenges due to rapidly changing channel conditions and the growing diversity of application-specific Quality of Service (QoS) requirements. Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack the flexibility to support emerging applications, such as Extended Reality (XR) and Virtual Reality (VR), which demand simultaneous guarantees for high data rates, ultra low latency, and high reliability. These stringent and multidimensional QoS needs call for more intelligent and adaptive solutions. In this paper, we propose QDRLLA (QoS-aware Deep Reinforcement Learning-based Link Adaptation), a novel framework that employs deep reinforcement learning to …

Privacy-Preserved And Incentivized Knowledge Sharing For Reinforced-Learning Based Iot Platform Security, Weichao Wang, Md Morshed Alam, Yu Wang

School of Cybersecurity Faculty Publications

With the fast development and deep penetration of IoT devices and smart environments, using localized machine learning models to detect malicious activities has also been developed and deployed. However, these isolated learning models and results cannot be effectively federated together because of privacy concerns and lack of incentivization. This paper proposed several mechanisms to solve the problem. A verification method was designed for phased learning results to protect user privacy and prevent individual parties from manipulating the verification selection. The paper also presented an incentive method based on delay of distribution of the latest federated learning results. Extensive simulations were …

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Problem-Centered Post-Secondary Computer Science Education: A Study Of The Private Artificial Intelligence Curriculum, Golnoush Haddadian, Prajwal Panzade, Daniel Takabi, Min Kyu Kim

School of Cybersecurity Faculty Publications

In response to the demand for Artificial Intelligence (AI) experts, this study introduced a curriculum development initiative. The aim was to design and implement a Private AI curriculum to understand the computer science (CS) students’ evaluations of the curricular activities and their levels of interest and motivation. Twenty-five students, a mix of undergraduates and graduates, were recruited and a scaled-down version of the curriculum was implemented. A parallel mixed-methods approach was employed. The results reinforced the significance of problem-centered curricula in CS context. Students rated the curricular activities highly and demonstrated strong motivation; however, graduates expressed more favorable view of …

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

An Integrated Machine Learning Approach For Identifying Emergency Rescue Messages On Social Media During Natural Disasters, Wael Khallouli, Jiang Li, Jingwei Huang, Ghaith Rabadi, Samuel Kovacic

School of Cybersecurity Faculty Publications

During large-scale disasters, emergency call centers are often overwhelmed by the large volume of rescue requests and calls for help. Consequently, people are turning to social media platforms to seek assistance. Rescue information posted on these platforms is extremely valuable for first responders to make informed rescue decisions. Therefore, the automatic identification of these requests from the vast amount of data posted on social media during crises is critical yet challenging. This work presents our ongoing research on applying deep learning techniques to extract actionable rescue information from social media during crises. We proposed a novel deep learning model that …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

Exploring The Impact Of Generative Ai Chatgpt On Critical Thinking In Higher Education: Passive Ai-Directed Use Or Human-Ai Supported Collaboration?, Nesma Ragab Nasr, Chih-Hsiung Tu, Jennifer Werner, Tonia Bauer, Cherng-Jyh Yen, Laura Sujo-Montes

STEMPS Faculty Publications

Generative AI is weaving into the fabric of many human aspects through its transformative power to mimic human-generated content. It is not a mere technology; it functions as a generative virtual assistant, raising concerns about its impact on cognition and critical thinking. This mixed-methods study investigates how GenAI ChatGPT affects critical thinking across cognitive presence (CP) phases. Forty students from a four-year university in the southwestern United States completed a survey; six provided their ChatGPT scripts, and two engaged in semi-structured interviews. Students’ self-reported survey responses suggested that GenAI ChatGPT improved triggering events (M = 3.60), exploration (M = 3.70), …

Applying Transfer Learning For Street-Scale Nuisance Flood Forecasting In Coastal-Urban Cities, Binata Roy, Jonathan L. Goodall, Diana Mcspadden, Chetan Kumar, Steven Goldenberg, Yidi Wang, Malachi Schram

VMASC Publications

An important challenge with Machine Learning (ML) is its transferability; i.e., whether a ML model trained on one set of data can be applied to a second set of data without requiring full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained for one …

Ai-Generated Messaging For Life Events Using Structured Prompts: A Comparative Study Of Gpt With Human Experts And Machine Learning, Christopher Lynch, Erik Jensen, Ross Gore, Virginia Zamponi, Kevin O'Brien, Brandon Feldhaus, Katherine Smith, Joseph Martínez, Madison H. Munro, Timur E. Ozkose, Tugce B. Gundogdu, Ann Marie Reinhold, Hamdi Kavak, Barry Ezell

VMASC Publications

Large Language Models (LLMs) play an increasingly integrated and pivotal role in generating diverse types of texts, such as social media messages, emails, narratives, and technical reports, among other textual communication forms. As AI-generated messaging filters into human communication, a systematic exploration of their effectiveness for mimicking human-like communication of life events is needed. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 life event messages for birth, death, hiring, and firing events using OpenAI's GPT-4. From this dataset, we manually classify 2880 messages and evaluate their validity in conveying these life events through the form …

Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in …

Examining Teaching Competencies And Challenges While Integrating Artificial Intelligence In Higher Education, Xinyue Ren, Min Lun Wu

STEMPS Faculty Publications

The rapid development of artificial intelligence (AI) technologies has demonstrated their affordances and limitations in revolutionizing pedagogical strategies in higher education. Given the lack of guidelines, policies, and resources to assist instructors in efficiently and ethically integrating AI into teaching and learning practices, this systematic review aimed to investigate AI integration competencies and challenges in higher education from the intelligent Technological Pedagogical Content Knowledge (TPACK) perspective. We first applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify 23 studies published between 2019 and 2023 that met the inclusion and exclusion criteria. After conducting open coding and …

Advancing Pedagogical And Instructional Design Through Artificial Intelligence (Ai) In Education And Training Contexts, Mohan Yang, Jewoong Moon, Tian Luo, Jinhee Kim

STEMPS Faculty Publications

[Introduction] "The only way to discover the limits of the possible is to go beyond them into the impossible." - Arthur C. Clarke

It is our pleasure and honor as guest editors for this special issue of the Journal of Applied Instructional Design (JAID) to present "Advancing Pedagogical and Instructional Design through Artificial Intelligence (AI) in Education and Training Contexts." This issue arrives at a truly timely moment, as the rapid and continuous development of generative artificial intelligence (GenAI) is fundamentally reshaping the traditional paradigms of teaching, learning, and training.

The Importance Of Atomic Charges For Predicting Site-Selective Ir-, Ru-, And Rh-Catalyzed C-H Borylations, Shannon M. Stephens, Kyle M. Lambert

Chemistry & Biochemistry Faculty Publications

A supervised machine learning model has been developed that allows for the prediction of site selectivity in late-stage C-H borylations. Model development was accomplished using literature data for the site-selective (≥95%) C-H borylation of 189 unique arene, heteroarene, and aliphatic substrates that feature a total of 971 possible sp² or sp³ C-H borylation sites. The reported experimental data was supplemented with additional chemoinformatic descriptors, computed atomic charges at the C-H borylation sites, and data from parameterization of catalytically active tris-boryl complexes resulting from the combination of seven different Ir-, Ru-, and Rh-based precatalysts with eight different ligands. Of the over …

A Framework For Task Offloading In Heterogeneous Computing Applications Within The Fog Ran Architecture, Samira Taheri, Neda Moghim, Naser Movahhedinia, Sachin Shetty

VMASC Publications

Fog Radio Access Network (Fog RAN) has recently emerged as a promising architecture for supporting low-latency applications by bringing fog nodes and cloud resources closer to end users. However, existing research on computational offloading in Fog RAN lacks a comprehensive framework that addresses three key aspects: where to offload tasks, which processing nodes to utilize, and how to allocate resources for tasks with varying latency requirements. To address this gap, we propose TOFRA (Task Offloading for Fog RAN), a novel latency-aware task offloading framework. TOFRA is a centralized system that determines the optimal offloading strategy, whether to execute tasks locally, …

Overcoming Resistance To Ai In Higher Education: A Case Study, C. Tomovic, M. Tomovic

Educational Leadership & Workforce Development Faculty Publications

While the deployment of AI in business and industry is widely acknowledged for its potential to improve efficiencies, decision-making, and automation, its implementation often encounters several challenges. These include integrating AI with existing systems, ensuring data quality and availability, bridging the employee skills gap, overcoming resistance to change, addressing ethical concerns, managing costs, complying with regulations, and handling ongoing maintenance and updates. In the context of higher education, however, the challenges take a different shape. Faculty members may express concerns about how AI could disrupt traditional teaching methods, necessitate changes in course delivery, and raise issues related to job security …

Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park

Engineering Management & Systems Engineering Faculty Publications

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model …

Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

Engineering Management & Systems Engineering Faculty Publications

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset …

Optimizing Port Logistics Through Generative Ai: Revolutionizing Efficiency And Resilience In The Maritime Industry, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

The maritime industry faces growing challenges in optimizing port logistics due to increasing trade volumes, environmental regulations, and supply chain disruptions. This comprehensive literature review examines the transformative role of artificial intelligence (AI), with particular focus on generative AI, in enhancing efficiency and resilience in port operations. Through systematic analysis of 23 peer-reviewed studies published between 2021-2025, this review synthesizes advancements in real-time data integration, machine learning, digital twins, IoT, and autonomous systems that collectively improve operational decision-making, risk management, and environmental sustainability. Key findings reveal that machine learning applications achieve 90% effectiveness ratings in operational optimization, while predictive analytics …

A Bibliometric Analysis Of Ai-Driven Healthcare Literature Containing Kos Keywords: Trends, Themes, And Gaps, Julaine Clunis, Eric Asare

STEMPS Faculty Publications

As artificial intelligence (AI) becomes increasingly embedded in healthcare applications, concerns have emerged around the trustworthiness, interpretability, and context-awareness of these systems. Knowledge Organization Systems (KOS) hold considerable potential to address these challenges by supporting semantic standardization, explainability, and domain alignment. This study presents a bibliometric analysis of scholarly publications referencing both AI and healthcare concepts to examine how KOS are positioned within this evolving discourse. The findings indicate that while early literature frequently and explicitly referenced KOS—such as ontologies, controlled vocabularies, and classification systems—their visibility has declined relative to newer paradigms such as machine learning and large language models. …

Comprehensive Benchmarking Of Several Machine Learning And Bayesian Models For Early-Stage Diabetes Risk Prediction: A Large-Scale Comparative Study, Md. Iqbal Hossain, Najila Alam Porno

Mathematics & Statistics Faculty Publications

Diabetes remains a critical global health challenge, with early detection is crucial for effective management. This study presents a comprehensive benchmarking analysis of 14 diverse machine learning and Bayesian models for early-stage diabetes risk prediction using clinical data [2] from Sylhet, Bangladesh. This research evaluated traditional methods (Logistic Regression, Decision Trees), ensemble techniques (Random Forest, XGBoost, LightGBM), Bayesian approaches (BART, Bayesian Logistic Regression), and advanced neural architectures (Deep Belief Networks) using both 70-30 train-test splits and 10-fold cross-validation. The results demonstrate that ensemble methods consistently outperformed other approaches, with Random Forest(RF) achieving the highest cross-validated AUC (0.9951) and accuracy (0.9699). …

Is It Getting Better? An Evaluation Of Two Successive Generations Of Chatgpt In Answering Specialized Vascular Surgery Questions, Dongjin Suh, Quang Le, Leana Dogbe, Kedar Lavingia, Michael Amendola

Department Surgery Faculty Publications

Purpose: Large language models (LLMs) can generate clinically relevant text; however, their performance in highly specialized medical domains remains uncertain. This study evaluated ChatGPT-3.5 and ChatGPT-4 (OpenAI) using vascular surgery board–style questions from the Vascular Education and Self-Assessment Program, version 4 (VESAP4) and compared the two public model versions (June and November 2023).

Materials and Methods: All non-image VESAP4 questions (n=384) were presented independently three times to each model version (ChatGPT-3.5 June/November; ChatGPT-4, June/November). Outcomes included accuracy (proportion correct), consistency (same option letter across all three attempts and “consistently correct”), explanation length (word count), and modes of failure classified for …

Leveraging Custom Gpts For Formative Assessment And Feedback, Nari Kim, Jason Vickers, Shaimaa Alyehaibi, Chikezie Ozuzu, Xinyue Ren, Suhasini Kotcherlakota

STEMPS Faculty Publications

As artificial intelligence (AI) advances, the use of custom generative pre-trained transformers (Custom GPTs) in formative assessments opens new opportunities for improving student learning and engagement. Custom GPTs increase cognitive and emotional engagement by providing timely, context-sensitive, and tailored responses based on unique learner profiles. This paper aims to investigate how custom GPT-driven formative feedback can enhance individualized learning experiences and improve student performance. This paper will discuss the promise of this AI tool, the challenges associated with its application, and recommendations for educators and academics looking to use these technologies for formative assessments.

Leveraging Transformer-Based Ocr Model With Generative Data Augmentation For Engineering Document Recognition, Wael Khallouli, Mohammad Shahab Uddin, Andres Sousa-Poza, Jiang Li, Samuel Kovacic

Engineering Management & Systems Engineering Faculty Publications

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced …

T3-Ciders: Train-The-Trainer And Community Building To Increase Cyberinfrastructure Adoption In Cybersecurity Research And Education, Wirawan Purwanto, Mohan Yang, Peng Jiang, Shanan Chappell Moots, Masha Sosonkina, Hongyi Wu

University Administration Publications

T³-CIDERS is a train-the-trainer program to increase the adoption of advanced cyberinfrastructure (CI) and data skills into the fabric of research and education in cybersecurity and cyber-related disciplines. T³-CIDERS trains faculty, researchers, and students as “future trainers” (FTs) with hands-on technical and instructional skills to enable more people to effectively leverage CI in cybersecurity. The program includes a series of technical pre-training modules, a weeklong summer institute, ongoing learning engagements conducted over an academic year; it culminates with the FTs conducting locally tailored CI-infused training events at their respective home institutions. Ultimately, T³-CIDERS aims to build a “CI+cybersecurity” community of …

Point Cloud-Based Diffusion Models For The Electron-Ion Collider, Jack Y. Araz, Vinicius Mikuni, Felix Ringer, Nobuo Sato, Fernando Torales Acosta, Richard Whitehill

Physics Faculty Publications

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We …

Socially Shared Regulation Of Learning And Artificial Intelligence: Opportunities To Support Socially Shared Regulation, Jinhee Kim, Rita Detrick, Seongryeong Yu, Yukyeong Song, Linda Bol, Na Li

STEMPS Faculty Publications

Supporting learners in achieving high-level socially shared regulation of learning (SSRL) in the online collaborative learning (OCL) context presents challenges that the utilization of artificial intelligence (AI) technologies may help solve. However, the effective uses of AI to support multifaceted areas (cognition, metacognition, and motivation) and phases (forethought, performance, and reflection) of SSRL remain elusive. Furthermore, research on developing an educational AI and what pedagogical attributes and elements are required for AI to support students' SSRL effectively is limited. This study, therefore, aims to investigate students' perceptions of AI applications in enhancing SSRL and to explore the essential pedagogical elements …

The Integration Of Artificial Intelligence And Ontologies: Transformations In Knowledge Representation And Application, Grazia Serratore, Julaine Clunis

STEMPS Faculty Publications

Artificial Intelligence (AI) is reshaping the landscape of knowledge representation. There is an increasingly strong bidirectional relationship, between AI techniques and ontologies. AI techniques revolutionized traditional, manual ontology development and contribute to automated ontology construction, while ontologies enhance the performance of AI systems and their semantic accuracy. Through a comprehensive review of current literature, this paper aims to examine: i) how Machine Learning (ML) techniques contribute to the automated construction, refinement, and validation of ontologies; ii) the most widely used and effective ML approaches for ontology construction; iii) how domain-specific requirements influence the selection and adaptation of AI techniques for …