Computer Sciences Commons^™

Affordable Course Content And Open Education Resources For Undergraduate Courses Teaching Fundamentals Of Wireless Communications And Networking, Dimitrie C. Popescu, Otilia Popescu

Electrical & Computer Engineering Faculty Publications

Wireless communication systems and networks along with the services they provide have become an essential component of the modern 21st century society, fueling job growth in the wireless industry and increasing the need for engineers specialized in wireless communication systems. As a consequence, over the past two decades, undergraduate courses teaching fundamentals of wireless communication systems and networks have become common in electrical and computer engineering and technology programs. At the same time, the number of textbooks dedicated to wireless systems and networks published by mainstream publishers has also grown, with availability in various formats and offerings and a significant …

Stabilized Multi-Objective Optimization For Camouflage Attack In Remote Sensing, Omid Rajabi Rostami, Kazi Aminul Islam, Liuwan Zhu, Rui Ning, Chungsheng Xin, Hongyi Wu, Jiang Li

Electrical & Computer Engineering Faculty Publications

We investigate the vulnerability of multi-task maritime surveillance systems in which object detection and semantic segmentation operate concurrently. We propose Stabilized Multi-Objective Optimization (SMO), a framework that jointly targets Mask R-CNN and U-Net with a single universal adversarial texture in a digital white-box setting, with additional cross-model transfer evaluation. SMO combines a logarithmic-inversion loss transformation, feature inconsistency, and a systematic comparison of six optimization strategies for seven heterogeneous objectives. The loss transformation controls loss-dependent gradient scaling, while a U-Net generator supplies a spatially coherent texture and a hull-aligned application function accommodates target geometry without explicit Expectation over Transformation (EoT) sampling. …

Bridging Governance And Empirical Threat Intelligence: An Integrated Framework For Cybersecurity In Smart Farming, Radwan Rouzky, Abdolhossein Sarrafzadeh, Evelyn Sowells-Boone, Jason Green, Hannaneh B. Pasandi, Gregory Goins

Electrical & Computer Engineering Faculty Publications

Modern agriculture’s integration of Internet of Things (IoT), Industrial Control Systems (ICSs), and data analytics boosts productivity but introduces significant cybersecurity and data governance challenges. Existing scholarship is divided between policy-focused governance and technical attack analyses, hindering the development of comprehensive, enforceable defenses. This paper introduces an integrated framework that bridges normative data governance in smart farming (SF) with empirical, honeynet-derived threat intelligence. Drawing on the authors’ previous systematic review of SF data governance and a honeynet simulating agricultural IoT/ICS, the study maps governance challenges to quantitative attack indicators from honeynet logs, classifying each pairing as directly supported by telemetry, …

Stabilized Multi-Objective Optimization For Camouflage Attack In Remote Sensing, Omid Rajabi Rostami, Kazi Aminul Islam, Liuwan Zhu, Rui Ning, Chunsheng Xin, Hongyi Wu, Jiang Li

Electrical & Computer Engineering Faculty Publications

We investigate the vulnerability of multi-task maritime surveillance systems in which object detection and semantic segmentation operate concurrently. We propose Stabilized Multi-Objective Optimization (SMO), a framework that jointly targets Mask R-CNN and U-Net with a single universal adversarial texture in a digital white-box setting, with additional cross-model transfer evaluation. SMO combines a logarithmic-inversion loss transformation, feature inconsistency, and a systematic comparison of six optimization strategies for seven heterogeneous objectives. The loss transformation controls loss-dependent gradient scaling, while a U-Net generator supplies a spatially coherent texture and a hull-aligned application function accommodates target geometry without explicit Expectation over Transformation (EoT) sampling. …

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; …

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 …

Technology Directors' Perspectives On Cybersecurity Preparedness In West Virginia Public Schools, Angela Dawn Urling

Theses, Dissertations and Capstones

Cybersecurity threats targeting K-12 school districts continue to present challenges for educational leaders responsible for protecting district operations, networks, and sensitive student information. The purpose of this explanatory mixed-methods case study was to examine technology directors' perceptions of cybersecurity preparedness in West Virginia public school districts and to identify factors influencing preparedness and barriers to implementing effective cybersecurity measures.

Quantitative data were collected through a 31-item survey distributed to technology directors employed by West Virginia public school districts. Twenty-one participants completed the survey, resulting in a response rate of 38.2%. Qualitative data were collected through semi-structured interviews with six technology …

Towards Sample-Efficient Deep Reinforcement Learning, Guang Yang

Theses and Dissertations

Deep reinforcement learning (DRL), combining reinforcement learning and high-performance function approximations such as deep neural networks (DNN), is a powerful approach to solving complex sequential decision-making problems. However, due to the complex solution space of the sequential decision-making problems and the inefficient design of the DRL algorithms, DRL algorithms usually require a prohibitively large number of data samples to train effective strategies. Consequently, it is difficult to apply these DRL algorithms to complex real-world problems that require high costs to collect a large volume of data samples. This dissertation proposes new mechanisms to address this sample inefficiency issue, realizing sample-efficient …

Automated Writer And Acquisition-Condition Classification Of Digitally Captured Handwriting Using Statistical Dynamic Features And Support Vector Machines, Long-Huang Tsai, Hsiang-Ju Lai, Wen-Chao Yang, Jiajun Jiang, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

Digitally captured handwriting preserves pen trajectories and dynamic signals, but it also records hardware- and input-dependent properties that can confound forensic interpretation. This study revises a support vector machine (SVM) screening framework using 16,500 samples from 30 writers, 11 writing-content categories, and five acquisition conditions spanning three tablets and stylus or finger input. Twenty-four raw and derived time-series variables were summarized by maximum, minimum, mean, median, and standard deviation, yielding 120 features; the mode statistic was removed. Writing direction and angular velocity were recalculated with atan2-based vector formulas. Unavailable device/API channels were encoded as zero, and Z-score parameters were estimated …

Digital Twin Technologies For Battery Systems: Advancements, Applications, And Future Directions, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

The relationships among deep learning, edge computing, artificial intelligence (AI), and the most recent advancements in digital twin (DT) technology for battery energy storage systems are discussed in this paper. The study highlights the need for improved cloud-edge coordination, AI model development, and stronger cybersecurity features by demonstrating real-world applications of digital twin technology in electric vehicles (EVs), aircraft, and grid storage. It also described DT-based structures for fault detection, real-time monitoring, and optimization through standardization and battery management system (BMS) fusion. Because DT-based solutions for distributed energy resources (DERs) offer improved energy management systems, various studies have been conducted …

Healthcare Digital Twins: A Methodological Literature Review On Integrating Iot And Ai For Personalized Medicine And Predictive Care, Sara Shahnazinia, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini

Electrical & Computer Engineering Faculty Publications

Digital Twin (DT) technology has the potential to revolutionize healthcare delivery and enhance patient outcomes through personalized and precision medicine, simulation models for operations and interventions, and drug discovery. However, successful implementation of DTs in Internet of Things (IoT) and artificial intelligence (AI) healthcare is contingent upon addressing key challenges such as privacy, ethics, and robust data security. This paper presents a methodological literature review of DT applications in healthcare, systematically analyzing the current state of research, key enabling technologies, and implementation challenges. The review summarizes DT categorization approaches (application-based, technology-based, and real-time function-based); delineates core DT components such as …

Reinforcement Learning-Enabled Control And Design Of Rigid-Link Robotic Fish: A Comprehensive Review, Nhat Dinh, Darion Vosbein, Yuehua Wang, Qingsong Cui

Faculty Publications

With the rising demand for maritime surveys of infrastructure, energy resources, and environmental conditions, autonomous robotic fish have emerged as a promising solution with their biomimetic propulsion, agile motion, efficiency, and capacity for underwater inspection, monitoring, data collection, and exploration tasks in complex aquatic environments. Inspired by fish spines, rigid-link fish robots (RLFRs), a category of robotic fish, are widely utilized in robotics research and applications. Their rigid, actuated joints enable them to reproduce the undulatory locomotion and high maneuverability of biological fishes, while the modular nature of rigid links between joints makes them cost-effective and easy to assemble. This …

Teacher Culturally Sustaining Practices Supported By Generative Ai: Instrument Development, Guy Trainin, Azadeh Hassani, Shuling Yang

Research and Evaluation in Education, Technology, Art, and Design

Generative AI (GenAI) carries the dual potential to reproduce educational inequities and to expand opportunities for multilingual support, critical evaluation, and personalized learning. This paper reports the development and initial validation of an instrument measuring K-12 literacy teachers' perceptions of GenAI's capacity to support culturally sustaining pedagogy. Grounded in culturally sustaining pedagogy (CSP) and its asset-based commitments, the instrument operationalizes three constructs, namely academic success, cultural competence, and critical consciousness, across both teacher and student use. Items were drawn from the CSP literature and refined through cognitive interviews, then administered to 109 PK-12 language arts educators. Reliability was strong for …

Medical Students Report Coherent Ethical Orientations Towards Ai Across Academic And Clinical Domains, With No Clear Association With Perceived Technical Readiness: A Cross-Sectional Pilot Study, Ismail El Moudden, Madison Baldauf, John Millerhagen, Anca Dana Dobrian

Department of Obstetrics & Gynecology Faculty Publications

Background

Whether perceived technical AI competency is associated with greater ethical openness to AI integration, a relationship often assumed in curricular design, remains empirically untested. This pilot study examined relationships between perceived AI readiness, ethical perspectives toward AI integration, and curricular needs among medical students.

Methods

A cross-sectional survey was administered to medical students at a single U.S. institution from September through November 2025. The survey assessed self-reported AI readiness, ethical perspectives across academic and clinical domains, and responses to 22 scenario-based ethical items; all measures were student self-reports, and findings reflect student perceptions. Composite indices were created for Perceived …

Limits Of Machine Learning For Predicting Stillbirth At Term: A Population-Based Cohort Of 302,192 Pregnancies In Northern Italy, M. Benuzzi, T. Kawakita, F. Monari, B. Melis, G. Guariglia, A. La Marca, I. Neri, G. Saade, F. Facchinetti, I. El Moudden

Department of Obstetrics & Gynecology Faculty Publications

Background

Objective

Study Design

Emotional Patterns In Conversational Social Media Using Graph-Based Context, Sai Sanjay Yerunkar

Master's Projects

Most social media datasets for emotion detection have not been constructed to account for conversational structure‚ so we investigate whether it carries signal for emotion prediction. Using Sentiment140 and GoEmotions Reddit threads‚ we construct their thread-based conversation graphs and compute the aggregated features of neighbors as well as the transformer and TF-IDF representations of comments. Connected comments are 4.5× more similar in emotions than expected by chance. Pairwise emotional similarity decays exponentially with geodesic distance (e.g.‚ after 2 hops). Pairs separated by a 30s timestamp difference have the highest emotional similarity. These results suggest that emotion is structured locally and …

Modeling Sealed Deck Construction In Collectible Card Games Using Learning-To-Rank Approach, Michael Dinh Nguyen

Master's Projects

Artificial intelligence has demonstrated strong performance in complex decision-making domains such as chess and Go, motivating research into its application for games with even richer rules and combinatorial complexity. In collectible card games like Magic: The Gathering, deck construction from a constrained card pool is a critical and challenging task that requires evaluating card strength, synergy, and resource balance. This project explores whether machine learning, specifically learning-to-rank (LTR), can effectively model these human decision processes to construct competitive decks in a sealed format. The results found here can also be applicable to other areas of note, such as sports drafting …

An Empirical Framework For Evaluating Semantic Preservation Using Hugging Face, Nan Jia, Anita Raja, Raffi Khatchadourian

Publications and Research

As machine learning (ML) becomes an integral part of high-autonomy systems, it is critical to ensure the trustworthiness of learning-enabled software systems (LESS). Yet, the nondeterministic and run-time-defined semantics of ML complicate traditional software refactoring. We define semantic preservation in LESS as the property that optimizations of intelligent components do not alter the system's overall functional behavior. This paper introduces an empirical framework to evaluate semantic preservation in LESS by mining model evolution data from HuggingFace. We extract commit histories, $\textit{Model Cards}$, and performance metrics from a large number of models. To establish baselines, we conducted case studies in three …

Integrating Probabilistic Trees And Causal Networks For Clinical And Epidemiological Data, Sheresh Zahoor, Pietro Liò, Gaël Dias, Mohammed Hasanuzzaman

Department of Computer Science Publications

No abstract provided.

Dataless Neural Networks For Boolean Satisfiability And Network Optimization, Andrew Evan Gautier

Graduate Theses, Dissertations, and Problem Reports (ETD)

Combinatorial optimization problems (COPs) require searching over a finite solution space subject to constraints, with the goal of satisfying an objective function. They arise in operations research, scheduling, resource allocation, circuit design, and many other fields. Many problems in combinatorial optimization (including satisfiability and network design) are NP-hard. Traditionally, researchers have built approximate solvers that return near- optimal solutions efficiently by developing increasingly sophisticated heuristics and meta- heuristics. Deep learning has provided new opportunities for improving combinatorial solvers by leveraging neural guidance to prune the search space. Traditional neural networks have distinct drawbacks in this context: separate training and …

Multimodal Representation Learning For Face Understanding: From Caption Supervision To Foundation Model Adaptation, Md Mahedi Hasan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The rapid advancement of intelligent surveillance systems and the increasing demand for reliable biometric identification in border security, public safety, and digital forensics require robust face understanding under unconstrained conditions, including low resolution, pose variation, and occlusion. While Vision Transformer (ViT)-based foundation models have greatly improved visual representation learning, their patch-based tokenization and lack of spatial inductive bias limit their ability to capture fine-grained details in low-resolution inputs. This dissertation investigates multimodal representation learning for face understanding through natural language supervision, large-scale face-caption pre-training, and parameter-efficient foundation model adaptation. It hypothesizes that textual supervision provides complementary semantic cues that improve …

Safe-R2r: A Safety-Aware And Budget-Optimized Retrieval Controller For Rag Systems, Mandar Sunil Gondane

Master's Projects

Retrieval-Augmented Generation (RAG) improves factual grounding in large language models by incorporating external evidence during inference. However, most RAG systems rely on fixed retrieval strategies that ignore query difficulty, computational cost, and prediction uncertainty. This project introduces SAFE-R2R (Safety-Aware and Budget-Optimized Reason-to-Retrieve), a retrieval controller that treats retrieval as a query-dependent decision problem. SAFE-R2R organizes retrieval into a multi-rung ladder ranging from no retrieval to deeper retrieval with reranking. At each rung, the system generates an answer, computes reliability signals, and combines them into a risk score used within a conformal calibration framework to decide whether to accept the answer …

Proof Of Recovery: A Model To Enhance Data Integrity In Data Management Systems, Gustaf Barkstrom

Theses and Dissertations

Businesses lose millions of dollars every year when they can’t restore data from backups. Research shows that Disaster Recovery Plan (DRP) testing is not conducted frequently enough, nor are records maintained that demonstrate full data recovery from backups. This work introduces a design science artifact called PRTOK that aims to increase DRP testing. The design science artifact is a software solution that integrates with Data Management Systems (DMS)

such as iRODS and DSpace, and can work with formats such as HDF5 and BagIt. Proof-of- recovery records, or tokens, are recorded in a replicated, resilient, and indelible proof-of- authority blockchain data …

Advancing Hate Speech Detection: Binary And Multiclass Approaches From Traditional Methods To Parameter-Efficient And Ontology-Guided Language Models, Mahmoud Abusaqer

Graduate Theses/Dissertations

The widespread proliferation of hate speech on social media platforms poses significant challenges for content moderation and user safety, requiring automated systems that are simultaneously accurate, efficient, and capable of fine-grained distinctions. This thesis investigates hate speech detection through five published manuscripts organized into two complementary threads: binary detection (hateful vs. non-hateful) and multiclass detection across demographic targeting categories. The binary thread progresses from a broad 38-model baseline spanning traditional machine learning, deep learning, and transformer architectures (where RoBERTa reaches 91.48% accuracy and CatBoost remains competitive at 88.60%) to parameter-efficient adaptation, in which Low-Rank Adaptation (LoRA) of large language models …

Computational Methods For Single-Cell And Multi-Omic Data Integration And Regulatory Network Inference, Jianlan Ren

Dissertations

Single-cell and multi-omic technologies have transformed the dissection of cellular heterogeneity and regulatory dynamics in health and disease. However, the high dimensionality, technical variability, and biological complexity of these datasets present significant challenges for integration, annotation, and interpretation. In this dissertation, a suite of computational approaches is introduced to address key problems in single-cell and multi-omic data analysis through model-based innovations and applied statistical frameworks.

First, a constrained deep learning framework for single-cell data integration, label transfer, and clustering is proposed. By incorporating biologically motivated constraints into the training process, robust performance is achieved across simulated and benchmark datasets spanning …

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

Faculty and Staff Publications & Presentations

This framework addresses the critical gap between post-quantum standards and workforce readiness. Shor's algorithm demonstrates that sufficiently powerful quantum computers can break the cryptographic foundations of internet security. While the cryptography research community has developed quantum-resistant algorithms, educational institutions have not prepared students to implement these solutions. Recent surveys show fewer than half of organizations have begun planning for post-quantum cryptography (PQC) transitions (Entrust Cybersecurity Institute, 2024; U.S. Government Accountability Office, 2023; (ISC)², 2024). The NICE Framework (Newhouse, Keith, Scribner, & Witte, 2017) outlines the knowledge and skills that cybersecurity professionals should possess. The framework omits post-quantum cryptography entirely. Organizations …

Patterns Of Llm Weaponization: A Comparative Analysis Of Exploitation Incidents Across Commercial Ai Systems, George Antoniou

Faculty and Staff Publications & Presentations

This comparative study examines patterns of Large Language Model (LLM) weaponization through systematic analysis of four major exploitation incidents spanning from 2023-2025. While existing research focuses on isolated incidents or theoretical vulnerabilities, this study provides one of the first comprehensive comparative frameworks analyzing exploitation patterns across state-sponsored cyber-espionage (Anthropic Claude incident), academic security research (GPT- 4 autonomous privilege escalation), social engineering platforms (SpearBot phishing framework), and underground criminal commoditization (WormGPT/FraudGPT ecosystem). Through comparative analysis across eight dimensions: Adversary sophistication, target selection, exploitation techniques, autonomy levels, detection evasion, attribution challenges, defensive gaps, and capability democratization, this research identifies critical cross-case patterns …

From Enhancement To Substitution: A Strategic Provocation On Simulation-Based Sport, Grant B. Morgan, Andreas Stamatis

Journal of Applied Sport Management

Advances in artificial intelligence, large-scale machine learning, and simulation technologies are rapidly transforming how sport is played, analyzed, and consumed. To date, most scholarly and industry discussions frame these technologies as tools that enhance embodied sport by improving performance, officiating, media production, and fan engagement. This paper extends that conversation by posing a more provocative strategic question: under what conditions might simulation move from enhancement to substitution? Focusing explicitly on sport as a business and entertainment enterprise, we argue that many of sport’s core sources of cultural and economic value—uncertainty of outcome, narrative continuity, legitimacy, and collective meaning—are structurally …

Instrument Response Functions Of The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Wenlei Chen, James H. Buckley, Marion Sudvarg

Computer Science Faculty Research & Creative Works

The Antarctic Demonstrator for the Advanced Particle-astrophysics Telescope (ADAPT) gamma-ray/cosmic-ray instrument serves as a precursor to the proposed APT mission. The APT mission is designed to improve sensitivity in the MeV-TeV gamma-ray range by an order of magnitude compared to current missions and is optimized for dark-matter and multimessenger research. The ADAPT instrument uses scintillating fibers for particle tracking and sodium-doped cesium iodide (CsI:Na) tiles read out with wavelength shifting (WLS) fibers for imaging, with solid-state silicon photomultipliers (SiPMs) for calorimetry. It includes four layers of imaging calorimeter detectors and scintillating-fiber trackers, functioning both as a Compton and Pair telescope …

Fpga-Based Data Processing Using High-Level Synthesis On The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Marion Sudvarg, Marion Sudvarg, Longhao Huang, Boran Yang, Blake Bal, Roger Chamberlain, Jeremy Buhler, Leonardo Di Venere, Leonardo Di Venere, Davide Serini, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler

Computer Science Faculty Research & Creative Works

FPGAs are widely deployed on high-energy astrophysics telescopes to read out sensor data from front-end electronics. To support continuous data streams or high trigger rates, FPGA logic may be employed to process raw sensor readout values, reducing the volume of data transmitted, processed, and stored by downstream CPU-based computational platforms. Across instruments, these FPGA-based processing pipelines often have similar semantics and share common stages. However, diverse telescope designs require unique implementations of the constituent algorithms, and the logic is often rewritten from scratch for a new instrument. Writing, simulating, and debugging firmware is difficult and time consuming. However, High-Level Synthesis …