Computer Sciences Commons^™

Dynamic Order Scheduling For Pick-And-Pass System Considering Workload Balance And Learning Effects, Weihong Liu, Sixiang Zhao, Dali Zhang, Zhenhui Jiang

Journal of System Simulation

Abstract: In e-commerce logistics, the hybrid pick-and-pass systems offer both complexity and flexibility, enabling adaptation to a wider range of order picking scenarios. Therefore, they have been widely used. However, this also complicates the order scheduling problem, particularly when both workload balance and pickers' learning effects need to be considered. Efficiently scheduling orders to reduce picking time under these conditions poses a significant challenge. This study began by constructing a mathematical model for the static scheduling problem with known orders. Based on this model, a simulation model of hybrid pick-and-pass zones was developed, and a scheduling rule incorporating multiple system …

Simulation And Optimization Of Continuous Motion Control Based On Spiking Reinforcement Learning, Xiaode Liu, Yufei Guo, Yuanpei Chen, Jie Zhou, Yuhan Zhang, Weihang Peng, Zhe Ma

Journal of System Simulation

Abstract: To improve the model robustness for multi-degree-of-freedom continuous motion control, an intelligent motion control algorithm was proposed based on the Actor-Critic reinforcement learning framework and spiking neural networks. This algorithm integrateed the Actor network with spiking population coding and enhanced model training performance by introducing feature transformation methods. The Critic network was used to evaluate the effectiveness of the motion control. The results show that, compared to other reinforcement learning algorithms, the average reward value of this method increases by more than 10%. The simulation results validate the effectiveness of the model in improving multi-degree-of-freedom continuous control performance.

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

Journal of System Simulation

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

Understanding The Role Of Sentiment And Emotion For Predicting Forced Displacement, Helge Marahrens, Ameeta Agrawal, Ali Arab, Katharine Donato, Yaguang Liu, Nathan Wycoff, Mohamed Ahmed, Colin Hwang, Lina Laghzaoui, Kate Liggio, Multiple Additional Authors

Computer Science Faculty Publications and Presentations

Digital trace data play an important role determining where and when people will move during migration crises because of their detailed temporal and spatial granularity. Yet, identifying variables that reliably serve as early indicators of movement remains a challenging task. Within this context, we conduct an in-depth analysis of two types of variables that can be constructed from social media data – sentiment and emotion. Sentiment is conceptually broad and easier to detect from social media posts, while emotion is conceptually nuanced and more difficult to determine. We investigate the potential of both sentiment and emotion of Twitter/X posts as …

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

Michigan Tech Publications

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

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

Faculty and Staff Publications & Presentations

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

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

Faculty and Staff Publications & Presentations

No abstract provided.

Securing The Digital Harvest: Cybersecurity As A Core Agribusiness Skill, Jody Herchenbach, George Grispos

Mountain Plains Business Conference

The digitization of agriculture, through IoT-enabled equipment, cloud platforms, and precision technologies, has improved efficiency and profitability while also introducing significant cybersecurity risks. These vulnerabilities can disrupt supply chains, compromise sensitive data, and undermine financial stability. Yet agribusiness degree programs often overlook cybersecurity education. This paper proposes integrating cybersecurity content on threat awareness, incident response, and data protection into agribusiness curricula. Embedding these elements equips graduates to manage both digital and financial risks, enhancing resilience and competitiveness. Such curricular innovation aligns technical and managerial training, preparing future agribusiness professionals to lead securely and sustainably in an increasingly connected industry.

A Lightweight Microchained Architecture For Unmanned Aerial Vehicle Network Reputation Systems, Simeon Ogunbunmi, Ronghua Xu, Yu Chen

Michigan Tech Publications

Unmanned Aerial Vehicle (UAV) networks are widely adopted for diverse applications in modern Internet of Things (IoT) ecosystems, ranging from last-mile deliveries to infrastructure inspections. The growing reliance on UAVs makes a secure and trustworthy network environment essential. However, deploying a practical reputation framework in resource-constrained UAV networks necessitates a lightweight, high-throughput, and scalable solution. This paper introduces a Lightweight Microchained ARchitecture (LiMAR) for UAV network reputation systems. LiMAR presents a novel lightweight microchained blockchain model optimized for UAV networks, addressing key blockchain overheads such as storage bloat, high validation costs, and consensus delays. By separating operational data from on-chain …

When Cybersecurity Becomes A Reason For Amending Or Terminating An International Commercial Contract: Proposed Solutions, Mohammed El Hadi El Maknouzi, Enas Mohammed Alqodsi, Iyad Mohammad Jadalhaq, Ashraf Khalil

All Works

This study investigates the issue of a country's cybersecurity evolving into a justification for amending the scope of or terminating an international commercial contract. Therefore, the hypothesis is grounded in the neglect of cybersecurity-related issues during the negotiation of certain types of international commercial contracts, as well as in the drafting of their clauses. This underscores the need for an analytical approach to trace the emergence of such risks. The identification of these risks by the public authority responsible for overseeing cybersecurity may result in the suspension of the performance of the international commercial contract. This measure affects the national …

Web3-Based Identity And Kyc Innovations For Next-Generation Fintech, Usama Arshad, Abdallah Tubaishat, Sajid Anwar, Zahid Halim, Abedallah Abualkishik, Abrar Ullah

All Works

The growing reliance on digital financial services necessitates a secure, efficient, and privacy-centric approach to identity verification and Know Your Customer (KYC) compliance. Traditional identity management systems rely on centralized databases, making them susceptible to data breaches, inefficiencies, and regulatory constraints. Over 10 billion identity records have been exposed in centralized KYC breaches, leading to a 60% increase in financial fraud cases. The rise of Decentralized Finance (DeFi) has further complicated KYC compliance, requiring innovative solutions that balance privacy and regulatory requirements. This paper proposes a Web3-powered decentralized identity framework that leverages blockchain technology, self-sovereign identity (SSI), verifiable credentials (VCs), …

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

Feminist Pedagogy

No abstract provided.

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

Feminist Pedagogy

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

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

Journal of Human-Centered AI: Creativity and Practice

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

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

Publications

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

Graphrag-Enabled Local Large Language Model For Gestational Diabetes Mellitus: Development Of A Proof-Of-Concept, Edmund Evangelista, Fathima Ruba, Salman Bukhari, Amril Nazir, Ravishankar Sharma

All Works

Background: Gestational diabetes mellitus (GDM) is a prevalent chronic condition that affects maternal and fetal health outcomes worldwide, increasingly in underserved populations. While generative artificial intelligence (AI) and large language models (LLMs) have shown promise in health care, their application in GDM management remains underexplored. Objective: This study aimed to investigate whether retrieval-augmented generation techniques, when combined with knowledge graphs (KGs), could improve the contextual relevance and accuracy of AI-driven clinical decision support. For this, we developed and validated a graph-based retrieval-augmented generation (GraphRAG)–enabled local LLM as a clinical support tool for GDM management, assessing its performance against open-source LLM …

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

College of Population Health Faculty Papers

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

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

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

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

USF Tampa Graduate Theses and Dissertations

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

Skin Cancer Image Classification Using Deep Learning With Data Segmentation Technique, Akhilesh Kumar Shrivas, Hema Vastrakar

Karbala International Journal of Modern Science

The human skin is an impressive organ and structural element often impacted by a diverse range of recognized and unknown diseases. Diagnosing disorders that affect the outermost layer of the body is the most uncertain and difficult component in the scientific field. Dermatological diseases are one of the most significant health concerns in the 21st century since their identification is challenging and costly, plagued with challenges and the subjectivity that comes with human interpretation. The main objective of this piece of research work is to develop a robust model for the classification of skin cancer diseases using deep convolution neural …

Prompt Engineering For Genai In Cybersecurity Incident Response: A Multi-Platform Evaluation Based On Nice Pr-Ir-001, Yuanyuan Liu

Journal of Cybersecurity Education, Research and Practice

This study explores the application of prompt engineering in cybersecurity education, mainly by evaluating the performance of different generative artificial intelligence (GenAI) platforms when performing tasks consistent with the NICE framework role pr-ir-001 - Network Defense Incident Responder. The study employed structured prompts designed for a medical technology environment compliant with HIPAA and NIST SP 800-53, while the tasks of the three GenAI models (GPT-4, Gemini, and DeepSeek) were to generate event response scenarios. Their outputs will be evaluated from four aspects: accuracy, relevance, clarity and completeness.

The results show that the three models differ in depth and consistency, but …

Arizona’S Experiential Learning Opportunities: Regional Security Operations Centers And Cybersecurity Clinics, Joshua Kipers, Paul Wagner, Robert J. Honomichl

Journal of Cybersecurity Education, Research and Practice

The increasing frequency, sophistication, and economic impact of cybersecurity incidents have intensified the global demand for a skilled cybersecurity workforce. Traditional academic programs often fail to provide the applied experience necessary to prepare graduates for the rapidly evolving threat landscape. This paper examines Arizona’s innovative approaches to experiential cybersecurity education through the establishment of Regional Security Operations Centers (RSOCs) and the Arizona Cybersecurity Clinic. These initiatives integrate Kolb’s Experiential Learning Theory and the NICE Cybersecurity Workforce Framework to align academic preparation with real-world practice. The RSOCs, supported by the Arizona Department of Homeland Security, provide paid student internships focused on …

Cortenmm: Efficient Memory Management With Strong Correctness Guarantees, Junyang Zhang, Xiangcan Xu, Yonghao Zou, Zhe Tang, Xinyi Wan, Kang Hu, Siyuan Wang, Wenbo Xu, Di Wang, Hao Chen, Lin Huang, Shoumeng Yan, Yuval Tamir, Yingwei Luo, Xiaolin Wang, Huashan Yu, Zhenlin Wang, Hongliang Tian, Diyu Zhou

Michigan Tech Publications

Modern memory management systems suffer from poor performance and subtle concurrency bugs, slowing down applications while introducing security vulnerabilities. We observe that both issues stem from the conventional design of memory management systems with two levels of abstraction: a software-level abstraction (e.g., VMA trees in Linux) and a hardware-level abstraction (typically, page tables). This design increases portability but requires correctly and efficiently synchronizing two drastically different and complex data structures, which is generally challenging.We present CortenMM, a memory management system with a clean-slate design to achieve both high performance and synchronization correctness. Our key insight is that most OSes no …

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

Research outputs 2022 to 2026

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

Improved Streamflow Forecasting Through Swe-Augmented Spatio-Temporal Graph Neural Networks, Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

Computer Science Student Research

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 …

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

Department of Medicine Faculty Papers

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

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

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

A Case Study On The Effectiveness Of Llms In Verification With Proof Assistants, Barış Bayazıt, Yao Li, Xujie Si

Computer Science Faculty Publications and Presentations

Large language models (LLMs) can potentially help with verification using proof assistants by automating proofs. However, it is unclear how effective LLMs are in this task. In this paper, we perform a case study based on two mature Rocq projects: the hs-to-coq tool and Verdi. We evaluate the effectiveness of LLMs in generating proofs by both quantitative and qualitative analysis. Our study finds that: (1) external dependencies and context in the same source file can significantly help proof generation; (2) LLMs perform great on small proofs but can also generate large proofs; (3) LLMs perform differently on different verification projects; …

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

School of Medicine Faculty Publications

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

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

International Journal of Undergraduate Research and Creative Activities

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

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

Analyzing Adversarial Strategies And Countermeasures For Cyberbullying Detection, Maddie Juarez, Eldor Abdukhamidov, Manuel Sandoval, Deborah Hall, Mujtaba Nazari, George Thiruvathukal, Tamer Abuhmed, Yasin Silva, Mohammed Abuhamad

Computer Science: Faculty Publications and Other Works

Cyberbullying on social networking sites has become more prevalent. Most cyberbullying detection models often lack consideration of adversarial threads, leaving them vulnerable. This study evaluates the resilience of text-based cyberbullying detection models, constrained by limited available datasets, against word-level substitutions and character-level perturbations. We consider well-established ML techniques with real-world data and more recent LLM-based approaches to uncover model weaknesses. The results reveal that adversarial attacks can significantly reduce detection accuracy, e.g., most models are vulnerable to word- and character-level attacks with success rates up to 88% and 44%, respectively. We also find that LLM-based models such as CyberBERT are …

Maximum Likelihood Symbol Timing Algorithm Based On Cyclic Prefix For Ofdm Systems, Kwame S. Ibwe

Tanzania Journal of Engineering and Technology (TJET)

In this paper, a blind symbol synchronization algorithm is presented for orthogonal frequency-division multiplexing (OFDM) systems, and a timing function based on the redundancy of the cyclic prefix (CP) is introduced. The existing algorithms rely on the prior knowledge of the channel energy distribution i.e. channel power profile. In practical environment the channel power profile is unknown to the receiver and its statistics are expected to be highly changing. Nevertheless, the use of pilot symbols in channel profile estimation reduces efficiency as data subcarriers are used to carry pilots instead of payload. In this paper a timing function that accounts …