Digital Commons Network^™

Lightweight Tinyml-Enhanced Task Offloading In Vanets For Next-Generation Intelligent Transportation Systems, Muhammad Ali, Tariq Qayyum, Asadullah Tariq, Zouheir Trabelsi, Irfan Ud Din, Shabir Ahmed

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Vehicular Ad Hoc Networks (VANETs) face resource constraints, high node mobility, and stringent latency requirements, especially in safety-critical applications such as collision avoidance, path planning, and emergency braking. Task offloading to nearby vehicles or Roadside Units (RSUs) mitigates local computational limits, but dynamic conditions, unreliable nodes, and rapid topology changes complicate dependable node selection. This paper proposes a Tiny Machine Learning (TinyML)-enhanced, credibility-based task offloading framework for real-time decision-making in vehicular networks. RSUs evaluate vehicle reliability through a three-component Credibility Assessment Module: a Task Assignment Component that distributes lightweight test tasks and filters unreliable nodes via TinyML inference; a Verification …

A Design Science Research Architecture For Xr-Based Pre-Visit Cultural Heritage Learning Applications, Mousa Al-Kfairy, Omar Alfandi, Saed Alrabaee

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Pre-Visit preparation plays a critical role in shaping visitors’ learning and engagement in cultural heritage sites; however, existing approaches largely rely on static and passive materials that fail to foster meaningful understanding before the physical visit. Extended Reality (XR) technologies offer new opportunities to address this gap by enabling immersive, narrative-driven pre-visit learning experiences. This paper proposes a conceptual architecture for XR-based pre-visit cultural heritage learning applications, grounded in Design Science Research (DSR). Drawing on museum pedagogy, experiential learning, and XR interaction design, the study identifies key educational and technical requirements and translates them into a layered, modular system architecture. …

Aoi-Aware Agentic Federated Mixture-Of-Digital-Twin Experts For 6g Vehicular Edge Intelligence, Asadullah Tariq, Mohamed Adel Serhani, Ikbal Taleb, Shayma Alkobaisi, Tariq Qayyum, Irfan Ud Din

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Digital twin-enabled vehicular edge intelligence is expected to become a fundamental service paradigm for sixth-generation (6G) intelligent transportation systems. However, the performance of such systems depends not only on model accuracy, but also on the freshness of digital twin states, timeliness of inference, privacy-preserving model training, and efficient use of heterogeneous edge resources. Existing DT-assisted federated learning and edge mixture-of-experts solutions optimize digital twin synchronization, distributed learning, and sparse inference largely independently, without allowing digital twin states to actively govern expert specialization, expert refreshing, and distributed orchestration. Nevertheless, the joint problem of how digital twins should guide federated expert specialization, …

Mapping Llm Misuse In Computing Education: A Survey-Based Risk Analysis Of Faculty And Student Contexts, Noura Alzaabi, Mohamed El-Attar, Sarah Kohail, Mahmood Niazi

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Large Language Models (LLMs) have become deeply embedded in computing higher education, yet the misuse risks they introduce for faculty and students remain insufficiently understood from a cybersecurity and data privacy perspective. This paper presents an empirical study in which a structured survey of 105 participants at a computing college was used to identify and systematically risk-score thirteen LLM misuse cases across faculty and student contexts. Using a Likelihood × Impact scoring model, the resulting taxonomy classifies misuse cases as Critical, High, or Medium severity, with over-reliance and skill atrophy, academic integrity violations, and research integrity risks emerging as the …

Evaluating Chatgpt-5 For Misuse Case Diagram Generation: An Empirical Evaluation, Alia Alzarooni, Yasser Khan, Hassan Alsayegh, Mohamed El-Attar, Rima Grati

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Misuse case diagrams are a widely adopted technique in security requirements engineering, enabling analysts to model adversarial threats and derive countermeasures early in the software development lifecycle. However, manual construction of these diagrams is prone to incompleteness and subjectivity, requiring significant security expertise. Large language models (LLMs) such as ChatGPT present a promising opportunity to automate this process, yet their effectiveness for generating structured security modeling artifacts remains largely unexplored. This paper presents an exploratory study evaluating ChatGPT-5's ability to generate misuse case diagrams directly from textual security requirements, using 12 case studies of varying complexity spanning small, medium, and …

From Image To Insight: Evaluating Llm Accuracy In Understanding Uml Use Case Diagrams With Claude, Mohamed El-Attar, Yasser Khan, Mahmood Niazi, Sajjad Mahmood, Mohammad Alshayeb

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UML use case diagrams are a prominent artefact of requirements engineering, capturing the functional scope of a software system in terms of actors, use cases, and their stereotyped relationships. The emergence of multimodal large language models with image understanding capabilities raises the question of whether such models can reliably extract structured construct-level information from use case diagram images. This paper reports an empirical evaluation of Claude on the task of counting 14 notational construct types from a corpus of 78 computer-generated UML use case diagrams, assessed against manually verified ground truth annotations. Results reveal a strongly differentiated accuracy profile: Claude …

Cliffinsight: An Educational Web Application That Visualizes The Calculation Of Effect-Sizes Using Cliff's Delta, Mohamed El-Attar, Ahmed Shuhaiber, Rima Grati, Sarah Kohail

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The purpose of calculating effect sizes in statistics is to quantify the practical significance of observed differences beyond mere statistical significance. While standardized mean difference measures such as Cohen’s d are widely used, they require normally distributed data, an assumption frequently violated in educational and social science research. Non-parametric alternatives such as Cliff’s delta (δ) are more robust under these conditions yet remain underused due to perceived computational complexity and limited accessible resources. Existing web-based tools for Cliff’s delta function primarily as numerical calculators and do not expose the underlying dominance structure that gives the statistic its meaning. This paper …

A Preliminary Exploratory Assessment Of Chatgpt To Generating Stride Data Flow Diagrams, Hassan Alsayegh, Mohamed El-Attar

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Threat modeling is a core activity in security-by-design practices, enabling early identification of architectural weaknesses before system implementation. The drawings used during STRIDE analysis are typically Data Flow Diagrams (DFDs), referred to as “STRIDE diagrams” in this paper. STRIDE diagrams provide a visual approach for categorizing security threats; however, constructing accurate STRIDE diagrams require experience and is often time-consuming. Recent advances in Large Language Models (LLMs), such as ChatGPT, raise important questions about their suitability for supporting structured security modeling tasks. This study presents a preliminary exploratory assessment of ChatGPT’s ability to generate, analyse, and iteratively refine STRIDE diagrams from …

Sustainable Paper Production From Date Palm And Reed Leaves Through The Valorization Of Agricultural Waste Products, Imane Belyamani, Alreem Alameri, Jacqueline Soghman

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The environmental consequences of wood-based paper production, including greenhouse gas emissions, have accelerated the search for sustainable alternatives. This study investigates the use of reed and date palm fibers as eco-friendly raw materials for paper production, focusing on starch's influence on their thermal, structural, and mechanical properties. Reed fibers exhibited a higher pulp yield (58.2 %) and lower lignin content (7.8 %) compared to date palm fibers (55.9 % yield, 14.1 % lignin), contributing to papers with smoother textures and greater flexibility. The incorporation of starch into both fiber types resulted in notable performance improvements, though the effects were more …

Leveraging Quantum Storage Mechanism For Digital Forensic Readiness Towards Smart City Security, Bashaer Aljeneibi, Richard Ikuesan

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The increasing digitization of urban infrastructure has introduced advanced efficiency and connectivity in smart cities while exposing them to sophisticated cybersecurity threats. This study explores how Quantum Storage Mechanisms (QSM) can be integrated with digital forensic readiness systems to enhance smart city security and incident response. Through a simulated environment, the research evaluates the effectiveness of QSM against three critical cyberattack scenarios: Distributed Denial of Service (DDoS), sensor spoofing, and supply chain firmware attacks. The findings reveal that QSM-enabled systems outperform traditional cybersecurity tools by ensuring tamper-proof evidence collection, real-time threat detection, and secure long-term data retention. The study also …

Drone Authentication System Using Radio Frequency Fingerprinting, Jamila Muhsen Alnuaimi, Shamma Ghaleb Almansoori, Noura Ahmed Alrumeithi, Richard Ikuesan

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The widespread integration of unmanned aerial vehicles (UAVs) across domains such as logistics, surveillance, and emergency response has introduced critical security challenges, particularly unauthorized access, identity spoofing, and drone cloning. Traditional software-based authentication methods, including GPS tracking and encryption, have proven inadequate against advanced cyber-physical threats. This paper proposes a secure and automated drone authentication framework based on Radio Frequency (RF) fingerprinting, leveraging intrinsic hardware-level signal imperfections to generate unique and unclonable drone identities. Using Random Forest classifiers, the system captures, preprocesses, and analyses RF features to distinguish between authorized and unauthorized UAVs. Validation with real-world RF datasets demonstrates high …

Towards A Context-Aware Driving Assistance System (Ca-Das): Advancing Intelligent Vehicular Safety Through Multimodal Context Integration, Fatma Outay, Siham Farrag, Anjum Zameer, Ansar Yassar

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Driving-related behavioural factors are responsible for 90% of traffic collisions. The rapid growth of urbanization and the complexity of the traffic conditions demand a smart, efficient, and flexible transportation system. The advancement of transportation through technologies such as the Internet of Things (IoT) and AI have reshaped the way that drivers interact with their vehicles and the surrounding environment. In this paper, we propose a comprehensive Context-Aware Driving Assistance System (CA-DAS) that employs sensor fusion, semantic context modelling, along with a machine-learning-based approach to provide personalised and proactive driving assistance across dynamic scenarios. The proposed CA-ADS was developed using a …

Missouri_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Missouri

No abstract provided.

Illinois_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Illinois

No abstract provided.

Nevada_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Nevada

No abstract provided.

Connecticut_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Connecticut

No abstract provided.

Arkansas_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Arkansas

No abstract provided.

Alaska_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Alaska

No abstract provided.

California_Raw_Data_06.02.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

California

No abstract provided.

Model Data Of "Aerial Imagery Resolution Optimization And Machine Learning Model Comparison For Land Cover Classification", Jessica Suoja

Research Data Sets

Downloadable zip file contents:

• 1MeterModel.csv (CSV file, 8.5 KB)
• 1MeterPrediction.csv (CSV file, 81.4 MB)
• 2MeterModel.csv (CSV file, 9.6 KB)
• 2MeterPrediction.csv (CSV file, 20.8 MB)
• 5MeterModel.csv (CSV file, 9.1 KB)
• 5MeterPrediction.csv (CSV file, 2.4 MB)
• 50CentimeterModel.csv (CSV file, 9.7 KB)
• 50CentimeterPrediction.csv (CSV file, 333.3 MB)
• 75CentimeterModel.csv (CSV file, 9.6 KB)
• 75CentimeterPrediction.csv (CSV file, 147.0 MB)
• 1125MeterModel.csv (CSV file, 9.0 KB)
• 1125MeterPrediction.csv (CSV file, 65.8 MB)
• 125MeterModel.csv (CSV file, 9.6 KB)
• 125MeterPrediction.csv (CSV file, 53.1 MB)
• 1375MeterModel.csv (CSV file, 9.5 KB)
• 1375MeterPrediction.csv (CSV file, 44.1 MB)
• 150MeterModel.csv (CSV file, 9.0 KB)
• 150MeterPrediction.csv (CSV file, 37.0 MB)
• 175MeterModel.csv (CSV file, 9.6 KB)
• 175MeterPrediction.csv …

Model Data Of "Land Cover Classification Of A Desert Riparian Ecosystem Using A Basic Stacked Ensemble Modeling Approach", Jessica Suoja

Research Data Sets

Downloadable zip file contents:

• CARTscript.R (R script, 2.2 KB)
• ForModel.csv (CSV file, 9.5 KB) 
• ForPrediction.csv (CSV file, 44.1 MB)

Artificial Sense-Making Dataset, Jason A. Bengtson, John Sandstrom, Nathan Camp

NMSU Library: Datasets

No abstract provided.

Hawaii_Raw_Data_06.01.26, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Hawaii

No abstract provided.

Maryland_Raw_Data_06.01.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Maryland

No abstract provided.

Colorado_Raw_Data_06.01.2026, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Colorado

No abstract provided.

Ny_Buds.Csv, Yasha Kahn, Panduka Nagahawatte, Lori Nichols

CSVs

No abstract provided.

Ny_Concentrates.Csv, Yasha Kahn, Panduka Nagahawatte, Lori Nichols

CSVs

No abstract provided.

Missouri_Raw_Data_06.01.26, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Missouri

No abstract provided.

Long Biogeographic Separation Limits Arthropod Associations On Eastern Asian Plants Introduced To Eastern North America, Robert J. Warren

Biology Faculty Datasets

No abstract provided.

Mississippi_Raw_Data_06.01.26, Yasha Kahn, Lori Nichols, Panduka Nagahawatte

Mississippi

No abstract provided.