Digital Commons Network^™

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 …

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 …

Revisiting Underwater Image Enhancement For Object Detection: A Unified Quality–Detection Evaluation Framework, Ali Awad, Ashraf Saleem, Sidike Paheding, Evan Lucas, Serein Al-Ratrout, Timothy C. Havens

Michigan Tech Publications

Underwater images often suffer from severe color distortion, low contrast, and reduced visibility, motivating the widespread use of image enhancement as a preprocessing step for downstream computer vision tasks. However, recent studies have questioned whether enhancement actually improves object detection performance. In this work, we conduct a comprehensive and rigorous evaluation of nine state-of-the-art enhancement methods and their interactions with modern object detectors. We propose a unified evaluation framework that integrates (1) a distribution-level quality assessment using a composite quality index (Q-index), (2) a fine-grained per-image detection protocol based on COCO-style mAP, and (3) a mixed-set upper-bound analysis that quantifies …

Panda-Plus: Improved Dataset Of Prostate Whole Slide Images From Panda Challenge With Pixel-Level Expert Annotations, Spencer Hopson, Carson Mildon, Corbyn Kubalek, Joshua L. Ebbert, Ryan Vance, Lauren Laverty, Paul Urie, Dennis Della Corte

Faculty Publications

Artificial intelligence (AI)-based prostate cancer detection through whole slide images (WSIs) offers promising potential to address the global pathologist shortage while improving clinical consistency. Digital slides and improving image analysis methods encourage the creation of tools to aid in WSI classification. Despite promising advances, these tools are still limited by available training data. Current publicly available datasets, such as Kaggle's PANDA Challenge, while large in scale, rely on slide-level labels that may introduce noise and limit model reliability. Others contain detailed annotations, but are smaller in size due to manual processing efforts. In this work, we introduce PANDA-PLUS, a 546-image …

Supply Chain Network Based On Blockchain And Intelligent Agent, Hiba Hamdi Hassan, Rana Fareed Ghani

Journal of Soft Computing and Computer Applications

In agricultural supply chains, the complexity and indeterminacy pose serious challenges to traceability, reliability and confidence today. This challenge is especially acute in the olive oil industry where adulteration, wrong labeling, and uneven chemical quality threaten the actual well-being of producers and consumers. The project aims to design a blockchain-based hybrid architecture with intelligent agents (FNNs) to enhance transparency, reliability and responsiveness in the olive oil supply chain. The Blockchain component enables a completely open, tamper-proof ledger to be built in a very decentralized way and preserved as an archive of every account of its transactions. The intelligent agents contribute …

Intelligent Extensible Markup Language Encryption Using Type-2 Fuzzy Logic, Faiez Musa Lahmood Alrufaye, Seham Ahmed Hashem

Journal of Soft Computing and Computer Applications

Financial and commercial institutions increasingly rely on Extensible Markup Language (XML) files as a standard means of exchanging data. However, this extensive use has created serious security challenges due to the fact that these files contain sensitive information such as bank card numbers and expiration dates. Relying on traditional full file encryption methods achieves a high degree of security, but it causes problems related to the large file sizes that consume memory and the long encryption and decryption times, which reduces the efficiency of systems when dealing with a large number of daily transactions. Methods based on Type-1 Fuzzy Logic …

Enhanced Generative Convolutional Networks: A Hybrid Algorithm For Refinement Video Classification, Dalal Thair Mahjoub, Hala Bahjat Abdulwahab, Kesra Nermend

Journal of Soft Computing and Computer Applications

Video classification is a vital area of research due to the growing volume of video content in various applications. Accurate category across various resolutions poses challenges, which include adapting to scaling, resizing, and compression. Therefore, this paper introduces an innovative Generative Convolutional Network (GCN) set of rules tailored for multi-resolution video classes. The proposed GCN model utilizes Convolutional Neural Networks (CNNs) combined with generative modeling to enhance the extraction of functions across varying video resolutions, which is crucial for maintaining class robustness in the face of common video adjustments, such as scaling, resizing, and compression. In contrast, traditional fashions frequently …

Review Of Video Steganography By Using Deep Learning Methods: Datasets, Techniques, And Evaluations, Noor Fahem Sahib, Soukaena Hassan Hashem, Ekhlas Falih Naser

Journal of Soft Computing and Computer Applications

The growing prevalence of cyber threats, including fraud and attacks, has intensified the demand for secure methods of safeguarding confidential information exchanged between users. As telecommunications increasingly rely on multimedia data, video steganography has become a prominent technique to address these concerns. By embedding sensitive data within video files, this approach enhances protection against unauthorized access and common internet-based attacks, offering a robust layer of security in an era of escalating digital risks. With the introduction of Deep Learning (DL) steganography methods recently, video steganography can be defined as a rapidly developing subject within information security. This study provides a …

Real-Time Hand Gesture Recognition System For Abductees Rescue Using Deep Learning Techniques, Aws Saood Mohamed, Nidaa Flaih Hassan, Abeer Salim Jamil

Journal of Soft Computing and Computer Applications

Hand gesture recognition is a challenging problem in computer vision, particularly in terms of security surveillance applications. This study presents the first efficient system for abduction-related hand gesture real-time detection based on deep learning. The most critical problem is to detect and recognize hand gestures in real surveillance conditions and to be computationally effective for real-time multi-hand tracking in various lighting situations while allowing reliable surveillance beyond the 1–4 meters limitation. The proposed system consists of three main parts: The adaptive hand tracking algorithm, which has been used to create the Abductees-Rescue dataset. Introduced pose estimation You Only Look Once …

Hate Speech Detection Using Optimized Feature Representation Via Spiral-Grey Wolf Optimizer-Based Machine Learning Approaches, Noor S. Farhan, Matheel E. Abdulmunim, Hasanen S. Abdullah

Journal of Soft Computing and Computer Applications

Hate speech detection is crucial as social media diversifies. This research present a lightweight, scalable system using traditional machine learning methods along with a new approach called Spiral-Grey Wolf Optimizer (S-GWO).

S-GWO effectively selects key features that consider both meaning and content from the Term Frequency Inverse Document Frequency (TF-IDF) space, leading to high-quality representation without excessive computing power.

The propoused system was tested on Arabic and another English datasets using six machine learning methods: SVM, RF, LR, KNN, NB, and SGD. It achieved 92% accuracy and F1 score on the Arabic dataset, while reaching 100% accuracy on the English …

Robust Security Assurance In Cyber-Physical Systems: From Attack Diagnosis To Attack Resilience, Zifan Wang

Dissertations - ALL

Cyber-Physical Systems (CPS) have become integral to critical infrastructure, from autonomous vehicles to industrial control systems. However, their increased connectivity and sophistication introduce new vulnerabilities, making security a paramount concern. This dissertation presents a comprehensive pipeline for enhancing CPS security, focusing on accurate diagnosis of trustworthy time frames and subsystems, followed by system resilience measures to restore safe states. The foundation of system resilience lies in an advanced checkpointing protocol for real-time multi-process systems. A novel three-step approach uniquely addresses both logical and timing correctness, which are crucial for modern CPS applications. By partitioning processes into directed acyclic graphs, implementing …

Fine-Tuning Llama2 For Summarizing Discharge Notes: Evaluating The Role Of Highlighted Information, Mahshad Koohi Habibi Dehkordi, Yehoshua Perl, Fadi P. Deek, Hao Liu

School of Computing Faculty Scholarship and Creative Works

This study investigates whether incorporating highlighted information in discharge notes improves the quality of the summaries generated by Large Language Models (LLMs). Specifically, it evaluates the effect of using highlighted versus unhighlighted inputs for fine-tuning LLaMA2-13B model for summarization tasks. We fine-tuned LlaMA2-13B in two variants using MIMIC-IV-Ext-BHC dataset: one variant fine-tuned with the highlighted discharge notes (H-LLaMA), and the other on the same set of notes without highlighting (U-LLaMA). Highlighting was performed automatically using a Cardiology Interface Terminology (CIT) presented in our previous work. H-LLaMA and U-LLaMA were evaluated on a randomly selected test set of 100 discharge notes …

Mixed Linear Equation–Inequality Systems Over The Pura Vida Neutrosophic Algebra, Muhammad Rayyanu Abdullahi, Abdulhadi Aminu

Neutrosophic Systems with Applications

This paper proposes a neutrosophic extension of max-plus algebra for solving mixed systems of linear equations and inequalities.Classical max-plus algebra is a powerful tool for modeling synchronization in discrete-event systems LastNatpreClose LastNatClose, but it assumes fully deterministic data.To incorporate uncertainty and indeterminacy, we reformulate the framework so that coefficients and variables are expressed as neutrosophic numbers

γ+λI,γ,λ∈ℝ,I∈[0,1],

where I quantifies the degree of indeterminacy.

We redefine the max-plus semiring in this neutrosophic setting, extend solvability and uniqueness results, and adapt the ONEMLP-EI algorithm LastNatpreClose LastNatClose to handle neutrosophic …

Mapping Sustainability To Cybernetic-Generative Artificial Intelligence-Based Education: An Innovative Neutrosophic Orbifold-Lattice Methodology, Mona Mohamed, Ahmed A. Metwaly

Neutrosophic Systems with Applications

The lightning-fast development of artificial intelligence (AI), notably generative artificial intelligence (Gen AI) technologies, has intrigued multiple disciplines, particularly education. In this context, Large Language Models (LLMs) serve as beneficial cognitive resources that address knowledge deficits and provide tailored educational support for learners and staff. For learners, LLMs are regarded as knowledgeable educators who offer prompt, focused responses and rationales to tricky queries. Whereby LLMs for staff, Strength enhancer, automating tedious tasks, and creating intelligent resources.Gen AI’s rapid growth offers enormous obstacles for educational systems, compelling them to discover solutions for these obstacles.

Conceptually, cybernetics is leveraged for bridging the …

Neutrosophic Σ - Baire Spaces, R. Vijayalakshmi, F. Josephine Daisy, M. Simaringa

Neutrosophic Systems with Applications

A Neutrosophic Baire Space extends the concept of Baire Space from classical topology to the realm of neutrosophic topology which deals with sets and spaces where truth, falsehood and indeterminancy are explicitly considered. In this paper the concept of neutrosophic σ - baire spaces are introduced in Neutrosophic topological spaces. Also Neutrosophic σ - dense, Neutrosophic σ - nowhere dense, Neutrosophic σ -first category and Neutrosophic σ - second category sets are defined. Several characterizations of neutrosophic σ - baire spaces are investigated and explained using examples and the conditions under which a neutrosophic topological space becomes a neutrosophic σ …

Advancements In Perfect Matchings Within Neutrosophic Fuzzy Graphs: Theory And Applications, Muhammad Saeed, Fatima Razaq

Neutrosophic Systems with Applications

Graph theory has been widely used in exemplifying relational structure, and a greater generalization to fuzzy and neutrosophic space enables the exemplification of uncertainty, indeterminacy, and inconsistency in complex systems. Some of these extensions include the neutrosophic fuzzy graphs that provide a more detailed description of the loose relations between the vertices and the edges. However, unlike in classical graph theory, where the concepts of matching and perfect matching are well developed, very little has been studied on how the two concepts can be extended to neutrosophic fuzzy graphs. To seal this gap, the current paper develops and defines the …

Chaos Engineering In Multi-Gigahertz Solid-State Lasers: A Novel Approach To Optoelectronic Control, Mikhail V. Gorbunkov, Yulia Ya. Maslova, Yulia A. Sinichkina

Karbala International Journal of Modern Science

This paper presents a comprehensive study of a multi-gigahertz chaotic generator of light pulses based on solid-state laser sources, including fiber lasers, governed by carefully designed positive and negative feedback loops. It harnesses the inherent nonlinear dynamics within a solid-state laser controlled by a combination of two inertial feedback loops, enabling the realization of complex chaotic behavior, including the logistic map scenario, under moderate amplification conditions. The laser system dynamics are rigorously investigated through theoretical modeling, employing a nonlinear map approach, and high-resolution picosecond simulations. The results of our numerical simulations highlight the efficacy of fast electro-optical feedback system with …

Unveiling The Anticancer Potential Of Syzygium Cumini: In Silico Insights Into Its Mechanistic Action Against Non-Small Cell Lung Cancer, Nur Sofiatul Aini, Win Darmanto

Karbala International Journal of Modern Science

Non-small cell lung carcinoma (NSCLC) is the most periodic type of lung cancer and the second most diagnosed cancer globally. Syzygium cumini is plant that extensively used in cuisine and traditional medicine. However, its potential for NSCLC treatment has not yet been elucidated. This study determined the potential of S. cumini as anti-NSCLC using in silico approaches. The in silico study was applied to perform active compound analysis, selection of target candidates, network pharmacology, functional annotation, molecular docking, and molecular dynamics simulation, respectively. Based on open source databases, S. cumini contained 115 compounds and 14 of them predicted to have …

Three Decades Of Chinese Internet Technology: Social Risks And Prevention Pathways, Zhaokai Yin, Weifu Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

With the rapid advancement and application of big data, artificial intelligence, and mobile Internet technologies, network technology has been driving social development, while its negative effects have gradually emerged. Based on a critical perspective and logical reasoning, this study systematically reviews the evolution and characteristic manifestations of social risks in China’s network technology over the past 30 years, revealing multiple hidden dangers it has caused in data governance, capital operation, and political communication. Under the new situation, to prevent and defuse the social risks of network technology, precise measures should be taken from aspects such as value guidance, institutional regulation, …

Application Of Reinforcement Learning To Precision Aerial Delivery System Control In Adverse Wind Conditions, Radman Zarbock

The Journal of Purdue Undergraduate Research

Precision aerial delivery systems (PADS) are a subset of airdropped parachute-leveraging package delivery systems that use autonomous guidance, navigation, and control (GNC) to reach targets with high degrees of accuracy. This technology emerged in the 1990s, and strides have been made since to improve the reliability of traditional physics-based controllers that guide PADS. However, these algorithms still struggle to deliver acceptable performance results when PADS are subjected to austere operating environments, such as those with unpredictable wind. Building on a foundational study in 2022 that used artificial intelligence (AI) and machine learning to improve PADS GNC performance, this study aims …

What Does Graptolite Origination And Extinction Reveal About The Cause Of The Late Ordovician Mass Extinction?, Charles E. Mitchell, H. David Sheets, Michael J. Melchin, Chris Holmden

Computer and Data Science Faculty Publications

Assesses the macroevolutionary turnover of paleotropical planktic graptolites during the Late Ordovician Mass Extinction (LOME) via automated sequencing and capture-mark-recapture modeling. Graptolites exhibited a succession of turnover pulses (sensu Elizabeth Vrba) that were coincident with the main phases of the Hirnantian glaciation and during which the Diplograptina experienced declining metapopulation size, elevated extinction, zero species originations, and ultimately, complete extermination. Concurrently, the Neograptina (latest Katian temperate zone immigrants) exhibit pulses of both extinction and adaptive radiation. Thus, the LOME involved intense species selection and the wholesale alteration of the clade diversity structure of a major element of the zooplankton. The …

Visualizing And Evaluating Binary Classifier Performance With Contingency Space, Colin D. Kehoe, Azim Ahmadzadeh

Undergraduate Research Symposium

Traditional metrics for evaluating binary classifiers, such as Accuracy, F1 Score, and True Skill Statistic (TSS), often obscure the underlying tradeoffs between true positive and true negative performance—particularly in imbalanced or high-stakes domains. This poster introduces the Contingency Space, a two-dimensional representation of classifier behavior defined by true positive rate (TPR) and true negative rate (TNR). Within this space, scalar performance metrics become geometric surfaces, revealing how scores vary across the entire landscape of possible classifier outputs.

We present a Python package that implements this framework, enabling users to map model predictions into the Contingency Space, visualize metric surfaces …

Pixel-Perfect Segmentation Of Solar Filaments, Jamie Harris

Undergraduate Research Symposium

The observation and classification of solar filaments has a drastic impact on the ability to predict solar-magnetic weather phenomena that threatens to put both satellite infrastructure and astronauts at risk. Using the Hɑ filter provided by the Global Oscillations Network Group (GONG), a network of six telescopes around the world dedicated to 24/7 surveillance of the sun, we are able to get images that clearly and prominently display filament activity. With the vast amount of images the GONG takes, it is not possible to manually analyze every image. Using the U-Net model for computer vision, we were able to train …

Face Value: A Computational Approach To Subjective Impressions Of Faces, Kevin Kpankou

Undergraduate Research Symposium

Various computational models of first impressions have been developed to uncover the mechanisms driving these judgments. However, the implicit notion of a singular ``human'' often overlooks meaningful individual differences in beliefs, attitudes, and associations, as well as culturally grounded group-level constructs. In this paper, we extend Cultural Consensus Theory (CCT) to estimate culturally shared beliefs about faces by incorporating latent constructs structured around interpretable facial features extracted via computer vision algorithms. We apply our model to a large-scale dataset of people’s first impressions of faces. Our approach reveals a robust mapping between facial features and culturally constructed impressions, allowing us …

Gauss’S Method For Orbital Determination, Milagros Tamara Giraldo

Honors Program Theses and Projects

Accurately predicting the orbital trajectory of celestial objects is essential for precise spacecraft navigation, planning planetary missions, avoiding potential collisions with space debris, and studying the long-term stability of planetary systems. Gauss’s method for orbital determination provides a way to predict the path of a celestial body accurately using only a small number of observations. In this project, we create an implementation that is not just a tool for executing Gauss’s method, but also an opportunity to study the formulation of the method itself. It allows for a practical and detailed examination of how different inputs, assumptions, and numerical choices …

Cyber-Physical Framework For Smart Paint Manufacturing: Hybrid Integration Of Plc And Recipe Management Simulation, Stephen A. Michael, Anas M. Atieh, Emmanuel Nkwocha, Nathir A. Rawashdeh

Michigan Tech Publications

This study investigates the implementation of Industry 4.0 paradigms in the context of smart paint manufacturing, focusing on process control and recipe management through the integration of Ignition SCADA, a Siemens programmable logic controller (PLC), and a MySQL Workbench database. The developed architecture employs Ignition as an interoperable communication interface that facilitates bidirectional data exchange between the PLC and the database, thereby establishing a cyber-physical system for automated monitoring and control. The digital recipe management module formalizes paint formulations into parameterized datasets specifying paint and solvent ingredient ratios, process variables, and operational constraints, which are executed autonomously by the control …

Tests Without Borders: A Global Approach To Measuring Visualization Literacy, Olivia A. Guess

McKelvey School of Engineering Graduate Student Theses & Dissertations

Visualization literacy assessments shape how we understand people's ability to interpret data, yet most existing instruments embed Western datasets and assumptions that limit their relevance for global audiences. This thesis argues that because data is personal, assessments must also be culturally grounded. We introduce a unified framework for adapting the Mini-VLAT into 22 regionally responsive short-form assessments, each retaining the structure of the original test while incorporating datasets and scenarios tailored to specific regions around the world. To demonstrate how such adaptations can be customized and validated, we present a detailed case study of a Ghana-adapted Mini-VLAT, developed in collaboration …

Is Ai Replacing Human Mental Health Professionals?, Michiko Ueda

Population Health Research Brief Series

An increasing number of people are turning to generative artificial intelligence (AI) tools and AI-assisted chatbots to manage mental health concerns. This data slice presents findings from a national survey of U.S. adults aged 18-49 (N = 1,805) conducted in October 2025. Among respondents, 35.2% reported using AI tools more than once a week for mental health support. Among those who had ever seen a human mental health professional, 28.4% reported visiting human providers less often since beginning to use AI for the same purpose. The findings suggest that a subset of users may be using AI to replace, rather …

Integrating Post-Quantum Cryptography Into A Cdn-Style Pki: Tls And Quic Performance Across Pqc Suites, Katrina Mary Parsom

UNLV Theses, Dissertations, Professional Papers, and Capstones

Post-quantum cryptography (PQC) adds larger certificates and extra workload to the Transport Layer Security (TLS) handshake. In order to analyze the effect of that change on content delivery, a controlled testbed with conditions replicating a Content Delivery Network (CDN) is used. The system uses a standard X.509 certificate chain over TLS 1.3. It compares RSA with classical key exchange, RSA with hybrid key encapsulation mechanism (KEM), and Module Lattice-based Digital Signature Algorithm (ML-DSA) with hybrid KEM. The test harness generates and provides certificates and establishes TLS parameters. The test harness also supports mixed web workloads and records key metrics, including …

Ankimedbench: Evaluating Hierarchical Medical Knowledge In Language Model Embeddings, Neel Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Despite achieving over 90% accuracy on medical benchmarks, recent studies show physicians cannot effectively leverage language models to improve clinical reasoning. Current benchmarks test isolated factual recall, but clinical practice requires hierarchical navigation through diagnostic categories—starting broad and narrowing systematically from chest pain to cardiovascular pathology to myocardial infarction to specific STEMI types. Existing evaluations cannot measure whether models preserve this taxonomic structure essential for clinical reasoning.

We introduce AnkiMedBench, built from 16,512 medical flashcards used by students preparing for licensing exams. Cards are organized across six hierarchy levels spanning 16 broad medical specialties to 672 specific diseases and conditions. …