Digital Commons Network^™

Psychological Needs And Ai Delegation Across Four Social Domains - A Cross-Cultural Analysis Of 35 Nations, Magnus Liebherr, Ala Yankouskaya, Mohamed Basel Almourad, Justin Thomas, Guandong Xu, Raian Ali

All Works

As artificial intelligence (AI) systems increasingly assume roles with social, educational, and emotional significance, understanding the psychological drivers behind individuals' readiness to delegate such roles to AI is crucial. Drawing on Self-Determination Theory (SDT), this study examines how the satisfaction of basic psychological needs (autonomy, competence, and relatedness) predicts individuals' readiness to delegate socially significant roles to AI across four domains (education, healthcare, mental health, and companionship) and 35 nations. Using data from over 35,000 participants in the 2023 Global Digital Wellbeing Survey, we applied Bayesian multilevel multivariate modelling to assess both global and culture-specific motivational associations. Results revealed that …

Vision Transformers And Convolutional Neural Networks For Land Use Scene Classification, Arun D. Kulkarni

Computer Science Faculty Publications and Presentations

Land use scene classification (LUSC) from remote sensing imagery plays a critical role in environmental monitoring, urban planning, and sustainable resource management. In recent years, deep learning methods have significantly advanced the state-of-the-art, with Convolutional Neural Networks (CNNs) dominating the field because of their strong ability to capture local spatial features. However, the emergence of Vision Transformers (ViTs) has introduced a new paradigm that models long-range dependencies through self attention mechanisms, potentially enabling improved global context understanding. This study presents a comparative assessment of Vision Transformers and CNN-based architectures for remote sensing land use scene classification. Representative CNN models, such …

Attention-Based Ensemble Deep Learning Model For Arabic And English Fake News Classification, Ameer Alhaq Alshamery

Journal of Intelligent Informatics, Networking, and Cybersecurity

It is difficult to classify articles as fake news since one article may consist of true facts with only some statements being fake. Moreover, classification becomes complicated for the Arabic language owing to its morphology and several ways of spelling, as well as the lack of well-classified and marked data sets. This paper presents an Ensemble Deep Learning Model (EDLM) used for Arabic and English fake news classification. The EDLM consists of CNN, Bi-LSTM with attention, and Bi-GRU with attention networks. Each of them produces one probability of the article, which is then summed up to a final probability via …

A Data-Driven Framework For Mitigating Breast Cancer Overdiagnosis: From Estimation To Risk-Adjusted Computer-Aided Diagnosis, William M. Brown Jr.

LSU Doctoral Dissertations

In Computer-Aided Diagnosis (CAD) of cancer, standard cost metrics (false-positives and false-negatives) fundamentally fail to account for overdiagnosis. Overdiagnosis is a critical scenario where a disease is correctly detected (true-positive) but is biologically indolent and would never have caused the patient harm or symptoms. While widely recognized in the medical community as a major healthcare crisis driving stressful and invasive overtreatment, overdiagnosis remains severely under-researched within computer science and engineering. This dissertation addresses this interdisciplinary gap by defining the three key computational challenges of overdiagnosis: (i) accurate estimation, (ii) harm quantification, and (iii) algorithmic mitigation. To overcome the estimation challenge, …

A Mathematical Decision-Making Framework For Athlete Development In A Collegiate Taekwondo Community: Prioritizing Coaching Interventions Using Statistical Analysis And The Analytic Hierarchy Process, King Harold A. Recto, Hazel Jade L. Antonio, Jhyrald Anthony P. Dalida

Electronics, Computer, and Communications Engineering Faculty Publications

Athlete development within collegiate sports communities requires informed decisions regarding the prioritization of coaching interventions and allocation of developmental resources. However, such decisions are frequently guided by experience and intuition, limiting opportunities for systematic and evidence-based decision-making. This study develops a mathematical decision-making framework for athlete development by integrating statistical analysis and the Analytic Hierarchy Process (AHP) within a collegiate taekwondo community. Data were collected from 25 collegiate taekwondo athletes who satisfied established eligibility criteria, including participation in University Athletic Association of the Philippines (UAAP) competitions during the previous three seasons. Athletes evaluated coaching practices across five dimensions: Training and …

Ai-Powered Resume Screening, Sang Suh, Numery Zaber

Faculty Publications

Traditional resume screening is manual, slow, and susceptible to bias, and it struggles to keep pace with today’s application volumes. This paper presents a dual-engine, AI-powered resume screening system designed for transparency and reproducibility. The primary (classical) pipeline encodes resumes and job descriptions using Sentence-BERT (SBERT), computes a resume–job match score via cosine similarity, classifies candidates into 25 job categories using XGBoost, and provides model interpretability through SHAP. In parallel, a prompted large language model (LLM) baseline (GPT-4o/4o-mini) outputs a match score and predicted category for comparative analysis. A Streamlit-based interface integrates both engines to support recruiter workflows and human-in-the-loop …

Information Theory Analysis Of Water Vapor Stable Isotopes From The Sail Campaign, Matthew John Rybecky

Earth and Planetary Sciences ETDs

Understanding the processes that control water vapor isotopic composition in mountain environ- ments is essential for interpreting isotope records and predicting water resource responses to cli- mate change. This thesis applies information theory to continuous, high-resolution water vapor stable isotope measurements from the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in the East River watershed of Colorado’s Upper Gunnison Basin, spanning the winter- to-spring transition of 2022–2023. The analysis employs Shannon entropy, mutual information, transfer entropy, and joint transfer en- tropy (JTE) to quantify how environmental variables, including surface meteorology, radiation, tur- bulent fluxes, and ERA5 reanalysis products, transfer information …

Surveyception: An Exploration Of Deceptive Survey Forms, Muhammad Danish

Computer Science ETDs

Survey platforms such as Google Forms and Microsoft Forms are widely used for feedback, data collection, and engagement, but scammers increasingly exploit them to distribute phishing and deceptive attacks. This thesis presents a large-scale study of survey-form abuse across ten major providers. We collected 140,000 forms from three sources: public posts on X, search-engine results, and web pages from the top 10 million DomCop-ranked domains. Using automated filtering and manual qualitative review, we identified 2,645 forms requesting sensitive information and classified 566 as scams. These forms used techniques including phishing, private-secret theft, account and personal-data harvesting, financial deception, and psychological …

A Hybrid Deep Learning Model Combining Cnn And Extreme Learning Machine For Cyberattack Classification, Israa S. Kamil

Journal of Intelligent Informatics, Networking, and Cybersecurity

As ransomware attacks and zero-day exploits grow sophisticated, the need for intelligent, accurate systems to detect such threats becomes clearer. In this paper, a hybrid learning model based on Convolutional Neural Networks (CNNs) and Extreme Learning Machine (ELM) is presented to improve multiclass classification performance for cybersecurity applications. The framework combines CNNs' hierarchical feature learning with ELMs' fast classification. An attention mechanism that assigns weights to each feature based on importance is included in the final model. The hybrid model performed well on the metrics: precision = 0.97, recall = 0.98, and F1- score = 0.97, and, as expected from …

Towards Intelligent Iot-Ndn Security: Ai-Driven Pit Attack Detection And Cache Attack Analysis, Sura Haidar Ali, Alaa Shawqi Jaber

Journal of Intelligent Informatics, Networking, and Cybersecurity

Beginning with Named Data Networking (NDN), an early form of information-centric networks, the paradigm of how data is transmitted over a network was changed through the use of ``content-based'' communication instead of ``host-based'', while creating native caching at intermediate points along the path to each destination, and improving upon the security of all previous paradigms. NDN contains many inherent benefits such as caching, security, etc., but like any other paradigm, NDN creates new types of vulnerabilities, particularly within some of the key elements of this paradigm; namely the Content Store (CS), Pending Interest Table (PIT), and the Forwarding Information Base …

Stop Blaming My Users: Illumination Of The Technocentric Mythos Bias, Ervin H. Frenzel, Richard Lightcap

Journal of Cybersecurity Education, Research and Practice

 Abstract -This conceptual essay addresses the need for systemic and systematic transdisciplinary analytical techniques within cybersecurity and technical security. This conceptual essay is contingent upon recognition that cybersecurity is not simply technical in nature, it does not need an adversary, and more importantly it is based upon systems engineering and systems thinking.  The essay contributes a socio-technical attribution chain and field-specific ontology/taxonomy which distinguish user-triggered events from root causes, latent conditions, technical debt, validation failures, governance failures, and attribution bias before assigning responsibility to end users. It systematically defines an ontology inclusive of developer technical debt, organizational debt arising from …

Escaping The Cyberstorm: A Gamified Social Engineering Training Program, Noah Mcclanahan, Fadi Abu-Amara, Ali Khattab, Travis Jett, Andre Jackson

Journal of Cybersecurity Education, Research and Practice

In this research work, we explored the effectiveness of gamification in improving cybersecurity awareness and training users on targeted social engineering attacks. Traditional cybersecurity training focuses on lectures and videos. These training methods may not actively engage employees, which reduces their knowledge retention and ability to recognize social engineering attacks. This lack of involvement is a concern, as social engineering continues to be one of the most prevalent attack methods faced by end-users. A gamified training program, Escaping the Cyberstorm, was developed using the Godot game engine to address key challenges in spreading cybersecurity awareness. The game includes real-life …

Accessibility Fairness Practices In Ai Applications For People With Disabilities, Megan Gross

Master's Theses

Advancements in artificial intelligence (AI) improve the lives of people every day with tools like the auto-captioning of videos, improved screen-reader capabilities, and advanced mobility control through speech. However, are all groups of people benefiting from AI or are some being overlooked and left out? Although AI tools made for people with disabilities (PWDs) have improved their lives, AI for the general population generally ignores the experiences of PWDs, making them unable to interact with and benefit from technology. This research evaluates ChatGPT and Gemini in Gmail for usability fairness and analyzes how current regulations and development processes fail to …

Artificial Intelligence Mechanisms In The Limit Of Crimes And Law Enforcement, Saad Mefleh Alsuwaileh

Journal of Police and Legal Sciences

This study explores the potential of employing technological mechanisms and modern innovations brought about by the Fourth Industrial Revolution, particularly advancements in the field of information technology, in the domains of criminal investigation, crime prevention, and law enforcement. It aims to analyze the impact of these technologies on crime control efforts and the promotion of justice.

The significance of the study lies in highlighting the power of technology in processing and analyzing massive volumes of data with greater speed and accuracy, thereby enhancing the efficiency of criminal investigations and the ability to predict and prevent crimes. The core research question …

Applying Artificial Intelligence Within Decision Support Systems And Its Role In Improving Proactive Thinking And Reducing Security Threats: The Mediating Role Of Data Quality, Hany Shaaban El Anany

Journal of Police and Legal Sciences

The study aimed to identify the impact of applying artificial intelligence within decision support systems in improving the level of proactive thinking and reducing security threats in government institutions in the Arab Republic of Egypt, as well as to examine the mediating role of data quality in this relationship, at a significance level of (α ≤ 0.05). The study sample consisted of (360) participants working in the departments of information technology, decision support, and cybersecurity within government institutions and national authorities that rely on AI-enhanced decision support systems.

The study adopted the descriptive analytical method and used a questionnaire as …

Match Made In Ml: Developing Compatibility Relationships In Evidential Reasoning Approaches With Machine Learning, Ella Jolie Thomas

Master's Theses

The presented expectation maximization informed evidential reasoning model extends the ability of the evidential reasoning calculus to support decision making by integrating an adaptive model learning capability. Compatibility relationships in Evidential Reasoning models are traditionally built by human domain experts. This process is labor-intensive, especially for large and complex models. Additionally, when new data becomes available, compatibility relationships must be reconstructed. Using machine learning and the expectation maximization algorithm, it is demonstrated that compatibility relationships can be constructed that learn relationships between domain knowledge that is used to make decisions. Using drug development as a domain of application, a traditional …

Can Machines Testify? Llms And The Boundaries Of Testimonial Epistemology, Michael J. Cummins

Philosophy Summer Fellows

As Large Language Models and AI chatbots become increasingly prevalent, pressing questions are raised about whether beliefs formed through LLM interactions carry the same epistemic weight as beliefs formed through human testimony. How we answer this question depends on whether LLMs can function as testifiers, a role which is typically assumed to require a human or human-like agent. This assumption has gone largely unexamined, yet its consequences are significant: if LLM outputs cannot constitute testimony, then the justificatory tools of testimonial epistemology are unavailable to any beliefs formed through LLM interaction. This paper challenges that assumption. It first argues that …

Stylometric And Formal Patterns In The Scholarly Impact Of Scientific Literature, Joshua Ange, Eric Godat, Rajani Sudan

SMU Journal of Undergraduate Research

Scientific communication is typically tied to promoting public engagement and interest in science, increasing scientific literacy, and playing an essential role in policymaking. The success of public communication of scientific findings is largely associated with secondary characteristics of research (e.g. the style of writing and presentation), rather than the primary content or research quality. But it is unclear to what extent the success of scientific literature intended for working scientists is influenced by those same secondary characteristics. Does the writing style of scientific articles impact their success in academic spheres? In this study, we explore the stylometric and formal characteristics …

Image Fusion Based On Deep Learning With Different Locations And Sizes Of Objects, Baneen Al-Kalabi, Tawfiq Al-Assadi

Journal of Intelligent Informatics, Networking, and Cybersecurity

Multi-focus image fusion combines partially focused images into a single all-in-focus composite. Existing object-based methods assume precise spatial and scale alignment across source images, an assumption that frequently fails in Misaligned Multi-Focus Dataset scenarios due to camera displacement and focal length variation. This paper proposes a novel training-free, object-aware fusion framework to address this limitation through a five-stage pipeline: YOLOv8x detection, SAM2-L segmentation, LoFTR correspondence matching, a novel Scale-Aware Area Resize Algorithm, and GLCM-guided MSB/LSB bit-level fusion. The framework was evaluated on the EDMF benchmark (20 image pairs, synthetically modified to simulate Misaligned Multi-Focus Dataset shifts) and a Misaligned Multi-Focus …

Lossless Medical Image Compression Using Integer Discrete Wavelet Transform With Adaptive Subband Differencing And Context-Adaptive Entropy Coding, Rasha F. Nadhim, Ibrahim Adel Ibrahim, Ashwaq T. Hashim

Journal of Intelligent Informatics, Networking, and Cybersecurity

From transform-domain decorrelation and adaptive entropy coding, we propose a method for efficient lossless compression of medical images in this work. We implement the Integer Discrete Wavelet Transform (IDWT) to decompose the input image into four subbands of LL, LH, HL, HH, encompassing approximation and directional detail elements, in the initial implementation. It also removes spatial redundancy in image information and decomposes image information into a less correlated and more sparsely distributed set of coefficients. To decrease redundancy further, it proposes a subband-dependent differencing scheme, which decorrelates neighbouring wavelet coefficients with directional prediction. So horizontal differencing is done on LH, …

A Blockchain-Integrated Federated Learning Model And Autoencoder-Based Feature Reduction For Improving Iot Intrusion Detection, Tahseen A. Wotaifi

Journal of Intelligent Informatics, Networking, and Cybersecurity

The rapid growth of Internet of Things (IoT) environments has brought forth a wealth of security challenges in detecting network intrusions in diverse and resource-restricted systems. Privacy, scalability, and single point of failure issues plague traditional centralized intrusion detection solutions. To address these challenges, the study proposes a secure and adaptive intrusion detection model using Federated Learning (FL) and Blockchain, augmented with autoencoder-based feature reduction. The ToN-IoT dataset is pre-processed, and then an unsupervised autoencoder is used to build informative low-dimensional feature representations. The processed data is deployed to various clients to mimic a real federated situation. Every client will …

A Dual- Task Hierarchical Graph Attention Network For Protein-Protein Interaction Sites Prediction, Oras A. Hussein, Eman S. Al-Shamery

Journal of Intelligent Informatics, Networking, and Cybersecurity

In computational structural biology, it is still very hard to accurately find protein-protein interaction (PPI) sites and estimate how strong the interaction would be. In this research, we provide an innovative two-stage deep learning framework that combines residue-level graph representation learning with protein-level regression to achieve a thorough modeling of protein interactions. Protein structures first encoded as residue graphs, with nodes that stand for amino acids and edges that show how close they are to each other in space. To find binding residues, a deep residual Graph Attention Network v2 (GATv2) uses multi-head attention, residual connections, and Jumping Knowledge aggregation …

Tile-Based Knot Assembly With Celtic!, Divya Bajaj, Ryan Knobel, Juan Manuel Perez, Rene Reyes, Ramiro Santos, Tim Wylie

Computer Science Faculty Publications

In this paper we focus on the intersection of tile assembling systems, edge-matching puzzles, combinatorial games, and knot construction and identity. As a basis, we utilize the game Celtic!, which is a 2-player board game where the goal of the game is to construct knots where one knot uses more of a player’s pieces than the other player over all knots. All pieces must build off an existing knot and a valid knot must be closed. We consider three variations: a 0-player self-assembly variation that deterministically places pieces to form a closed knot of some length, a 1-player puzzle variation …

Cytotoxicity Of Bioactive Peptides From Cowpea (Vigna Unguiculata L. Walp) Against Breast Cancer Cells (Mcf-7), Dian R. Ningsih, Zusfahair Zusfahair, Ely Setiawan, Purwati Purwati, Kasta Gurning, Fitri A. Puspita Sari, Elya Widiawati

Karbala International Journal of Modern Science

Cancer is characterized by dysregulated metabolic signaling pathways, leading to uncontrolled cellular proliferation. Current cancer treatments, which involve surgery, radiotherapy, and chemotherapy, still cause adverse systemic toxicities. Accordingly, one possible effort that can be undertaken is to explore safe anticancer substances from protein sources such as peptides. Bioactive peptides can be isolated by hydrolyzing cowpea (Vigna unguiculata L. Walp) protein with trypsin. The objectives of this study were to isolate and fractionate bioactive peptides from cowpea, to determine the anticancer activity of peptide fractions, to identify active peptide fractions as anticancer agents using Liquid Chromatography High Resolution Mass Spectrometry …

Exogenous Ribosome Incorporation Induces Lineage Transdifferentiation In Intestinal And Liver Cancer Cells, Haoxuan Gu, Nadia Wahyuningsih, Keisuke Yamashita, Shota Inoue, Fuchao Tan, Mani Sharaki, Muhaimin Rifa’I, Kunimasa Ohta

Karbala International Journal of Modern Science

It has previously been shown that exogenous bacterial ribosomes added to somatic cells and various cancer cell lines generate ribosome-induced cell clusters (RICs) capable of transdifferentiating into multiple cellular lineages. However, the scope of ribosome-induced transdifferentiation in cancer cells remains poorly understood. This study aimed to analyze the effects of exogenous ribosome incorporation on human cancer cell lines, specifically the intestinal epithelial cell line Caco-2 and the hepatocellular carcinoma cell line HepG2. Caco-2 and HepG2 cells were cultured with purified bacterial ribosomes in human ES/iPS cell medium after trypsinization, resulting in the formation of ribosome-induced Caco-2 (RICs-Ca) and HepG2 (RICs-He) …

Mathematically Driven Enhancement In Information Security, Suhaib Badran, Asmaa Alqassab

Karbala International Journal of Modern Science

A new mathematically based cryptographic method has been proposed to improve information security; it involves transforming data into a matrix and performing bit-level operations. This decryption then follows the basic mechanism of a deterministic, fully invertible algorithm, with this invertible algorithm having the same principle as the existing ones: the process of decryption is symmetrical, where the process is reversed based on the input of the ciphertext. The plaintext is transformed into a square matrix, which includes matrix rotation, permutation of rows, circular bit shifting, and finally an affine linear transformation followed by an additional Base62-like encoding layer to further …

Tapping Into The Ocean’S Hidden Energy: Feasibility Of A 5 Mw Otec Installation In North Bali, Widodo Setiyo Pranowo, Yani Permanawati, Gisela Malya Asoka Anindita, Agung Kurniawan, Albertus Sulaiman, Johar Setiyadi, Safri Burhanuddin, Ivonne Milichristi Radjawane, Hansan Park, Endro Sigit Kurniawan

Karbala International Journal of Modern Science

Indonesia is an archipelagic country surrounded by water and faces energy challenges due to the low use of renewable energy. Among the potential renewable options, marine energy is a particularly suitable resource given the country’s geographical nature. Based on this discussion, seawater temperature can be used as an alternative ocean thermal en-ergy known as ocean thermal energy conversion (OTEC) by using the difference in sea surface and deep-sea water temperatures. Therefore, this study aimed to examine OTEC installations in North Bali waters using closed-cycle OTEC calculations for a 5 MW system. Following this objective, we examined the water conditions by …

Assessment And Evidence Practices In Cybersecurity Education: A Systematic Review (2015–2025), James K. Mayberry

Journal of Cybersecurity Education, Research and Practice

This study presents a PRISMA-based systematic review of 412 cybersecurity education intervention studies, coding assessment methods, evidence types, claimed outcomes, use of established assessment instruments, and artifact availability. Despite frequent claims of skill development and workforce preparation, 45.4% of studies reported no identifiable assessment. Knowledge tests appeared in 11.4% of studies, while performance assessments appeared in 10.2%. From 2015 to 2025, assessment practices remained dominated by post-only designs or no assessment, with no statistically detectable increase in pre/post-capable designs. Use of established assessment instruments was rare, with 94.2% of assessed studies using ad hoc measures or not identifying an established …

Coordinating Meaning With Ai System Cards: A Thematic Analysis, Jennifer Rene French Cyrek

Doctoral Dissertations and Projects

As the meaning of AI risk remains unsettled across sociotechnical and public discourse, AI system cards are an emergent, yet understudied, genre of technical documentation through which AI technology developers publicly frame new AI system capabilities including risks. This thematic content analysis study examines how AI technology developers coordinate meaning regarding risk and responsible development in stewardship of AI. Guided by a constitutive view of communication and systems theory, second-order cybernetics, and the cybernetic tradition, this study analyzes a purposive corpus of AI system cards collected from 2023-2025 using thematic content analysis and the hierarchy of meaning heuristic from coordinated …

New Paradigm Of Forestry And Grassland Research Driven By Artificial Intelligence, Zhang Huaiqing, Jiaojun Zhu, Yang Liu, Tingdong Yang, Tian Gao, Jing Zhang, Xueyan Zhu, Yan Chen, Xian Jiang, Zeyu Cui, Jingwei Tan, Kexin Lei

Bulletin of Chinese Academy of Sciences (Chinese Version)

Addressing the current limitations in forestry and grassland research, particularly in cross-scale system cognition, complex process mechanism representation, and multi-scenario simulation, this study proposes an artificial intelligence-driven paradigm reconstruction framework, to shift the research model from experience-oriented approaches toward data- and intelligence-driven integration. On this basis, the study systematically establishes a multidimensional mapping between artificial intelligence and forestry and grassland research across research objects, processes, and objectives, and clarifies their intrinsic coupling mechanisms and technical pathways. Furthermore, it develops a foundational capability system to support the new paradigm from four key dimensions: data, computing power, models, and applications. By examining …