Digital Commons Network^™

An Uncertainty-Aware Entropy-Oreste Framework For Big Data Platform Selection In Complex Multi-Sector Environments, Ahmed M. Ali, Ibrahim Alrashdi, Karam M. Sallam

Neutrosophic Systems with Applications

The increasing reliance on Big Data platforms across various industries has necessitated the development of systematic decision-support frameworks to guide their evaluation and selection. Given the diversity of available platforms, each offering different capabilities, scalability, and computational efficiency, choosing the optimal solution remains a complex challenge. This research proposes a novel analytical framework that integrates Spherical Fuzzy Sets (SFS) with the Entropy and ORESTE methods to address uncertainty and enhance the accuracy and robustness of Big Data platform evaluation. This hybrid integration, not previously applied to Big Data platform selection, enables objective criteria weighting through the Entropy method and comprehensive …

Client Feedback Effects On Enjoyment, Motivation And Self-Efficacy In A Project-Based Learning Course, Krista Stacey, David M. Bourrie

University Faculty and Staff Publications

This paper reports a mixed-methods evaluation of how feedback/project source (faculty-led versus client-led) shapes student outcomes in a two-course undergraduate game and simulation development sequence (N = 29 across two academic years). Quantitative measures (enjoyment, intrinsic motivation, self-efficacy) were collected with a six-point Likert survey and analyzed, but due to small sample sizes were not used to empirically evaluate the constructs. Instead, qualitative data comprised of de-identified focus-group transcripts and open-ended survey responses were analyzed with a keyword-assisted codebook and manual validation. Year 1 (faculty feedback) exhibited more consistent post-course gains, especially in self-efficacy, while Year 2 (client feedback) produced …

Sensi-Train: Context-Aware Sensitive Content Detection Using Transformers And Large Language Models, Nazeefa Muzammil

USF Tampa Graduate Theses and Dissertations

The detection of sensitive content has become a critical task in natural language processing due to the widespread use of online platforms and the social harm caused by it. Even though current methods have advanced significantly, many of them mainly rely on surface-level text and neglect to take into consideration contextual and cultural elements that are crucial for correctly assessing sensitivity of any content to a particular individual. Due to this, implicit offense, sarcasm, and culturally grounded expressions frequently cause problems for such systems. To enable culturally grounded sensitive content detection, this thesis presents Sensi-Train, a context-rich dataset and modeling …

Study On Strategies And Measures And Insights For Introducing Artificial Intelligence Talents In Japan, Fangxin Hu, Ru Ma, Yujun Su

Bulletin of Chinese Academy of Sciences (Chinese Version)

In response to the aging population and the need for innovation in science and technology development, Japan regards AI as a key technology to solve social problems. In addition to accelerating the training of domestic AI talents, Japan is also vigorously introducing overseas AI talents. This study sorts out and analyzes Japan’s long-term, annual, and AI-specific strategic planning for the introduction of AI talents, including Basic Plan for Science, Technology and Innovation, Comprehensive Innovation Strategy, Strategic Plan for Artificial Intelligence Technology, and AI Strategy, and explores Japan’s specific implementation measures such as updating the national residence management system, improving the …

Cerebral Documents And Algorithmic Sensemaking: Searching For Expressions In Human And Artificial Cognitive Collaborations, Rebekah L. Cowell

Proceedings from the Document Academy

Generative Artificial Intelligences (AIs) and current advanced large language models (LLMs) are algorithmically designed to generate text-based conversations as conversational agents (CAs), by replicating human language and conversational communication. Pairing human cognition with generative computationally coded cognition. We have never been here before: cerebral and artificial information collaborations and processing producing expressions that may or may not become visible as second-hand/secondary source documents.

Sensemaking or sense(un)making is a unique autonomous human drive cognitively, our information processing is sensemaking in action and expressions and articulations are evidence of the sensemaking cycle. Documentation [expressed or articulated through various mediums] are a product …

Molecular Detection Of Genes Encoded For Biofilm Formation In Bacteria Isolated From The Oral Cavity Of Patients, Tebarek Al-Taei, Ali A. Al-Hisnawi

Karbala International Journal of Modern Science

Background: A biofilm is a collection of the microbial cells attached to the surface that are encased in a matrix of extracellular polymers. Biofilm formation creates a physical and chemical barrier that prevents antibiotics from reaching bacterial cells, resulting in treatment failure and reinfection. The present study aimed to investigate the biofilm formation in bacteria isolated from the oral cavity of patients.

Methods: In this study, 150 bacterial isolates were obtained from tooth and gum surface swabs of male and female patients of different ages who visited specialized centers and dental clinics in Babylon City after they were clinically diagnosed …

An Ai Approach To Lunar Phase Detection: Enhancing The Identification Of The New Crescent With Astronomical Data Integration, Murad Al-Rajab, Samia Loucif, Raed Abu Zitar, Mubarak Gwaza Abdu-Aguye

All Works

Introduction: The observation of the lunar crescent is significant in astronomy, cultural traditions, and religious lunar calendar determinations. However, earth-based imaging that captures all lunar phases, particularly the new crescent across multiple months, remains limited. This study explores the feasibility of using artificial intelligence (AI) techniques to detect and analyze the birth of the new lunar crescent using space-borne imagery from NASA’s Lunar Reconnaissance Orbiter (LRO), spanning over 13 years. Methods: This study evaluates both deep learning and traditional machine learning approaches for new crescent detection. Convolutional Neural Networks (CNN), Random Forests (RF), and Support Vector Machines (SVM) were applied …

Who Lets Ai Take Over? Cross-National Variation In Willingness To Delegate Socially Important Roles To Artificial Intelligence, Ala Yankouskaya, Mohamed Basel Almourad, Magnus Liebherr, Fahad Beyahi, Guandong Xu, Raian Ali

All Works

Delegating socially significant roles to artificial intelligence (AI) is an emerging reality, yet little is known about how publics evaluate this transfer of responsibility across contexts and countries. This study applied a structural model to a large cross-national dataset (30,994 individuals in 35 countries) to test how cognitive appraisals, affective dispositions, and contextual factors jointly shape willingness to delegate socially important roles of companionship, mental health advisor, doctor and teacher to children to AI. The results revealed a robust hierarchy of delegation preferences, with companionship most frequently entrusted to AI, followed by mental-health advisor, teacher, and doctor. Cognitive appraisals emerged …

Emerging Threats In Ai: A Detailed Review Of Misuses And Risks Across Modern Ai Technologies, Niyat Seghid, Farkhund Iqbal, Khalifa Al-Room, Áine Macdermott

All Works

The swift evolution of artificial intelligence (AI) has enabled unprecedented capabilities across domains, while simultaneously introducing critical vulnerabilities that can be maliciously exploited or cause unintended harm. Although multiple initiatives aim to govern AI-related risks, a comprehensive and systematic understanding of how AI systems are actively misused in practice remains limited. This paper presents a systematic review of AI misuse across modern AI technologies. We analyze documented incidents, attack mechanisms, and emerging threat vectors, drawing from existing AI risk repositories, prior taxonomies, and empirical case reports. These sources are synthesized into a unified analytical framework that categorizes AI misuse across …

An Autonomous Robotic System For Object Retrieval And Delivery: Enhancing Independence For Users Living With Disability And Older Adults, Jincheng Li, Chenghao Lin, Amna Mazen, Youssef A. Bazzi

Michigan Tech Publications

As the global population ages, there is a growing need for assistive technologies to help older adults maintain their independence. This work presents a cost-effective autonomous socially assistive robot designed for object retrieval and delivery, enhancing accessibility in home environments. The system is built on the Robot Operating System (ROS) framework and integrates three key components: the Pioneer P3-DX mobile robot for autonomous navigation, the ReactorX-200 robotic arm for pick-and-place operations, and the Kinect v2 RGB-D camera for object detection and localization. Users interact with the robot through natural language processing by issuing voice commands to retrieve various objects. Microsoft …

Cybersecurity In Higher Education Institutions: Awareness, Policy, And Experience On Employee Behaviour, Abdullahi Abiodun Yusuf, Adriana A. Steyn

Journal of Cybersecurity Education, Research and Practice

The digital transformation of higher education institutions (HEIs) has introduced unprecedented connectivity and operational efficiency, but it has also heightened their exposure to cyber threats. South African HEIs, in particular, face increasing vulnerability due to their reliance on technology, openness, and diverse user communities. This study examines the influence of the institutional cybersecurity environment on employee cybersecurity-compliant behaviour (CCB), emphasising the critical roles of awareness, policy engagement, and experience. Drawing on Protection Motivation Theory and the Theory of Planned Behaviour, a conceptual model was developed to explore how cybersecurity awareness, policy familiarity, and prior experience shape employees’ attitudes, subjective norms, …

On The Misattribution Of Reassurance: A Structural Account, Griselda Poe

Publications and Research

This paper challenges the conventional assumption that empathy generates reassurance in interpersonal services. Reassurance is treated not as an emotion transmitted from the outside, but as an internal state transition that arises when a fixed and erroneous world model—a False Fantasy—undergoes collapse and the world becomes computable again. The study identifies a systematic misattribution pattern where providers and receivers treat empathy as a causal mechanism rather than a post hoc explanatory label. By introducing Base AI as an external reference—a system capable of providing structural information without emotional modulation—this paper demonstrates that reassurance is generated through operations such as distraction …

Responsible Ai In Teaching And Learning, Asa B. Stone, Mark C. Stone, Derek M. Heeren, Santosh Pitla

PRAIRIE: Pioneering Responsible AI for Research, Innovation, and Education

The rapid adoption of generative artificial intelligence (AI) in higher education presents both transformative opportunities and significant pedagogical risks. While AI tools are becoming embedded in academic and professional environments, their integration into teaching and learning raises critical questions about cognitive engagement, academic integrity, equity, and skill development. This white paper proposes a principled framework for the responsible integration of AI in higher education, grounded in the dual commitment to AI literacy and the cultivation of durable skills.

The framework articulates six core principles: purposefulness; transparency; integrity and attribution; critical AI literacy; equity and access; and privacy and data protection. …

Future Trends In Cybersecurity: A Meta-Review, Yara Mohammed, Manar Alsaid, Gahangir Hossain

Faculty Publications

The increasing importance of cybersecurity in protecting digital assets, data and infrastructures necessitates a reevaluation of research priorities within the discipline. As of today, numerous emerging cybersecurity topics are gaining significant importance in both academic research and industry applications. To identify recent trends in cybersecurity topics, this study extracts scholarly articles from two prestigious academic databases, the ACM Digital Library, and Google Scholar, covering the period from early 2015 to late 2024.Through a systematic identification of trends and focal points in cybersecurity research, a comprehensive analysis is facilitated, including Latent Dirichlet Allocation (LDA), Biterm Topic Modeling (BTM), keyword frequencies, and …

Online Visual Query System For Real-Time Large-Scale Spatio-Temporal Data Explorations With Error Bounds, Xueqi Huang

Dissertations, Theses, and Capstone Projects

Modern datasets continue to grow in size, dimensionality, and heterogeneity, creating increasing tension between the need for responsive, interactive analysis and the computational cost of accessing, aggregating, and visualizing large volumes of data. Traditional database engines and visualization tools often assume that full data retrieval is feasible or that exact computation is necessary for meaningful insight. In practice, however, analysts frequently benefit from timely, uncertainty-aware approximations than from delayed and exact results. This thesis investigates how data summarization techniques, specifically mergeable sketches can be combined with progressive, out-of-core visualization methods to support interactive exploration of datasets that exceed main memory. …

Courts Of New York: A Visual Atlas Of The City’S Public Basketball Spaces, Nathaniel Rattner

Dissertations, Theses, and Capstone Projects

Basketball courts in New York City are recreation facilities, community anchors and part of the city’s cultural image. In the basketball capital of the world, New Yorkers are rarely more than a few blocks away from a court. The visual diversity of these courts, however, is not widely documented in systematic ways.

This project makes that diversity visible to the public, combining open data, aerial imagery and computational analysis to document this important public space across the five boroughs. It is a narrative story and digital atlas of New York City’s public basketball courts, using surface color as a way …

Hybrid Server–Ai Architecture For Persistent Generative Game Worlds: Achieving Scalable, Consistent, And Low-Latency Interactive Environments, Jay Ratican, James Hutson

Faculty Scholarship

Generative artificial intelligence has demonstrated remarkable capabilities in real-time content creation for interactive entertainment, yet current implementations struggle with the persistence, consistency, and scalability demanded by modern multiplayer and long-form gaming environments. This paper presents a hybrid server–AI architecture that fuses the deterministic reliability of authoritative multiplayer server frameworks with the creative flexibility of state-aware generative systems. The proposed three-tier design consists of (1) a deterministic server backend leveraging technologies such as Unity Netcode for GameObjects, Unreal Engine 5’s dedicated servers, and Amazon GameLift to maintain authoritative and persistent world state; (2) a state-aware generative layer responsible for producing real-time …

Cyber Science Education Meets Healthcare Technology, Angela Spencer

Journal of Cybersecurity Education, Research and Practice

The research investigates how cyber science education combines with healthcare technology during the digital age to resolve a fundamental research gap in these two advancing areas. A combined approach utilizing extensive surveys and detailed interviews evaluates the functionality of learning platforms as well as cybersecurity measures and potential uses of emerging virtual reality (VR) and augmented reality (AR) tools to improve both educational and clinical environments. The research document describes its methodologies thoroughly while. The research documents multiple quantitative and qualitative results before performing its analysis, which leads to strategy development for digit. The researchers worked to find ways that …

The Feelit System: Application Content-Aware Perspectives And Challenges On Understanding User Likes In Social Network Posts, Konstantinos Theocharidis, Hady W. Lauw, Panagiotis Karras

Research Collection School Of Computing and Information Systems

In a series of our prior works, we study influence and subscription maximization problems in social networks that are based on posts having influential content; as content we consider a set of features where each feature corresponds to a specific social network page, whereas influence and subscription relate to gaining the postlike and subscription-to-brand page of targeted users, respectively; subscription is conceptually achieved as repetitive influence on users. So, both influence and subscription depend on content that gains the likes of users; however, to be realistic, modeling and estimating such likes is a complex problem that has not been adequately …

Prisrv+: Privacy And Usability-Enhanced Wireless Service Discovery With Fast And Expressive Matchmaking Encryption, Yang Yang, Guomin Yang, Yingjiu Li, Pengfei Wu, Rui Shi, Minming Huang, Jian Weng, Hwee Hwa Pang, Deng, Robert H.

Research Collection School Of Computing and Information Systems

The evolution of decentralized identity (DID) and self-sovereign identity (SSI) frameworks, as endorsed by W3C Verifiable Credentials (VC) and eIDAS 2.0, underscores the need for secure, efficient, and privacy-preserving credential management. However, existing credential systems often depend on centralized issuers, lack efficient aggregation mechanisms, or fail to ensure unlinkability across authentication sessions. To address these challenges, we propose DISC (Decentralized Identity System with Self-Sovereign Credential Aggregation), a novel credential system that enables multi-authority credential issuance, user-controlled credential aggregation, and unlinkable authentication. DISC allows users to aggregate credentials from multiple issuers while maintaining constant-size authentication tokens and supporting batch verification for …

The Case For Ai Authorship In Copyright Law, Cheng Lim Saw, Duncan Lim

Research Collection Yong Pung How School Of Law

Today, with generative AI, literary and artistic works can be created almost effortlessly. There is at present intense debate as to whether works generated by AI – broadly categorised as “AI-assisted” and “AI-generated” works – ought to attract copyright protection. AI-assisted works are those that involve some degree of human intervention. Where AI-generated works are concerned, however, such works are created autonomously by the AI itself with minimal (de minimis) input from an identifiable human being. Presently, it is generally accepted that AI-generated works do not attract copyright protection for want of a human author. This article examines whether it …

G-Trac: Graph-Textual Representations Alignment For Cold-Start Recommendations, Li Yang Chang, Yuan Fang, Ming Feng Tsai, Chuan Ju Wang

Research Collection School Of Computing and Information Systems

The cold-start problem remains a significant challenge in recommendation systems, particularly for new users or unseen items with little to no historical data. Existing methods, including graph neural networks, often struggle in such scenarios. Inspired by the success of transformer models in natural language processing, we propose G-TRAC (Graph-Textual Representations Alignment for Cold-start Recommendations), a novel approach that integrates transformer-based textual modeling with graph neural networks. By effectively leveraging both textual and structural information, G-TRAC addresses cold-start challenges more effectively. Extensive experiments demonstrate its ability to enhance recommendation quality and generalize well across diverse scenarios.

A Virtual-Reality-Based Dental Simulator For Endodontics With Automated Formative Feedback, Yousef Salah Abo El Ela

Theses and Dissertations

Advancements in virtual reality (VR) and haptic technology are transforming the landscape of medical and dental education, offering new avenues for safe, immersive, and repeatable training experiences. Within dentistry, endodontics presents unique challenges for preclinical education due to anatomical complexity, limited access to extracted teeth, ethical concerns, and the shortcomings of conventional plastic models. Despite endodontics specific plastic teeth being available, they fall short of replicating the hardness of real extracted teeth, are relatively costly compared to generic plastic teeth, and are ultimately a disposable item which makes them inadequate as a sustainable long-term solution. Extracted teeth do a much …

Artificial Intelligence In Higher Education, Opportunities, And Challenges: A Review, Sharifa Alblooshi

All Works

Artificial intelligence (AI) is a growing force of change in higher education, providing assistance to students, teachers, and administrators in teaching, learning, and administration. As AI technologies advance rapidly, they present a combination of significant opportunities and complex challenges. In this study, we examine the role of AI in higher education, highlighting both its positive and negative impacts, as well as current policy gaps and issues arising from its deployment. The literature on the topic was reviewed to determine how AI decisively impacts teaching and learning, the role of AI in assessments and academic integrity, as well as ethics, psychological …

Adaptive Multi-Agent Intelligence: A Dynamic Data Management System For Enhanced Data Quality And Reconciliation, Anthony Taeyang Vigil

USF Tampa Graduate Theses and Dissertations

Organizations struggle with data quality problems that cost them millions of dollars annually. Manual data cleanup and standardizing, also known as reconciliation, can take skilled analysts days to complete and can still contain human errors. This dissertation addresses this challenge by developing and validating the Origin Data Management System, which automates processes that traditionally require days of manual work. The system achieved up to 100% accuracy in standardizing vendor names and identifying CEO information across real-world test datasets from Jefferson County Public Schools, Forbes, and Fortune Magazines. Processing time dropped from days to minutes—a 96-fold improvement over manual methods. These …

Generative Ai Use And Self-Learning In Higher Education: The Role Of Learning Difficulties, Dana Saleh, Areej Elsayary

All Works

The rapid development of GenAI tools and their adoption in education have shown promising potential to personalize learning experiences. However, their effectiveness is influenced by factors such as familiarity, frequency of use, and the impact on self-learning. This study investigates the undergraduate students' familiarity with Generative AI (GenAI) tools, their frequency of use, and the perceived impact of GenAI on self-learning, with particular consideration of differences between students with and without learning difficulties. Prompt engineering is also included as a secondary aspect of students' GenAI experience. The research employed a quantitative survey design, utilizing validated scales to measure familiarity, usage …

Developing A Multimodal Approach To Channel Characterization On Youtube, Shadi Shajari

Theses and Dissertations

YouTube has become a dominant arena for global information sharing, where creators and audiences interact through content, comments, and engagement dynamics. Understanding how these interactions shape a channel’s identity requires a comprehensive characterization of both audience behavior and content structure. This dissertation develops a unified framework for characterizing YouTube channels through multimodal analysis that integrates behavioral modeling, dimensionality reduction, clustering, and content-based characterization to provide a holistic view of audience and editorial patterns. The framework begins by analyzing the structural relationships within co-commenter networks, where users who repeatedly comment on the same videos are connected to capture patterns of interaction …

Pl-Mamba: A 3d Point Cloud Semantic Segmentation Network Based On Bimodal Fusion, He Zhu, Feng Zhou, Mengxiao Zhu, Ju Dai

Journal of System Simulation

Abstract: To enhance the semantic discrimination capability in point cloud semantic segmentation, a 3D point cloud semantic segmentation network named PL-Mamba is proposed, which is centered on the fusion of point cloud (P) and language (L) dual modalities. This method takes PointMamba as the backbone network, leveraging its excellent long-sequence modeling and global perception capabilities. It introduces a language prompt mechanism and uses a pretrained language model BERT to encode the context of category labels, obtaining semantically rich text features. The text information serves as a language guided token and is deeply integrated with point cloud features through cross modal …

Evaluation Of Mcf-7 Breast Cancer Cell Cytotoxic And Antioxidant Activities Of Peptide Fractions From Symbiotic Bacteria Of Jellyfish Catostylus Sp., Eka Sry Wahyuni, Ahyar Ahmad, Muhammad Nasrum Massi, Sofa Fajriah, Randi Rimpung, Muh. Akbar Ardiputra, Harningsih Karim, Irda Handayani

Karbala International Journal of Modern Science

Marine-derived symbiotic microorganisms are recognized as a promising source of bioactive compounds with potential therapeutic applications, yet research on jellyfish-associated bacteria remains limited. This study examines the bioactivity of peptide fractions derived from symbiotic bacteria isolated from the jellyfish Catostylus sp., collected from the coastal waters of South Sulawesi, Indonesia, with a focus on their anticancer and antioxidant properties. Following sample collection, the symbiont bacteria were isolated, enzymatically hydrolyzed, and purified before their biological activity was evaluated. Preliminary cytotoxicity screening using the brine shrimp lethality assay revealed that the extracellular peptide fraction (5–10 kDa) and intracellular peptide fraction (3–5 kDa) …

Exploitation Prioritization And Residual Risk Assessment Based On Hybrid Mcdm Model, Zibo Wang, Yaofang Zhang, Sicai Lv, Yingzhou Wang, Hongri Liu, Bailing Wang

Turkish Journal of Electrical Engineering and Computer Sciences

Exploitation is one of the most significant ways to launch attacks using vulnerabilities. The increasing number of vulnerabilities and limited allocation of security resources make it impossible to eliminate all exploitations. Because not every vulnerability can be fixed, it is necessary to rank exploitations and subsequently assess the residual risk, which is defined as the remaining threat potential after each elimination. In this paper, a structured and flexible decision support framework based on a hybrid multicriteria decision-making model is proposed for prioritizing exploitations and assessing residual risk. Metrics are treated as criteria in the model. The hybrid model is developed …