Digital Commons Network^™

Generative Ai's Copyright Enigma: A Comparative Study Of Fair Use And Fair Dealing, Taysir Awad

IP Theory

At the dawn of this decade, generative Artificial Intelligence (AI) models were at the apogee of modern science and technology. Their emergence introduced the world to a new paradigm of creativity and innovation, where machines can synthesize art, literature, and design with unprecedented sophistication, blurring the boundaries between human ingenuity and algorithmic computation. These models have the capacity to regenerate Oscar Wilde with the depiction of Ansel Adams, rewrite Harry Potter with William Shakespear’s proverbial tongue, and redesign St. Peter’s Basilica with Gothic arches, Seljuk carved stones, and an Antoni Gaudi roof architecture, relocated in the heart of New York …

Neuro-Symbolic Ai: A Future Of Tomorrow, Pankaj Chandre, Parikshit Mahalle, Gitanjali Shinde, Bhagyashree Shendkar, Shraddha Kashid

ASEAN Journal on Science and Technology for Development

Neuro-Symbolic AI: A Future of Tomorrow" explores the convergence of neural learning and symbolic reasoning to advance artificial intelligence (AI) systems. Symbolic reasoning makes use of knowledge representation techniques and rule-based systems, whereas neural learning analyzes data using deep learning models. AI becomes more adept at fusing data-driven insights with deductive reasoning when these methods are integrated using hybrid models and differentiable reasoning techniques. Applications show enhanced diagnostic precision and decision-making skills in a variety of industries, including robotics, healthcare, and finance. To promote responsible AI development, regulatory frameworks and ethical principles address issues like bias and transparency. Future paths …

Ai-Powered Image-Based Assessment Of Pressure Injuries Using You Only Look Once (Yolo) Version 8 Models, Mehedi Hasan Tusar, Fateme Fayyazbakhsh, Niloofar Zendehdel, Eduard Mochalin, Igor Melnychuk, Lisa Gould, Ming C. Leu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Objective: The primary objective of this study is to enhance the detection and staging of pressure injuries using machine learning capabilities for precise image analysis. This study explores the application of the You Only Look Once version 8 (YOLOv8) deep learning model for pressure injury staging. Approach: We prepared a high-quality, publicly available dataset to evaluate different variants of YOLOv8 (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x) and five optimizers (Adam, AdamW, NAdam, RAdam, and stochastic gradient descent) to determine the most effective configuration. We followed a simulation-based research approach, which is an extension of the Consolidated Standards of Reporting Trials …

Artificial Intelligence In Science And Society: The Vision Of Usern, Tommaso Dorigo, Gary D. Brown, Carlo Casonato, Artemi Cerda, Joseph Ciarrochi, Mauro Da Lio, Nicole D'Souza, Nicolas R. Gauger, Steven C. Hayes, Stefan G. Hofmann, Robert Johansson, Marcus Liwicki, Fabien Lotte, Juan J. Nieto, Giulia Olivato, Peter Parnes, George Perry, Alice Plebe, Idupulapati M. Rao, Nima Rezaei, Fredrik Sandin, Andrey Ustyuzhanin, Giorgio Vallortigara, Pietro Vischia, Niloufar Yazdanpanah

All Peer-Reviewed Publications

The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in …

Artificial Intelligence Should Get To Play Games Too: Large Language Models Playing Picobot And Generative Artificial Intelligence As A Teaching Assistant, Jonathan J. Wise

CMC Senior Theses

This thesis identifies strengths and weaknesses of Large Language Models

(LLMs) in a game-based, academic computer science setting. Specifically, it builds a

platform for LLMs to compete on a foundational assignment for entry-level Computer

Science students: playing the game of Picobot. To enable the analysis, it reconstructs

an earlier Picobot simulation: translating from Python2 to Python3, and introducing an

LLM mode that interacts with the Application Program Interfaces (APIs) of multiple

classes of LLMs.

We find that, while the LLM models understand the existence of the Picobot

game, the LLMs require specific instructions to generate valid moves. Therefore, this

thesis …

Towards Dynamic Learner State: Orchestrating Ai Agents And Workplace Performance Via The Model Context Protocol, Mohan Yang, Nolan Lovett, Belle Li, Zhen Hou

Educational Leadership & Workforce Development Faculty Publications

Current learning and development approaches often struggle to capture dynamic individual capabilities, particularly the skills they acquire informally every day on the job. This dynamic creates a significant gap between what traditional models think people know and their actual performance, leading to an incomplete and often outdated understanding of how ready the workforce truly is, which can hinder organizational adaptability in rapidly evolving environments. This paper proposes a novel dynamic learner-state ecosystem—an AI-driven solution designed to bridge this gap. Our approach leverages specialized AI agents, orchestrated via the Model Context Protocol (MCP), to continuously track and evolve an individual’s multi-dimensional …

The Future Of Ai Regulation In Drug Development: A Comparative Analysis, Gabriela Lenarczyk, Timo Minssen, Nicholson Price, Arti Rai

Faculty Scholarship

As artificial intelligence (AI) transforms drug development, regulatory frameworks are evolving to oversee its implementation, particularly at the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This paper makes three contributions to understanding emerging regulatory approaches. First, we offer a comparative analysis of how these agencies have responded to AI-driven advances, incorporating new US executive orders and the European Union (EU)’s AI Act. Second, we propose a novel analytical framework to understand regulatory divergence: the FDA’s flexible, dialog-driven model contrasts with the EMA’s structured, risk-tiered approach, reflecting broader institutional and political-economic differences. While the former encourages …

A Position Indicator With Applications In The Field Of Designing Forms With Artificial Intelligence, Ovidiu Ilie Șandru, Florentin Smarandache, Alexandra Șandru

Branch Mathematics and Statistics Faculty and Staff Publications

The indicators used so far within the Theory of Extension can be synthetically expressed by the notion of “position indicators”. More exactly, these indicators can be grouped in two main sub-categories: point-set position indicators and point-two sets position indicators. The secondary goal of this paper is to define these classifications, while the primary goal is that to extend the two notions to the most general notion of set-set position indicator.

Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

Engineering Management & Systems Engineering Faculty Publications

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset …

Optimizing Port Logistics Through Generative Ai: Revolutionizing Efficiency And Resilience In The Maritime Industry, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

The maritime industry faces growing challenges in optimizing port logistics due to increasing trade volumes, environmental regulations, and supply chain disruptions. This comprehensive literature review examines the transformative role of artificial intelligence (AI), with particular focus on generative AI, in enhancing efficiency and resilience in port operations. Through systematic analysis of 23 peer-reviewed studies published between 2021-2025, this review synthesizes advancements in real-time data integration, machine learning, digital twins, IoT, and autonomous systems that collectively improve operational decision-making, risk management, and environmental sustainability. Key findings reveal that machine learning applications achieve 90% effectiveness ratings in operational optimization, while predictive analytics …

Artificial Intelligence In Achieving Sustainable Development: Expectations Of Undergraduate Students, Jinhee Kim

STEMPS Faculty Publications

While there has been ample discussion regarding Artificial Intelligence (AI)’s contributions and challenges on the development agenda at the policy level, little is known about how students translate the potential and barriers of AI in achieving Sustainable Development Goals (SDGs). Drawing upon various qualitative data, including class observation, focus group interviews, and learning activity outcomes generated by 240 students across 7 different majors, this case study explores the expected roles of AI as well as barriers to AI adoption for sustainable development perceived by undergraduate students. The study revealed that students anticipated AI to play diverse roles, including data analyst, …

Utilizing Artificial Intelligence As A Strategic Risk Management Tool For Public Sector Operations And Auditing Processes, Oğuz Ümit Tamer, Bruce D. Mcdonald Iii, Farouk Hemici, Georgia Kontogeorga

School of Public Service Faculty Publications

Symbolizing a significant turning point in the historical landscape, AI is becoming an effective tool in today's public administration, not only for increasing capacity, quality, and speed in services, but also for strategic risk management. Regulators and algorithmic auditing play a central role in implementing fairness, transparency, and persistent controls against risks in AI systems. Discussing modern applications of AI, such as anomaly-based fraud detection, resource estimation, and continuous auditing, and their respective strengths and weaknesses, this study concludes that AI significantly enhances efficiency and oversight but also poses the risk of enshrining bias, opacity, and accountability gaps. By considering …

Assessing Iot Intrusion Detection Computational Costs When Using A Convolutional Neural Network, Mathew Nicho, Brian Cusack, Christopher D. Mcdermott, Shini Girija

All Works

IoT systems face vulnerabilities due to their data processing requirements and resource constraints. With 13 billion connected devices globally, this research investigates the economic viability of AI-based intrusion detection systems (IDSs), specifically analyzing the automation costs of implementing a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) for classifying malicious sensor traffic. This study introduces an innovative framework that evaluates six distinct architectural components of CNN and LSTM: image input processing, convolutional layer operations, max pooling layer functionality, fully connected layer characteristics, softmax output activation, and class determination mechanisms. The framework employs six metrics: matrix size, feature vector number, …

Data-Driven Gradient Optimization For Field Emission Management In A Superconducting Radio-Frequency Linac, S. Goldenberg, K. Ahammed, A. Carpenter, J. Li, R. Suleiman, C. Tennant

Electrical & Computer Engineering Faculty Publications

Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission-induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings.

Leveraging Large Language Models To Create Learner Personas For Training Design, N. Lovett, M. Yang, K. Herman, B. Li, M. Sosonkina, W. Purwanto, P. Jiang, M. H. Wu

Electrical & Computer Engineering Faculty Publications

This case examines the innovative use of Large Language Models (LLMs) to generate learner personas for developing learner-centered cybersecurity training materials when direct access to initial learner data is not available. The team developed a nine-stage iterative process for creating and refining AI-generated personas to address this constraint, integrating ethical review, stakeholder feedback, and action research principles. The process expanded upon Kouprie and Visser’s (2009) empathic design framework to ensure cultural responsiveness and mitigate potential biases in LLM outputs. Through multiple refinement cycles, initial generic personas evolved into detailed, context-rich archetypes which informed the development of effective and context-responsive training …

Advances In Battery Modeling And Management Systems: A Comprehensive Review Of Techniques, Challenges, And Future Perspectives, Seyed Saeed Madani, Yasmin Shabeer, Ananthu Shibu Nair, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, Shi Xue Dou, Khay See, Saad Mekhilef, Françios Allard

Electrical & Computer Engineering Faculty Publications

Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include …

Ai-Based Steganography Method To Enhance The Information Security Of Hidden Messages In Digital Images, Nhi Do Ngoc Huynh, Jiajun Jiang, Chung-Hao Chen, Wen-Chao Yang

Electrical & Computer Engineering Faculty Publications

With the increasing sophistication of Artificial Intelligence (AI), traditional digital steganography methods face a growing risk of being detected and compromised. Adversarial attacks, in particular, pose a significant threat to the security and robustness of hidden information. To address these challenges, this paper proposes a novel AI-based steganography framework designed to enhance the security of concealed messages within digital images. Our approach introduces a multi-stage embedding process that utilizes a sequence of encoder models, including a base encoder, a residual encoder, and a dense encoder, to create a more complex and secure hiding environment. To further improve robustness, we integrate …

Problem-Based Learning And Artificial Intelligence, Varna Taranikanti, Srineil Vuthaluru

Books and Book Chapters

In the current digital era, significant advancements in Artificial Intelligence (AI) have influenced various fields worldwide, particularly within the health professional education sector. This progress has also percolated into the medical education system, where Problem-Based Learning (PBL) is routinely used as a key pedagogical strategy. In PBL, students are able to fully exert their exploratory skills by solve problems through a student-centered learning approach. PBL also fosters critical thinking and encourages self-directed learning through the inquiry of clinical case scenarios. This chapter investigates the integration of AI, specifically tools like ChatGPT, into PBL frameworks to enhance learning outcomes for medical …

A Framework For Task Offloading In Heterogeneous Computing Applications Within The Fog Ran Architecture, Samira Taheri, Neda Moghim, Naser Movahhedinia, Sachin Shetty

VMASC Publications

Fog Radio Access Network (Fog RAN) has recently emerged as a promising architecture for supporting low-latency applications by bringing fog nodes and cloud resources closer to end users. However, existing research on computational offloading in Fog RAN lacks a comprehensive framework that addresses three key aspects: where to offload tasks, which processing nodes to utilize, and how to allocate resources for tasks with varying latency requirements. To address this gap, we propose TOFRA (Task Offloading for Fog RAN), a novel latency-aware task offloading framework. TOFRA is a centralized system that determines the optimal offloading strategy, whether to execute tasks locally, …

Advancing Natural Resource Management Through Artificial Intelligence, Jonathan Fisk

Graduate Theses/Dissertations

Natural aquatic systems are characterized by complex ecological processes and interactions occurring across multiple scales, and quantifying them is essential for proper management. However, managers often face decreasing budgets, increasing responsibilities, and limited staffing, resulting in challenges related to data prioritization, digitization, and management. While modern analytical technologies offer powerful tools, their effectiveness relies heavily on timely and accurate data input from diverse sources, including written, audio, and visual formats. Traditional data management practices are time-consuming, particularly due to the need for manual data verification. To address these challenges, innovative technologies leveraging artificial intelligence (AI)—including deep learning, machine learning, and …

Frameworks For Multi-Source Image Search Using Latent Space Feature Matching, Praveen Vijayakumar Phatate

Dissertations

In this dissertation we study frameworks for image matching for three distinct application scenarios: the first relates to positioning in GPS agnostic environments, the second relates to matching and retrieving similar images in an indoor environment sourced from multiple uncalibrated cameras and the third relates to matching and retrieving satellite images across multiple uncalibrated satellite cameras. We propose three different frameworks the first one based on the idea of transformers, the second one based on the idea of autoencoders and the third one based on the idea of Siamese networks. We report extensive experiments with novel datasets for each of …

Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi

School of Cybersecurity Faculty Publications

With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the …

Leveraging Transformer-Based Ocr Model With Generative Data Augmentation For Engineering Document Recognition, Wael Khallouli, Mohammad Shahab Uddin, Andres Sousa-Poza, Jiang Li, Samuel Kovacic

Engineering Management & Systems Engineering Faculty Publications

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced …

Certain Knowledge Of Administrative Decisions Issued By Artificial Intelligence Systems In The Public Sector: A Comparative Analysis, Nayel Musa Alomran, Odai Mohammad Alheilat

All Works

This paper aims to elucidate the matter of certain knowledge pertaining to administrative decisions, those issued by artificial intelligence. It also clarifies the position of the administrative judiciary with regard to the adoption of this presumption and through a comparative analysis of administrative judicial applications in Jor-dan, Egypt, and Morocco. The text addresses the most significant evidence for achieving certain knowledge of an administrative decision – and thereby initiating the appeal period against the appellant. The question at hand is whether the administrative judiciary applies the traditional theory of certain knowledge to its counterpart issued by artificial intelligence systems, especially …

Novel Ai/Ml-Based Frameworks For Protein Conformation Selection In Drug Discovery Applications, Shivangi Gupta

Dissertations

Drug development is a lengthy, expensive process with a high failure rate. The urgency brought on by the COVID-19 pandemic has accelerated the integration of artificial intelligence (AI) and machine learning (ML) to enhance drug discovery by increasing target specificity, reducing toxicity, and optimizing formulation strategies. Building on this momentum, this dissertation introduces novel AI/ML-driven frameworks for protein conformation selection and classification, addressing critical challenges in modern drug discovery. However, designing such a novel AI/ML data-driven framework is a difficult task because most real-world biomedical datasets suffer from class imbalance issues, which can significantly skew AI/ML model training, resulting in …

The Integration Of Artificial Intelligence And Ontologies: Transformations In Knowledge Representation And Application, Grazia Serratore, Julaine Clunis

STEMPS Faculty Publications

Artificial Intelligence (AI) is reshaping the landscape of knowledge representation. There is an increasingly strong bidirectional relationship, between AI techniques and ontologies. AI techniques revolutionized traditional, manual ontology development and contribute to automated ontology construction, while ontologies enhance the performance of AI systems and their semantic accuracy. Through a comprehensive review of current literature, this paper aims to examine: i) how Machine Learning (ML) techniques contribute to the automated construction, refinement, and validation of ontologies; ii) the most widely used and effective ML approaches for ontology construction; iii) how domain-specific requirements influence the selection and adaptation of AI techniques for …

Perceptions Of Stem Education And Artificial Intelligence: A Twitter (X) Sentiment Analysis, Demetrice Smith-Mutegi, Yoseph Mamo, Jinhee Kim, Helen Crompton, Matthew Mcconnell

Teaching & Learning Faculty Publications

Background, context, and purpose of the study: Artificial intelligence (AI) is becoming increasingly prevalent in science, technology, engineering, and mathematics (STEM) education, holding promising potential for supporting the design and implementation of quality STEM education. However, there is a lack of data-based research studying the diverse perceptions of AI in STEM education as conveyed on social media, the factors that influence those perceptions, or the change in those perceptions over time among public audiences. Results, the main findings: The purpose of this study was to examine public perceptions of AI in STEM education by analyzing X posts (Tweets) between 04/28/2020 …

Generative Ai's Impact On Student's Motivation And Well-Being In Online Graduate Programs: A Phenomenological Study Through Self-Determination Theory, Sohee Linda Lee

Theses and Dissertations

While higher education has increasingly embraced emerging technologies, limited research explores how adult learners at the graduate level navigate AI-driven feedback, content generation, and learning supports, particularly within online programs that require self-regulation and advanced academic rigor. To address this gap, this qualitative, phenomenological study examined how graduate students enrolled in online degree programs experience and interpret the use of generative AI (GenAI) tools, such as ChatGPT, in their coursework. Drawing on Self-Determination Theory (SDT), this study focused on how the use of GenAI tools may influence students’ perceptions on autonomy (volition and choice in tasks), competence (confidence in academic …

Artificial Intellectual Property, Daniel J. Gervais

Vanderbilt Law School Faculty Publications

The year is 2031. You just got up. Your smartwatch has registered the movement, and the coffee maker is on. After your shower, you pick up your phone or read the latest news on the New York Times app on your VR goggles or palm. Except for a few remaining columnists, the news is all written by AI machines. At work, you are responsible for a report on a new product for your company, and you use Al to design the product and prepare the pitch, including the visuals and text of your presentation to the C-suite. The Al system …

Copyright And Artificial Intelligence, Part 2: Copyrightability

Copyright, Fair Use, Scholarly Communication, etc.

This report by the United States Copyright Office addresses the legal and policy issues related to artificial intelligence (AI) and copyright as outlined in the Office’s August 2023 Notice of Inquiry (NOI).

The report will be published in several parts each one addressing a different topic. This part addresses the copyrightability of works created using generative AI. The first part, published in 2024, addresses the topic of digital replicas—the use of digital technology to realistically replicate an individual’s voice or appearance. A subsequent part will turn to the training of AI models on copyrighted works, licensing considerations, and allocation of …