Digital Commons Network^™

A Critical Realist Erp Implementation In Zimbabwean Mining Industry Organisations, Jairos Mukwenha

Tanzania Journal of Engineering and Technology (TJET)

This research uses a critical realist framework to examine the factors influencing the success of enterprise resource planning (ERP) system implementation in Zimbabwean mining industry organisations. From the perspective of critical realism, the mining industry in Zimbabwe faces a complex interplay of opportunities and obstacles while implementing ERP systems. The deployment of ERP in mining firms is critically examined in this paper, emphasising how these systems might improve operational efficiency while considering Zimbabwe's particular socioeconomic circumstances. By exploring underlying structures, mechanisms, and outcomes, the research aims to identify critical challenges and opportunities and develop practical recommendations for improving ERP adoption …

Development Of A Microcontroller-Based Intelligent Traffic Light Control System For Vehicular Movement In T-Junctions, Frederick O. Ehiagwina

Tanzania Journal of Engineering and Technology (TJET)

This research is devoted to the issue of regulating traffic congestion in major cities using light-dependent resistors coupled with the PIC16F877A microcontroller. This study proposes an intelligent traffic control system for T-Junctions, utilizing sensing and control to optimize traffic flow through dynamic phase adjustments and congestion reduction, enabled by a microcontroller-based decision-making system. The proposed system reduces traffic congestion, automates control, and enhances safety, minimizing accidents and lowering infrastructure costs. Under simulated environment, it demonstrates an average response time of 50 ms and achieves 99% accuracy in displaying the correct countdown. Finally, the number of state transitions handled per minute …

Infusing Aboriginal Perspectives In Cyber Education, John Shannahan, Mohiuddin Ahmed

Research outputs 2022 to 2026

While human factors are important in cyber security, the discipline has largely not explored incorporating indigenous perspectives—or, more specifically, in an Australian context, Aboriginal perspectives—in its curricula. In this paper, we introduce a promising approach for aligning Aboriginal perspectives with the needs of cyber security graduates and incorporating diverse perspectives into cyber degrees. The approach advocates for the centrality of good curriculum design fundamentals: backward design, constructive alignment, and student outcomes. The paper ends by reflecting on challenges and lessons from the first implementation and review of the material. It provides recommendations for other cyber practitioners exploring ways of incorporating …

An Efficient Blockchain-Based Privacy Preservation Scheme For Smart Grids, Mohamad Badra, Rouba Borghol

All Works

Smart grids have revolutionized electricity management and distribution, but they also generate and transmit vast amounts of consumer data, raising privacy concerns. In this paper, we propose a blockchain-based solution to preserve user's privacy in smart grids and to mitigates data forgery, profiling, and man-in-the-middle attacks. Moreover, our solution provides security services such as authentication and non-repudiation to prevent unauthorized access to sensitive data and ensure accountability and traceability. We validate our approach through testing and show that it is a simple, scalable, cost-effective solution with minimal computational processing overhead.

Generating More Equitable Fair Use, Jacqueline Kessel

Pepperdine Law Review

From advancing healthcare and education to threatening democratic systems, generative artificial intelligence (AI) has demonstrated a capacity to positively and negatively impact society. And these benefits and consequences are not shared equitably. Copyright law, however, stands as a powerful mechanism in monitoring AI system development. Several complaints have charged generative AI system developers with copyright infringement, alleging that (1) ingesting copyrighted works as training data infringes the copyright owner’s exclusive right to reproduce works in copies and (2) generating AI outputs infringes the exclusive right to prepare derivative works because the outputs are based upon the works on which the …

Self-Supervised Learning And Its Applications In Medical Image Analysis, Siladittya Manna

Doctoral Theses

Self-supervised learning (SSL) enables learning robust representations from unlabeled data and it consists of two stages: pretext and downstream. The representations learnt in the pretext task are transferred to the downstream task. Self-supervised learning has appli- cations in various domains, such as computer vision tasks, natural language processing, speech and audio processing, etc. In transfer learning scenarios, due to differences in the data distribution of the source and the target data, the hierarchical co-adaptation of the representations is destroyed, and hence proper fine-tuning is required to achieve satisfactory performance. With self-supervised pre-training, it is possible to learn repre- sentations aligned …

Autonomous Intelligence In Fashion: A Comprehensive Analysis Of Agentic Ai Across The Fashion Ecosystem, Andrew Burnstine

Faculty and Staff Publications & Presentations

The fashion industry is undergoing a paradigm shift with the emergence of agentic artificial intelligence (AI), a sophisticated class of intelligent systems exhibiting autonomous decision-making, continuous learning, and adaptive action with minimal human intervention. Moving beyond traditional AI applications in fashion focused on predictive analytics, generative tools, and supervised automation, agentic AI introduces a transformative paradigm wherein intelligent agents proactively navigate the complexities inherent in design, manufacturing, supply chain optimization, and consumer personalization. This paper presents a comprehensive exploration of the evolving role of agentic AI across the multifaceted fashion ecosystem, offering an in-depth analysis of its technological underpinnings, operational …

One Size Doesn’T Fit All: Towards Design And Evaluation Of Developmentally Appropriate Parental Control Tool, Prakriti Dumaru, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

As children progress through developmental stages, they undergo substantial biological, cognitive, and social changes, creating unique needs for online safety across different age groups (e.g., young children, tweens, teens). The existing parental control tools fail to account for these differences, leaving a notable gap in the literature on parental mediation. To this end, we conducted 10 focus group sessions with a total of 20 parents to understand their preferences for age-appropriate design components that promote self-regulation and open communication, followed by an ideation workshop with four UX design experts to translate these preferences into customized features. We then evaluated these …

The Attitudes And Perspectives Of Laboratory Professionals On The Use Of Machine Learning Combined With Maldi For Viral Identification: A Qualitative Study, Grace Johnson

Honors Projects

The use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with machine learning (ML) has been proposed by numerous studies as a novel approach for viral identification. However, the development and implementation of this instrumentation is still in its early stages, and laboratory professionals' perspectives on its feasibility, accuracy, implementation, and effect on current laboratory operating procedures remain underexplored.

This study aimed to investigate laboratory professionals’ attitudes and opinions regarding the use of MALDI-TOF-MS coupled with machine learning for viral identification, focusing on perceived benefits, barriers, and factors that would affect participants’ opinions on implementation.

A qualitative descriptive research …

Fact-Based Counter Narrative Generation To Combat Hate Speech, Brian Wilk, Homaira Huda Shomee, Suman Kalyan Maity, Sourav Medya

Computer Science Faculty Research & Creative Works

Online hatred has become an increasingly pervasive issue, affecting individuals and communities across various digital platforms. To combat hate speech in such platforms, counter narratives (CNs) are regarded as an effective method. In recent years, there has been growing interest in using generative AI tools to construct CNs. However, most of the generative models produce generic responses to hate speech and can hallucinate, reducing their effectiveness. To address the above limitations, we propose a counter narrative generation method that enhances CNs by providing non-aggressive, fact-based narratives with relevant background knowledge from two distinct sources, including a web search module. Furthermore, …

Real-Time Active-Learning Method For Audio-Based Anomalous Event Identification And Rare Events Classification For Audio Events Detection, Farkhund Iqbal, Ahmed Abbasi, Ahmad Almadhor, Shtwai Alsubai, Michal Gregus

All Works

Introduction: Audio event detection, the application of scientific methods to analyze audio recordings, can be helpful in examining and analyzing audio recordings to preserve, analyze, and interpret sound evidence. Furthermore, it can be helpful in safety and compliance, security, surveillance, maintenance, and predictive analysis. Audio event detection aims to recover meaningful information from audio recordings, such as determining the authenticity of the recording, identifying the speakers, and reconstructing conversations. However, filtering out noise for better accuracy in audio event detection is a major challenge. A greater sense of public security can be achieved by developing automated event detection systems that …

Autoradai: A Versatile Artificial Intelligence Framework Validated For Detecting Extracapsular Extension In Prostate Cancer, Pegah Khosravi, Shady Saikali, Abolfazl Alipour, Saber Mohammadi, Maxwell Boger, Dalanda M. Diallo, Christopher J. Smith, Marcio C. Moschovas, Iman Hajirasouliha, Andrew J. Hung, Srirama S. Venkataraman, Vipul Patel

Publications and Research

Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and …

Systemization Of Knowledge (Sok): Goals, Coverage, And Evaluation In Cybersecurity And Privacy Games, Yue Huang, Marthie Grobler, Lauren S. Ferro, Georgia Psaroulis, Sanchari Das, Jing Wei, Helge Janicke

Research outputs 2022 to 2026

This paper systematized existing knowledge on cybersecurity and privacy game-based approaches, exploring their goals, scope, and evaluation methods. Our review of 93 academic papers revealed that these approaches serve multiple purposes and target diverse player types. We identified 11 key aspects of cybersecurity and privacy that these approaches addressed, such as threats, defensive strategies, and data privacy. Additionally, we analyzed the effectiveness evaluation methods of these approaches, emphasizing the connections between evaluation techniques, types of data used, and their alignment with the approaches' goals. We also summarized the aspects of user experience evaluated in the literature and the types of …

The Role Of Ai In Risk Management: Benefits, Challenges, And Adoption Strategies, Lukas Ludwig

Honors Projects in Information Systems and Analytics

This study explores the role of artificial intelligence (AI) in risk management, focusing on its integration within business operations. The primary objective of this research is to examine both the potential benefits and associated risks of adopting AI technologies, with a specific emphasis on data security, ethical concerns, and governance. A mixed methodology was employed, combining a comprehensive literature review on AI's applications and limitations with qualitative interviews conducted with professionals from various industries, including risk management, higher education, and IT development. The findings highlight key challenges in AI adoption, such as data privacy issues, bias in AI algorithms, and …

Navigating Ethical Dimensions In The Metaverse: Challenges, Frameworks, And Solutions, Mousa Al-Kfairy, Saed Alrabaee, Omar Alfandi, Amr Taha Mohamed, Souheil Khaddaj

All Works

The Metaverse is rapidly evolving into a transformative digital ecosystem, bringing with it unprecedented opportunities and a complex array of ethical challenges. This narrative review, based on an in-depth analysis of 105 full publications, explores the key ethical themes associated with the Metaverse, including privacy and data security, identity and behavior, digital inclusivity, mental and physical health, ethical AI, content moderation, intellectual property, governance, environmental sustainability, harassment, cultural representation, and economic implications. Proposed solutions for these challenges encompass privacy-by-design frameworks, robust identity verification systems, equitable access initiatives, explainable AI, and blockchain-based intellectual property protections. Additionally, the review examines governance and …

Fictional Failures And Real-World Lessons: Ethical Speculation Through Design Fiction On Emotional Support Conversational Ai, Fayle Kollig, Jessica Pater, Fayika Farhat Nova, Casey Fiesler

Health Services and Informatics Research

Conversational artificial intelligence (CAI), which replicates human-to-human interaction as human-to-machine, is increasingly developed to address insufficient access to healthcare. In this paper, we use design fiction methods to speculate on ethical consequences of CAI that offers emotional support to complement or replace mental healthcare. Through a near-future news article about a fictional, failed CAI, we explore safety and privacy concerns associated with mismatches between what an emotional support CAI is advertised to do, what it technically can do, and how it is likely to be used. We pose the following questions to researchers, regulators, and developers: How might we jointly …

The Role Of Natural Language Processing In Abstract Dataset To Improve Virtual Assistant Devices, Reem Alshahoomi, Salma Alameri, Sanaa Alfalasi, Feras Al-Obeidat

All Works

Natural Language Processing (NLP) has transformed human-computer interaction, especially in the realm of virtual assistants. NLP enables machines to understand, interpret, and generate human language, driving innovations in applications ranging from virtual assistants to customer service chatbots. This paper delves into the intersection of NLP and virtual assistants, examining advanced models like BERT and RoBERTa, which enhance contextual understanding and user intent recognition. Through a comprehensive evaluation using the dataset of research abstracts to explore new methods and improve response for virtual assistant devices, it explores methods to improve model efficiency, precision, and scalability. By leveraging machine learning techniques and …

Cryptology With Bitcoin And Blockchain Applications, Seth D. Bergmann

OER Textbooks

This book is intended to be used for a first course in cryptography for computer science students. It assumes that the student has had at least one programming course and a discrete structures or discrete math course. This book places an emphasis on algorithms and the internals of cryptographic systems.

The book begins with some classical cryptographic algorithms used for confidentiality. Then it exposes modern cryptographic algorithms for confidentiality, integrity, and authenticity. Both private key (symmetric) and public key (asymmetric) algorithms are covered. This book also describes the workings of the most common cryptocurrency, Bitcoin, as well as blockchain technology. …

Romance Scam Victimization: A Survey-Based Examination Of Financial, Psychological, And Reporting Factors, Ld Herrera

Research & Publications

Romance scams are a growing type of cybercrime in which perpetrators develop and exploit fraudulent romantic relationships with victims to obtain financial resources. These schemes cause substantial economic and psychological damage, yet they are significantly underreported. Official 2022 reports indicate only \$1.3 billion lost to romance scams in the US, but the true financial toll is likely much higher.

Using survey data from 366 victims, this study examines the financial and psychological toll of romance scams, reporting patterns, obstacles to seeking help, and victims' perceptions of received help. Most of the victims (60.9\%) did not seek help from any source, …

A Hybrid Deep Learning-Based Approach For Visual Field Test Forecasting, Ashkan Abbasi, Sowjanya Gowrisankaran, Wei-Chun Lin, Xubo Song, Bhavna Josephine Antony, Gadi Wollstein, Joel Schuman, Hiroshi Ishikawa

Wills Eye Hospital Papers

OBJECTIVE: Longitudinal assessment of visual field (VF) testing is essential in glaucoma management. Conventional VF forecasting methods require numerous prior tests, while deep learning techniques have shown promising results with fewer tests. This study introduces a hybrid deep learning framework to enhance flexibility and accuracy in VF test forecasting.

DESIGN: A retrospective longitudinal study using deep learning-based VF forecasting models.

SUBJECTS AND CONTROLS: A total of 1750 subjects (healthy and glaucoma patients) with 19 437 Humphrey VF (24-2 Swedish Interactive Threshold Algorithm) tests collected from longitudinal glaucoma cohorts at the University of Pittsburgh and New York University.

METHODS: Three deep …

2025 Acssc Program, Acssc Planning Committee

Annual Celebration for Student Scholarship and Creativity

No abstract provided.

Using Generative Artificial Intelligence To Improve Software-Defined Network Security: A Brief Survey, Anthony Smith

Honors Theses

This paper aims to explore various approaches to using generative artificial intelligence (GenAI) to improve network security in software-defined networking. While software-defined networks provide a more programmable infrastructure, they are not immune to network security threats. Through a combination of Software-Defined Networking (SDN) technologies and generative AI, it is possible to facilitate improved SDN security approaches that promise enhanced network efficiency and protection. Among these approaches, generative adversarial networks (GAN) based models can be employed to generate adversarial traffic samples to train the proposed AI engines proven to be effective in detecting malicious network traffic. Additionally, generative artificial intelligence can …

Volume 16, Maggie Duncan, Madeline Little, Alicia Hoffman, Megan Livesay, Gabrielle Quaresma, Serenity Allen, Laina Pfountz, Ainslie Allred, Sabrina Robles, Nicholas J. Duellman, Trinity L. Deguzman, Melissa H. Savage, Margaret Dudley, Jocelyn Escobar, Olivia Hildreth, Olivia Hopkins, Benjamin Gettier, Lee Kassay, Jade Riddle, Ashley Seiders

Incite: The Journal of Undergraduate Scholarship

Introduction Dr. Amorette Barber, Director, Office of Student Research

From the Editor Dr. Hannah Dudley-Shotwell

Artist’s Statement Maggie Duncan

On Mentoring Dr. Lee Millar Bidwell

The Hujum Campaign in Uzbekistan and its Consequences by Madeline Little

Wet Cupping Compared to Dry Needling for Treatment of Patients with Low Back Pain: A Critically Appraised Topic by Alicia Hoffman and Megan Livesay

Optimization of eDNA Air Sampling Via 3D Printed Fan by Gabrielle Quaresma

Beyond the Classroom: A Qualitative Study of Teacher Attrition and Retention by Serenity Allen and Laina Pfountz

The Treatment of Subacromial Impingement Syndrome with Platelet-Rich plasma Injections Verses …

Quantifying The Transfer Effectiveness Of An Artificial Intelligence-Based Simulator Pre-Training Program For Student Pilots, Ryan Guthridge

Journal of Aviation/Aerospace Education & Research

Since the airline pilot shortage was initially studied in 2016, the pilot hiring model has been significantly impacted, with airlines hiring qualified pilots at unprecedented rates. The COVID-19 pandemic has slowed this hiring rate, however it is expected that airline hiring will soon increase to a rate higher than initially expected (Bureau of Transportation Statistics, 2022). With this dynamic, certified flight instructors are often the most qualified recruits for airlines, due to the number of hours and experience they have gained in the flight training organization. In turn, certified flight instructors are in short supply for flight training organizations worldwide. …

Ensemble Learning With Explainable Ai For Improved Heart Disease Prediction Based On Multiple Datasets, Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Zhongming Zhao

Faculty, Staff and Student Publications

Heart disease is one of the leading causes of death worldwide. Predicting and detecting heart disease early is crucial, as it allows medical professionals to take appropriate and necessary actions at earlier stages. Healthcare professionals can diagnose cardiac conditions more accurately by applying machine learning technology. This study aimed to enhance heart disease prediction using stacking and voting ensemble methods. Fifteen base models were trained on two different heart disease datasets. After evaluating various combinations, six base models were pipelined to develop ensemble models employing a meta-model (stacking) and a majority vote (voting). The performance of the stacking and voting …

Securing Edge-Iiot Networks: A Comprehensive Ensemble-Based Ddos Detection System, Fariba Laiq, Feras Al-Obeidat, Adnan Amin, Fernando Moreira

All Works

As the number of Internet of Things (IoT) devices increases daily due to the rapid growth in technology, every device and network is vulnerable to attacks because it is exposed to the internet. Denial of Service (DoS) is a prevalent type of intrusion on the IoT network in which the server becomes down due to flooding requests. Distributed Denial of Service (DDoS) is a special type of DoS attack where the network of malicious computers called botnet consumes the target's system resources by flooding the requests. Edge computing is closely related to Industrial Internet of Things (IIoT), and industry 4.0. …

Appraisal Of Uncertainty-Driven Financial Performance: Bringing Multi-Criteria Decision Making Techniques With Neutrosophic Theory, Mona Mohamed, Nurhan Alaa, Eman Sayed

Neutrosophic Systems with Applications

Evaluating financial performance is essential yet challenging due to various factors, including ambiguity, insufficient information, and conflicting evaluation criteria. Conventional Multi-Criteria Decision-Making (MCDM) techniques often struggle to manage these complexities effectively. To address these limitations and enhance financial performance assessments, this research proposes an innovative approach integrating Single-Valued Neutrosophic Sets (SVNS) with established MCDM methodologies. SVNS uniquely manages uncertainty by concurrently quantifying degrees of truth, indeterminacy, and falsity within financial data. This research specifically employs entropy-based weighting methods integrated with Additive Ratio Assessment (ARAS) and Multi-Objective Optimization by Ratio Analysis (MOORA) methodologies in the SVNS environment to systematically rank enterprises …

Leveraging Hypersoft Set To Optimize Livestock In The Era Of Unmanned Aerial Vehicles, Alaa Salem, Mona Mohamed, Nebojsa Bacanin, Mohamed Abouhawwash

Neutrosophic Systems with Applications

Due to urbanization and industrialization, rapid global change and the potential loss of arable land, agricultural output must rise in production levels and harvest, distribute, and use resources more efficiently. It is believed that using technology on livestock would help meet the expanding population's demand for food. Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) integration in conventional farming has transformed operations, providing farmers with greater productivity, improved decision-making, and sustainability. We assume that there are enough UAVs to cover the entire pasture, and our goal is to identify the best UAVs. Accordingly, determining the best type of UAVs …

A Comprehensive Intelligent Traffic Monitoring System Based On A Novel Integration Of Neutrosophic Multi-Criteria Decision-Making Techniques, Mai Mohamed, Amira Salam, Rana Muhammad Zulqarnain, Muhammad Gulistan

Neutrosophic Systems with Applications

With the spread of road accidents and traffic congestion that costs countries and governments a lot of money in addition to the loss of human lives, and since traditional methods of monitoring traffic have not been as effective as desired, attention has been drawn to the search for more effective solutions to the problem of monitoring and regulating traffic. With the spread of technology and the Internet of Things, UAVs have emerged as a promising tool for monitoring traffic, as they can fly for a sufficient period and operate in difficult climatic conditions, in addition to their ability to monitor …

Nash Equilibrium Solutions For Continuous Static Games Under Neutrosophic Environment, M. G. Brikaa

Neutrosophic Systems with Applications

Neutrosophic set theory plays an important role in dealing with the impreciseness and inconsistency in data encountered in solving real life problems. This paper presents a novel approach to solving a new class of continuous static games within a neutrosophic framework. In the proposed methodology, the neutrosophic continuous static games are redefined into two separate crisp problems: the lower problem and the upper problem. The study further establishes the necessary conditions for determining equilibrium strategies in neutrosophic continuous static games. To demonstrate its effectiveness and practical applicability, the proposed method is validated through a numerical example.