Digital Commons Network^™

Towards Age-Inclusive Human Computer Interaction: A Study Of Text Message Adoption By Seniors, Sam Takavarasha, Varaidzo Mapepa

African Conference on Information Systems and Technology

Since mobile phones are increasingly becoming livelihoods-enablers to people in developing economies, inclusive interaction design are critical. This research on the age inclusivity of feature phones was motivated by some observation that elderly users had challenges with adoption of text messaging. We, therefore, hypothesized that elderly people had challenges with texting ‘cash’, ‘cheque’ or ‘visa’ on a feature phone. The paper investigates if the required agility and interface ergonomics were age discriminatory inhibitors of the adoption of text messaging given the diminishing dexterity and cognitive skills of seniors. Using Sen’s (1999) ‘heterogeneity of capabilities’ theory and Davis et al (1989)’s …

Creepy, Invasive, And Exploitative Algorithms: A Cpm Analysis Of Users' Privacy Breakdowns And Recalibration Practices With Social Media Algorithms, Matthew J. A. Craig, Jeffrey T. Child

Human-Machine Communication

Social media content filtering algorithms can both provide desired personalized content and ads for users. However, sometimes these recommendations can resemble individual private information. How might users navigate these experiences to best manage their private information? The present exploratory study utilizes the rules- and systems-based framework of communication privacy management (CPM) theory to explore social media users’ experiences of privacy breakdowns with social media algorithms and investigates what users do in response to said breakdowns. These responses were refined using content analysis and divided into different categories of privacy breakdowns and recalibration strategies. Implications for future research surrounding human-machine communication …

The Integration Of Agile Methodologies In Devops Practices Within The Information Technology Industry, Ashley Hourigan, Ridewaan Hanslo

African Conference on Information Systems and Technology

The demand for rapid software delivery in the Information Technology (IT) industry has significantly intensified, emphasising the need for faster software products and service releases with enhanced features to meet customer expectations. Agile methodologies are replacing traditional approaches such as Waterfall, where flexibility, iterative development and adaptation to change are favoured over rigid planning and execution. DevOps, a subsequent evolution from Agile, emphasises collaborative efforts in development and operations teams, focusing on continuous integration and deployment to deliver resilient and high-quality software products and services. This study aims to critically assess both Agile and DevOps practices in the IT industry …

Cybersecurity And Intention To Use Mobile Banking Applications, Ishmael Chikoo, Salah Kabanda

African Conference on Information Systems and Technology

The adoption rate of mobile banking amongst consumers remains low, especially in developing countries where there is a knowledge gap in understanding why consumers do not engage in the frequent use of mobile banking applications. Given that most financial institutions see mobile banking as a strategy for their competitive advantage; it is important that they understand how best to address consumer’s fears brought about by cybersecurity threats. The purpose of this study is to investigate the perceived influence of cybersecurity on the user’s intentions to use mobile banking applications. Data collected from 90 participants was statistically analysed in Smart PLS …

Machine Learning And Crime Prevention, Emily Lizewski

Student Theses

Predictive policing uses machine learning to analyze crime patterns and help law enforcement better efficient use their resources. These tools can improve accuracy by highlighting complex trends in large sets of data. While this technology has its advantages, it also raises important ethical and social questions. Within this paper we looks at how predictive policing works, focusing on the machine learning models often used such as decision trees, random forests, gradient boosting, and models that factor in both time and location. It also explores how these tools might unintentionally reinforce biases already present in historical crime data. In reviewing the …

Mapping The Research Landscape Of Covid-19 And Artificial Intelligence Using The Lens Database, Aichetou Mohamed Sidiya, Jailan Fouad Aljizawi

Effat Undergraduate Research Journal

Due to the rapid spread of the COVID-19 pandemic, all countries faced a significant challenge in their efforts to monitor and halt the spread of the virus. Moreover, researchers around the world raced into publishing to get to understand the effects of this pandemic on every aspect of our life whether that was economic, medical, or social. Thus, this research seeks to assess the quality of research on the application of Artificial Intelligence in the Covid-19 pandemic with an emphasis on Saudi Arabia. The research methodology is based on Bibliometric Analysis techniques and VosViewer for various bibliometric visualizations based on …

An Envisioned And Efficient Design Of Next Generation Decentralized Iot Bot Detection Model, Ahmed Abdullah Almalki

Doctoral Dissertations

The Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are revolutionizing critical infrastructures, but their expansion has also introduced severe cybersecurity vulnerabilities. Traditional IoT Bot Detection Systems (IBDS) struggle to scale in environments characterized by high-dimensional, large-scale, and redundant network traffic. These challenges hinder the development of reliable cloud-based intrusion detection systems. The limitations of static and rulebased methods in detecting evolving IoT botnet attacks—such as those launched by Mirai and Gafgyt—underscore the need for intelligent, adaptive approaches. To address this, the present study proposes a machine learning and deep learning-driven IoT Botnet Detection Model, validated through …

Protecting Networks Against Entry-Point Attacks, Shivani Mishra

Theses - ALL

In many network applications, it is critical to protect sensitive nodes from discovery by malicious crawlers. This thesis addresses the network protection problem from the data protector’s perspective, focusing on strategically deleting edges to hide target nodes from entry-point attacks. Earlier work on this problem proposed node-level scores to identify key edges for deletion. We propose two novel edge-level scoring functions to identify critical edges for removal: the Shortest Path Change Score (SPCS), which quantifies the damage an edge’s removal causes to shortest paths, and the PageRank Edge Flow Score (PEFS), which estimates an edge’s usage in random walks from …

Mathematics And Mental Health: An Interdisciplinary Analysis Of Ai In Counselor Education, Jennifer M. Hightower, Catrina A. May

Journal of Counselor Preparation and Supervision

Although use of Artificial Intelligence (AI) in mental health care has become increasingly common, many professional counselors remain under informed about foundational components of AI. Fundamental issues associated with AI, including model bias and the Black Box Problem, must be considered as these tools become integrated into the counseling profession. This manuscript provides an interdisciplinary, theoretical analysis of AI use in counseling and counselor education grounded in foundational knowledge of AI and the American Counseling Association’s recommendations for the ethical integration of AI (Butler et al., 2023). The paper summarizes these recommendations, establishes accessible definitions of AI terms, explains the …

Anatomy Of An Ai Arms Race: U.S. And China Technological Dispute For Ai Leadership, Denisse I. Rojas Maldonado

Dartmouth College Master’s Theses

The history of societies and the emergence of powerful states have been marked by cycles of conflict and war, followed by periods of cooperation that foster international stability. Similarly, the Cold War era saw a significant rise in military and economic capabilities, which highlighted a security dilemma as the former USSR and the United States sought to protect their national interests. Currently, artificial intelligence has expanded the scope of invisible warfare beyond the atomic bomb. Some scholars like Paul Scharre advocate that there is no arms race in place, and others support the idea of a healthy competition and collaboration …

Cybersecurity: Digital Stewardship In A Violent World, Rocky K. C. Chang

University Faculty Publications and Creative Works

This paper defines cybersecurity based on the principle of biblical stewardship. The focus of this principle is God, not the technologies, who is the creator and owner of cyberspace and everything in it. Humankind is called by God to steward them by protecting the digital property of our neighbors in cyberspace from malicious attacks. In the context of cybersecurity, a neighbor can be any individual cyber user, practitioner, educator, organization, tech company, or even hacker. However, as argued from the perspectives of human sin and human finitude, the defending stewards can never win over evil in this spiritual battle. Instead, …

Application Of Multi-Criteria Decision Analysis For Quantifying Responsibility In Ml-Based Intrusion Detection System, Mona Mohamed, Zekra Sakr

Neutrosophic Systems with Applications

With the swift growth in Internet of Things (IoT), certifying secure and trustworthy networks has turned to be a critical challenge, particularly as IoT devices are increasingly vulnerable to sophisticated cyberattacks. As a remedy, intelligent intrusion detection systems (IDS) evolved as promising solutions in recent years, but deciding on the appropriate model remains difficult because of competing performance and trustworthiness criteria. To this end, this paper explores a novel application of an ML-augmented decision-making framework to enhance security-related decision-making in IoT environments. The framework systematically evaluates and ranks ML-based IDS systems according to different evaluation criteria with distinct trade-offs, including …

Topnet R1: A Multi-Stage Ai Framework For Topic Discovery In Scientific Abstracts, Md Elias Hossain

Theses and Dissertations

Scientific abstracts are rich sources of knowledge, yet extracting meaningful topics remains challenging due to limitations in existing topic modeling techniques. Traditional methods often struggle with interpretability, scalability, and contextual understanding. To overcome these issues, we introduce TopNet R1, a multi-stage ensemble framework that integrates traditional topic models with contextual embeddings from Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer 4 (GPT-4) large language models (LLMs). Top- Net R1 operates in three phases: (1) topic generation using Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Latent Semantic Analysis (LSA), and Hierarchical Dirichlet Process (HDP); (2) LLM-assisted pattern recognition …

Bring Back The Blue-Book Exam: In An Age Of Ai, We Need To Return To Handwritten Assignments., Katie Day Good

University Faculty Publications and Creative Works

When ChatGPT was released three years ago, its ability to mimic human writing unsettled me. I’m a professor of communication; what did it mean that my students now had access to a machine that could communicate for them? My initial unease led to a half-joke with my colleagues. Universities could survive this threat, I ventured, but only if we reverted back to 19th-century teaching methods like Socratic dialogue, oral defenses, and lengthy essay exams. This once-laughable scenario is now a serious consideration for many faculty, including me.

Like many professors, I’ve recently abandoned take-home essays in favor of blue-book exams. …

Antifungal Peptides From Casein Milk Of Etawa Crossbreed (Capra Hircus) As Biopreservation Agent For Bread And Molecular Docking Studies, Dian Riana Ningsih, Winarto Haryadi, Rachma Wikandari, Tri Joko Raharjo

Karbala International Journal of Modern Science

Bread without peptide fraction treatment started to grow mold on the fourth day with a total of 24x10⁷ fungi colonies/ml. Meanwhile, the addition of peptide fractions GMC5, GMC6, GMC7, and GMC8 effectively preserved bread for up to 4 days, indicated by no mold growth. The best treatment was the GMC6 peptide fraction on day 8 which grew the least amount of fungus at 7x10⁷ spores/ml. Peptide interaction with the CaATPase receptor Aspergillus sp. was carried out using HADDOCK 2.4. The 3D CaATPase structure prediction resulted in a model with good structural quality. This was supported by residues in …

Exploitation For All: The Factors That Enable Pig Butchering Schemes To Weaponize Cybersecurity's Weakest Link, Melaney Freeman

Boise State Graduate Student Projects

This paper discusses pig butchering schemes and how they leverage human weaknesses through advanced social engineering and manipulation techniques that are enabled through the use of human trafficking, forced labor, and government corruption in Southeast Asia. Details regarding scammer exploitation and the formation of a victim-offender identity is presented, followed by the explanation of factors that allow organized crime groups to exist. Essential tactics used by scammers are examined to understand how they weaponize people's vulnerabilities for the duration of the scam to build a relationship with the victim and earn their trust in order to financially exhaust them through …

Bibliography For "Ai 2.0: Is Ai A Tool, A Threat, Or A Teammate?", Annikah Carpio, Sally Park, Melody Madrigal

Library Displays and Bibliographies

A bibliography created to support a display about AI 2.0 in August 2025 at the Leatherby Libraries at Chapman University.

Role Of Social Media Mindfulness In Combatting Fake News Propagation, Gaurav Bansal, Fiona Fui-Hoon Nah, Jason B. Thatcher

Research Collection School Of Computing and Information Systems

The dissemination of fake news by social media users is a key factor in the escalation of misinformation. Research suggests that social media networks are becoming increasingly homophilic, which leads to an overreliance on social media friends that contributes to the spread of fake news. However, little is known about how social media mindfulness can reduce the sharing of fake news. To investigate this research question, we conceptualized a social media mindfulness construct and developed the social media mindfulness scale. We also hypothesize that social media mindfulness lowers overreliance on friends’ knowledge, which increases skepticism about social media news that …

Wildfires Classification In Canadian Boreal Forest: A Comparative Study Of Logistic Regression And Xgboost Models, Brandon Tran, Elijah James Duran, Mike Luu, Hesham Morgan, Surendra Maharjan, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

In recent years, Canada has faced a growing number of wildfires. These events have devastated ecosystems, displaced communities, and posed severe health risks. To minimize the damage caused by such disasters, this study aims to develop an early warning system that predicts wildfire occurrences. Two machine learning models for binary classification of wildfire occurrence in Canadian wild forests, Logistic regression and XGBoost, will be compared and evaluated. The models are used to predict the likelihood of wildfire events based on various environmental and climatic factors. The models are evaluated using a 70-30 split validation approach and their performance is assessed …

Robustness Investigation, Detection, And Defense Of Deep Learning Models Against False Data Injection, Amirhossein Nazeri

All Dissertations

This dissertation addresses the critical challenge of adversarial robustness in deep learning systems, focusing on two fundamental domains: time-series prediction and object detection. As these AI systems become increasingly deployed in safety-critical applications from power grid management to autonomous vehicles their vulnerability to adversarial attacks poses significant risks to infrastructure and human safety.

The first contribution introduces a novel stealthy black-box False Data Injection (FDI) attack specifically designed for quasi-periodic time-series data. Unlike existing attacks that produce easily detectable anomalies, our method generates adversarial perturbations that preserve the underlying periodicity and statistical properties of the data, effectively bypassing traditional anomaly …

Evaluating Immersion And Agency In Ai-Assisted Live Murder Mystery Games, Seraphina C. Courtney

LSU New Orleans Theses and Dissertations

This thesis explores the impact of AI-assisted narrative generation on player immersion and agency in a live-action roleplaying (LARP) experience. A live-action murder mystery game was designed and run in two formats: a static version with GPT-4 generated characters, dialogue, and stage directions, and a dynamic version where players created their own characters and improvised freely, guided by AI-generated narrative beats, a story element that moves the plot forward [21], that provided a narrative scaffolding for the players. The dynamic version employed a distributed computer vision system that tracks the movement of key items in the play-space so their relevance …

Greening The Virtual: An Interdisciplinary Narrative Review On The Environmental Sustainability Of The Metaverse, Mousa Al-Kfairy

All Works

As the Metaverse continues to evolve as a transformative digital ecosystem, its environmental implications remain insufficiently examined within academic discourse. Despite growing interest in its technological and societal impacts, there is a lack of comprehensive evaluations that synthesize existing knowledge on its sustainability potential. This interdisciplinary narrative review addresses this gap by critically exploring how Metaverse technologies intersect with environmental sustainability across key sectors, including education, healthcare, tourism, e-commerce, manufacturing, and urban development. Employing a narrative review methodology informed by a systematic selection of scholarly and industry sources, the study consolidates current practices, emerging opportunities, and notable trade-offs. While the …

Intersecting Realities And Evolving Landscapes: Mapping Generative Ai Within The Framework Of Digital Rhetoric, Joshua Troy Nieubuurt

English Theses & Dissertations

The increased usage of [Generative] AI technologies (GenAI) in the 21st century has called into the question the rhetorical agency of these digital things. [Gen]AI has historically been framed within a Heideggerian “readiness-to-hand” dynamic in which it has been unilaterally conceived as a tool to be used by humans. This dissertation proposes that the GenAI assemblage is capable of being a co-actor in rhetorical spaces. To provide evidence for this stance This dissertation utilizes Actor Network Theory to map the actants within a GenAI assemblage. In doing so it allows for an understanding of the stakeholders (both human and non-human) …

Fact-Checker: A Web Application For Leveraging Large Language Models For Fact-Checking Youtube Videos, Andrew R. Craig

Electronic Theses, Projects, and Dissertations

Fact-Checker is a web application that allows users to fact-check YouTube videos. It feeds YouTube’s closed captioning transcript to a large language model (LLM) to extract claims. It then uses multiple LLMs, such as Gemini, Llama, and Claude, to verify these claims. The modular design makes it easy to change to a different LLM or model if needed. The application is built using Python for access to Application Programming Interfaces (APIs) and Streamlit as the front-end framework. The utilization of Docker and Dockerfiles enables easy distribution and deployment. It enables the application to be deployed on almost any hardware platform …

Computational Fact-Checking With Limited Resources, Fengzhu Zeng

Dissertations and Theses Collection (Open Access)

The rapid dissemination of information through online platforms has sparked widespread concern about the propagation of misinformation. Manual fact-checking by pro- fessional fact-checkers is time-consuming and lacks scalability to address the vast volume of daily information. Consequently, computational fact-checking, driven by automated techniques in natural language processing (NLP), has garnered interest as
a potential solution. However, computational fact-checking faces critical challenges limited resources, particularly due to the issues of data scarcity and computing resource constraints. One key challenge is data scarcity, which arises from the constant generation of new information and emerging events on social media. This scarcity manifests in …

Ev Charging Management In A Real-Time Optimization Framework Considering Operational Constraints, Hilmi Cihan Güldorum, Ayşe Kübra Erenoğlu, İbrahim Şengör, Barry P. Hayes, Ozan Erdinç

Department of Computer Science Publications

The electrification of transportation plays a central role in the decarbonization of energy systems. Although electric vehicles (EVs) are expected to reduce energy related emissions, the increasing demand imposed by large scale EV adoption presents serious challenges for distribution systems (DSs), which were not originally designed to accommodate such loads. This study proposes a mixed integer quadratically constrained programming (MIQCP) framework to optimize the operation of an EV parking lot (EVPL) under DS constraints. The model compares three widely adopted objective functions: minimization of active power loss, charging cost, and uncontrolled charging impact, which is represented by minimizing the total …

A Comprehensive Analysis Of Evolving Permission Usage In Android Apps: Trends, Threats, And Ecosystem Insights, Ali Alkinoon, Trung Cuong Dang, Ahod Alghuried, Abdulaziz Alghamdi, Soohyeon Choi, Manar Mohaisen, An Wang, Saeed Salem, David Mohaisen

Research Collection School Of Computing and Information Systems

The proper use of Android app permissions is crucial to the success and security of these apps. Users must agree to permission requests when installing or running their apps. Despite official Android platform documentation on proper permission usage, there are still many cases of permission abuse. This study provides a comprehensive analysis of the Android permission landscape, highlighting trends and patterns in permission requests across various applications from the Google Play Store. By distinguishing between benign and malicious applications, we uncover developers’ evolving strategies, with malicious apps increasingly requesting fewer permissions to evade detection, while benign apps request more to …

Explainable Multimodal Sentiment Analysis Of Social Media Visual Content For Child Safety, Yee Sen Tan, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Ensuring the safety and well-being of children is increasingly important, especially in a world where visual content is pervasive. This paper proposes a novel multimodal, multilingual, and multiclass sentiment analysis method for social media content, aimed at improving content moderation for child safety. Our approach integrates textual, visual, and audio data from videos, categorizing sentiment into four levels: positive, slightly negative, negative, and strongly negative, enabling granular detection of harmful content. To enhance explainability and trust, we also leverage interpretable mechanisms to analyze the contributions of each modality. Evaluation of our method demonstrates strong generalization across diverse video types, and …

Eformer: An Effective Edge-Based Transformer For Vehicle Routing Problems, Dian Meng, Zhiguang Cao, Yaoxin Wu, Yaqing Hou, Hongwei Ge, Qiang Zhang

Research Collection School Of Computing and Information Systems

Recent neural heuristics for the Vehicle Routing Problem (VRP) primarily rely on node coordinates as input, which may be less effective in practical scenarios where real cost metrics--such as edge-based distances--are more relevant. To address this limitation, we introduce EFormer, an Edge-based Transformer model that uses edge as the sole input for VRPs. Our approach employs a precoder module with a mixed-score attention mechanism to convert edge information into temporary node embeddings. We also present a parallel encoding strategy characterized by a graph encoder and a node encoder, each responsible for processing graph and node embeddings in distinct feature spaces, …

Fine‑Tuning Multimodal Large Language Models For Product Bundling, Xiaohao Liu, Jie Wu, Zhulin Tao, Yunshan Ma, Yinwei Wei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recent advances in product bundling have leveraged multimodal information through sophisticated encoders, but remain constrained by limited semantic understanding and a narrow scope of knowledge. Therefore, some attempts employ In-context Learning (ICL) to explore the potential of large language models (LLMs) for their extensive knowledge and complex reasoning abilities. However, these efforts are inadequate in understanding mulitmodal data and exploiting LLMs' knowledge for product bundling. To bridge the gap, we introduce Bundle-MLLM, a novel framework that fine-tunes LLMs through a hybrid item tokenization approach within a well-designed optimization strategy. Specifically, we integrate textual, media, and relational data into a unified …