Digital Commons Network^™

Towards A Comprehensive Metaverse Forensic Framework Based On Technology Task Fit Model, Amna Almutawa, Richard Adeyemi Ikuesan, Huwida Said

All Works

This article introduces a robust metaverse forensic framework designed to facilitate the investigation of cybercrime within the dynamic and complex digital metaverse. In response to the growing potential for nefarious activities in this technological landscape, the framework is meticulously developed and aligned with international standardization, ensuring a comprehensive, reliable, and flexible approach to forensic investigations. Comprising seven distinct phases, including a crucial incident pre-response phase, the framework offers a detailed step-by-step guide that can be readily applied to any virtualized platform. Unlike previous studies that have primarily adapted the existing digital forensic methodologies, this proposed framework fills a critical research …

Participatory Ethical Regulations: Risk Challenges Of Artificial Intelligence Era And Construction Of Governance Logic, Chenggang Zhang, Lu Pan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Participatory ethical norms emphasize the involvement of diverse stakeholders, aiming to construct a more comprehensive and balanced ethical governance framework. The rapid development of artificial intelligence (AI) technology is leading society through unprecedented transformations, significantly impacting ethical perspectives, social governance models, and the symbiotic relationship between humans and technology. The participatory ethical norms, characterized by multi-stakeholder participation, interactivity, and openness, represent a crucial pathway for addressing the challenges posed by the rapid development of AI technology. Constructing an AI governance framework based on participatory ethical norms provides solutions for the sustainable, fair, and transparent development of AI from multiple aspects …

Gmc-157 Text-To-Digital Person Video Generator: Digitalavatargen, Akansha Kesharwani, Nisha Bagdwal, Md E Hossain, Drashti Patel, Nikhil Adigoppula

C-Day Computing Showcase

The Text-to-digital person video generator: DigitalAvatarGen project uses AI to create lifelike videos of 2D digital avatars from user text input. Users enter text, select a voice and select or upload an avatar, and generate a video using DigitalAvatarGen web application which uses Google TTS and SadTalker, to synchronize voice, expressions, and lip movements. Key contributions include a customizable user interface, personalized voice and avatar options, and an optimized backend for efficient video generation. This tool provides an engaging, realistic solution for applications in education, media, and customer interaction.

Gmr-124 Emotion-Based Synthetic Feature Binary Classification Of Human Vs Llm Generated Text/Essay, Tong Xu, Rene P Lisasi, Patrick Wu

C-Day Computing Showcase

Sentimental analysis is a popular method to classify text into various emotional tones and intentions. In the meanwhile, the emergence of large language models (LLMs) has become ever more capable, their potential to cause harm through information fabrication, misleading propagation, or mere lack of capability has also increased. Therefore, our project is designed to discover any patterns that could potentially uncover texts origins of human and LLM during sentimental analysis. Our dataset covers over 57,000 lengthier essay samples (70% human vs 30% LLM), we use the state-of-art pre-trained DistilRoBERTa-base, a powerful pre-trained language model that is a more condensed and …

Gmc-219 Athlete-Agent Connect Mobile App, Ayokunle Ijagbemi, Esther N Uzoka, Foluke Omoniyi

C-Day Computing Showcase

The Athlete-Agent Connect app aims to bridge the gap between athletes and agents, simplifying the process of professional engagement. By providing a digital space for talent acquisition and event coordination, the app fosters networking, recruitment, and collaboration within the sports industry. The platform’s features are tailored to meet the needs of athletes looking for representation and agents seeking clients, with tools for direct communication, event planning, and a calendar of relevant sports gatherings. This mobile app serves as a dedicated platform for athletes and sports agents to connect, collaborate, and enhance professional opportunities. The app enables athletes to hire agents …

Gmr-215 Efficient Sentiment Analysis Using Encoder-Only Transformer, Rohan Jonnalagadda, Srinidhi Kandimalla, Siri Yellu

C-Day Computing Showcase

In the era of social media, sentiment analysis has emerged as a vital instrument for comprehending public opinion, especially on sites like LinkedIn and Twitter. Because user-generated content is informal and noisy, traditional sentiment classification techniques like Naive Bayes and Support Vector Machines sometimes find it difficult to capture context, sarcasm, and long-term interdependence. In order to improve sentiment analysis accuracy for social media datasets with a specific focus on sentiments related to corporate layoffs, this study suggests an encoder-only transformer model. Our method successfully captures intricate phrase patterns and contextual subtleties in textual data by leveraging the self-attention mechanism …

Gpr-233 Human-Assisted Ai For Detecting Mental Health Indicators In Social Media, Abm Adnan Azmee, Francis E Nweke, Mason V Pederson

C-Day Computing Showcase

Mental health is essential to overall well-being, and mental illness includes conditions that affect a person’s psychological health, causing significant distress and limiting daily functioning. With advancements in technology, social media has become a platform where individuals openly share their emotions and thoughts, offering a unique window into their psychological states. However, traditional machine learning models struggle to interpret social media data's wide range of linguistic nuances. To analyze this data effectively, collaboration with human experts is crucial. This study proposes an innovative human-AI teaming framework that integrates human expertise with artificial intelligence (AI) to address these challenges. Our framework …

Improving Data Curation With Spectral Clustering And Shannon Entropy: An Unsupervised Approach Within The Data Washing Machine, Erin Hathorn

Theses and Dissertations

In the ever-expanding landscape of digital technologies, the exponential growth of data presents both challenges and opportunities, demanding innovative approaches to data curation. Effective data curation is pivotal for extracting meaningful insights from vast and complex datasets. This study explores the integration of spectral clustering and Shannon Entropy within the Data Washing Machine (DWM), a novel tool designed to streamline unsupervised data curation processes. The DWM incorporates Shannon Entropy into its clustering process, allowing for adaptive refinement of clustering strategies based on entropy levels observed within data clusters. Spectral clustering, known for its ability to handle complex and non-linearly separable …

Strategies To Alleviate Flickering: Bayesian And Smoothing Methods For Deep Learning Classification In Video, Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce A. Cox, Trevor Bihl

Faculty Publications

Excerpt: Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers from “flickering” of results. Flickering occurs when the CV algorithm switches between declared classes over successive frames. Such behavior causes a loss of trust and confidence in their operations.

First Amendment Roadblock? Regulating The Misuse Of Generative Ai Technologies: Impersonation And Appropriation Of Likeness Without Permission, Muhammad Rabiu

Cybersecurity Undergraduate Research Showcase

This paper initially explores the misuse of generative AI technologies, particularly their role in impersonating or appropriating individuals' likeness without consent. It will then analyze technical and legal mitigation strategies and propose recommendations to address this issue in light of the First Amendment’s constitutional Freedom of Speech provision.

Integrating Humanities Into Cybersecurity Education: Enhancing Ethical, Historical, And Sociopolitical Understanding In Technical Training, Joseph Frusci

Journal of Cybersecurity Education, Research and Practice

The increasing complexity of cybersecurity challenges necessitates a holistic educational approach that integrates both technical skills and humanistic perspectives. This article examines the importance of infusing humanities disciplines such as history, ethics, political science, sociology, law, and anthropology—into cybersecurity education. Through a pilot course developed for Staten Island Technical High School, aligned with the New York State K-12 Computer Science and Digital Fluency Standards, students were introduced to an interdisciplinary curriculum that combined technical cybersecurity training with historical analysis, ethical reasoning, and sociopolitical context. The results of pre- and post-course assessments demonstrated significant improvements in critical thinking, ethical decision-making, and …

From Classroom To Cloud: Why Cybersecurity Education Is Crucial For Tomorrow's Learners, Kiori Edwards

Cybersecurity Undergraduate Research Showcase

This paper examines the transformative impact of cloud computing on education, highlighting how cloud technologies are reshaping teaching and learning environments, making education more accessible, flexible, and collaborative. It also explores the critical importance of incorporating cybersecurity education into school curricula. As digital devices and cloud-based platforms become integral to students' daily lives, they are increasingly vulnerable to cyber threats. This paper argues that early cybersecurity education, starting at the elementary level, is essential for equipping future generations with the knowledge and skills needed to navigate the digital world safely, protect their personal information, and avoid cybercrime.

Between Copyright And Computer Science: The Law And Ethics Of Generative Ai, Devin R. Desai, Mark Riedl

Northwestern Journal of Technology and Intellectual Property

Copyright and computer science continue to intersect and clash, but they can coexist. The advent of new technologies such as digitization of visual and aural creations, sharing technologies, search engines, social media offerings, and more, challenge copyright-based industries and reopen questions about the reach of copyright law. Breakthroughs in artificial intelligence research, especially Large Language Models that leverage copyrighted material as part of training, are the latest examples of the ongoing tension between copyright and computer science. The exuberance, rush-to-market, and edge problem cases created by a few misguided companies now raises challenges to core legal doctrines and may shift …

Intersections Of Living And Machine Agencies: Art-Based Models Of Adaptive Conversation With The More-Than-Human World, Carlos Castellanos

Tradition Innovations in Arts, Design, and Media Higher Education

Today’s AI systems are not built to have reciprocal interplay with their environments and thus they demonstrate little interest in emergence, adaptation or developing mutually productive relationships with the natural world. Is a different kind of AI possible? How can artists contribute to its development? In this essay, I will discuss ways in which the arts might help guide us towards a new kind of AI, built upon adaptive conversation (i.e. shared construction of meaning) with nature. I will discuss how can work with AI while also challenging its prevailing ontology, and even suggest alternative ontologies and epistemologies. I will …

Internet Of Medical Things: Integrating Machine Learning And Wearables For Healthcare, Jingwen Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Internet of Medical Things (IoMT), which integrates Internet of Things technologies into healthcare, has become a powerful tool for enhancing health monitoring and predicting clinical outcomes. By leveraging wearable devices, IoMT facilitates continuous, cost-effective, and convenient tracking of patients' health conditions over time. This dissertation applies data-driven methods to address critical clinical challenges involving wearable devices. Specifically, it focuses on three significant clinical problems: (1) indoor contact tracing for healthcare workers using Bluetooth Low Energy (BLE) beacons, (2) predicting surgical outcomes using wearable data, and (3) developing robust models for surgical outcome prediction that account for patient variability using …

Internet Of Medical Things: Integrating Machine Learning And Wearables For Healthcare, Jingwen Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Internet of Medical Things (IoMT), which integrates Internet of Things technologies into healthcare, has become a powerful tool for enhancing health monitoring and predicting clinical outcomes. By leveraging wearable devices, IoMT facilitates continuous, cost-effective, and convenient tracking of patients' health conditions over time. This dissertation applies data-driven methods to address critical clinical challenges involving wearable devices. Specifically, it focuses on three significant clinical problems: (1) indoor contact tracing for healthcare workers using Bluetooth Low Energy (BLE) beacons, (2) predicting surgical outcomes using wearable data, and (3) developing robust models for surgical outcome prediction that account for patient variability using …

Misinformation, Fraud, And Stereotyping: Towards A Typology Of Harm Caused By Deepfakes, Paulina Trifonova , '25, Sukrit Venkatagiri

Computer Science Faculty Works

Scholars, politicians, and journalists have raised alarm over the potential for AI-generated photos, video, and audio—often referred to as deepfakes—to reduce trust in one another and our institutions. Despite these clarion calls, little empirical work exists on how deepfakes are being used to harm individuals outside of non-consensual intimate imagery (NCII). This research provides a preliminary analysis of 50 wide-ranging incidents of deepfake harm. We find that the most common types of harm are relational, systemic, financial, and emotional. Apart from AI-generated NCII, the most prevalent uses of deepfakes to cause harm were instances of mis- and disinformation, fraud, and …

Video Label Refinement And Temporal Localization Using Motion Signal Patterns, Jennifer Piane

College of Computing and Digital Media Dissertations

Performing video analysis for activity recognition presents challenges beyond classification, including obtaining class labels and performing temporal localization. One such challenge is precisely labeling a video with class labels having the exact start and end frames of an activity - a difficult task for a human to perform. Moreover, the task of annotating a video at any level of precision can quickly become tedious, impacting the attentiveness of the annotator and resulting in class label errors. Temporally localizing an activity within a video presents a second challenge. This dissertation investigates novel signal and image processing methods for motion features extracted …

Hacker, Their Actions, And Fear Appeal: A First Look Through The Lens Of Children, Rizu Paudel, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

With the increasing use of computers and smartphones by children, their online safety has become a major concern due to the lack of security awareness. Prior studies pointed to children's poor password habit and vague perceptions on the significance of passwords. While users must be sufficiently motivated to perform a target behavior, a little study to date, focused on understanding how we can encourage children towards strong password creation. As we begin to address this gap, we examined children's perceptions of adversary's actions that instill fear in the context of password compromise. Our semi-structured interviews with 20 children (aged between …

Flowgpt: Exploring Domains, Output Modalities, And Goals Of Community-Generated Ai Chatbots, Xian Li, Yuanning Han, Di Liu, Pengcheng An, Shuo Niu

Computer Science

The advent of Generative AI and Large Language Models has not only enhanced the intelligence of interactive applications but also catalyzed the formation of communities passionate about customizing these AI capabilities. FlowGPT, an emerging platform for sharing AI prompts and use cases, exemplifies this trend, attracting many creators who develop and share chatbots with a broader community. Despite its growing popularity, there remains a significant gap in understanding the types and purposes of the AI tools created and shared by community members. In this study, we delve into FlowGPT and present our preliminary findings on the domain, output modality, and …

Harnessing Llms For Automated Video Content Analysis: An Exploratory Workflow Of Short Videos On Depression, Jiaying (Lizzy) Liu, Yunlong Wang, Yao Lyu, Yiheng Su, Shuo Niu, Orson Xuhai Xu, Yan Zheng

Computer Science

Despite the growing interest in leveraging Large Language Models (LLMs) for content analysis, current studies have primarily focused on text-based content. In the present work, we explored the potential of LLMs in assisting video content analysis by conducting a case study that followed a new workflow of LLM-assisted multimodal content analysis. The workflow encompasses codebook design, prompt engineering, LLM processing, and human evaluation. We strategically crafted annotation prompts to get LLM Annotations in structured form and explanation prompts to generate LLM Explanations for a better understanding of LLM reasoning and transparency. To test LLM's video annotation capabilities, we analyzed 203 …

Ethical Responsibility In The Design Of Artificial Intelligence (Ai) Systems, David K. Mcgraw

International Journal on Responsibility

This article aims to provide an overview of the ethical questions surrounding the responsibilities of designers of artificial intelligence (AI) systems. First, the author delves into the philosophical underpinnings of this responsibility, examining various ethical theories to grasp the moral obligations individuals have towards others and society. The author contends that designers of technology bear the responsibility of considering the broader societal implications of their creations. Subsequently, the author scrutinizes the fundamental question of whether AI systems present unique ethical concerns compared to conventional technologies, pinpointing factors such as complexity, opacity, autonomy, unpredictability, uncertainty, and the potential for significant social …

Competitive Conquest: Charting The Climb To Pokémon Supremacy, Robert Dilworth

BCoE Publications

This manuscript presents a comprehensive exploration of optimizing Pokémon gameplay through data-driven methodologies, aimed at enhancing competitive performance in high-stakes environments. In the first section, we introduce a robust Pokémon teambuilding algorithm that leverages statistical analysis of championship-winning compositions. By employing multiple linear regression techniques, we predict team performance based on critical factors such as Base Stat Totals (BSTs) and various coverage types. This integration of data science principles into Pokémon strategy underscores the importance of offensive capabilities over defensive considerations, ultimately contributing to advancements in teambuilding strategies. Our proficiency in R programming facilitated the development of an efficient codebase …

Tackling Toxicity And Harassment In Online Environments Through The Use Of Artificial Intelligence, Heba Saleous

Thesis/ Dissertation Defenses

With the increase in popularity of online communities, such as social media platforms, online games, and chatroom servers, there is a need to improve chat and content moderation. Platforms have reported an increase in the prevalence of toxic behavior and hate speech. Meanwhile, moderators are reporting difficulties in keeping up with the amount of data to check as well and the type of content they are exposed to, which further harms their own mental health. The main objective of this work is to address the challenges that exist within online communities with the rising prevalence of hate speech. Additionally, some …

Bacterial Cellulose Production From Fermented Fruits And Vegetables Byproducts: A Comprehensive Study On Chemical And Morphological Properties, Yati Maryati, Hakiki Melanie, Windri Handayani, Yasman Yasman

Karbala International Journal of Modern Science

This study aimed to produce α-cellulose from Bacterial Cellulose SCOBY (BCS) using alternative substrates from fermented fruit and vegetable byproducts: katuk leaves (KT), kale leaves (KL), guava (JB), dragon fruit (NG), and banana (PS). BCS production involved juice extraction, SCOBY inoculation, and sucrose addition, followed by 21 days of fermentation. Initially, the NG medium had the highest concentration of Total Reducing Sugars (TRS), but all media showed a decline as sugars were consumed. Fermentation reduced pH and increased total polyphenols, with KL and JB showing the highest rise (0.13-0.15 mg GAE/mL). Flavonoid levels varied, decreasing in KL and PS but …

Gray Advice, Keith Porcaro

Duke Law & Technology Review

Debates over economic protectionism or the technology flavor-of-the-month obscure a simple, urgent truth: people are going online to find help that they cannot get from legal and health professionals. They are being let down, by products with festering trust and quality issues, by regulators slow to apply consumer protection standards to harmful offerings, and by professionals loath to acknowledge changes to how help is delivered. The status quo cannot continue. Waves of capital and code are empowering ever more organizations to build digital products that blur the line between self-help and professional advice. For good or ill, “gray advice” is …

Rubrics Informed By The Cognitive Theory Of Multimedia Learning That Support Research On Personalized Learning Paths, Sean A. Mochocki, Mark G. Reith, Jonathan Zemmer

AFIT Documents

Personalized Learning Paths (PLP)s are a popular area of research in E-Learning where sequences of Learning Materials (LM)s and activities are returned based on a learner profile, the LM metadata, and a knowledge structure that describes the relationship between the underlying topics. Unfortunately, PLP researchers tend to not use an empirically supported cognitive science framework for their research, instead relying on such unsupported theories as learning styles or developing their own ad hoc approaches. While many of these researchers present and solve challenging PLP problems using a variety of algorithmic approaches, the PLP community in general would benefit from a …

Toward A Responsible Future: Recommendations For Ai-Enabled Clinical Decision Support, Steven Labkoff, Bilikis Oladimeji, Joseph Kannry, Anthony Solomonides, Russell Leftwich, Eileen Koski, Amanda L Joseph, Monica Lopez-Gonzalez, Lee A Fleisher, Kimberly Nolen, Sayon Dutta, Deborah R Levy, Amy Price, Paul J Barr, Jonathan D Hron, Baihan Lin, Gyana Srivastava, Nuria Pastor, Unai Sanchez Luque, Tien Thi Thuy Bui, Reva Singh, Tayler Williams, Mark G Weiner, Tristan Naumann, Dean F Sittig, Gretchen Purcell Jackson, Yuri Quintana

Faculty, Staff and Student Publications

BACKGROUND: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging.

OBJECTIVES: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients.

MATERIALS AND METHODS: In May …

From Biased Data Inputs To Your Discriminatory Diagnosis Outputs: A Review Of Legal Liability For Artificial Intelligence In Healthcare, Amber Bolden

Michigan Technology Law Review

While health disparities in America occur due to non-medical circumstances, certain protected classes experience healthcare disparities due to the biases of medical professionals. Biased diagnoses, both intentional or unintentional, have existed throughout the history of the medical profession. That those biases are becoming data for training algorithms raises concerns as the medical field increasingly incorporates and standardizes artificial and augmented intelligence in patient diagnosis and treatment. Currently unregulated but with lifedetermining potential, artificial intelligence (AI) when used in patient treatment leads to important questions: should the doctor, the provider, or the AI developers be liable, and for what? Section II …

Left, Then Right Internal Carotid Artery Dissection: A Case Report, Jeffrey M. Kalczynski, John Douds, Michael E. Silverman

SKMC Student Presentations and Publications

INTRODUCTION: We present a unique case of a patient who presented to the emergency department with stroke-like symptoms found to have a spontaneous, left-sided internal carotid artery dissection (ICAD).

CASE REPORT: The patient was treated successfully with thrombectomy and subsequently developed contralateral symptoms caused by a right-sided ICAD. This was managed with a second contra-lateral thrombectomy. The patient's course was complicated by persistent and mild hypotension, postulated to be secondary to bilateral carotid baroreceptor trauma from the dissections.

CONCLUSION: This case highlights the importance of close neurological monitoring for patients, preferably in a neurologic critical care setting, during and after …