Digital Commons Network^™

A Prototype Application For The Measurement Of Data Breach Event Intensity, James Palazzolo

Master's Theses and Doctoral Dissertations

The data breach phenomenon is not new. People have valued information, its protection, and its security for centuries. A data breach is the loss of control over one’s information. In contemporary society (circa 2024), a data breach can occur in several ways: through accidental disclosure, negligence, or malicious action. Likewise, the study of the data breach phenomenon is not new. As with other studies, this research effort seeks to expand the current body of knowledge concerning data breaches. However, unlike previous research, we adopt a novel approach to further our understanding of the phenomenon. By employing topological and phenomenological thinking, …

Embracing Ai In English Composition: Insights And Innovations In Hybrid Pedagogical Practices, James Hutson, Daniel Plate, Kadence Berry

Faculty Scholarship

In the rapidly evolving landscape of English composition education, the integration of AI writing tools like ChatGPT and Claude 2.0 has marked a significant shift in pedagogical practices. A mixed-method study conducted in Fall 2023 across three sections, including one English Composition I and two English Composition II courses, provides insightful revelations. The study, comprising 28 student respondents, delved into the impact of AI tools through surveys, analysis of writing artifacts, and a best practices guide developed by an honors student. Initially, the study observed a notable anxiety and mistrust among students regarding the use of AI in writing. However, …

Privacy Vs. Social Capital: Examining Information Disclosure Patterns Within Social Media Influencer Networks, Eidan James Rosado

CCAC Theses and Dissertations

Adversaries have several ways to leverage and expose the data disclosed in social media network engagements for different motives including but not limited to fraud, discreditation, or social engineering. Previous research on social media interactions discussed increased engagements where influencers and viral trends were involved. Studies also discussed engagements declining over time. Within posts or engagements, personal identifiable information (PII) can be shared with varying rate of risk severity. Publicly available data such as this can be leveraged by adversaries. With the absence of insights of whether influencers impact engagements and disclosures, the goal of this study was to obtain …

Design Science Nutrition Label Approach: Satiating U.S. Consumer Concerns For Information Privacy (Cfip) With Privacy Home Automation Assessment Scorecards (Phaats), Andrew S. Ramos

CCAC Theses and Dissertations

In today’s technology-dependent society, information privacy and cybersecurity boundaries between human-to-machine-to-web are non-existent or built on questionable business practices. Consumers are becoming more reliant on and even addicted to their technological enabling, or internet of things (IoT), life tools (e.g., smartphones, smarthome devices, smart-wearable devices). Nevertheless, consumer information privacy rights in the United States have not fully matured to legal ramifications against businesses like in other countries, e.g., the European Union’s 2018 General Data Protection Regulation (GDPR). The real issue lies in consumers not understanding the opportunity cost of associated privacy trade-offs. While some are unaware, others disregard such trade-offs …

An Algorithm Based On Priority Rules For Solving A Multi-Drone Routing Problem In Hazardous Waste Collection, Youssef Harrath Dr., Jihene Kaabi Dr.

Research & Publications

This research investigates the problem of assigning pre-scheduled trips to multiple drones to collect hazardous waste from different sites in the minimum time. Each drone is subject to essential restrictions: maximum flying capacity and recharge operation. The goal is to assign the trips to the drones so that the waste is collected in the minimum time. This is done if the total flying time is equally distributed among the drones. An algorithm was developed to solve the problem. The algorithm is based on two main ideas: sort the trips according to a given priority rule and assign the current trip …

Consistent Monte Carlo Methods For Non-Linear Applications In Light Transport, Zackary T. Misso

Dartmouth College Ph.D Dissertations

The study of light transport focuses on describing the propagation of light from emitters to sensors through accurately describing the interactions light can undergo with everything in between. Physically-based rendering is the process of applying the laws of light transport to formulate practical algorithms which simulate the flow of light for the purpose of synthesizing images of virtual environments.

Unfortunately, there are very few interesting scene configurations which can be computed analytically. Instead, modern solutions predominantly rely on Monte Carlo integration to stochastically estimate the transfer of light since the process is both unbiased and consistent. Meaning, it is expected …

Scalable Methods For Resource-Constrained Adaptive Sampling, Kizito Masaba

Dartmouth College Ph.D Dissertations

As the global community confronts the pressing issues of climate change, the importance of environment monitoring cannot be overstated. This process is essential in identifying and tracking critical environmental trends to facilitate efficient and effective conservation efforts. Traditionally, scientists perform this task using hand-held, in-situ instruments. With the recent robot advancements, this task can be fully automated by applying multi-robot systems to a task referred to as multi-robot adaptive sampling. However, the cost of purchasing and operating robot systems for this application are still prohibitive due to various environmental, technological, and logistical constraints. Some of the outstanding constraints to the …

Privacy And Security Of The Windows Registry, Edward L. Amoruso

Graduate Thesis and Dissertation 2023-2024

The Windows registry serves as a valuable resource for both digital forensics experts and security researchers. This information is invaluable for reconstructing a user's activity timeline, aiding forensic investigations, and revealing other sensitive information. Furthermore, this data abundance in the Windows registry can be effortlessly tapped into and compiled to form a comprehensive digital profile of the user. Within this dissertation, we've developed specialized applications to streamline the retrieval and presentation of user activities, culminating in the creation of their digital profile. The first application, named "SeeShells," using the Windows registry shellbags, offers investigators an accessible tool for scrutinizing and …

Breaking The Cycle: Countering Popularity Bias For Diverse Content Discovery, Brandon J. Weaver

Master's Projects

The ways most people consume the media have become very much driven by some pre-set algorithms. It is increasingly important to examine the outcome of these artificial intelligence (AI) models and ensure that any potentially dangerous long-term effects are addressed before they have a significant negative impact in our society. Popularity bias is one of these potentially harmful impacts, which stemmed from the shift from human intelligence to AI, or machine intelligence/machine learning (ML), when one explores the media and receives recommendations (often without requesting). In ML, three key steps usually occur; i.e, pre-processing, in-processing, and post- processing steps. The …

Digital Resurrection Of Historical Figures: A Case Study On Mary Sibley Through Customized Chatgpt, James Hutson, Paul Huffman, Jeremiah Ratican

Faculty Scholarship

This study investigates the emerging realm of digital resurrection, focusing on Mary Sibley (1800–1878), the esteemed founder of Lindenwood University. The core objective was to demonstrate the capability of advanced artificial intelligence, specifically a customized version of ChatGPT, in revitalizing historical figures for educational and engagement purposes. By integrating comprehensive diaries from Sibley with Claude 2.0, the research utilized a substantial autobiographical dataset to develop a GPT beta version that replicates her distinct voice and tone. The incorporation of her official portrait and diaries into the GPT Builder was pivotal, creating an interactive platform that accurately reflects her perspectives on …

Enhancing Multilingual Sentence Representation Learning For The Job Recruitment Domain, Napat Laosaengpha

Chulalongkorn University Theses and Dissertations (Chula ETD)

With the advancement in natural language processing (NLP), there has been significant development in multilingual pretraining sentence encoder. Typically, these pretraining models are trained on large-scale datasets that consist of general text data from various sources such as Wikipedia. However, the general proposed models aren't enough to understand contexts in such a domain-specific, especially in the job recruitment domain. It is due to its niche nature and the lack of readily available related information. To enhance the existing multilingual pretraining sentence encoder and mitigate the aforementioned problems, we first propose multi-task dual-encoder framework to improve the sentence encoder for general-purpose …

Ai-Based Defect Detection In Aerospace Ultrasonic Signals, Rami Issac Lake

Graduate Research Theses & Dissertations

Ensuring the safety and integrity of materials and structures throughout the manufacturing cycle is a critical concern across various industries, including aerospace, automotive, oiland gas, and civil engineering. Non-Destructive Inspection (NDI) techniques allow for the examination of materials without causing damage or alteration, enabling the early detection of potential issues before materials are utilized in the field. The inspection of fuselage composites presents a particular challenge due to their complex structures, diverse materials, and differences in thickness, making defect detection a challenging yet crucial task. Moreover, defects of various types and causes can emerge across all depths of the material …

Volume 15, Connor Thompson, Emily Steffenhagen, Emily Robertson, Luis Fernando Dos Reis, Emily Farmer, Samuel Villa, Robert Allison, Zachary Chessor, Megan Borden, Austin Burnett, Larry W. Grant Jr., Tristan Marowski, Emma Moore, Pearl Siff

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 Connor Thompson

On Mentorship Dr. John Miller

The Meat of the Matter: Alien, Human, and Animal in Terry Bisson’s “They’re Made Out of Meat” by Emily Steffenhagen

“Please REBLOG!”: An Ethical Analysis of Doxxing, Internet Vigilantism and Racists Getting Fired by Emily Robertson

Journaling: Paper Has More Patience Than People by Luis Fernando Dos Reis

The Effects of Climate Change on the Archaeological World by Emily Farmer

Lowered Seat Height Does Not Impair Wingate Performance in Untrained Cyclists by Samuel Villa, Robert Allison, …

Heed The Warning Signs: The Effectiveness Of Message Popup Warnings For Deterring The Spread Of Misinformation, Hollis Greenberg

CCAC Theses and Dissertations

As false news can propagate to others rapidly, social media platforms employ multiple methods to combat misinformation. Debunking techniques are warning features embedded into a platform’s interface that alert readers of misinformation. These warnings have two goals: to “debunk” false information and to prevent the further spread of misinformation. Researchers have evaluated the effectiveness of debunking techniques to understand how users increase their awareness of misinformation, and what users do with the information given in warning messages. Message popup warnings are a newer and understudied type of debunking technique.

The overarching research question of this study was: Are message popup …

Empirical Assessment Of Remote Workers’ Cyberslacking And Computer Security Posture To Assess Organizational Cybersecurity Risks, Ariel Luna

CCAC Theses and Dissertations

No abstract provided.

Understanding The Role Of Tacit And Explicit Knowledge Hiding In Organizations, Darren Wiggins

CCAC Theses and Dissertations

Knowledge Hiding (KHi) is the deliberate act of withholding knowledge from others, driven by distrust. This distrust stems from three key factors: rationalized hiding, evasive hiding, and playing dumb. The latter two, evasive hiding and playing dumb, are particularly detrimental as they foster a cycle of mutual distrust within the workplace. To counteract this, organizations have significantly invested in promoting Tacit Knowledge (TK) and Explicit Knowledge (EK) sharing. These initiatives aimed to facilitate knowledge transfer, foster collaboration, enhance problem-solving capabilities, and strengthen social and interpersonal relationships.

Recent studies highlighted the importance of understanding the attributes linked to TK and EK. …

Combating Disinformation On Social Media Networks With Media And Information Literacy Training For Social Media Network Users, Oscar Kwok Chao Ho

CCAC Theses and Dissertations

In the Internet age, social media networks (SMNs), such as Facebook (FB), Instagram (IG), and Twitter (TW), have gained popularity and become an essential part of human life. SMNs provide ease of connection to family, friends, and communities; however, they increase the chances social media network users (SMN users) will disclose private information (PI), causing critical harm to SMN users’ information privacy (IP). Furthermore, SMN users are exposed to significant amounts of disinformation, misinformation, or fake news, which they share without realizing the information is untrustworthy.

The goal of this developmental research was to investigate, examine, and understand the effects …

Assessment Of The Impact Of Various Feature Extraction Techniques On The Effectiveness Of Music Genre Classification In Neural Network Models., Sabhdh Grace

ICT

This research focuses on Music Genre Classification (MGC) using Convolutional Neural Networks (CNNs) and various datasets, including raw audio files (WAV) and extracted features such as Mel Spectrograms (MS), Mel-Frequency Cepstral Coefficients (MFCC), and Chroma Features (CF). The study employs Explanatory Sequential Mixed Methods (ESMM), combining qualitative research and experimental analysis to explore different model inputs and their performance. Several CNN-based models, including 2D CNN, 2D CNN-LSTM, 1D CNN, and 1D CNN-LSTM, were tested. However, the models generally underperformed, with most achieving accuracy of 10% or lower, and the best model (raw audio 1D CNN) reaching only 20%. The research …

Using Machine Learning To Identify Hate Speech And Offending Language On Twitter., Mayara Lorens, Thayene Lorens

ICT

This project focuses on applying Machine Learning (ML) techniques to detect hate speech and offensive language on Twitter, addressing ethical concerns like cyberbullying and fostering a safer online environment. The topic is chosen for its societal significance and business relevance, as hostile online behaviour negatively impacts user experiences and platform credibility.

To achieve this, the study implements four distinct ML models to develop an automated system capable of identifying and categorising content as offensive, non-offensive, or neutral. The system aims to contribute to mitigating harmful interactions on social media and improving user safety by effectively classifying potentially problematic content.

The …

Constructed Language (Conlang) Audio Honing, Ronald B. Oakes

CCAC Theses and Dissertations

An important aspect of a constructed language (conlang) is how it sounds when spoken. This dissertation designs and implements a tool to allow the user to hear how their conlang sounds when spoken. This tool will generate spoken language based on sample text in the constructed language. Further, it will enable the user to manipulate the phonetics of the language and hear how these changes impact the language in its spoken form.

This tool also allows users to assess the preferability of the language’s phonetics using Net Auditory Distance under the framework of Beats-and-Bindings Phonology. It was shown that the …

(Meta-)Physical Artworks: Digital Augmentation In Art Observation, Macy A. Toppan

Dartmouth College Master’s Theses

Augmented art— the subgenre of art that incorporates physical and digital artwork— is a rapidly growing field driven by advancing technology and a new generation for whom that tech is a given. Yet the presence of media like augmented and virtual reality in exhibition remains a controversial subject. Rather than focusing on the many theoretical debates about whether digital pieces can qualify as "good" art, we study it in practice through the eyes of the casual art observer. This paper highlights the audience in a within-participant study that asked viewers to take in a physical sculpture intentionally built with virtual …

A Benchmark Framework For Data Visualization And Explainable Ai (Xai), Murat Kuzlu, Gokcen Ozdemir, Umut Ozdemir

Engineering Technology Faculty Publications

This research introduces a benchmark framework, called EDUMX, designed for machine learning (ML)-based forecasting and XAI tasks, leveraging the Streamlit open-source Python library. The framework offers a comprehensive suite of functionalities, including data loading, feature selection, relationship analysis, data preprocessing, model selection, metric evaluation, training, and real-time monitoring. Users can easily upload data in diverse formats, explore relationships between variables, preprocess data using various techniques, and assess the performance of the ML model using customizable metrics. With its user-friendly interface, this framework offers invaluable insights for forecasting tasks in various domains, catering to the evolving needs of predictive analytics. EDUMX …

Enhancing 21 U.S.C. §§ 355, 356, And 360 To Encompass Artificial Intelligence-Based Drug Design And Manufacturing Methods, Aj Tsang

Michigan Technology Law Review

Despite newfound attention to how artificial intelligence (AI) may accelerate pharmaceutical development, federal regulators may find that current statutes are ambiguous or silent about their applicability to AI-based drug design and manufacturing methods. This poses a serious problem in the era of Loper Bright and the Major Questions Doctrine. As federal agencies struggle to adjust to courts’ growing demand for Congress to craft clear, explicit, and express delegations of authority, this note develops a statutory framework in which the Food and Drug Administration (FDA) would have more flexibility to regulate the use of AI in advanced drug manufacturing. Guided by …

Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao

Research Collection School Of Computing and Information Systems

In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) …

Conversational Localization: Indoor Human Localization Through Intelligent Conversation, Sheshadri Smitha, Kotaro Hara

Research Collection School Of Computing and Information Systems

We propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that …

The Hazard Prediction Problem, Mary E. Helander, Brendan Smith, Sylvia Charchut, Erika Swiatowy, Calvin Nau, Gregory Cavaretta, Timothy Schuler, Adam Schunk, Héctor Ortiz-Peña

Social Science - All Scholarship

This work formulates the hazard prediction problem while addressing the research question: Can machine learning create a model to automatically recognize patterns that correspond to hazard state conditions during a mission-critical operation? Supervised learning models were trained and tested on data observed from mission simulators, which allowed for safe observation of dynamic system states and undesirable casualty events. The prediction task was formulated as a binary classification problem, producing the probability of being in a hazard state at time t and providing situational awareness of a possible imminent loss. Several modeling architectures were investigated: neural networks, logistic regression, a support …

Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim

CMC Senior Theses

Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …

A Scoping Review On Mobile Health Technology For Assessment And Intervention Of Upper Limb Motor Function In Children With Motor Impairments, Md Raihan Mia, Sheikh Iqbal Ahamed, Alissa V. Fial, Samuel Nemanich

Computer Science Faculty Research and Publications

Upper limb (UL) motor dysfunctions impact residual movement in hands/shoulders and limit participation in play, sports, and leisure activities. Clinical and laboratory assessments of UL movement can be time-intensive, subjective, and/or require specialized equipment and may not optimally capture a child's motor abilities. The restrictions to in-person research experienced during the COVID-19 pandemic have inspired investigators to design inclusive at-home studies with child participants and their families. Relying on the ubiquity of mobile devices, mobile health (mHealth) applications offer solutions for various clinical and research problems. This scoping review article aimed to aggregate and synthesize existing research that used health …

Unveiling User Perspectives: Exploring Themes In Femtech Mobile App Reviews For Enhanced Usability And Privacy, Nidhi Nellore, Tania Mishra, Michael Zimmer

Computer Science Faculty Research and Publications

Femtech, a growing sector in mobile healthcare technology, caters to women's needs across various life stages with digital solutions like period tracking and pregnancy management apps. Maintaining robust data privacy is crucial due to the sensitive nature of the information involved, such as menstrual cycles and pregnancy status. Our research analyzes user feedback from platforms like the Apple App Store and Google Play Store to understand perceptions of femtech apps, covering accessibility, interface, features, and privacy concerns. Understanding user perspectives helps developers enhance usability and trust, driving further adoption. Prioritizing privacy also fosters industry advancement. This paper stresses the importance …

Remember The Human: A Systematic Review Of Ethical Considerations In Reddit Research, Casey Fiesler, Michael Zimmer, Nicholas Proferes, Sarah A. Gilbert, Naiyan Jones

Computer Science Faculty Research and Publications

Reddit is one of the world's most prominent social media platforms, and also a valuable source of data for internet researchers. However, working with this kind of data also presents novel ethical complications for researchers, including issues around privacy, vulnerable populations, and unintended consequences. This paper describes an analysis of 134 papers that rely on Reddit data while also including some discussion of ethical implications and/or considerations by the researchers. Our analysis of these papers reveals common ethical issues and ethically motivated methodological decisions, as described by the researchers themselves, while also exposing some gaps for further ethical contemplation for …