Computer Engineering Commons^™

Assessing The Prevalence And Archival Rate Of Uris To Git Hosting Platforms In Scholarly Publications, Emily Escamilla

Computer Science Theses & Dissertations

The definition of scholarly content has expanded to include the data and source code that contribute to a publication. While major archiving efforts to preserve conventional scholarly content, typically in PDFs (e.g., LOCKSS, CLOCKSS, Portico), are underway, no analogous effort has yet emerged to preserve the data and code referenced in those PDFs, particularly the scholarly code hosted online on Git Hosting Platforms (GHPs). Similarly, Software Heritage is working to archive public source code, but there is value in archiving the surrounding ephemera that provide important context to the code while maintaining their original URIs. In current implementations, source code …

An Advanced Simulation Architecture For Testing Autonomous And Connected Vehicles Enabled By Virtual Reality, Defu Cui

Electrical & Computer Engineering Theses & Dissertations

With the advancement of intelligent transportation systems, autonomous driving and connected driving are seen as potential solutions to alleviate traffic congestion, enhance traffic safety, and improve efficiency. Extensive testing and validation of autonomous vehicles (AVs) and connected vehicles (CVs) including connected autonomous vehicles are crucial to ensure their safety and reliability. However, testing and validating AVs and CVs on public roads faces challenges such as high costs, inadequate support from transportation infrastructure with communication technologies, and safety concerns, among others. Simulations have become essential tools for testing autonomous driving and connected driving. As mixed traffic involves multiple domains including traffic …

Towards Intelligent Runtime Framework For Distributed Heterogeneous Systems, Polykarpos Thomadakis

Computer Science Theses & Dissertations

Scientific applications strive for increased memory and computing performance, requiring massive amounts of data and time to produce results. Applications utilize large-scale, parallel computing platforms with advanced architectures to accommodate their needs. However, developing performance-portable applications for modern, heterogeneous platforms requires lots of effort and expertise in both the application and systems domains. This is more relevant for unstructured applications whose workflow is not statically predictable due to their heavily data-dependent nature. One possible solution for this problem is the introduction of an intelligent Domain-Specific Language (iDSL) that transparently helps to maintain correctness, hides the idiosyncrasies of lowlevel hardware, and …

Towards A Robust Defense: A Multifaceted Approach To The Detection And Mitigation Of Neural Backdoor Attacks Through Feature Space Exploration And Analysis, Liuwan Zhu

Electrical & Computer Engineering Theses & Dissertations

From voice assistants to self-driving vehicles, machine learning(ML), especially deep learning, revolutionizes the way we work and live, through the wide adoption in a broad range of applications. Unfortunately, this widespread use makes deep learning-based systems a desirable target for cyberattacks, such as generating adversarial examples to fool a deep learning system to make wrong decisions. In particular, many recent studies have revealed that attackers can corrupt the training of a deep learning model, e.g., through data poisoning, or distribute a deep learning model they created with “backdoors” planted, e.g., distributed as part of a software library, so that the …

Iot Health Devices: Exploring Security Risks In The Connected Landscape, Abasi-Amefon Obot Affia, Hilary Finch, Woosub Jung, Issah Abubakari Samori, Lucas Potter, Xavier-Lewis Palmer

School of Cybersecurity Faculty Publications

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of …

Systemic Risk Analysis Of Human Factors In Phishing, Mark Guilford

Engineering Management & Systems Engineering Theses & Dissertations

The scope of this study is the systemic risk of the role of humans in the risk of phishing. The relevance to engineering managers and systems engineers of the risks of phishing attacks is the theft of data which has significantly increased in the past couple of years. Phishing has become a systemic persistent threat to all internet users. Understanding the role of humans in phishing from a systemic perspective is a critical objective towards creating a strong defense against complex and manipulative phishing attacks. The systemic view of phishing concentrates on how phishing affects the entire organizational system, not …

Roboretrieve--In A Dual Role As A Hand-Held Surgical Robot And A Collaborative Robot End-Effector To Perform Spillage-Free Specimen Retrieval In Laparoscopy, Siqin Dong

Mechanical & Aerospace Engineering Theses & Dissertations

Recent advances in surgical robotics attempt to overcome limitations of manual surgery by augmenting the surgeon’s capabilities while performing suturing, incision, retraction, and retrieval tasks. This dissertation presents novel approaches for spillage-free specimen retrieval in confined spaces, targeted toward the surgical domain of minimally invasive robotic surgery. The retrieval task involves extraction of a resected specimen, residing in the abdominal cavity, completely outside of the patient’s body. A major challenge in this context is the spillage of content being retrieved, which may cause dissemination of malignancy. To address this challenge, this dissertation develops RoboRetrieve, a portable hand-held robot that …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Opensim-Based Musculoskeletal Modeling: Foundation For Interactive Obstetric Simulator, Bahador Dodge

Electrical & Computer Engineering Theses & Dissertations

The use of mathematical and computational models to understand complex biological systems, such as the human birth process, is a rapidly growing field in medicine. These models can be used to optimize and personalize medical treatments for individual patients, enhance training, and aid in educational efforts. While recent advancements in healthcare, particularly in obstetrics, have improved care for mothers and babies, studies and government reports indicate a rising rate of maternal mortality in the United States.

Despite this rising trend, there is a lack of detailed studies concerning the use of modeling and simulation to develop an interactive obstetrics simulator …

Ransomware: What Is Ransomware, And How To Prevent It, Brandon Chambers

Cybersecurity Undergraduate Research Showcase

This research paper answers the question, “What is Ransomware, and How to prevent it?”. This paper will discuss what ransomware is, its history about ransomware, how ransomware attacks Windows systems, how to prevent ransomware, how to handle ransomware once it is already on the network, ideas for training professionals to avoid ransomware, and how anti-virus helps defend against ransomware. Many different articles, case studies, and professional blogs will be used to complete the research on this topic.

Hampton Roads' Building Resilient Communities Flood Game, Gul Ayaz, Katherine Smith, Rafael Diaz, Joshua G. Behr

Modeling, Simulation and Visualization Student Capstone Conference

As rising sea levels and subsequent recurrent flooding disproportionately affects coastal areas, it is crucial to develop a heightened awareness of the impacts of natural disasters on communities and the environments they live in. The Hampton Roads’ Building Resilient Communities (BRC) Flood Game is a simulation role-playing game designed to allow players to increase their understanding of the impact of various community response interventions to sea level rise and recurrent flooding. Players will examine and assess the tradeoffs of resiliency investments, the impact policies may have on the population, and the amount of time return on investment takes. The BRC …

Digital Game-Based Approach To Math Learning For Students, Gul Ayaz, Katherine Smith

Modeling, Simulation and Visualization Student Capstone Conference

Mathematics is an important subject that is pervasive across many disciplines. It is also a subject that has proven to be challenging to both teach and learn. Students face many challenges with learning math such as a lack of motivation and anxiety. To address these challenges, game-based learning has become a popular approach to stimulate students and create a more positive classroom environment. It can serve as an alternative or supplement to traditional teaching and can better engage students while developing a positive attitude toward learning. The use of games in a classroom can create a more exciting and engaging …

Gpu Utilization: Predictive Sarimax Time Series Analysis, Dorothy Dorie Parry

Modeling, Simulation and Visualization Student Capstone Conference

This work explores collecting performance metrics and leveraging the output for prediction on a memory-intensive parallel image classification algorithm - Inception v3 (or "Inception3"). Experimental results were collected by nvidia-smi on a computational node DGX-1, equipped with eight Tesla V100 Graphic Processing Units (GPUs). Time series analysis was performed on the GPU utilization data taken, for multiple runs, of Inception3’s image classification algorithm (see Figure 1). The time series model applied was Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX).

Behind Derogatory Migrants' Terms For Venezuelan Migrants: Xenophobia And Sexism Identification With Twitter Data And Nlp, Joseph Martínez, Melissa Miller-Felton, Jose Padilla, Erika Frydenlund

Modeling, Simulation and Visualization Student Capstone Conference

The sudden arrival of many migrants can present new challenges for host communities and create negative attitudes that reflect that tension. In the case of Colombia, with the influx of over 2.5 million Venezuelan migrants, such tensions arose. Our research objective is to investigate how those sentiments arise in social media. We focused on monitoring derogatory terms for Venezuelans, specifically veneco and veneca. Using a dataset of 5.7 million tweets from Colombian users between 2015 and 2021, we determined the proportion of tweets containing those terms. We observed a high prevalence of xenophobic and defamatory language correlated with the …

An Algorithm For Finding Data Dependencies In An Event Graph, Erik J. Jensen

Modeling, Simulation and Visualization Student Capstone Conference

This work presents an algorithm for finding data dependencies in a discrete-event simulation system, from the event graph of the system. The algorithm can be used within a parallel discrete-event simulation. Also presented is an experimental system and event graph, which is used for testing the algorithm. Results indicate that the algorithm can provide information about which vertices in the experimental event graph can affect other vertices, and the minimum amount of time in which this interference can occur.

Automatic Generation Of Virtual Work Guide For Complex Procedures: A Case, Shan Liu, Yuzhong Shen

Modeling, Simulation and Visualization Student Capstone Conference

Practical work guides for complex procedures are significant and highly affect the efficiency and accuracy of on-site users. This paper presents a technique to generate virtual work guides automatically for complex procedures. Firstly, the procedure information is extracted from the electronic manual in PDF format. And then, the extracted procedure steps are mapped to the virtual model parts in preparation for animation between adjacent steps. Next, smooth animations of the procedure are generated based on a 3D natural cubic spline curve to improve the spatial ability of the work guide. In addition, each step's annotation is automatically adjusted to improve …

U-Net Based Multiclass Semantic Segmentation For Natural Disaster Based Satellite Imagery, Nishat Ara Nipa

Modeling, Simulation and Visualization Student Capstone Conference

Satellite image analysis of natural disasters is critical for effective emergency response, relief planning, and disaster prevention. Semantic segmentation is believed to be on of the best techniques to capture pixelwise information in computer vision. In this work we will be using a U-Net architecture to do a three class semantic segmentation for the Xview2 dataset to capture the level of damage caused by different natural disaster which is beyond the visual scope of human eyes.

Simulating Function Generators And Oscilloscopes In A Virtual Laboratory Environment, Yiyang Li, Yuzhong Shen, Charles I. Sukenik

Modeling, Simulation and Visualization Student Capstone Conference

This paper discusses the development of a virtual laboratory for simulating electronic instruments commonly used in science and engineering courses, such as function generators and digital storage oscilloscopes. Mathematical equations are used to represent continuous signals and ensure signal integrity, while C# delegates are adopted to enable communication between simulated devices. The approach allows for loose coupling between software components and high cohesion of individual components, and can be applied to other virtual laboratory developments. The virtual laboratory provides a means for students to gain hands-on experience with electronic instruments and improve their understanding of theoretical concepts.

Assessing Frustration Towards Venezuelan Migrants In Columbia: Path Analysis On Newspaper Coded Data, Brian Llinás, Guljannat Huseynli, Erika Frydenlund, Katherine Palacia, Jose Padilla

Modeling, Simulation and Visualization Student Capstone Conference

This study analyzes the impact of Venezuelan migrants on local frustration levels in Colombia. The study found a relationship between the influx of Venezuelan migrants and the level of frustration among locals towards migrants, infrastructure, government, and geopolitics. Additionally, we identified that frustration types have an impact on other frustrations. The study used articles from a national newspaper in Colombia from 2015 to 2020. News articles were coded during a previous study qualitatively and categorized into frustration types. The code frequencies were then used as variables in this study. We used path modeling to statistically study the relationship between dependent …

Lidar Buoy Detection For Autonomous Marine Vessel Using Pointnet Classification, Christopher Adolphi, Dorothy Dorie Parry, Yaohang Li, Masha Sosonkina, Ahmet Saglam, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive on-the-fly the external environment through onboard sensors. In this paper, buoy detection for LiDAR images is explored by using several tools and techniques: machine learning methods, Unity Game Engine (herein referred to as Unity) simulation, and traditional image processing. The Unity Game …

Role Of Ai In Threat Detection And Zero-Day Attacks, Kelly Morgan

Cybersecurity Undergraduate Research Showcase

Cybercrime and attack methods have been steadily increasing since the 2019 pandemic. In the years following 2019, the number of victims and attacks per hour rapidly increased as businesses and organizations transitioned to digital environments for business continuity amidst lockdowns. In most scenarios cybercriminals continued to use conventional attack methods and known vulnerabilities that would cause minimal damage to an organization with a robust cyber security posture. However, zero-day exploits have skyrocketed across all industries with an increasingly growing technological landscape encompassing internet of things (IoT), cloud hosting, and more advanced mobile technologies. Reports by Mandiant Threat Intelligence (2022) concluded …

Lack Of Black Female Diversity Within The Cybersecurity Workforce, Eric Preston

Cybersecurity Undergraduate Research Showcase

Cybersecurity methods, strategies, and programs for protecting computers and electronic devices have become critically important in all aspects of the technological infrastructure. However, while this is a vastly growing field that has a large workforce, it suffers from a lack of representation, specifically for Black/African American females. This study examines the barriers that prevent Black women from having representation within the cybersecurity workforce and solutions that address them. Some barriers noted include a lack of role models and resources, historical discrimination and systemic barriers, and cultural norms. Additionally, implications created by these barriers include less diversity, a lack of career …

Leveraging Artificial Intelligence And Machine Learning For Enhanced Cybersecurity: A Proposal To Defeat Malware, Emmanuel Boateng

Cybersecurity Undergraduate Research Showcase

Cybersecurity is very crucial in the digital age in order to safeguard the availability, confidentiality, and integrity of data and systems. Mitigation techniques used in the industry include Multi-factor Authentication (MFA), Incident Response Planning (IRP), Security Information and Event Management (SIEM), and Signature-based and Heuristic Detection.

MFA is employed as an additional layer of protection in several sectors to help prevent unauthorized access to sensitive data. IRP is a plan in place to address cybersecurity problems efficiently and expeditiously. SIEM offers real-time analysis and alerts the system of threats and vulnerabilities. Heuristic-based detection relies on detecting anomalies when it comes …

The Rise And Risks Of Internet Of Things, Diamond E. Hicks

Cybersecurity Undergraduate Research Showcase

Internet of Things (IoT) has become a necessary part of our everyday lives. IoT is the network in which many different devices communicate, connect, and share data. Though how IoT got to where it is today, the issues it faced, and how it affects our lives today is not common knowledge. Despite the fact that IoT has advanced our technology to what it is today, people do not completely understand what it does.

Modeling Wealth Distribution In A Society, Dylan Berns, Peihsien Sun, Adrian V. Gheorghe

Engineering Management & Systems Engineering Faculty Publications

The interconnectedness of social mood, changing dynamics, income inequality, and wealth distribution underscores the complexity of understanding and addressing these issues. This complexity inspires researchers to develop models and conduct further research to gain insights into the mechanisms driving income inequality and wealth distribution. By studying these phenomena more comprehensively, one can aim to develop strategies and policies that promote a more equitable distribution of wealth and opportunities, thereby fostering social stability and economic prosperity. In the present paper, there was build a model on wealth distribution and income inequality to help people understand the complexities of wealth inequality and …

Quantum Computing And Its Applications In Healthcare, Vu Giang

OUR Journal: ODU Undergraduate Research Journal

This paper serves as a review of the state of quantum computing and its application in healthcare. The various avenues for how quantum computing can be applied to healthcare is discussed here along with the conversation about the limitations of the technology. With more and more efforts put into the development of these computers, its future is promising with the endeavors of furthering healthcare and various other industries.

An Optimized And Scalable Blockchain-Based Distributed Learning Platform For Consumer Iot, Zhaocheng Wang, Xueying Liu, Xinming Shao, Abdullah Alghamdi, Md. Shirajum Munir, Sujit Biswas

School of Cybersecurity Faculty Publications

Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) models using end-user data. Federated learning (FL) uses privacy-preserving ML techniques to forecast customers' needs and consumption habits, and blockchain replaces the centralized aggregator to safeguard the ecosystem. However, blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research introduces a novel architecture, emphasizing …

Robustembed: Robust Sentence Embeddings Using Self-Supervised Contrastive Pre-Training, Javad Asl, Eduardo Blanco, Daniel Takabi

School of Cybersecurity Faculty Publications

Pre-trained language models (PLMs) have demonstrated their exceptional performance across a wide range of natural language processing tasks. The utilization of PLM-based sentence embeddings enables the generation of contextual representations that capture rich semantic information. However, despite their success with unseen samples, current PLM-based representations suffer from poor robustness in adversarial scenarios. In this paper, we propose RobustEmbed, a self-supervised sentence embedding framework that enhances both generalization and robustness in various text representation tasks and against diverse adversarial attacks. By generating high-risk adversarial perturbations to promote higher invariance in the embedding space and leveraging the perturbation within a novel contrastive …

Past Challenges And The Future Of Discrete Event Simulation, Andrew J. Collins, Farinaz Sabz Ali Pour, Craig A. Jordan

Engineering Management & Systems Engineering Faculty Publications

The American scientist Carl Sagan once said: “You have to know the past to understand the present.” We argue that having a meaningful dialogue on the future of simulation requires a baseline understanding of previous discussions on its future. For this paper, we conduct a review of the discrete event simulation (DES) literature that focuses on its future to understand better the path that DES has been following, both in terms of who is using simulation and what directions they think DES should take. Our review involves a qualitative literature review of DES and a quantitative bibliometric analysis of the …

A Structured Narrative Prompt For Prompting Narratives From Large Language Models: Sentiment Assessment Of Chatgpt-Generated Narratives And Real Tweets, Christopher J. Lynch, Erik J. Jensen, Virginia Zamponi, Kevin O'Brien, Erika Frydenlund, Ross Gore

VMASC Publications

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent-based models and simulations consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Simulated agents generate large volumes of data and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents' perspectives on key life events by providing natural language narratives. However, these narratives should be factual, transparent, and reproducible. Therefore, we present a structured narrative prompt …