Computer Sciences Commons^™

A Call For Research: Ethical Dilemmas Of Autonomous Vehicle Manufacturers, Remy Harwood

Cybersecurity Undergraduate Research Showcase

While autonomous vehicles accounted for about 31.4 million vehicles on the road in 2019 (Placek). They have continued to flood the market and have a projected growth to 58 million in just 8 years from now (Placek) As well as a market cap in the billions of dollars. Even the comparatively new AV company Tesla has over 3 times the market cap value of the leading non AV brand Toyota (Market Cap) who are also working toward AVs as well, like their level 2 teammate driver assistance. Following Moore’s Law, as technology continues to improve, their social impact and ethical …

Ethical Concerns In Self-Driving Cars, Victoria Shand

Cybersecurity Undergraduate Research Showcase

Automobiles have been in existence since 1672 and only went up from there. Self-driving cars are a fantastic piece of technology; they can self-park, laser ranger finder, and near vision; they can map the road in advance, understand road signs, and, in some cases, handle certain variables on the road.

When talking about any form of technology, the possibilities are endless technology is a continuously evolving idea. When discussing self-driving cars, they have some fantastic and encouraging benefits and ideas. Some ideas would be that vehicles could communicate with each other, we could eliminate the need for traffic lights, and …

A Brief Review Of Dns, Root Servers, Vulnerabilities And Decentralization, Mallory Runyan

Cybersecurity Undergraduate Research Showcase

Since the 1980’s and creation of the World Wide Web, Internet utilization is a common and arguably, necessary, part of daily life. The internet is young and still relatively new, but as of 2016, 3.4 billion people were online, and that number has since grown [1]. This is a significant number, but as such a common part of daily life, how elements of the internet or its infrastructure work is complex. The world would very likely be thrown into dark ages if DNS or any other significant aspect of the internet's infrastructure were to succumb to an attack. The Colonial …

Cyber Resilience Analytics For Cyber-Physical Systems, Md Ariful Haque

Electrical & Computer Engineering Theses & Dissertations

Cyber-physical systems (CPSs) are complex systems that evolve from the integrations of components dealing with physical processes and real-time computations, along with networking. CPSs often incorporate approaches merging from different scientific fields such as embedded systems, control systems, operational technology, information technology systems (ITS), and cybernetics. Today critical infrastructures (CIs) (e.g., energy systems, electric grids, etc.) and other CPSs (e.g., manufacturing industries, autonomous transportation systems, etc.) are experiencing challenges in dealing with cyberattacks. Major cybersecurity concerns are rising around CPSs because of their ever-growing use of information technology based automation. Often the security concerns are limited to probability-based possible attack …

Hard-Real-Time Computing Performance In A Cloud Environment, Alvin Cornelius Murphy

Engineering Management & Systems Engineering Theses & Dissertations

The United States Department of Defense (DoD) is rapidly working with DoD Services to move from multi-year (e.g., 7-10) traditional acquisition programs to a commercial industrybased approach for software development. While commercial technologies and approaches provide an opportunity for rapid fielding of mission capabilities to pace threats, the suitability of commercial technologies to meet hard-real-time requirements within a surface combat system is unclear. This research establishes technical data to validate the effectiveness and suitability of current commercial technologies to meet the hard-real-time demands of a DoD combat management system. (Moreland Jr., 2013) conducted similar research; however, microservices, containers, and container …

Applications Of Blockchain In Business Processes: A Comprehensive Review, Wattana Viriyasitavat, Li Xu, Dusit Niyato, Zhuming Bi, Danupol Hoonsopon

Information Technology & Decision Sciences Faculty Publications

Blockchain (BC), as an emerging technology, is revolutionizing Business Process Management (BPM) in multiple ways. The main adoption is to serve as a trusted infrastructure to guarantee the trust of collaborations among multiple partners in trustless environments. Especially, BC enables trust of information by using Distributed Ledger Technology (DLT). With the power of smart contracts, BC enforces the obligations of counterparties that transact in a business process (BP) by programming the contracts as transactions. This paper aims to study the state-of-the-art of BC technologies by (1) exploring its applications in BPM with the focus on how BC provides the trust …

Smart Manufacturing—Theories, Methods, And Applications, Zhuming Bi, Lida Xu, Puren Ouyang

Information Technology & Decision Sciences Faculty Publications

(First paragraph) Smart manufacturing (SM) distinguishes itself from other system paradigms by introducing ‘smartness’ as a measure to a manufacturing system; however, researchers in different domains have different expectations of system smartness from their own perspectives. In this Special Issue (SI), SM refers to a system paradigm where digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the internet to expand system capabilities, (3) supporting data-driven decision making at all domains and levels of businesses, or (4) reconfiguring systems to adapt changes and uncertainties in dynamic environments. …

Cyber Deception For Critical Infrastructure Resiliency, Md Ali Reza Al Amin

Computational Modeling & Simulation Engineering Theses & Dissertations

The high connectivity of modern cyber networks and devices has brought many improvements to the functionality and efficiency of networked systems. Unfortunately, these benefits have come with many new entry points for attackers, making systems much more vulnerable to intrusions. Thus, it is critically important to protect cyber infrastructure against cyber attacks. The static nature of cyber infrastructure leads to adversaries performing reconnaissance activities and identifying potential threats. Threats related to software vulnerabilities can be mitigated upon discovering a vulnerability and-, developing and releasing a patch to remove the vulnerability. Unfortunately, the period between discovering a vulnerability and applying a …

Evaluation Of Generative Models For Predicting Microstructure Geometries In Laser Powder Bed Fusion Additive Manufacturing, Andy Ramlatchan

Computer Science Theses & Dissertations

In-situ process monitoring for metals additive manufacturing is paramount to the successful build of an object for application in extreme or high stress environments. In selective laser melting additive manufacturing, the process by which a laser melts metal powder during the build will dictate the internal microstructure of that object once the metal cools and solidifies. The difficulty lies in that obtaining enough variety of data to quantify the internal microstructures for the evaluation of its physical properties is problematic, as the laser passes at high speeds over powder grains at a micrometer scale. Imaging the process in-situ is complex …

Predictors Of Email Response: Determinants Of The Intention Of Not Following Security Recommendations, Miguel Angel Toro-Jarrin

Engineering Management & Systems Engineering Theses & Dissertations

Organizations and government leaders are concerned about cyber incidents. For some time, researchers have studied what motivates people to act in ways that put the confidentiality, integrity, and availability of information in organizations at risk. Still, several areas remained unexplored, including the role of employees’ evaluation of the organizational systems and the role of value orientation at work as precursors of secure and insecure actions in relation to information technologies (information security [IS] action). The objective of this research project was to examine how the evaluations of formal and informal security norms are associated with the intention to follow them …

Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray

Electrical & Computer Engineering Theses & Dissertations

Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …

Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque

Electrical & Computer Engineering Theses & Dissertations

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover …

Adaptive Risk Network Dependency Analysis Of Complex Hierarchical Systems, Katherine L. Smith

Computational Modeling & Simulation Engineering Theses & Dissertations

Recently the number, variety, and complexity of interconnected systems have been increasing while the resources available to increase resilience of those systems have been decreasing. Therefore, it has become increasingly important to quantify the effects of risks and the resulting disruptions over time as they ripple through networks of systems. This dissertation presents a novel modeling and simulation methodology which quantifies resilience, as impact on performance over time, and risk, as the impact of probabilistic disruptions. This work includes four major contributions over the state-of-the-art which are: (1) cyclic dependencies are captured by separation of performance variables into layers which …

The Message Design Of Raiders Of The Lost Ark On The Atari 2600 & A Fan’S Map, Quick Start, And Strategy Guide, Miguel Ramlatchan, William I. Ramlatchan

Distance Learning Faculty & Staff Books

The message design and human performance technology in video games, especially early video games have always been fascinating to me. From an instructional design perspective, the capabilities of the technology of the classic game consoles required a careful balance of achievable objectives, cognitive task analysis, guided problem solving, and message design. Raiders on the Atari is an excellent example of this balance. It is an epic adventure game, spanning 13+ distinct areas, with an inventory of items, where those hard to find items had to be used by the player to solve problems during their quest (and who would have …

Runtime Energy Savings Based On Machine Learning Models For Multicore Applications, Vaibhav Sundriyal, Masha Sosonkina

Electrical & Computer Engineering Faculty Publications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.

Development Of Modeling And Simulation Platform For Path-Planning And Control Of Autonomous Underwater Vehicles In Three-Dimensional Spaces, Sai Krishna Abhiram Kondapalli

Mechanical & Aerospace Engineering Theses & Dissertations

Autonomous underwater vehicles (AUVs) operating in deep sea and littoral environments have diverse applications including marine biology exploration, ocean environment monitoring, search for plane crash sites, inspection of ship-hulls and pipelines, underwater oil rig maintenance, border patrol, etc. Achieving autonomy in underwater vehicles relies on a tight integration between modules of sensing, navigation, decision-making, path-planning, trajectory tracking, and low-level control. This system integration task benefits from testing the related algorithms and techniques in a simulated environment before implementation in a physical test bed. This thesis reports on the development of a modeling and simulation platform that supports the design and …

Deep Learning Object-Based Detection Of Manufacturing Defects In X-Ray Inspection Imaging, Juan C. Parducci

Mechanical & Aerospace Engineering Theses & Dissertations

Current analysis of manufacturing defects in the production of rims and tires via x-ray inspection at an industry partner’s manufacturing plant requires that a quality control specialist visually inspect radiographic images for defects of varying sizes. For each sample, twelve radiographs are taken within 35 seconds. Some defects are very small in size and difficult to see (e.g., pinholes) whereas others are large and easily identifiable. Implementing this quality control practice across all products in its human-effort driven state is not feasible given the time constraint present for analysis.

This study aims to identify and develop an object detector capable …

Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw

Electrical & Computer Engineering Theses & Dissertations

Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are …

Data-Driven Framework For Understanding & Modeling Ride-Sourcing Transportation Systems, Bishoy Kelleny

Civil & Environmental Engineering Theses & Dissertations

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for …

Deapsecure Computational Training For Cybersecurity: Third-Year Improvements And Impacts, Bahador Dodge, Jacob Strother, Rosby Asiamah, Karina Arcaute, Wirawan Purwanto, Masha Sosonkina, Hongyi Wu

Modeling, Simulation and Visualization Student Capstone Conference

The Data-Enabled Advanced Training Program for Cybersecurity Research and Education (DeapSECURE) was introduced in 2018 as a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. By its third year, DeapSECURE, like many other educational endeavors, experienced abrupt changes brought by the COVID-19 pandemic. The training had to be retooled to adapt to fully online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, …

Applications Of Parallel Discrete Event Simulation, Erik J. Jensen

Modeling, Simulation and Visualization Student Capstone Conference

This work presents three applications of parallel discrete event simulation (PDES), which describe the motivation for and the benefits of using PDES, the kinds of synchronization algorithms that are used, and scaling behavior with these different synchronization algorithms.

Analysis Of An Existing Method In Refinement Of Protein Structure Predictions Using Cryo-Em Images, Maytha Alshammari, Jing He, Willy Wriggers, Jiangwen Sun

College of Sciences Posters

Protein structure prediction produces atomic models from its amino acid sequence. Three-dimensional structures are important for understanding the function mechanism of proteins. Knowing the structure of a given protein is crucial in drug development design of novel enzymes. AlphaFold2 is a protein structure prediction tool with good performance in recent CASP competitions. Phenix is a tool for determination of a protein structure from a high-resolution 3D molecular image. Recent development of Phenix shows that it is capable to refine predicted models from AlphaFold2, specifically the poorly predicted regions, by incorporating information from the 3D image of the protein. The goal …

Cova Cci Undergrad Cyber Research, Nana Jeffrey

Cybersecurity Undergraduate Research Showcase

Is your digital assistant your worst enemy? Modern technology has impacted our lives in a positive way making tasks that were once time consuming become more convenient. For example a few years ago writing down your grocery list with a paper and pen was a norm, now with technology we have access to IoT devices such as smart fridges that can inform us on what items are low in stock, send a message to our digital assistants such as iOS Siri and Amazon's Alexa to remind us to buy those groceries. Although these digital assistants have helped make our daily …

Understanding The Mechanism Of Deep Learning Frameworks In Lesion Detection For Pathological Images With Breast Cancer, Wei-Wen Hsu, Chung-Hao Chen, Chang Hao, Yu-Ling Hou, Xiang Gao, Yun Shao, Xueli Zhang, Jingjing Wang, Tao He, Yanhong Tai

Electrical & Computer Engineering Faculty Publications

With the advances of scanning sensors and deep learning algorithms, computational pathology has drawn much attention in recent years and started to play an important role in the clinical workflow. Computer-aided detection (CADe) systems have been developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing misdetections. In this study, we conducted four experiments to demonstrate that the features learned by deep learning models are interpretable from a pathological perspective. In addition, classifiers such as the support vector machine (SVM) and random forests (RF) were used in experiments to replace the fully connected layers and decompose the end-to-end …

Two-Stage Transfer Learning For Facial Expression Classification In Children, Gregory Hubbard, Megan Witherow, Khan Iftekharuddin

Undergraduate Research Symposium

Studying facial expressions can provide insight into the development of social skills in children and provide support to individuals with developmental disorders. In afflicted individuals, such as children with Autism Spectrum Disorder (ASD), atypical interpretations of facial expressions are well-documented. In computer vision, many popular and state-of-the-art deep learning architectures (VGG16, EfficientNet, ResNet, etc.) are readily available with pre-trained weights for general object recognition. Transfer learning utilizes these pre-trained models to improve generalization on a new task. In this project, transfer learning is implemented to leverage the pretrained model (general object recognition) on facial expression classification. Though this method, the …

Ransombuster Iot: A Intrusion Detection And Dataset Creation Tool For Ransomware Attacks Within Iot Networks, Jackson M. Walker

Cybersecurity Undergraduate Research Showcase

The proposed research follows the design-science guidelines(Hevner, 2004). This paper uses these design-science methods for developing the guidelines for the implementation of the proposed architecture, understanding previous research contributions, and evaluating of research. This paper proposes a network artifact for studying ransomware IoT intrusion detection techniques and offers a proposed network architecture to serve as a framework for creating a publicly available dataset for IoT research on ransomware.

Human Interaction With Fake News, Autumn Woodson, Sampath Jayarathna (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Precursors Of Email Response To Cybersecurity Scenarios: Factor Exploration And Scale Development, Miguel A. Toro-Jarrin, Pilar Pazos-Lago, Miguel Padilla

Engineering Management & Systems Engineering Faculty Publications

In the last decade, information security research has further expanded to include human factors as key elements of the organization's cybersecurity infrastructure. Numerous factors from several theories have been explored to explain and predict the multitude of information security-related behaviors in organizations. Lately, there has been a call for the study of specific cybersecurity behaviors in contextualized scenarios that reflect specific and realistic situations of a potential cyber-attack. This paper focuses on precursors of email response in situations that can be the origin of cybersecurity incidents in organizations (i.e., phishing attacks, ransomware, etc.). This study explores participants' intentions to follow …

The Effects Of Antecedents And Mediating Factors On Cybersecurity Protection Behavior, Ling Li, Li Xu, Wu He

Information Technology & Decision Sciences Faculty Publications

This paper identifies opportunities for potential theoretical and practical improvements in employees' awareness of cybersecurity and their motivational behavior to protect themselves and their organizations from cyberattacks using the protection motivation theory. In addition, it contributes to the literature by examining additional variables and mediators besides the core constructs of the Protection Motivation Model (PMT). This article uses empirical data and structural equation modeling to test the antecedents and mediators of employees' cybersecurity motivational behavior. The study offers theoretical and pragmatic guidance for cybersecurity programs. First, the model developed in this study can partially explain how people may change their …

Segmenting Technical Drawing Figures In Us Patents, Md Reshad Ul Hoque, Xin Wei, Muntabir Hasan Choudhury, Kehinde Ajayi, Martin Gryder, Jian Wu, Diane Oyen

Computer Science Faculty Publications

Image segmentation is the core computer vision problem for identifying objects within a scene. Segmentation is a challenging task because the prediction for each pixel label requires contextual information. Most recent research deals with the segmentation of natural images rather than drawings. However, there is very little research on sketched image segmentation. In this study, we introduce heuristic (point-shooting) and deep learning-based methods (U-Net, HR-Net, MedT, DETR) to segment technical drawings in US patent documents. Our proposed methods on the US Patent dataset achieved over 90% accuracy where transformer performs well with 97% segmentation accuracy, which is promising and computationally …