Computer Sciences Commons^™

Security Hardening Of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks, Ferhat Ozgur Catak, Murat Kuzlu, Haolin Tang, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the …

Defensive Distillation-Based Adversarial Attack Mitigation Method For Channel Estimation Using Deep Learning Models In Next-Generation Wireless Networks, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Ozgur Guler

Engineering Technology Faculty Publications

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those …

Development Of Experiential Learning Experiences For K-12 Students Focusing On Smart Cities, Murat Kuzlu, Vukica Jovanovic, Nathan Puryear, Patrick J. Martin, Sherif Abdelwahed, Özgür Güler

Engineering Technology Faculty Publications

The main objective of this paper is to describe a project focused on the development of experiential learning experiences for undergraduate and graduate students focusing on smart cities. The future workforce needs students with various data analytics skills, service reliability, and sustainability. The team of researchers from Old Dominion University and Virginia Commonwealth University is developing a virtual smart city lab environment at both universities and collaborating on multiple research projects. The main purpose of this virtual labs is to provide a testbed that can be used for students who are interested in careers related to cyber-physical systems (CPS). These …

A Real-Time 3d Object Detection, Recognition And Presentation System On A Mobile Device For Assistive Navigation, Jin Chen

Dissertations and Theses

This thesis proposes an integrated solution for 3D object detection, recognition, and presentation to increase accessibility for various user groups in indoor areas through a mobile application. The system has three major components: a 3D object detection module, an object tracking and update module, and a voice and AR-enhanced interface. The 3D object detection module consists of pre-trained 2D object detectors and 3D bounding box estimation methods to detect the 3D poses and sizes of the objects in each camera frame. This module can easily adapt to various 2D object detectors (e.g., YOLO, SSD, Mask RCNN) based on the requested …

A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir

Dissertations and Theses

Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches toward the comprehension of urban flood risk using data science techniques on crowd-sourced data. This is accomplished by developing a series of data-driven models to identify flood factors of significance and localized areas of flood vulnerability in New York City (NYC). First, the infrastructural (catch basin clogs, …

Advanced Full-Text Search Based On Synonyms In Postgres, Joey Bodoia

CMC Senior Theses

This paper discusses the advanced full-text search queries based on synonyms that are supported in Chajda, which is a postgres extension and corresponding python library for highly multi-lingual full-text search in postgres. This discussion will include the motivations for using advanced queries based on synonyms, examples of how to use these advanced queries in Chajda, current limitiations of the advanced queries, and performance testing of the advanced queries.

Dynamic Nonlinear Gaussian Model For Inferring A Graph Structure On Time Series, Abhinuv Uppal

CMC Senior Theses

In many applications of graph analytics, the optimal graph construction is not always straightforward. I propose a novel algorithm to dynamically infer a graph structure on multiple time series by first imposing a state evolution equation on the graph and deriving the necessary equations to convert it into a maximum likelihood optimization problem. The state evolution equation guarantees that edge weights contain predictive power by construction. After running experiments on simulated data, it appears the required optimization is likely non-convex and does not generally produce results significantly better than randomly tweaking parameters, so it is not feasible to use in …

Examination Of Strategies To Implementing Chip-And-Personal Identification Number Credit Card Authentication Infrastructures, Neville Arthur Gallimore

Walden Dissertations and Doctoral Studies

Chip-and-Personal Identification Number (PIN) technology is seen as a game changer in many e-commerce industries and a transformational technology in the 21st century. However, security concerns have made chip-and-PIN adoption relatively slow. Massive unauthorized card payment transactions in the United States (U.S.) cost victims an estimate totaling billions of dollars. Information Technology (IT) managers are concerned with credit card fraud's financial loss and liability cost. Grounded in Rogers’s diffusion of innovation theory, the purpose of this qualitative pragmatic study was to explore strategies used by IT managers to transition their e-commerce organizations to chip-and-PIN credit card authentication infrastructures. The participants …

The 4c’S Of Pal – An Evidence-Based Model For Implementing Peer Assisted Learning For Mature Students, Nevan Bermingham, Frances Boylan, Barry J. Ryan

Articles

Peer Assisted Leaning (PAL) programmes have been shown to enhance learner confidence and have an overall positive effect on learner comprehension, particularly in subjects traditionally perceived as difficult. This research describes the findings of a three-cycle Action Research study into the perceived benefits of implementing such a programme for mature students enrolled on a computer science programming module on an Access Foundation Programme in an Irish University. The findings from this study suggest that peer learning programmes offer students a valued support structure that aids transition and acculturation into tertiary education whilst simultaneously improving their subject-matter comprehension and confidence. An …

Satdbailiff- Mining And Tracking Self-Admitted Technical Debt, Mohamed Wiem Mkaouer, Eman Abdullah Alomar, Ben Christians, Mihal Busho, Ahmed Hamad Alkhalid, Christian D. Newman

Articles

Self-Admitted Technical Debt (SATD) is a metaphorical concept to describe the self-documented addition of technical debt to a software project in the form of source code comments. SATD can linger in projects and degrade source-code quality, but it can also be more visible than unintentionally added or undocumented technical debt. Understanding the implications of adding SATD to a software project is important because developers can benefit from a better understanding of the quality trade-offs they are making. However, empirical studies, analyzing the survivability and removal of SATD comments, are challenged by potential code changes or SATD comment updates that may …

Design And Implementation Of An Automatic Word Generator For Word Matching Interactives, Evan Miles Gertis

College of Graduate Studies: Theses & Dissertations

An Automatic Word Match Generator is a software tool that can be used to generate word-matching interactives automatically. The purpose of a word-matching interactive is to provide students with the mechanism to learn new vocabulary and improve their reading comprehension skills. This thesis will present the design and implementation of an Automatic Word Match Generator, as well as the research and algorithms used in the program.

Modernization Of Legacy Information Technology Systems, Rabie Khabouze

Walden Dissertations and Doctoral Studies

Large enterprises spend a large portion of their Information Technology (IT) budget on maintaining their legacy systems. Legacy systems modernization projects are a catalyst for IT architects to save cost, provide new and efficient systems that increase profitability, and create value for their organization. Grounded in sociotechnical systems theory, the purpose of this qualitative multiple case study was to explore strategies IT architects use to modernize their legacy systems. The population included IT architects in large enterprises involved in legacy systems modernization projects, one in healthcare, and one in the financial services industry in the San Antonio-New Braunfels, Texas metropolitan …

Relationship Between Vendor/Client Complementarity, Vendor Technology Maturity, Vendor Financial Stability, And It Outsourcing Project Outcomes, Everton A. Wilson

Walden Dissertations and Doctoral Studies

Business and IT leaders in financial services are concerned with the limited benefits they are reaping from information technology outsourcing (ITO) projects, despite continued heavy investments in ITO. Grounded in the transaction cost, agency, and resource-based view theories, the purpose of this quantitative correlational study was to examine the relationship between vendor/client complementarity, vendor technology maturity, vendor financial stability, and ITO success. Participants were 65 business and IT leaders in financial institutions engaged in ITO projects. The result of the multiple linear regression was significant, F(3, 61) = 4.845, p = .004, R2 = .192. In the final analysis, vendor/client …

Increasing Nurse Leader Knowledge And Awareness Of Information And Communication Technologies, Cory Stephens

Walden Dissertations and Doctoral Studies

Due to the recent coronavirus disease (COVID-19) pandemic, rapid technological innovation and nursing practice transformation exposed a deepening divide in the knowledge and awareness of information and communication technologies (ICT) among nurses. This technological skills gap undermines the benefits of ICT to nursing practice such as increased nurse satisfaction, improved care quality, and reduced costs. Nurse leaders are positioned to promote the use of ICT among nurses but may suffer from the same knowledge deficit of ICT as their followers. Guided by Locsin’s technological competencies as caring in nursing theory, Staggers and Parks’ nurse-computer interaction framework, and Covell’s nursing intellectual …

Strategies For Cybercrime Prevention In Information Technology Businesses, Sophfronia G. Tucker

Walden Dissertations and Doctoral Studies

Cybercrime continues to be a devastating phenomenon, impacting individuals and businesses across the globe. Information technology (IT) businesses need solutions to defend and secure their data and networks from cyberattacks. Grounded in general systems theory and transformational leadership theory, the purpose of this qualitative multiple case study was to explore strategies IT business leaders use to protect their systems from a cyberattack. The participants included six IT business leaders with experience in cybersecurity or system security in the Midlands region of South Carolina. Data were collected using semistructured interviews and reviews of government standards documents; data were analyzed using thematic …

Employee Training Strategies For New Technology Implementation In Small Business, Eddy Varela

Walden Dissertations and Doctoral Studies

Failure to implement new technology creates a barrier to success for small businesses. Small business owners must create competitive advantages by implementing new technology as there is a need to maintain an advantage when competing in the local market economy. Grounded in the human capital theory, the purpose of this qualitative multiple case study was to explore the employee training strategies small business owners use to implement new technology. The participants were five small business owners in Central Florida who used employee training strategies to implement new technologies Data were collected using (a) semistructured interviews, (b) member checking interviews, (c) …

Supporting Mastery Learning Through A Multiple-Submission Policy For Assignments In A Purely Online Programming Class, Joseph Benjamin R. Ilagan, Marianne Kayle Amurao, Jose Ramon Ilagan

Quantitative Methods and Information Technology Faculty Publications

The Learning Edge Momentum (LEM) theory suggests that once students fall behind, it gets more difficult to catch up with the course material. It then becomes increasingly more difficult to connect new, higher-level concepts to those solid edges of knowledge with mastery of basic concepts. Learning for Mastery (LFM) acknowledges that students learn at different paces by allowing students unable to master tests the first time to catch up eventually. This paper describes how an online introductory Python programming course offered to business students followed a multiple-submission policy for assignments to support LFM. The multiple submission policy contributed to the …

Deeply Learning Deep Inelastic Scattering Kinematics, Markus Diefenthaler, Abdullah Farhat, Andrii Verbytskyi, Yuesheng Xu

Mathematics & Statistics Faculty Publications

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables Q² and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection …

Towards Automated Data Mining: Reinforcement Intelligence For Self-Optimizing Feature Engineering, Kunpeng Liu

Electronic Theses and Dissertations, 2020-2023

Feature engineering is one of the most important components in data mining and machine learning. One of the key thrusts in data mining is to answer: How should a low-dimensional geometry structure be extracted and reconstructed from high-dimensional data? To solve this issue, researchers proposed feature selection, PCA, sparsity regularization, factorization, embedding, and deep learning. However, existing techniques are limited in achieving full automation, globally optimal, and explainable explicitness. Can I address the automation, optimal, and explainability challenges in data geometry reconstruction? A low-dimensional data geometry structure is crucial for SciML methods (e.g., GP models), and the accuracy of these …

Exploring The Privacy Dimension Of Wearables Through Machine Learning-Enabled Inference, Ulku Meteriz Yildiran

Electronic Theses and Dissertations, 2020-2023

Today's hyper-connected consumers demand convenient ways to tune into information without switching between devices, which led the industry leaders to the wearables. Wearables such as smartwatches, fitness trackers, and augmented reality (AR) glasses can be comfortably worn on the body. In addition, they offer limitless features, including activity tracking, authentication, navigation, and entertainment. Wearables that provide digestible information stimulate even higher consumer demand. However, to keep up with the ever-growing user expectations, developers keep adding new features and interaction methods to augment the use cases without considering their privacy impacts. In this dissertation, we explore the privacy dimension of wearables …

Vision-Based Human Fall Detection In Smart Homes, Snigdha Chaudhari

Graduate Theses/Dissertations

Falling is one of the leading causes of death from unintentional injuries in older adults. They are more common in people over the age of 65. Wearable sensor-based solutions are commercially available, but they possess limitations like recharging the sensors, and wearing them can be intrusive to the user. Consequently, vision-based fall detection approaches offer a feasible alternative due to the ever-increasing presence of cameras in smart homes. This thesis presents a novel two-stage human fall detection system for smart homes. The proposed approach uses humans as a sensor. It is a vision-based two-stage process where Stage-1 is dedicated to …

Deep Features To Analyze Pulmonary Abnormalities In Chest X-Rays Due To Covid-19, Supriti Ghosh

Dissertations and Theses

Artificial Intelligence (AI) has contributed a lot since the beginning. Healthcare is no exception. Detecting anomaly/abnormality in (bio)medical image is crucial. In this thesis, we aim at detecting/screening pulmonary abnormalities due to Covid-19 in chest X-rays using deep features. We study CheXNet, DenseNet169, ResNet50 and VggNet16 to analyze CXRs to detect the evidence of Covid-19 in this research. CheXNet was primarily designed for radiologist-level pneumonia detection in Chest X-rays (CXRs). We created a benchmark dataset size of 4,716 CXRs (2,358 Covid-19 positive cases and 2,358 non-Covid cases (Healthy and Pneumonia cases)) and with k(=5) fold cross-validation technique, using the DenseNet, …

What Is Explainable Ai?: A Focus On Ethical Theory, Casey Wall

Dissertations and Theses

A lack of a standardized set of principles within the computer science community is a major issue that impacts the entire field while also creating a level of mistrust among both practitioners of computer science as well as the public. This statement is particularly true when artificial intelligence(AI) is of concern. To create trust in anything one must show that the method of creation is done in a responsible manner. To standardize a set of principles within the computer science community would be to show that the creation of programs and AI can be done responsibly, though this standardization of …

Analyzing Cough Sounds For The Evidence Of Covid-19 Using Deep Learning Models, Muntasir Mamun

Dissertations and Theses

Early detection of infectious disease is the must to prevent/avoid multiple infections, and Covid-19 is an example. When dealing with Covid-19 pandemic, Cough is still ubiquitously presented as one of the key symptoms in both severe and non-severe Covid-19 infections, even though symptoms appear differently in different sociodemographic categories. By realizing the importance of clinical studies, analyzing cough sounds using AI-driven tools could help add more values when it comes to decision-making. Moreover, for mass screening and to serve resource constrained regions, AI-driven tools are the must. In this thesis, Convolutional Neural Network (CNN) tailored deep learning models are studied …

2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice

Dissertations and Theses

In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients (MFCC), …

Accelerating Spatial Autocorrelation Computation With Parallelization, Vectorization And Memory Access Optimization, Anmol Paudel, Satish Puri

Computer Science Faculty Research and Publications

No abstract provided.

A Novel Framework For Mixed Reality–Based Control Of Collaborative Robot: Development Study, Md. Tanzil Shahria, Md. Samiul Haque Sunny, Md. Ishrak Islam Zarif, Md. Mahafuzur Rahaman Khan, Preet Parag Modi, Sheikh Iqbal Ahamed, Mohammad H. Rahman

Computer Science Faculty Research and Publications

Background:

Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research.

Objective:

This study aims to present the development of a novel framework for the collaborative robot using mixed reality.

Methods:

The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. …

Evaluation Of Continuous Power-Down Schemes, James Andro-Vasko, Wolfgang Bein

Computer Science Faculty Research

We consider a power-down system with two states—“on” and “off”—and a continuous set of power states. The system has to respond to requests for service in the “on” state and, after service, the system can power off or switch to any of the intermediate power-saving states. The choice of states determines the cost to power on for subsequent requests. The protocol for requests is “online”, which means that the decision as to which intermediate state (or the off-state) the system will switch has to be made without knowledge of future requests. We model a linear and a non-linear system, and …

Novel Architecture Of Onem2m-Based Convergence Platform For Mixed Reality And Iot, Seungwoon Lee, Woogeun Kil, Byeong Hee Roh, Si-Jung Kim, Jin Suk Kang

College of Engineering Faculty Research

There have been numerous works proposed to merge augmented reality/mixed reality (AR/MR) and Internet of Things (IoT) in various ways. However, they have focused on their specific target applications and have limitations on interoperability or reusability when utilizing them to different domains or adding other devices to the system. This paper proposes a novel architecture of a convergence platform for AR/MR and IoT systems and services. The proposed architecture adopts the oneM2M IoT standard as the basic framework that converges AR/MR and IoT systems and enables the development of application services used in general-purpose environments without being subordinate to specific …

A Machine Learning Approach To Intended Motion Prediction For Upper Extremity Exoskeletons, Justin Berdell

Graduate Research Theses & Dissertations

A fully solid-state, software-defined, one-handed, handle-type control device built around a machine-learning (ML) model that provides intuitive and simultaneous control in position and orientation each in a full three degrees-of-freedom (DOF) is proposed in this paper. The device, referred to as the “Smart Handle”, and it is compact, lightweight, and only reliant on low-cost and readily available sensors and materials for construction. Mobility chairs for persons with motor difficulties could make use of a control device that can learn to recognize arbitrary inputs as control commands. Upper-extremity exoskeletons used in occupational settings and rehabilitation require a natural control device like …