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Generalized Model To Enable Zero-Shot Imitation Learning For Versatile Robots, Yongshuai Wu 2024 Kennesaw State University

Generalized Model To Enable Zero-Shot Imitation Learning For Versatile Robots, Yongshuai Wu

Master's Theses

The rapid advancement in Deep Learning (DL), especially in Reinforcement Learning (RL) and Imitation Learning (IL), has positioned it as a promising approach for a multitude of autonomous robotic systems. However, the current methodologies are predominantly constrained to singular setups, necessitating substantial data and extensive training periods. Moreover, these methods have exhibited suboptimal performance in tasks requiring long-horizontal maneuvers, such as Radio Frequency Identification (RFID) inventory, where a robot requires thousands of steps to complete.

In this thesis, we address the aforementioned challenges by presenting the Cross-modal Reasoning Model (CMRM), a novel zero-shot Imitation Learning policy, to tackle long-horizontal robotic …


Brain Computer Interface-Based Drone Control Using Gyroscopic Data From Head Movements, Ikaia Cacha Melton 2024 Georgia Southern University

Brain Computer Interface-Based Drone Control Using Gyroscopic Data From Head Movements, Ikaia Cacha Melton

Honors College Theses

This research explores the potential of using gyroscopic data from a person’s head movement to control a DJI Tello quadcopter via a Brain-Computer Interface (BCI). In this study, over 100 gyroscopic recordings capturing the X, Y and Z columns (formally known as GyroX, GyroY, GyroZ) between 4 volunteers with the Emotiv Epoc X headset were collected. The Emotiv Epoc X data captured (left, right, still, and forward) head movements of each participant associated with the DJI Tello quadcopter navigation. The data underwent thorough processing and analysis, revealing distinctive patterns in charts using Microsoft Excel. A Python condition algorithm was then …


Improving Ethics Surrounding Collegiate-Level Hacking Education: Recommended Implementation Plan & Affiliation With Peer-Led Initiatives, Shannon Morgan, Dr. Sanjay Goel 2024 University at Albany, SUNY

Improving Ethics Surrounding Collegiate-Level Hacking Education: Recommended Implementation Plan & Affiliation With Peer-Led Initiatives, Shannon Morgan, Dr. Sanjay Goel

Military Cyber Affairs

Cybersecurity has become a pertinent concern, as novel technological innovations create opportunities for threat actors to exfiltrate sensitive data. To meet the demand for professionals in the workforce, universities have ramped up their academic offerings to provide a broad range of cyber-related programs (e.g., cybersecurity, informatics, information technology, digital forensics, computer science, & engineering). As the tactics, techniques, and procedures (TTPs) of hackers evolve, the knowledge and skillset required to be an effective cybersecurity professional have escalated accordingly. Therefore, it is critical to train cyber students both technically and theoretically to actively combat cyber criminals and protect the confidentiality, integrity, …


Using Digital Twins To Protect Biomanufacturing From Cyberattacks, Brenden Fraser-Hevlin, Alec W. Schuler, B. Arda Gozen, Bernard J. Van Wie 2024 Washington State University

Using Digital Twins To Protect Biomanufacturing From Cyberattacks, Brenden Fraser-Hevlin, Alec W. Schuler, B. Arda Gozen, Bernard J. Van Wie

Military Cyber Affairs

Understanding of the intersection of cyber vulnerabilities and bioprocess regulation is critical with the rise of artificial intelligence and machine learning in manufacturing. We detail a case study in which we model cyberattacks on network-mediated signals from a novel bioreactor, where it is important to control medium feed rates to maintain cell proliferation. We use a digital twin counterpart reactor to compare glucose and oxygen sensor signals from the bioreactor to predictions from a kinetic growth model, allowing discernment of faulty sensors from hacked signals. Our results demonstrate a successful biomanufacturing cyberattack detection system based on fundamental process control principles.


Characterizing Advanced Persistent Threats Through The Lens Of Cyber Attack Flows, Logan Zeien, Caleb Chang, LTC Ekzhin Ear, Dr. Shouhuai Xu 2024 University of Colorado, Colorado Springs (UCCS)

Characterizing Advanced Persistent Threats Through The Lens Of Cyber Attack Flows, Logan Zeien, Caleb Chang, Ltc Ekzhin Ear, Dr. Shouhuai Xu

Military Cyber Affairs

Effective cyber defense must build upon a deep understanding of real-world cyberattacks to guide the design and deployment of appropriate defensive measures against current and future attacks. In this abridged paper (of which the full paper is available online), we present important concepts for understanding Advanced Persistent Threats (APTs), our methodology to characterize APTs through the lens of attack flows, and a detailed case study of APT28 that demonstrates our method’s viability to draw useful insights. This paper makes three technical contributions. First, we propose a novel method of constructing attack flows to describe APTs. This abstraction allows technical audiences, …


Machine Learning Security For Tactical Operations, Dr. DeNaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu 2024 Virginia Tech

Machine Learning Security For Tactical Operations, Dr. Denaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu

Military Cyber Affairs

Deep learning finds rich applications in the tactical domain by learning from diverse data sources and performing difficult tasks to support mission-critical applications. However, deep learning models are susceptible to various attacks and exploits. In this paper, we first discuss application areas of deep learning in the tactical domain. Next, we present adversarial machine learning as an emerging attack vector and discuss the impact of adversarial attacks on the deep learning performance. Finally, we discuss potential defense methods that can be applied against these attacks.


Securing The Void: Assessing The Dynamic Threat Landscape Of Space, Brianna Bace, Dr. Unal Tatar 2024 University at Albany

Securing The Void: Assessing The Dynamic Threat Landscape Of Space, Brianna Bace, Dr. Unal Tatar

Military Cyber Affairs

Outer space is a strategic and multifaceted domain that is a crossroads for political, military, and economic interests. From a defense perspective, the U.S. military and intelligence community rely heavily on satellite networks to meet national security objectives and execute military operations and intelligence gathering. This paper examines the evolving threat landscape of the space sector, encompassing natural and man-made perils, emphasizing the rise of cyber threats and the complexity introduced by dual-use technology and commercialization. It also explores the implications for security and resilience, advocating for collaborative efforts among international organizations, governments, and industry to safeguard the space sector.


Commercial Enablers Of China’S Cyber-Intelligence And Information Operations, Ethan Mansour, Victor Mukora 2024 Virginia Tech

Commercial Enablers Of China’S Cyber-Intelligence And Information Operations, Ethan Mansour, Victor Mukora

Military Cyber Affairs

In a globally commercialized information environment, China uses evolving commercial enabler networks to position and project its goals. They do this through cyber, intelligence, and information operations. This paper breaks down the types of commercial enablers and how they are used operationally. It will also address the CCP's strategy to gather and influence foreign and domestic populations throughout cyberspace. Finally, we conclude with recommendations for mitigating the influence of PRC commercial enablers.


Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark 2024 University of South Alabama

Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark

Poster Presentations

Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, …


Automated Brain Tumor Classifier With Deep Learning, venkata sai krishna chaitanya kandula 2024 California State University – San Bernardino

Automated Brain Tumor Classifier With Deep Learning, Venkata Sai Krishna Chaitanya Kandula

Electronic Theses, Projects, and Dissertations

Brain Tumors are abnormal growth of cells within the brain that can be categorized as benign (non-cancerous) or malignant (cancerous). Accurate and timely classification of brain tumors is crucial for effective treatment planning and patient care. Medical imaging techniques like Magnetic Resonance Imaging (MRI) provide detailed visualizations of brain structures, aiding in diagnosis and tumor classification[8].

In this project, we propose a brain tumor classifier applying deep learning methodologies to automatically classify brain tumor images without any manual intervention. The classifier uses deep learning architectures to extract and classify brain MRI images. Specifically, a Convolutional Neural Network (CNN) …


Diegetic Sonification For Low Vision Gamers, Jhané Dawes 2024 Kennesaw State University

Diegetic Sonification For Low Vision Gamers, Jhané Dawes

Master's Theses

There are not many games designed for all players that provide accommodations for low vision users. This means that low vision users may not get to engage with the gaming community in the same way as their sighted peers. In this thesis, I explore how diegetic sonification can be used as a tool to support these low vision gamers in the typical gaming environment. I asked low vision players to engage with a prototype game level with two diegetic sonification techniques applied, without the use of their corrective lenses. I found that participants had more enjoyment and experienced less difficulty …


Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark 2024 University of South Alabama

Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark

Honors Theses

Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, …


Building Software At Scale: Understanding Productivity As A Product Of Software Engineering Intrinsic Factors, Gauthier Ingende Wa Boway 2024 Kennesaw State University

Building Software At Scale: Understanding Productivity As A Product Of Software Engineering Intrinsic Factors, Gauthier Ingende Wa Boway

Master's Theses

During our education at KSU, we have learned about various factors that affect productivity such as schedule, budget, and risks, but those are often controlled outside of what we could learn as software engineering principles, patterns, or practices. On top of that, other off-work factors such as health conditions, emotional distress, or political climate, just to name a few, could drastically affect the productivity of a software engineering team. We see a demarcation between those factors that affect productivity in software engineering but are not inherent to the discipline itself, which we call resistance factors, and the factors that are …


Secured Blockchain And Fractional Discrete Cosine Transform-Based Framework For Medical Images, Abhay Kumar Yadav, Virendra P. Vishwakarma 2024 Guru Gobind Singh Indraprastha University, New Delhi

Secured Blockchain And Fractional Discrete Cosine Transform-Based Framework For Medical Images, Abhay Kumar Yadav, Virendra P. Vishwakarma

Makara Journal of Technology

Images can store large amounts of data and are useful for transmitting large amounts of information across different geographical locations using different cloud services. This data sharing increases the chances of cyber-attacks on digital images. Blockchain has properties that enable it to work as a solution to this problem, providing enhanced security and unchangeable storage. However, image size poses a challenge in image storage, as it increases the related storage cost. Compressing images using fractional discrete cosine transform (fctDCT) reduces the amount of data required to express an image securely. This paper presents a novel framework for securely storing and …


Breast Cancer Classification With Machine Learning, Rahanuma Tarannum 2024 Rahanuma Tarannum

Breast Cancer Classification With Machine Learning, Rahanuma Tarannum

ATU Research Symposium

Breast cancer is one of the foremost causes of death amongst women worldwide. Breast tumours are characteristically classified as either benign (non-cancerous) or malignant (cancerous). Benign tumours do not spread external side of the breast and are not fatal, whereas malignant tumours can metastasize and be incurable if untreated. Rapidly and accurate diagnosis of malignant tumours is significant for efficient treatment and advanced outcomes. In 2022, breast cancer claimed 670 000 lives worldwide. Women without any particular risk factors other than age and sex account for half of all cases of breast cancer. In 157 out of 185 nations, breast …


Pyroscan: Wildfire Behavior Prediction System, Derek H. Thompson, Parker A. Padgett, Timothy C. Johnson 2024 Arkansas Tech University

Pyroscan: Wildfire Behavior Prediction System, Derek H. Thompson, Parker A. Padgett, Timothy C. Johnson

ATU Research Symposium

During a wildfire, it is of the utmost importance to be updated about all information of the wildfire. Wind speed, wind direction and dry grass often works as fuel for the fire allowing it to spread in multiple directions. These different factors are often issues for any firefighting organization that is trying to help fight the fire. An uncontrolled wildfire is often a threat to wildlife, property, and worse, human and animal lives. In our paper, we propose an artificial intelligence (AI) powered fire tracking and prediction application utilizing Unmanned Aerial Vehicles (UAV) to inform fire fighters regarding the probability …


League Of Learning: Deep Learning For Soccer Action Video Classification, Musfikur Rahaman 2024 Arkansas Tech University

League Of Learning: Deep Learning For Soccer Action Video Classification, Musfikur Rahaman

ATU Research Symposium

The field of sports video analysis using deep learning is rapidly advancing. Proper classification and analysis of sports videos are essential to manage the growing sports media content. It offers numerous benefits for the media, advertising, analytics, and education sectors. Soccer, also known as football, worldwide, is among the most popular sports. This research study used a deep learning-based approach for soccer action detection. Deep learning has become a popular machine learning technique, especially for image and video classification. We have used the SoccerAct dataset, which consists of ten soccer actions like corner, foul, freekick, goal kick, long pass, on …


Predictive Ai Applications For Sar Cases In The Us Coast Guard, Joshua Nelson 2024 Old Dominion University

Predictive Ai Applications For Sar Cases In The Us Coast Guard, Joshua Nelson

Cybersecurity Undergraduate Research Showcase

This paper explores the potential integration of predictive analytics AI into the United States Coast Guard's (USCG) Search and Rescue Optimal Planning System (SAROPS) for deep sea and nearshore search and rescue (SAR) operations. It begins by elucidating the concept of predictive analytics AI and its relevance in military applications, particularly in enhancing SAR procedures. The current state of SAROPS and its challenges, including complexity and accuracy issues, are outlined. By integrating predictive analytics AI into SAROPS, the paper argues for streamlined operations, reduced training burdens, and improved accuracy in locating drowning personnel. Drawing on insights from military AI applications …


Human-Machine Communication: Complete Volume. Volume 7 Special Issue: Mediatization, 2024 University of Central Florida

Human-Machine Communication: Complete Volume. Volume 7 Special Issue: Mediatization

Human-Machine Communication

This is the complete volume of HMC Volume 7. Special Issue on Mediatization


First-Year Engineering Students And Genai: Experience, Attitudes, Trust, And Ethics., Elisabeth Thomas, Cenetria Crockett, Campbell Rightmyer Bego 2024 University of Louisville

First-Year Engineering Students And Genai: Experience, Attitudes, Trust, And Ethics., Elisabeth Thomas, Cenetria Crockett, Campbell Rightmyer Bego

Undergraduate Research Events

Generative AI (GenAI) has the potential to benefit student learning by offering personalized feedback, idea generation, research, and analysis support, writing aid, and administrative support (Chan and Hu, 2023; Zhang, 2023). However, if used inappropriately, the same tools can lead to false/biased content creation and reduced ethical awareness leading to possible academic dishonesty and privacy issues (Schwartz, 2016; Wu, 2023). At this early stage, ethical standards and professorial guidance are unavailable, so it is important to understand what students are thinking about the recent technologies (Shen et al., 2013). Spring 2023 survey results revealed that some students used ChatGPT, a …


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