Artificial Intelligence and Robotics Commons^™

Machine Learning Multimodal Framework For Fake News Detection And Mitigation, Nada A. Gaballah

Theses and Dissertations

Social media has become our new reality, people wake up every morning and the first thing they do before getting out of bed, is check their social media. Nowadays, people rarely read newspapers, they even rarely watch TV news or listen to radio broadcasts. In recent years, we have witnessed lots of fake news roaming social media every second, with people simply believing it and spreading it even more without checking the credibility of this news. This fake news affected several domains like what happened in the US election in 2016 and again in 2020, the false information about Covid-19 …

Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco

Theses and Dissertations

Artificial Intelligence (AI) has exploded into mainstream consciousness with commercial investments exceeding $90 billion in the last year alone. Inasmuch as consumer-facing applications such ChatGPT offer astounding access to algorithms that were hitherto restricted to academic research labs, public focus of attention on AI has created an avalanche of misinformation. The nexus of investor-driven hype, “surprising” inaccuracies in the answers provided by AI models – now anthropomorphically labeled as “hallucinations”, and impending legislation by well-meaning and concerned governments has resulted in a crisis of confidence in the science of AI. The primary driver for AI’s recent growth is the convergence …

Customization Of Large Language Models For Causal Inference And Data Quality, Xingqiao Wang

Theses and Dissertations

In the burgeoning fields of artificial intelligence (AI) and natural language processing (NLP), Large Language Models (LLMs) have emerged as powerful tools for understanding complex textual data. This dissertation focuses on the novel customization of LLMs for enhancing causal inference in pharmacovigilance and improving entity matching for data quality—two critical challenges in healthcare analytics and data management. Through an in-depth exploration of encoder and decoder LLMs, this study illustrates how domain-specific customization can significantly advance the processing and interpretation of textual information. For pharmacovigilance, it demonstrates how tailored LLMs can extract causal relationships from adverse event reports, offering a new …

Denoising Diffusion Probabilistic Models Based Accelerated Mri, Alexander Francis Bugielski

Theses and Dissertations

Magnetic Resonance Imaging (MRI) is a cornerstone in obtaining intricate visualizations of anatomy and physiological processes within the human body. However, its extensive scan duration not only causes patient discomfort but also increases the likelihood of motion-induced artifacts in the images. To address such a challenge, this study investigates deep neural network models for reconstructing high-resolution MRI images from noisy and significantly undersampled data in a supervised learning manner. Specifically, it compares three models: a conventional U-Net, a self-attentive U-Net, and an innovative probabilistic diffusion model that builds upon the self-attentive U-Net architecture. These models are evaluated on their ability …

Representation Learning For Generative Models With Applications To Healthcare, Astronautics, And Aviation, Van Minh Nguyen

Theses and Dissertations

This dissertation explores applications of representation learning and generative models to challenges in healthcare, astronautics, and aviation.

The first part investigates the use of Generative Adversarial Networks (GANs) to synthesize realistic electronic health record (EHR) data. An initial attempt at training a GAN on the MIMIC-IV dataset encountered stability and convergence issues, motivating a deeper study of 1-Lipschitz regularization techniques for Auxiliary Classifier GANs (AC-GANs). An extensive ablation study on the CIFAR-10 dataset found that Spectral Normalization is key for AC-GAN stability and performance, while Weight Clipping fails to converge without Spectral Normalization. Analysis of the training dynamics provided further …

Deep Learning In Indus Valley Script Digitization, Deva Munikanta Reddy Atturu

Theses and Dissertations

This research introduces ASR-net(Ancient Script Recognition), a groundbreaking system that automatically digitizes ancient Indus seals by converting them into coded text, similar to Optical Character Recognition for modern languages. ASR-net, with an 95% success rate in identifying individual symbols, aims to address the crucial need for automated techniques in deciphering the enigmatic Indus script. Initially Yolov3 is utilized to create the bounding boxes around each graphemes present in the Indus Valley Seal. In addition to that we created M-net(Mahadevan) model to encode the graphemes. Beyond digitization, the paper proposes a new research challenge called the Motif Identification Problem (MIP) related …

Measuring The Presentation Of Supporting Content For A Set Of Learning Objectives Throughout The States Of An Educational Game Tree, Michael L. Hastriter Jr.

Theses and Dissertations

In an era of evolving warfare, the Department of Defense (DoD) recognizes the value of serious games as immersive tools for teaching critical concepts. This thesis introduces a pioneering framework tailored to enhance learning objectives through the presentation of educational game content. This addresses the unique needs of the DoD and other educators who use games by providing measurements to assess educational games. This researches investigates how instructors might assess games as potential teaching tools. It establishes a five-phase process, providing a framework to assess educational games against predefined learning objectives and informing future game development. This thesis demonstrates games …

Opportunities And Limitations: Integrating Narrative Ai Into Game-Based Assessment Creation And Evaluating Student Impacts, Kevin B. Patterson

Theses and Dissertations

The Department of Defense (DoD) has identified the need for a technically proficient workforce in the areas of science, technology, engineering, and mathematics. To meet this need, the DoD is actively seeking innovative technology capable of creating workforce development opportunities that are both accessible and effective. Educational research indicates serious games provide a potential avenue to achieve this goal. Unfortunately, a limited number of tools that simplify the game development process and leverage artificial intelligence are available. Content-generating artificial intelligence might help reduce instructor and game-based assessment designers' workloads while promoting individualized learning in students. This research presents a novel …

Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs

Theses and Dissertations

Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …

Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil

Theses and Dissertations

This thesis investigates the impact of adjusting artificial intelligence explainability levels’ outputs on user perception. The overarching study extends within the Explainable Artificial Intelligence (XAI) domain. It examines a spectrum of variables, including performance, cognizance, familiarity, transparency, system bias, and the overall impact of AI, to understand their collective and individual effects that enable effective professional use in an organization. The study aims to illuminate the relationship between the degree of explainability provided by large language models such as ChatGPT, Bard, and Bing AI and the performance of these models when tasked with XAI adjustments.

Standardization Of Risk Classifications For Unmanned Space Vehicle Missions, Collin A. Gwaltney

Theses and Dissertations

This paper seeks to model risk classification levels (A-D) for 122 Space Vehicle programs. Models include multinomial logistic regression as well as random forest, a machine learning technique based on decision trees. We use independent variables (IVs) which are theoretically correlated to risk class for the regression and one random forest model. We then include all IVs and allow the random forest technique to use those which provide the most information on risk class before paring down the number of IVs to only 7. We show that the accuracy of predictions increases from 62% to 87% by using random forest …

A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae

Theses and Dissertations

A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …

A Model-Based And System-Theoretic Approach To Design Advanced Autonomy For Air Dominance Missions: A Loyal Wingman Case Study, Elizabeth S. Pennington

Theses and Dissertations

In contested air environments, safe coordination between decision-makers is paramount. Although the Department of Defense (DoD) prioritizes the development of Artificially Intelligent (AI) wingmen for air combat, a lack of methodology exists to design safe, holistic coordination between human and autonomous wingmen in the same environment. This thesis delivers a framework using Systems Theoretic Process Analysis Extended for Coordination (STPA-Coord) to analyze and design holistic coordination for the Loyal Wingman concept in an Air Dominance mission. STPA-Coord is a safety and hazard analysis process that uses Systems Theory to analyze and design coordination between decisionmakers in a system-of-systems architecture. Using …

Kodai: Framework Towards Data Augmentation Of Large Language Models In Machine Learning, Rick Rejeleene

Theses and Dissertations

Machine Learning is rapidly advancing at an incredible pace due to an increase in computational size, and availability of data. Data is the cornerstone of machine learning algorithms. Information quality has a profound impact on the performance of machine learning systems. Supervised, Unsupervised, and Reinforcement learning are three major ways of performing machine learning. Self-supervised learning, part of unsupervised learning, has made breakthroughs in engineering and research by using Large Language Models (LLM). LLM are a type of neural network, used for language understanding and generation. Recently, LLM has taken a distinct lead by demonstrating state-of-the-art capabilities in natural language …

Adaptive Multi-Label Classification On Drifting Data Streams, Martha Roseberry

Theses and Dissertations

Drifting data streams and multi-label data are both challenging problems. When multi-label data arrives as a stream, the challenges of both problems must be addressed along with additional challenges unique to the combined problem. Algorithms must be fast and flexible, able to match both the speed and evolving nature of the stream. We propose four methods for learning from multi-label drifting data streams. First, a multi-label k Nearest Neighbors with Self Adjusting Memory (ML-SAM-kNN) exploits short- and long-term memories to predict the current and evolving states of the data stream. Second, a punitive k nearest neighbors algorithm with a self-adjusting …

Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart

Theses and Dissertations

Enabling machines to learn measures of human activity from bioelectric signals has many applications in human-machine interaction and healthcare. However, labeled activity recognition datasets are costly to collect and highly varied, which challenges machine learning techniques that rely on large datasets. Furthermore, activity recognition in practice needs to account for user trust - models are motivated to enable interpretability, usability, and information privacy. The objective of this dissertation is to improve adaptability and trustworthiness of machine learning models for human activity recognition from bioelectric signals. We improve adaptability by developing pretraining techniques that initialize models for later specialization to unseen …

Designing An Artificial Immune Inspired Intrusion Detection System, William Hosier Anderson

Theses and Dissertations

The domain of Intrusion Detection Systems (IDS) has witnessed growing interest in recent years due to the escalating threats posed by cyberattacks. As Internet of Things (IoT) becomes increasingly integrated into our every day lives, we widen our attack surface and expose more of our personal lives to risk. In the same way the Human Immune System (HIS) safeguards our physical self, a similar solution is needed to safeguard our digital self. This thesis presents the Artificial Immune inspired Intrusion Detection System (AIS-IDS), an IDS modeled after the HIS. This thesis proposes an architecture for AIS-IDS, instantiates an AIS-IDS model …

Scalable And Explainable Self-Supervised Motif Discovery In Temporal Data, Somayeh Bakhtiari Ramezani

Theses and Dissertations

The availability of a scalable and explainable rule extraction technique via motif discovery is crucial for identifying the health states of a system. Such a technique can enable the creation of a repository of normal and abnormal states of the system and identify the system’s state as we receive data. In complex systems such as ECG, each activity session can consist of a long sequence of motifs that form different global structures. As a result, applying machine learning algorithms without first identifying the local patterns is not feasible and would result in low performance. Thus, extracting unique local motifs and …

Study Of Augmentations On Historical Manuscripts Using Trocr, Erez Meoded

Theses and Dissertations

Historical manuscripts are an essential source of original content. For many reasons, it is hard to recognize these manuscripts as text. This thesis used a state-of-the-art Handwritten Text Recognizer, TrOCR, to recognize a 16^th-century manuscript. TrOCR uses a vision transformer to encode the input images and a language transformer to decode them back to text. We showed that carefully preprocessed images and designed augmentations can improve the performance of TrOCR. We suggest an ensemble of augmented models to achieve an even better performance.

Phenotyping Cotton Compactness Using Machine Learning And Uas Multispectral Imagery, Joshua Carl Waldbieser

Theses and Dissertations

Breeding compact cotton plants is desirable for many reasons, but current research for this is restricted by manual data collection. Using unmanned aircraft system imagery shows potential for high-throughput automation of this process. Using multispectral orthomosaics and ground truth measurements, I developed supervised models with a wide range of hyperparameters to predict three compactness traits. Extreme gradient boosting using a feature matrix as input was able to predict the height-related metric with R2=0.829 and RMSE=0.331. The breadth metrics require higher-detailed data and more complex models to predict accurately.

A Hybrid Cognitive Model For Machine Agents In Project And Action Teams, Joshua A. Lapso

Theses and Dissertations

High performing human teams transcend complex domain uncertainty by achieving an emergent state of shared cognition, in which knowledge is organized, represented, and distributed to team members for rapid execution. However, this requires that individuals emit perceivable qualities upon which other members can make inferences about intent. In pursuit of future human and machine team studies, this research presents a hybrid cognitive model for machine agents in fully cooperative and semi-cooperative action and project teams. The hybrid cognitive model unifies the characteristics of the shared mental model and transactive memory system. The resultant model facilitates anytime selection over the two …

Marss: Multi-Agent Reinforcement Learning For Satellite Swarms, Nicholas J. Yielding

Theses and Dissertations

Multi-agent systems and swarms in spacecraft formation flying are of ever-increasing importance in a contested space environment—use of multiple spacecraft to contribute to a cooperative mission potentially increases positive outcomes on orbit, while autonomy becomes an ever more important requirement to reduce reaction time in dynamic situations and lower the burden on space operators. This research explores difficult swarm Guidance Navigation and Control (GNC) scenarios using Deep Reinforcement Learning (DRL). DRL polices are trained to provide guidance inputs to agents in multi-agent swarm environments for completing complex, teamwork focused objectives in geosynchronous orbit. An example scenario is explored for a …

Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad

Theses and Dissertations

Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …

Advances In Quaternion-Valued Neural Networks, Jeremiah P. Bill

Theses and Dissertations

This dissertation investigates the construction, optimization, and application of quaternion neural networks (QNNs) to Department of Defense (DoD) related problem sets. QNNs are a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. This work provides a critical evaluation of the myriad different quaternion backpropagation derivations that exist in the literature, testing the performance of each on a range of regression problem sets. The optimization dynamics of QNNs are explored, presenting visualizations of QNN loss surfaces and a novel method for assessing the “smoothness” of these loss surfaces. Finally, this dissertation presents …

Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney

Theses and Dissertations

Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …

Ide-Based Learning Analytics For Assessing Introductory Programming Skill, Phyllis J. Beck

Theses and Dissertations

Providing a sufficient level of personalized feedback on students' current level of strategic knowledge within the context of the natural programming environment through IDE-based learning analytics would transform learning outcomes for introductory programming students. However, providing sufficient insight into the programming process was previously inaccessible due to the need for more complex and scalable data collection methods and metrics with a wider variety for understanding programming metacognition and the full programming process.

This research developed a custom-built web-based IDE and event compression system to investigate two of the five components of a five-dimensional model of cognition for programming skill estimation …

The Use Of Artificial Intelligence In Higher Education: A Study On Faculty Perspectives In Universities In Egypt, Farah S. Sharawy

Theses and Dissertations

Artificial Intelligence (AI) is an emerging technology that is transforming various aspects of society, including higher education. This paper examines faculty perspectives from five different institutions; The American University in Cairo (AUC), The German University in Cairo (GUC), The Arab Academy for Science and Technology (AAST), Ain Shams University, and Cairo University, on the use of AI in higher education in teaching and learning in Egypt, with all its challenges and resources available to support it, and how it can be used to achieve equity and accessibility. This research was conducted through a qualitative study using semi-structured one- on-one interviews …

Adversary Aware Continual Learning, Muhammad Umer

Theses and Dissertations

Continual learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, these approaches are adversary agnostic, i.e., they do not consider the possibility of malicious attacks. In this dissertation, we have demonstrated that continual learning approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation to the model in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific class at test time. We then propose a novel defensive framework to counter …

Pruning Ghsom To Create An Explainable Intrusion Detection System, Thomas Michael Kirby

Theses and Dissertations

Intrusion Detection Systems (IDS) that provide high detection rates but are black boxes lead
to models that make predictions a security analyst cannot understand. Self-Organizing Maps
(SOMs) have been used to predict intrusion to a network, while also explaining predictions through
visualization and identifying significant features. However, they have not been able to compete with
the detection rates of black box models. Growing Hierarchical Self-Organizing Maps (GHSOMs)
have been used to obtain high detection rates on the NSL-KDD and CIC-IDS-2017 network traffic
datasets, but they neglect creating explanations or visualizations, which results in another black
box model.
This paper offers …

Development And Evaluation Of An Automated Tactical Tillage Tool To Control Weeds In Row-Crop Production Systems, Grace Mccormick Friday

Theses and Dissertations

Weed control is an integral part of a successful overall production strategy in row- cropping systems and has the potential to reduce or eliminate yield losses that negatively affect profitability. Timely and correctly selected herbicide applications are the major keys for effective weed control in a majority of instances. However, there are negative factors that contribute to ineffectiveness and weed escape issues that currently lack viable options for management. Sparsely populated late-season weeds that emerge after lay-by herbicide applications and weeds that have become tolerant and resistant to traditional herbicide chemistries are of greatest concern. Historically, these weeds would have …