Physical Sciences and Mathematics Commons^™

Robust And Trustworthy Deep Learning: Attacks, Defenses And Designs, Bingyin Zhao

All Dissertations

Deep neural networks (DNNs) have achieved unprecedented success in many fields. However, robustness and trustworthiness have become emerging concerns since DNNs are vulnerable to various attacks and susceptible to data distributional shifts. Attacks such as data poisoning and out-of-distribution scenarios such as natural corruption significantly undermine the performance and robustness of DNNs in model training and inference and impose uncertainty and insecurity on the deployment in real-world applications. Thus, it is crucial to investigate threats and challenges against deep neural networks, develop corresponding countermeasures, and dig into design tactics to secure their safety and reliability. The works investigated in this …

The Human Side Of Adaptive Autonomy: Design Considerations For Adaptive Autonomous Teammates, Allyson Hauptman

All Dissertations

Ground-breaking advances in artificial intelligence (AI) have led to the possibility of AI agents operating not just as useful tools for teams, but also as full-fledged team members with unique, interdependent roles. This possibility is fueled by the human desire to create more and more autonomous systems that possess computational powers beyond human capability and the promise of increasing the productivity and efficiency of human teams dramatically. Yet, for all the promise and potential of these human-AI teams, the inclusion of AI teammates presents several challenges and concerns for both teaming and human-centered AI.

An important part of teaming is …

Heterogeneous Federated Learning At Scale, Dmitry Lukyanov

All Theses

Federated learning has emerged as a solution to the challenges faced by traditional centralized machine learning approaches, such as data privacy, security, ownership, and computational bottlenecks. However, federated learning itself introduced new challenges, including system heterogeneity and scalability. Existing federated learning approaches, such as hierarchical and heterogeneous federated learning, address some of these challenges but have limitations in real-world scenarios where multiple issues coexist, particularly in large-scale, heterogeneous environments like mobile applications and IoT devices. This work proposes a new federated learning architecture that combines heterogeneous federated learning and hierarchical federated learning into a unified architecture. The proposed approach aims …

Leveraging Artificial Intelligence For Team Cognition In Human-Ai Teams, Beau Schelble

All Dissertations

Advances in artificial intelligence (AI) technologies have enabled AI to be applied across a wide variety of new fields like cryptography, art, and data analysis. Several of these fields are social in nature, including decision-making and teaming, which introduces a new set of challenges for AI research. While each of these fields has its unique challenges, the area of human-AI teaming is beset with many that center around the expectations and abilities of AI teammates. One such challenge is understanding team cognition in these human-AI teams and AI teammates' ability to contribute towards, support, and encourage it. Team cognition is …

Hpc-Enabled Fast And Configurable Dynamic Simulation, Analysis, And Learning For Complex Power System Adaptation And Control, Cong Wang

All Dissertations

This dissertation presents an HPC-enabled fast and configurable dynamic simulation, analysis, and learning framework for complex power system adaptation and control. Dynamic simulation for a large transmission system comprising thousands of buses and branches implies the latency of complicated numerical computations. However, faster-than-real-time execution is often required to provide timely support for power system planning and operation. The traditional approaches for speeding up the simulation demand extensive computing facilities such as CPU-based multi-core supercomputers, resulting in heavily resource-dependent solutions. In this work, by coupling the Message Passing Interface (MPI) protocol with an advanced heterogeneous programming environment, further acceleration can be …

Damage Detection With An Integrated Smart Composite Using A Magnetostriction-Based Nondestructive Evaluation Method: Integrating Machine Learning For Prediction, Christopher Nelon

All Dissertations

The development of composite materials for structural components necessitates methods for evaluating and characterizing their damage states after encountering loading conditions. Laminates fabricated from carbon fiber reinforced polymers (CFRPs) are lightweight alternatives to metallic plates; thus, their usage has increased in performance industries such as aerospace and automotive. Additive manufacturing (AM) has experienced a similar growth as composite material inclusion because of its advantages over traditional manufacturing methods. Fabrication with composite laminates and additive manufacturing, specifically fused filament fabrication (fused deposition modeling), requires material to be placed layer-by-layer. If adjacent plies/layers lose adhesion during fabrication or operational usage, the strength …

All Hands On Deck: Choosing Virtual End Effector Representations To Improve Near Field Object Manipulation Interactions In Extended Reality, Roshan Venkatakrishnan

All Dissertations

Extended reality, or "XR", is the adopted umbrella term that is heavily gaining traction to collectively describe Virtual reality (VR), Augmented reality (AR), and Mixed reality (MR) technologies. Together, these technologies extend the reality that we experience either by creating a fully immersive experience like in VR or by blending in the virtual and "real" worlds like in AR and MR.

The sustained success of XR in the workplace largely hinges on its ability to facilitate efficient user interactions. Similar to interacting with objects in the real world, users in XR typically interact with virtual integrants like objects, menus, windows, …

Cyber Attack Surface Mapping For Offensive Security Testing, Douglas Everson

All Dissertations

Security testing consists of automated processes, like Dynamic Application Security Testing (DAST) and Static Application Security Testing (SAST), as well as manual offensive security testing, like Penetration Testing and Red Teaming. This nonautomated testing is frequently time-constrained and difficult to scale. Previous literature suggests that most research is spent in support of improving fully automated processes or in finding specific vulnerabilities, with little time spent improving the interpretation of the scanned attack surface critical to nonautomated testing. In this work, agglomerative hierarchical clustering is used to compress the Internet-facing hosts of 13 representative companies as collected by the Shodan search …

Motion Synthesis And Control For Autonomous Agents Using Generative Models And Reinforcement Learning, Pei Xu

All Dissertations

Imitating and predicting human motions have wide applications in both graphics and robotics, from developing realistic models of human movement and behavior in immersive virtual worlds and games to improving autonomous navigation for service agents deployed in the real world. Traditional approaches for motion imitation and prediction typically rely on pre-defined rules to model agent behaviors or use reinforcement learning with manually designed reward functions. Despite impressive results, such approaches cannot effectively capture the diversity of motor behaviors and the decision making capabilities of human beings. Furthermore, manually designing a model or reward function to explicitly describe human motion characteristics …

The Effects Of Primary And Secondary Task Workloads On Cybersickness In Immersive Virtual Active Exploration Experiences, Rohith Venkatakrishnan

All Dissertations

Virtual reality (VR) technology promises to transform humanity. The technology enables users to explore and interact with computer-generated environments that can be simulated to approximate or deviate from reality. This creates an endless number of ways to propitiously apply the technology in our lives. It follows that large technological conglomerates are pushing for the widespread adoption of VR, financing the creation of the Metaverse - a hypothetical representation of the next iteration of the internet.

Even with VR technology's continuous growth, its widespread adoption remains long overdue. This can largely be attributed to an affliction called cybersickness, an analog to …

Understanding The Role Of Interactivity And Explanation In Adaptive Experiences, Lijie Guo

All Dissertations

Adaptive experiences have been an active area of research in the past few decades, accompanied by advances in technology such as machine learning and artificial intelligence. Whether the currently ongoing research on adaptive experiences has focused on personalization algorithms, explainability, user engagement, or privacy and security, there is growing interest and resources in developing and improving these research focuses. Even though the research on adaptive experiences has been dynamic and rapidly evolving, achieving a high level of user engagement in adaptive experiences remains a challenge. %????? This dissertation aims to uncover ways to engage users in adaptive experiences by incorporating …

Generalizable Deep-Learning-Based Wireless Indoor Localization, Ali Owfi

All Theses

The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To …

Music Mentor, Jacob Webb

Honors College Theses

Extra-curricular learning is on the rise, and many are interested in expanding their current knowledge by utilizing the recent increase in educational technology. While many forms of educational technology exist, there are few interactive and engaging platforms that teach music theory. Apps such as Perfect Ear and MyMusicTheory are great for becoming familiar with reading music and recognizing pitches, however, they often become dry with repetition and repeated tasks. By combining existing technologies that can complete real time conversions from raw audio to MIDI, our goal was to gather information such as harmonies, key and compatible chords from the user’s …

Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin

All Dissertations

Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …

Vanet Applications Under Loss Scenarios & Evolving Wireless Technology, Adil Alsuhaim

All Dissertations

In this work we study the impact of wireless network impairment on the performance of VANET applications such as Cooperative Adaptive Cruise Control (CACC), and other VANET applications that periodically broadcast messages. We also study the future of VANET application in light of the evolution of radio access technologies (RAT) that are used to exchange messages. Previous work in the literature proposed fallback strategies that utilizes on-board sensors to recover in case of wireless network impairment, those methods assume a fixed time headway value, and do not achieve string stability. In this work, we study the string stability of a …

Gaslight: Attacking Hard-Label Black-Box Classifiers Via Deep Reinforcement Learning, Rajat Sethi

All Theses

Through artificial intelligence, algorithms can classify arrays of data, such as images or videos, into a predefined set of categories. With enough labeled data, a classifier can analyze an input’s components and calculate confidence scores for each category. However, machine learning relies heavily on approximation, which allows attackers to exploit classifiers by providing adversarial
examples. Specifically, attackers can modify their input so that the victim classifier cannot correctly label it, while a human observer would be unable to notice the difference.
This thesis proposes Gaslight, a system that uses deep reinforcement learning to generate adversarial examples against a victim classifier. …

Using Immersive Technology To Improve Mechanical Design: Use Cases, A Review Of Current Technology, And An Experiment In Requirement Elicitation In Virtual Reality, William Hawthorne

All Theses

There has been a growing trend in the use of immersive technologies within engineering design. This research is focused on understanding how Virtual Reality (VR) technologies support design reviews. Modern tools including virtual reality hardware and software were tested for current capabilities and challenges. In a review of literature, two key gaps are identified: the rapid advancements of VR and AR technology and limited formal studies to determine the costs and benefits of immersive technology within engineering design reviews. This research has resulted in three key outcomes. First, use cases of immersive reality technologies are identified, through the lens of …

Individual Differences In Vulnerability To Phishing, Fake News, And Vishing, Jeff Black

All Theses

Digital deception, such as phishing emails, scam phone calls, and fake news, poses a threat to anyone using digital devices. Research on digital deception often points to individual differences like age, cognitive impulsivity, and digital literacy, but has only investigated different types of digital deception independent of each other. Therefore, it is unclear whether users vulnerable to one type of deception are also vulnerable to others, and why. The present research examined relationships between vulnerability to different types of deception, and how this vulnerability is associated with common individual differences like age, cognitive impulsivity, digital literacy, and gullibility, and exploratory …

Assessing Key Factors Influencing Fire-Induced Spalling Of Concrete Using Explainable Artificial Intelligence (Xai), Mohammad Khaled Gazi Albashiti

All Theses

This thesis adopts eXplainable Artificial Intelligence (XAI) to identify the key factors influencing the fire-induced spalling of concrete and to extract new insights into the fire-induced spalling phenomenon. In this pursuit, an XAI model was developed, validated, and then augmented with two explainability measures, namely, Shapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The proposed XAI model not only can predict the fire-induced spalling with high accuracy (i.e., >92 %) but can also articulate the reasoning behind its predictions (as in, the proposed model can specify the rationale for each prediction instance); thus, providing us with valuable insights …

Procedural City Generation With Combined Architectures For Real-Time Visualization, Griffin Poyck

All Theses

The work and research of this paper sought to build upon traditional city generation and simulation in creating a tool that both realistically simulates cities and their prominent features and also creates aesthetic and artistically rich cities using assets that combine several contemporary or near contemporary architectural styles. The major city features simulated are the surrounding terrain, road networks, individual buildings, and building placement. The tools used to both create and integrate these features were created in Houdini with Unreal Engine 5 as the intended final destination. This research was influenced by the city, town, and road networking of Ghost …

Accessible Virtual Reality For Older Adults, Aaron Gluck

All Dissertations

Virtual reality (VR) has grown significantly since the commercial release of the Oculus Rift in March 2016. This growth accelerated during the COVID-19 pandemic, revolutionizing how individuals and businesses work, socialize, exercise, and stay entertained. However, commercial VR systems are not designed to be accessible to people with disabilities. While researchers have explored aspects of VR accessibility for people with disabilities, there is minimal research on accessible VR for older adults. Older adults (65+) self-report the highest rate of disabilities which may result in VR accessibility barriers, making this the ideal group to study the accessibility of VR.

This dissertation …

Uni-Prover: A Universal Automated Prover For Specificationally Rich Languages, Nicodemus Msafiri John Mbwambo

All Dissertations

Formal software verification systems must be designed to adapt to growth in the scope and complexity of software, driven by expanding capabilities of computer hardware and domain of potential usage. They must provide specification languages that are flexible and rich enough to allow software developers to write precise and comprehensible specifications for a full spectrum of object-based software components. Rich specification languages allow for arbitrary extensions to the library of mathematical theories, and critically, verification of programs with such specifications require a universal automated prover. Most existing verification systems either incorporate specification languages limited to first-order logic, which lacks the …

Developing And Facilitating Temporary Team Mental Models Through An Information-Sharing Recommender System, Geoffrey Musick

All Dissertations

It is well understood that teams are essential and common in many aspects of life, both work and leisure. Due to the importance of teams, much research attention has focused on how to improve team processes and outcomes. Of particular interest are the cognitive aspects of teamwork including team mental models (TMMs). Among many other benefits, TMMs involve team members forming a compatible understanding of the task and team in order to more efficiently make decisions. This understanding is sometimes classified using four TMM domains: equipment (e.g., operating procedures), task (e.g., strategies), team interactions (e.g., interdependencies) and teammates (e.g., tendencies). …

Learning To Reason About Code With Assertions: An Exploration With Two Student Populations, Sarah Blankenship

All Theses

Code tracing is fundamental to students’ understanding of a program, and symbolic reasoning that entails learning to use assertions with abstract input and output values, as opposed to concrete values, enhances that understanding. Symbolic reasoning teaches students valuable abstraction and logic skills that will serve them well in all aspects of programming and their software
development careers.
We use lessons integrated into an online educational tool to supplement classroom instruction to help students learn symbolic reasoning. We explore two ways for students to learn about assertions: Writing assertions to capture the behavior of given code and solving Parsons-style problems in …

Unsupervised Contrastive Representation Learning For Knowledge Distillation And Clustering, Fei Ding

All Dissertations

Unsupervised contrastive learning has emerged as an important training strategy to learn representation by pulling positive samples closer and pushing negative samples apart in low-dimensional latent space. Usually, positive samples are the augmented versions of the same input and negative samples are from different inputs. Once the low-dimensional representations are learned, further analysis, such as clustering, and classification can be performed using the representations. Currently, there are two challenges in this framework. First, the empirical studies reveal that even though contrastive learning methods show great progress in representation learning on large model training, they do not work well for small …

Distributed Learning With Automated Stepsizes, Benjamin Liggett

All Theses

Stepsizes for optimization problems play a crucial role in algorithm convergence, where the stepsize must undergo tedious manual tuning to obtain near-optimal convergence. Recently, an adaptive method for automating stepsizes was proposed for centralized optimization. However, this method is not directly applicable to decentralized optimization because it allows for heterogeneous agent stepsizes. Furthermore, directly using consensus between agent stepsizes to mitigate stepsize heterogeneity can decrease performance and even lead to divergence.

This thesis proposes an algorithm to remedy the tedious manual tuning of stepsizes in decentralized optimization. Our proposed algorithm automates the stepsize and uses dynamic consensus between agents’ stepsizes …

Evaluating Privacy Adaptation Presentation Methods To Support Social Media Users In Their Privacy-Related Decision-Making Process, Moses Namara

All Dissertations

Several privacy scholars have advocated for user-tailored privacy (UTP). A privacy-enhancing adaptive privacy approach to help reconcile users' lack of awareness, privacy management skills and motivation to use available platform privacy features with their need for personalized privacy support in alignment with their privacy preferences. The idea behind UTP is to measure users' privacy characteristics and behaviors, use these measurements to create a personalized model of the user's privacy preferences, and then provide adaptive support to the user in navigating and engaging with the available privacy settings---or even implement certain settings automatically on the user's behalf. To this end, most …

Holistic Performance Analysis And Optimization Of Unified Virtual Memory, Tyler Allen

All Dissertations

The programming difficulty of creating GPU-accelerated high performance computing (HPC) codes has been greatly reduced by the advent of Unified Memory technologies that abstract the management of physical memory away from the developer. However, these systems incur substantial overhead that paradoxically grows for codes where these technologies are most useful. While these technologies are increasingly adopted for use in modern HPC frameworks and applications, the performance cost reduces the efficiency of these systems and turns away some developers from adoption entirely. These systems are naturally difficult to optimize due to the large number of interconnected hardware and software components that …

Fair, Equitable, And Just: A Socio-Technical Approach To Online Safety, Daricia Wilkinson

All Dissertations

Socio-technical systems have been revolutionary in reshaping how people maintain relationships, learn about new opportunities, engage in meaningful discourse, and even express grief and frustrations. At the same time, these systems have been central in the proliferation of harmful behaviors online as internet users are confronted with serious and pervasive threats at alarming rates. Although researchers and companies have attempted to develop tools to mitigate threats, the perception of dominant (often Western) frameworks as the standard for the implementation of safety mechanisms fails to account for imbalances, inequalities, and injustices in non-Western civilizations like the Caribbean. Therefore, in this dissertation …

Automated Parallel Optimization Of Simulation Parameters Using Modified Nelder-Mead Simplex Algorithm, Erina Mills

All Dissertations

Computational simulations used in many fields have parameters that define models that are used to evaluate simulated properties. When developing these models, the goal is to choose the parameters that best replicate a set of desired properties. Mathematical optimization methods can be used to optimize the simulation parameters by defining a function that uses simulation parameters as input and outputs a value describing how well a set of experimental properties are reproduced.

Because simulated properties are often calculated using stochastic sampling methods, this optimization involves an objective function that is noisy and expensive to evaluate. Also, optimization of the simulation …