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Description
Structural Magnetic Resonance Imaging analysis is a vital component in the study of Alzheimer’s Disease pathology and several techniques exist as part of the existing research conducted. In particular, volumetric approaches in this field are known to be beneficial due to the increased capability to express morphological characteristics when compared to manifold methods. To aid in the improvement of the field, this paper aims to propose an intrinsic volumetric conic system that can be applied to bounded volumetric meshes to enable a more effective study of subjects. The computation of the metric involves the use of heat kernel theory and conformal parameterization on genus-0 surfaces extended to a volumetric domain. Additionally, this paper also explores the use of the ’TetCNN’ architecture on the classification of hippocampal tetrahedral meshes to detect features that correspond to Alzheimer’s indicators. The model tested was able to achieve remarkable results with a measured classification accuracy of above 90% in the task of differentiating between subjects diagnosed with Alzheimer’s and normal control subjects.
ContributorsGeorge, John Varghese (Author) / Wang, Yalin (Thesis advisor) / Hansford, Dianne (Committee member) / Gupta, Vikash (Committee member) / Arizona State University (Publisher)
Created2023
Description
In recent years, a flood of devices has permeated our personal and professional lives, with increasingly interconnected networks playing an ever-growing role in day-to-day activities. As these systems expand in both importance and complexity, their value to attackers and their attack surface simultaneously increase. In this dissertation, I argue that traditional defensive approaches fail to acknowledge this changing landscape. Instead, by focusing on the twin concepts of permeable networks and counter-adversarial behavior, defenders will be able to achieve better outcomes. The dependencies, interactions, and relationships among the growing corpus of connected devices create complex systems that are difficult for both users and operators to reason about. Consequently, despite heightened awareness of security risks, the rate and scale of data breaches continue to accelerate. This underlying complexity renders networks {permeable} to attackers. In parallel, interest in low- and no-trust distributed computing, such as federated learning, ad hoc networking, and blockchain systems, has been on the rise. In these contexts, users and devices interact cooperatively toward common goals while contending with potentially adversarial participants. Inherently permeable, these cooperative systems benefit from adopting counter-adversarial behaviors. By discarding the traditional goal of strict prevention, defenders can shift their focus to {counter-adversarial} behaviors instead. These behaviors aim to limit attackers' choices, exhaust their resources, or reduce their effectiveness, collectively disincentivizing attacks. This dissertation leverages these dual concepts to explore counter-adversarial behaviors in the context of permeable networked systems. It further describes methods such as information partitioning, ensemble fusion, and resilience analysis that enable novel counter-adversarial strategies. In doing so, this dissertation provides solutions to the challenges faced by increasingly prevalent permeable systems. Collectively, these form a foundation for the development of more resilient communication architectures.
ContributorsBehrens, Hans Walter (Author) / Candan, Kasim Selçuk KS (Thesis advisor) / Ahn, Gail-Joon GJ (Thesis advisor) / Doupé, Adam A (Committee member) / Boscovic, Dragan D (Committee member) / Forrest, Stephanie S (Committee member) / Gelfand, Boris B (Committee member) / Arizona State University (Publisher)
Created2023
Description
Component-based models are commonly employed to simulate discrete dynamicalsystems. These models lend themselves to formalizing the structures of systems at multiple levels of granularity. Visual development of component-based models serves to simplify the iterative and incremental model specification activities. The Parallel Discrete Events System Specification (DEVS) formalism offers a flexible yet rigorous approach for decomposing a whole model into its components or alternatively, composing a whole model from components. While different concepts, frameworks, and tools offer a variety of visual modeling capabilities, most pose limitations, such as visualizing multiple model hierarchies at any level with arbitrary depths. The visual and persistent layout of any number of hierarchy levels of models can be maintained and navigated seamlessly. Persistence storage is another capability needed for the modeling, simulating, verifying, and validating lifecycle. These are important features to improve the demanding task of creating and changing modular, hierarchical simulation models. This thesis proposes a new approach and develops a tool for the visual development of models. This tool supports storing and reconstructing graphical models using a NoSQL database. It offers unique capabilities important for developing increasingly larger and more complex models essential for analyzing, designing, and building Digital Twins.
ContributorsMohite, Sheetal Chandrakant (Author) / Sarjoughian, Hessam S (Thesis advisor) / Bryan, Chris (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2023
Description
Interpreting answers to yes-no questions in social media is difficult. Yes and no keywords are uncommon, and when answers include them, they are rarely to be interpreted what the keywords suggest. This work presents a new corpus of 4,442 yes-no question answer pairs from Twitter (Twitter-YN). The corpus includes question-answer instances from different temporal settings. These settings allow investigating if having older tweets helps understanding more contemporary tweets. Common linguistic features of answers meaning yes, no as well as those whose interpretation remains unknown are also discussed. Experimental results show that large language models are far from solving this problem, even after fine-tuning and blending other corpora for the same problem but outside social media (F1: 0.59). In addition to English, this work presents a Hindi corpus of 3,409 yes-no questions and answers from Twitter (Twitter-YN-hi). Cross lingual experiments are conducted using a distant supervision approach. It is observed that performance of multilingual large language models to interpret indirect answers to yes-no questions in Hindi can be improved when Twitter-YN is blended with distantly supervised data.
ContributorsMathur, Shivam (Author) / Blanco, Eduardo (Thesis advisor) / Baral, Chitta (Thesis advisor) / Choi, YooJung (Committee member) / Arizona State University (Publisher)
Created2023
Description
Alongside the many challenges of Covid-19, the pandemic also disrupted the normal structure of education for students. As classes were being transferred to online formats, computer science students started learning more through constructivism principles rather than the traditionally taught, in-person lectures. This quantitative assessment hopes to determine whether constructivist principles or traditional/visual cognition principles are better for teaching computer science topics. Determinations will be made through a social behavioral experiment teaching pointers to participants. Participants were split into three groups: a control group, a constructivist group, and a visual cognition group. Each group took part in an assessment testing their knowledge retention about pointers after having a lecture based around each teaching method. The assessment evaluated retries per assessment, time per correct answer, time per question, and the average time taken in total. The results of the experiment led to a conclusion that, according to the resulting data, constructivism teaching principles benefited participant scores, and visual cognition teaching principles worsened participant scores. However, a definitive answer of which teaching method is better for computer science could not be made due to insufficient sample size. When reflecting on the first iteration of this experiment, it is clear that future iterations of this experiment would benefit from a higher sample size, an easier assignment for the constructivist group, a feedback survey, and a longer period to experiment.
ContributorsTiruchinapalli, Sai Santosh (Author) / Burger, Kevin (Thesis director) / Hartwell, Leland (Committee member) / Barrett, The Honors College (Contributor)
Created2023-05
Description
SLAM (Simultaneous Localization and Mapping) is a problem that has existed for a long time in robotics and autonomous navigation. The objective of SLAM is for a robot to simultaneously figure out its position in space and map its environment. SLAM is especially useful and mandatory for robots that want to navigate autonomously. The description might make it seem like a chicken and egg problem, but numerous methods have been proposed to tackle SLAM. Before the rise in the popularity of deep learning and AI (Artificial Intelligence), most existing algorithms involved traditional hard-coded algorithms that would receive and process sensor information and convert it into some solvable sensor-agnostic problem. The challenge for these sorts of methods is having to tackle dynamic environments. The more variety in the environment, the poorer the results. Also due to the increase in computational power and the capability of deep learning-based image processing, visual SLAM has become extremely viable and maybe even preferable to traditional SLAM algorithms. In this research, a deep learning-based solution to the SLAM problem is proposed, specifically monocular visual SLAM which is solving the problem of SLAM purely with a singular camera as the input, and the model is tested on the KITTI (Karlsruhe Institute of Technology & Toyota Technological Institute) odometry dataset.
ContributorsRupaakula, Krishna Sandeep (Author) / Bansal, Ajay (Thesis advisor) / Baron, Tyler (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2023
Description
Since the early 2000s the Rubik’s Cube has seen growing usage at speedsolving competitions and as an effective tool to teach Science, Technology, Engineering, Mathematics (STEM) topics at hundreds of schools and universities across the world. Recently, cube manufacturers have begun embedding sensors to enable digital face tracking. The live feedback from these so called “smartcubes” enables a new wave of immersive solution tutorials and interactive educational games using the cube as a controller. Existing smartcube software has several limitations. Manufacturers’ applications support only a narrow set of puzzle form factors and application platforms, fragmenting the ecosystem. Most apps require an active internet connection for key features, limiting where users can practice with a smartcube. Finally, existing applications focus on a single 3x3x3connection, losing opportunities afforded by new form factors. This research demonstrates an open-source smartcube application which mitigates these limitations. Particular attention is given to creating an Application Programming Interface (API) for smartcube communication and building representative solve analysis tools. These innovations have included successful negotiations to re-license existing open-source Rubik’sCube software projects to support deployment on multiple platforms, particularly iOS. The resulting application supports smartcubes from three manufacturers, runs on two platforms (Android and iOS), functions entirely offline after an initial download of remote assets, demonstrates concurrent connections with up to six smartcubes, and supports all current and anticipated smartcube form factors. These foundational elements can accelerate future efforts to build smartcube applications, including automated performance feedback systems and personalized gamification of learning experiences. Such advances will hopefully enhance the Rubik’s Cube’s value both as a competitive toy and as a pedagogical tool in educational institutions worldwide.
ContributorsHale, Joseph (Author) / Bansal, Ajay (Thesis advisor) / Heinrichs, Robert (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Recommendation systems provide recommendations based on user behavior andcontent data. User behavior and content data are fed to machine learning algorithms to train
them and give recommendations to the users. These algorithms need a large amount of data
for a reasonable conversion rate. But for small applications, the available amount of data
is minimal, leading to high recommendation aberrations. Also, when an existing large
scaled application with a high amount of available data uses a new recommendation
system, it requires some time and testing to decide which recommendation algorithm is
best suited to get higher conversion rates. This learning curve costs highly when the user
base and data size are significantly high.
In this thesis, A/B testing is used with manual intervention in the decision-making
of recommendation systems. To understand the effectiveness of the recommendations, user
interaction data is compared to compare experiences. Based on the comparisons, the
experiments conclude the effectiveness of A/B testing for the recommendation system.
ContributorsVaidya, Yogesh Vinayak (Author) / Bansal, Ajay (Thesis advisor) / Findler, Michael (Committee member) / Chakravarthi, Bharatesh (Committee member) / Arizona State University (Publisher)
Created2023
Description
Quantum computing holds the potential to revolutionize various industries by solving problems that classical computers cannot solve efficiently. However, building quantum computers is still in its infancy, and simulators are currently the best available option to explore the potential of quantum computing. Therefore, developing comprehensive benchmarking suites for quantum computing simulators is essential to evaluate their performance and guide the development of future quantum algorithms and hardware. This study presents a systematic evaluation of quantum computing simulators’ performance using a benchmarking suite. The benchmarking suite is designed to meet the industry-standard performance benchmarks established by the Defense Advanced Research Projects Agency (DARPA) and includes standardized test data and comparison metrics that encompass a wide range of applications, deep neural network models, and optimization techniques. The thesis is divided into two parts to cover basic quantum algorithms and variational quantum algorithms for practical machine-learning tasks. In the first part, the run time and memory performance of quantum computing simulators are analyzed using basic quantum algorithms. The performance is evaluated using standardized test data and comparison metrics that cover fundamental quantum algorithms, including Quantum Fourier Transform (QFT), Inverse Quantum Fourier Transform (IQFT), Quantum Adder, and Variational Quantum Eigensolver (VQE). The analysis provides valuable insights into the simulators’ strengths and weaknesses and highlights the need for further development to enhance their performance. In the second part, benchmarks are developed using variational quantum algorithms for practical machine learning tasks such as image classification, natural language processing, and recommendation. The benchmarks address several unique challenges posed by benchmarking quantum machine learning (QML), including the effect of optimizations on time-to-solution, the stochastic nature of training, the inclusion of hybrid quantum-classical layers, and the diversity of software and hardware systems. The findings offer valuable insights into the simulators’ ability to solve practical machine-learning tasks and pinpoint areas for future research and enhancement. In conclusion, this study provides a rigorous evaluation of quantum computing simulators’ performance using a benchmarking suite that meets industry-standard performance benchmarks.
ContributorsSathyakumar, Rajesh (Author) / Spanias, Andreas (Thesis advisor) / Sen, Arunabha (Thesis advisor) / Dasarathy, Gautam (Committee member) / Arizona State University (Publisher)
Created2023
Description
Abortion is a controversial topic internationally. Most current debates about abortion concern when, if at all, it should be legal. However, researchers have shown many times that after an abortion ban, maternal and infant mortalities rise significantly, as women who seek out abortions do so regardless of abortion legality. So, is it possible to reduce abortions in a population without delegalizing abortion and, if so, how? Why do some countries have higher abortion rates than others in the presence of the same law?This dissertation answers both questions. First, I present historical evidence in the first comprehensive comparative analysis of all 15 post-Soviet countries, which have very similar abortion laws originating from the Union of Soviet Socialist Republics (USSR). Second, I use those findings to build the first agent-based model (ABM) of unintended pregnancies in a hypothetical artificial population.
USSR was the only country in the world to complete its demographic transition through abortion instead of modern contraception, and the Soviet government passed the first law in the world to allow abortion upon request in 1920. After the USSR dissolution in 1991, post-Soviet countries maintained very similar abortion laws, but had very different abortion rates for most years. Analysis of fertility data from post-Soviet countries shows that the prevalence of some specific contraceptive methods, namely the rhythm method (r = 0.82), oral pill (r = 0.56), and male condom (r = 0.51) are most strongly correlated with high abortion rates, and that sex education is a factor that reduces the rates in otherwise similar countries (p = 0.02).
The ABM shows that even basic sex education results in fewer abortions than no sex education or abstinence-based sex education (p < 0.01). In scenarios without sex education, basic quality of post-abortion contraceptive counseling (PACC) is better than no PACC or low-quality PACC at reducing abortions (p < 0.01). Still, the higher the quality of sex education or PACC, the fewer abortions in the artificial population. The ABM is adaptive and policy makers can use it as a decision-support tool to make evidence-based policy decisions regarding abortion, and, potentially, other sociobiological phenomena with some adjustments to the code.
ContributorsZiganshina Lienhard, Dina A. (Author) / Maienschein, Jane (Thesis advisor) / Gaughan, Monica (Thesis advisor) / Laubichler, Manfred (Committee member) / Ellison, Karin (Committee member) / Arizona State University (Publisher)
Created2023