Electronic Thesis or Dissertation

Secure multiparty computation (secure MPC) is a computational paradigm that enables a group of parties to evaluate a public function on their private data without revealing the data (i.e., by preserving the privacy of their data). This computational approach, sometimes also referred to as secure function evaluation (SFE) and privacy-preserving computation, has attracted significant attention in the last couple of decades. It has been studied in different application domains, including in privacy-preserving data mining and machine learning, secure signal processing, secure genome analysis, sealed-bid auctions, etc. There are different approaches for realizing secure MPC. Some commonly used approaches include secret sharing schemes, Yao's garbled circuits, and homomorphic encryption techniques.
The main focus of this dissertation is to further investigate secure multiparty computation as an appealing area of research and to study its applications in different domains. We specifically focus on secure multiparty computation based on secret sharing and fully homomorphic encryption (FHE) schemes. We review the important theoretical foundations of these approaches and provide some novel applications for each of them. For the fully homomorphic encryption (FHE) part, we mainly focus on FHE schemes based on the LWE problem [142] or RLWE problem [109]. Particularly, we provide a C++ implementation for the ring variant of a third generation FHE scheme called the approximate eigenvector method (a.k.a., the GSW scheme) [67]. We then propose some novel approaches for homomorphic evaluation of common functionalities based on the implemented (R)LWE [142] and [109] and RGSW [38,58] schemes. We specifically present some constructions for homomorphic computation of pseudorandom functions (PRFs). For secure computation based on secret sharing [150], we provide some novel protocols for secure trust evaluation (STE). Our proposed STE techniques [137] enable the parties in trust and reputation systems (TRS) to securely assess their trust values in each other while they keep their input trust values private. It is worth mentioning that trust and reputation are social mechanisms which can be considered as soft security measures that complement hard security measures (e.g., cryptographic and secure multiparty computation techniques) [138, 171].

The field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding artifacts, saturation, and lighting, as well as for its’ aesthetic appeal. The purpose of such a mechanism could be detecting and discarding noisy, blurry, dark, or over exposed images, as well as detecting images that would be considered beautiful by a majority of viewers. In this dissertation, the detection of various quality and aesthetic aspects of an image using CNNs is explored. This research produced two datasets that are manually labeled for quality issues such as blur, poor lighting, and digital noise, and for their aesthetic qualities, and Convolutional Neural Networks were designed and trained using these datasets. Lastly, two case studies were performed to show the real-world impact of this research to traffic sign detection and medical image diagnosis.

The problem of the current study was the challenges experienced by those living in poverty can be propagated by poor attitudes and lack of empathy among the social service workers tasked with helping them. A key factor in individuals’ attitudes and empathy are their understanding of the experiences of others, as well as an awareness of their personal biases. While poverty simulations can help increase individuals’ awareness of personal biases and difficulties experienced by individuals living in poverty (ILP), little was known about how poverty simulations may influence the perceived social empathy and attitudes of participants who work for local government organizations. Accordingly, the purpose of the current phenomenological study was to examine the perceived effects of a poverty simulation on social service providers working for a local governmental agency tasked with distributing funds to assist ILPs. Specifically, the researcher explored participants’ perceptions of changes in social empathy and attitudes toward ILPs following participation in the Cost of Poverty Experience (COPE) poverty simulation exercise. Data were collected via semi structured interviews with 10 social service providers employed at the study site location, who had completed the COPE poverty simulation within the last 6 years. Data were analyzed following Groenewald’s approach to phenomenological analysis. The themes included: Participation in the COPE simulation influenced participants’ attitudes, participation in the COPE simulation influenced participants’ social empathy, and the system is broken, but participants feel disempowered to change it. The subthemes included: Developed an understanding of system flaws, developed an understanding of struggles faced by ILPs, uncovered personal attitudes/biases, the COPE simulation produced emotional reactions among participants, and the COPE simulation created empathy through simulated experiences of poverty.

An algorithm to determine IMRT optimization parameters within the Elekta Monaco® treatment planning system that increases dose homogeneity and dose conformity in the planning target volume was developed. This algorithm determines IMRT optimization parameters by calculating the difference between two pairs of dose points along the target volume’s dose volume histogram: Dmax – Dmin, and D2 – D98. The algorithm was tested on the Elekta Monaco® Treatment Planning System at GenesisCare of Coconut Creek, Florida using CT data from 10 anonymized patients with non-small cell lung cancer of various tumor sizes and locations. Nine iterations of parameters were tested on each patient. Once the ideal parameters were found, the results were evaluated using the ICRU report 83 homogeneity index as well as the Paddick conformity index. As an outcome of this research, it is recommended that at least three iterations of IMRT optimization parameters should be calculated to find the ideal parameters.

The aim of this thesis project was to design, develop, and test, a continuously variable transmission (CVT)-based power take off (PTO) sub-system, and its controller, for a small scale marine hydrokinetic turbine (MHK) developed for low-speed tidal currents. In this thesis, a CVT based PTO and controller was developed for a predefined MHK and validated through simulations. A testing platform was subsequently developed including an emulation system to replicate the MHK for testing of the coupled MHK/PTO system. Laboratory testing of the emulation system, PTO component efficiencies, and full system with controls was then conducted. The results showed the mechanical PTO design to be a valid solution and the control methods to be marginally stable with adequate power conversion at low-speed current conditions. The results also identified future work in continued controller development, alternate PTO component testing, and continued testing in parallel with that being done on the MHK prototype.

Marine and hydrokinetic (MHK) energy systems, including marine current turbines and wave energy converters, could contribute significantly to reducing reliance on fossil fuels and improving energy security while accelerating progress in the blue economy. However, technologies to capture them are nascent in development due to several technical and economic challenges. For example, for capturing ocean flows, the fluid velocity is low but density is high, resulting in early boundary layer separation and high torque. This dissertation addresses critical challenges in modeling, optimization, and control co-design of MHK energy systems, with specific case studies of a variable buoyancy-controlled marine current turbine (MCT). Specifically, this dissertation presents (a) comprehensive dynamic modeling of the MCT, where data recorded by an acoustic Doppler current profiler will be used as the real ocean environment; (b) vertical path planning of the MCT, where the problem is formulated as a novel spatial-temporal optimization problem to maximize the total harvested power of the system in an uncertain oceanic environment; (c) control co-design of the MCT, where the physical device geometry and turbine path control are optimized simultaneously. In a nutshell, the contributions are summarized as follows:

This study argues that settler women-in the all-inclusive sense of the word rather than just white, middle-and-upper class women-were crucial in founding and stabilizing Southeastern Florida communities. Historians have focused almost exclusively on men in studying this area's development and settlement. Henry Flagler, the railroad and hotel tycoon, for example, is given much credit for his role in bringing settlers to Palm Beach and building a home there for himself. Small towns use similar narratives. The reality was that diverse populations of women were critical for Southeastern Florida's growth in the late nineteenth and early twentieth centuries. This study thus seeks to recover the diverse actions, narratives, organizations, and systems of early Southeastern Florida and the roles women played to create, stabilize, and later maintain these aspects. This study will also discuss how these women subverted-whether subtly or overtly-factors of gender, race, and class to build unique and diverse communities in Southeastern Florida.

Time-series data in biomedical applications are gaining an increased interest to detect and predict underlying diseases and estimate their severity, such as Parkinson’s disease (PD) and cardiovascular diseases. This interest is driven by advances in wearable sensors and deep learning models to a large extent. In the literature, less attention has been paid to regression models for continuous outcomes in these applications, especially when dealing with limited data. Training deep learning models on raw limited data results in overfitted models, which is the main technical challenge we address in this dissertation. An example of limited and\or imbalanced time-series data is PD’s motion signals that are needed for the continuous severity estimation of Parkinson’s disease (PD). The significance of this continuous estimation is providing a tool for longitudinal monitoring of daily motor and non-motor fluctuations and managing PD medications.
The dissertation objective is to train generalizable deep learning models for biomedical regression problems when dealing with limited training time-series data. The goal is designing, developing, and validating an automatic assessment system based on wearable sensors that can measure the severity of PD complications in the home-living environment while patients with PD perform their activities of daily living (ADL). We first propose using a combination of domain-specific feature engineering, transfer learning, and an ensemble of multiple modalities. Second, we utilize generative adversarial networks (GAN) and propose a new formulation of conditional GAN (cGAN) as a generative model for regression to handle an imbalanced training dataset. Next, we propose a dual-channel auxiliary regressor GAN (AR-GAN) trained using Wasserstein-MSE-correlation loss. The proposed AR-GAN is used as a data augmentation method in regression problems.

In the past few years, the development of complex dynamical networks or systems has stimulated great interest in the study of the principles and mechanisms underlying the Internet of things (IoT). IoT is envisioned as an intelligent network infrastructure with a vast number of ubiquitous smart devices present in diverse application domains and have already improved many aspects of daily life. Many overtly futuristic IoT applications acquire data gathered via distributed sensors that can be uniquely identified, localized, and communicated with, i.e., the support of sensor networks. Soft-sensing models are in demand to support IoT applications to achieve the maximal exploitation of transforming the information of measurements into more useful knowledge, which plays essential roles in condition monitoring, quality prediction, smooth control, and many other essential aspects of complex dynamical systems. This in turn calls for innovative soft-sensing models that account for scalability, heterogeneity, adaptivity, and robustness to unpredictable uncertainties. The advent of big data, the advantages of ever-evolving deep learning (DL) techniques (where models use multiple layers to extract multi-levels of feature representations progressively), as well as ever-increasing processing power in hardware, has triggered a proliferation of research that applies DL to soft-sensing models. However, many critical questions need to be further investigated in the deep learning-based soft-sensing.

The present study applies a Gottman Method Couples Therapy (GMCT) intervention, the Trust Revival Method (TRM), to couples' relationships following an affair, using a randomized control waitlist design. Couples (n= 84) were recruited nationally and internationally and subsequently randomized to either an immediate treatment group or a 3-week waitlist group. A 6-month post-trial follow-up was conducted for couples that completed treatment. The revised Specific Affect Coding System (Coan & Gottman, 2007) was used to code couples' interactions during a 10–15-minute conflict discussion. Significant effects were found when comparing couples' codes against treatment retention and later relationship functioning. Couples also completed various assessments three times during the study, including the 480-question Gottman Connect (GC) assessment tool. Couples on the 3-week waitlist completed one additional pre-treatment assessment before their 3-week wait commenced. Multivariate statistics with appropriate univariate follow-up procedures were employed to determine group differences between the control and experimental groups. Follow-up procedures were also conducted to investigate any differential rates of symptom reduction or treatment success. The researcher used path analysis procedures following Actor Partner Interdependence Model (APIM- Kenny et al., 2020) assumptions to examine the effects of the intervention on overall relationship satisfaction and subsequent affair recovery, revealing significant effects between assessment scores and coded behaviors. Clinical significance testing also showed significant effects in specific relationship domains. The results add to the current research literature, validating GMCT as an effective broad-based couple therapy approach to repair relationships following infidelity. Implications for clinical practice, graduate training, and research are discussed.