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Title: High-precision and Personalized Wearable Sensing Systems for Healthcare Applications
Creator: Tu, Linlin
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The cyber-physical system (CPS) has been discussed and studied extensively since 2010. It provides various solutions for monitoring the user's physical and psychological health states, enhancing the user's experience, and improving the lifestyle. A variety of mobile internet devices with built-in sensors, such as accelerators, cameras, PPG sensors, pressure sensors, and the microphone, can be leveraged to build mobile cyber-physical applications that collected sensing data from the real world...
Show moreThe cyber-physical system (CPS) has been discussed and studied extensively since 2010. It provides various solutions for monitoring the user's physical and psychological health states, enhancing the user's experience, and improving the lifestyle. A variety of mobile internet devices with built-in sensors, such as accelerators, cameras, PPG sensors, pressure sensors, and the microphone, can be leveraged to build mobile cyber-physical applications that collected sensing data from the real world, had data processed, communicated to the internet services and transformed into behavioral and physiological models. The detected results can be used as feedback to help the user understand his/her behavior, improve the lifestyle, or avoid danger. They can also be delivered to therapists to facilitate their diagnose. Designing CPS for health monitoring is challenging due to multiple factors. First of all, high estimation accuracy is necessary for health monitoring. However, some systems suffer irregular noise. For example, PPG sensors for cardiac health state monitoring are extremely vulnerable to motion noise. Second, to include human in the loop, health monitoring systems are required to be user-friendly. However, some systems involve cumbersome equipment for a long time of data collection, which is not feasible for daily monitoring. Most importantly, large-scale high-level health-related monitoring systems, such as the systems for human activity recognition, require high accuracy and communication efficiency. However, with users' raw data uploading to the server, centralized learning fails to protect users' private information and is communication-inefficient. The research introduced in this dissertation addressed the above three significant challenges in developing health-related monitoring systems. We build a lightweight system for accurate heart rate measurement during exercise, design a smart in-home breathing training system with bio-Feedback via virtual reality (VR) game, and propose federated learning via dynamic layer sharing for human activity recognition.
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Title: Novel Depth Representations for Depth Completion with Application in 3D Object Detection
Creator: Imran, Saif Muhammad
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Depth completion refers to interpolating a dense, regular depth grid from sparse and irregularly sampled depth values, often guided by high-resolution color imagery. The primary goal of depth completion is to estimate depth. In practice methods are trained by minimizing an error between predicted dense depth and ground-truth depth, and are evaluated by how well they minimize this error. Here we identify a second goal which is to avoid smearing depth across depth discontinuities. This second...
Show moreDepth completion refers to interpolating a dense, regular depth grid from sparse and irregularly sampled depth values, often guided by high-resolution color imagery. The primary goal of depth completion is to estimate depth. In practice methods are trained by minimizing an error between predicted dense depth and ground-truth depth, and are evaluated by how well they minimize this error. Here we identify a second goal which is to avoid smearing depth across depth discontinuities. This second goal is important because it can improve downstream applications of depth completion such as object detection and pose estimation. However, we also show that the goal of minimizing error can conflict with the goal of eliminating depth smearing.In this thesis, we propose two novel representations of depths that can encode depth discontinuity across object surfaces by allowing multiple depth estimation in the spatial domain. In order to learn these new representations, we propose carefully designed loss functions and show their effectiveness in deep neural network learning. We show how our representations can avoid inter-object depth mixing and also beat state of the art metrics for depth completion. The quality of ground-truth depth in real-world depth completion problems is another key challenge for learning and accurate evaluation of methods. Ground truth depth created from semi-automatic methods suffers from sparse sampling and errors at object boundaries. We show that the combination of these errors and the commonly used evaluation measure has promoted solutions that mix depths across boundaries in current methods. The thesis proposes alternate depth completion performance measures that reduce preference for mixed depths and promote sharp boundaries.The thesis also investigates whether additional points from depth completion methods can help in a challenging and high-level perception problem; 3D object detection. It shows the effect of different depth noises originated from depth estimates on detection performances and proposes some effective ways to reduce noise in the estimate and overcome architecture limitations. The method is demonstrated on both real-world and synthetic datasets.
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Title: VISIONING THE AGRICULTURE BLOCKCHAIN : THE ROLE AND RISE OF BLOCKCHAIN IN THE COMMERCIAL POULTRY INDUSTRY
Creator: Fennell, Chris
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Blockchain is an emerging technology that is being explored by technologists and industry leaders as a way to revolutionize the agriculture supply chain. The problem is that human and ecological insights are needed to understand the complexities of how blockchain could fulfill these visions. In this work, I assert how the blockchain's promising vision of traceability, immutability and distributed properties presents advancements and challenges to rural farming. This work wrestles with the...
Show moreBlockchain is an emerging technology that is being explored by technologists and industry leaders as a way to revolutionize the agriculture supply chain. The problem is that human and ecological insights are needed to understand the complexities of how blockchain could fulfill these visions. In this work, I assert how the blockchain's promising vision of traceability, immutability and distributed properties presents advancements and challenges to rural farming. This work wrestles with the more subtle ways the blockchain technology would be integrated into the existing infrastructure. Through interviews and participatory design workshops, I talked with an expansive set of stakeholders including Amish farmers, contract growers, senior leadership and field supervisors. This research illuminates that commercial poultry farming is such a complex and diffuse system that any overhaul of its core infrastructure will be difficult to ``roll back'' once blockchain is ``rolled out.'' Through an HCI and sociotechnical system perspective, drawing particular insights from Science and Technology Studies theories of infrastructure and breakdown, this dissertation asserts three main concerns. First, this dissertation uncovers the dominant narratives on the farm around revision and ``roll back'' of blockchain, connecting to theories of version control from computer science. Second, this work uncovers that a core concern of the poultry supply chain is death and I reveal the sociotechnical and material implications for the integration of blockchain. Finally, this dissertation discusses the meaning of ``security’’ for the poultry supply chain in which biosecurity is prioritized over cybersecurity and how blockchain impacts these concerns. Together these findings point to significant implications for designers of blockchain infrastructure and how rural workers will integrate the technology into the supply chain.
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Title: Variational Bayes inference of Ising models and their applications
Creator: Kim, Minwoo
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Ising models originated in statistical physics have been widely used in modeling spatialdata and computer vision problems. However, statistical inference of this model and its application to many practical fields remain challenging due to intractable nature of the normalizing constant in the likelihood. This dissertation consists of two main themes, (1) parameter estimation of Ising model and (2) structured variable selection based on the Ising model using variational Bayes (VB).In Chapter 1,...
Show moreIsing models originated in statistical physics have been widely used in modeling spatialdata and computer vision problems. However, statistical inference of this model and its application to many practical fields remain challenging due to intractable nature of the normalizing constant in the likelihood. This dissertation consists of two main themes, (1) parameter estimation of Ising model and (2) structured variable selection based on the Ising model using variational Bayes (VB).In Chapter 1, we review the background, research questions and development of Isingmodel, variational Bayes, and other statistical concepts. An Ising model basically deal with a binary random vector in which each component is dependent on its neighbors. There exist various versions of Ising model depending on parameterization and neighboring structure. In Chapter 2, with two-parameter Ising model, we describe a novel procedure for the pa- rameter estimation based on VB which is computationally efficient and accurate compared to existing methods. Traditional pseudo maximum likelihood estimate (PMLE) can pro- vide accurate results only for smaller number of neighbors. A Bayesian approach based on Markov chain Monte Carlo (MCMC) performs better even with a large number of neighbors. Computational costs of MCMC, however, are quite expensive in terms of time. Accordingly, we propose a VB method with two variational families, mean-field (MF) Gaussian family and bivariate normal (BN) family. Extensive simulation studies validate the efficacy of the families. Using our VB methods, computing times are remarkably decreased without dete- rioration in performance accuracy, or in some scenarios we get much more accurate output. In addition, we demonstrates theoretical properties of the proposed VB method under MF family. The main theoretical contribution of our work lies in establishing the consistency of the variational posterior for the Ising model with the true likelihood replaced by the pseudo- likelihood. Under certain conditions, we first derive the rates at which the true posterior based on the pseudo-likelihood concentrates around the εn- shrinking neighborhoods of the true parameters. With a suitable bound on the Kullback-Leibler distance between the true and the variational posterior, we next establish the rate of contraction for the variational pos- terior and demonstrate that the variational posterior also concentrates around εn-shrinking neighborhoods of the true parameter.In Chapter 3, we propose a Bayesian variable selection technique for a regression setupin which the regression coefficients hold structural dependency. We employ spike and slab priors on the regression coefficients as follows: (i) In order to capture the intrinsic structure, we first consider Ising prior on latent binary variables. If a latent variable takes one, the corresponding regression coefficient is active, otherwise, it is inactive. (ii) Employing spike and slab prior, we put Gaussian priors (slab) on the active coefficients and inactive coefficients will be zeros with probability one (spike).
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Title: Solving Computationally Expensive Problems Using Surrogate-Assisted Optimization : Methods and Applications
Creator: Blank, Julian
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Optimization is omnipresent in many research areas and has become a critical component across industries. However, while researchers often focus on a theoretical analysis or convergence proof of an optimization algorithm, practitioners face various other challenges in real-world applications. This thesis focuses on one of the biggest challenges when applying optimization in practice: computational expense, often caused by the necessity of calling a third-party software package. To address the...
Show moreOptimization is omnipresent in many research areas and has become a critical component across industries. However, while researchers often focus on a theoretical analysis or convergence proof of an optimization algorithm, practitioners face various other challenges in real-world applications. This thesis focuses on one of the biggest challenges when applying optimization in practice: computational expense, often caused by the necessity of calling a third-party software package. To address the time-consuming evaluation, we propose a generalizable probabilistic surrogate-assisted framework that dynamically incorporates predictions of approximation models. Besides the framework's capability of handling multiple objectives and constraints simultaneously, the novelty is its applicability to all kinds of metaheuristics. Moreover, often multiple disciplines are involved in optimization, resulting in different types of software packages utilized for performance assessment. Therefore, the resulting optimization problem typically consists of multiple independently evaluable objectives and constraints with varying computational expenses. Besides providing a taxonomy describing different ways of independent evaluation calls, this thesis also proposes a methodology to handle inexpensive constraints with expensive objective functions and a more generic concept for any type of heterogeneously expensive optimization problem. Furthermore, two case studies of real-world optimization problems from the automobile industry are discussed, a blueprint for solving optimization problems in practice is provided, and a widely-used optimization framework focusing on multi-objective optimization (founded and maintained by the author of this thesis) is presented. Altogether, this thesis shall pave the way to solve (computationally expensive) real-world optimization more efficiently and bridge the gap between theory and practice.
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Title: Towards Accurate Ranging and Versatile Authentication for Smart Mobile Devices
Creator: Li, Lingkun
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Internet of Things (IoTs) was rapidly developed during past years. Smart devices, such as smartphones, smartwatches, and smart assistants, which are equipped with smart chips as well as sensors, provide users with many easy used functions and lead them to a more convenient life. In this dissertation, we carefully studied the birefringence of the transparent tape, the nonlinear effects of the microphone, and the phase characteristic of the reflected ultrasound, and make use of such effects to...
Show moreInternet of Things (IoTs) was rapidly developed during past years. Smart devices, such as smartphones, smartwatches, and smart assistants, which are equipped with smart chips as well as sensors, provide users with many easy used functions and lead them to a more convenient life. In this dissertation, we carefully studied the birefringence of the transparent tape, the nonlinear effects of the microphone, and the phase characteristic of the reflected ultrasound, and make use of such effects to design three systems, RainbowLight, Patronus, and BreathPass, to provide users with accurate localization, privacy protection, and authentication, respectively.RainbowLight leverages observation direction-varied spectrum generated by a polarized light passing through a birefringence material, i.e., transparent tape, to provide localization service. We characterize the relationship between observe direction, light interference and the special spectrum, and using it to calculate the direction to a chip after taking a photo containing the chip. With multiple chips, RainbowLight designs a direction intersection based method to derive the location. In this dissertation, we build the theoretical basis of using polarized light and birefringence phenomenon to perform localization. Based on the theoretical model, we design and implement the RainbowLight on the mobile device, and evaluate the performance of the system. The evaluation results show that RainbowLight achieves 1.68 cm of the median error in the X-axis, 2 cm of the median error in the Y-axis, 5.74 cm of the median error in Z-axis, and 7.04 cm of the median error with the whole dimension.It is the first system that could only use the reflected lights in the space to perform visible light positioning. Patronus prevents unauthorized speech recording by leveraging the nonlinear effects of commercial off-the-shelf microphones. The inaudible ultrasound scramble interferes recording of unauthorized devices and can be canceled on authorized devices through an adaptive filter. In this dissertation, we carefully studied the nonlinear effects of ultrasound on commercial microphones. Based on the study, we proposed an optimized configuration to generate the scramble. It would provide privacy protection againist unauthorized recordings that does not disturb normal conversations. We designed, implemented a system including hardware and software components. Experiments results show that only 19.7% of words protected by Patronus' scramble can be recognized by unauthorized devices. Furthermore, authorized recordings have 1.6x higher perceptual evaluation of speech quality (PESQ) score and, on average, 50% lower speech recognition error rates than unauthorized recordings. BreathPass uses speakers to emit ultrasound signals. The signals are reflected off the chest wall and abdomen and then back to the microphone, which records the reflected signals. The system then extracts the fingerprints from the breathing pattern, and use these fingerprints to perform authentication. In this dissertation, we characterized the challenge of conducting authentication with the breathing pattern. After addressing these challenges, we designed such a system and implemented a proof-of-concept application on Android platform.We also conducted comprehensive experiments to evaluate the performance under different scenarios. BreathPass achieves an overall accuracy of 83%, a true positive rate of 73%, and a false positive rate of 5%, according to performance evaluation results. In general, this dissertation provides an enhanced ranging and versatile authentication systems of Internet of Things.
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Title: IMPROVED DETECTION AND MANAGEMENT OF PHYTOPHTHORA SOJAE
Creator: McCoy, Austin Glenn
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Phytophthora spp. cause root and stem rots, leaf blights and fruit rots on agricultural and economically important plant species. Symptoms of Phytophthora infected plants, particularly root rots, can be difficult to distinguish from other oomycete and fungal pathogens and often result in devastating losses. Phytophthora spp. can lie dormant for many years in the oospore stage, making long-term management of these diseases difficult. Phytophthora sojae is an important and prevalent pathogen of...
Show morePhytophthora spp. cause root and stem rots, leaf blights and fruit rots on agricultural and economically important plant species. Symptoms of Phytophthora infected plants, particularly root rots, can be difficult to distinguish from other oomycete and fungal pathogens and often result in devastating losses. Phytophthora spp. can lie dormant for many years in the oospore stage, making long-term management of these diseases difficult. Phytophthora sojae is an important and prevalent pathogen of soybean (Glycine max L.) worldwide, causing Phytophthora stem and root rot (PRR). PRR disease management during the growing season relies on an integrated pest management approach using a combination of host resistance, chemical compounds (fungicides; oomicides) and cultural practices for successful management. Therefore, this dissertation research focuses on improving the detection and management recommendations for Phytophthora sojae. In Chapter 1 I provide background and a review of the current literature on Phytophthora sojae management, including genetic resistance, chemical control compounds (fungicides; oomicides) and cultural practices used to mitigate losses to PRR. In my second chapter I validate the sensitivity and specificity of a preformulated Recombinase Polymerase Amplification assay for Phytophthora spp. This assay needs no refrigeration, does not require extensive DNA isolation, can be used in the field, and different qPCR platforms could reliably detect down to 3.3-330.0 pg of Phytophthora spp. DNA within plant tissue in under 30 minutes. Based on the limited reagents needed, ease of use, and reliability, this assay would be of benefit to diagnostic labs and inspectors monitoring regulated and non-regulated Phytophthora spp. Next, I transitioned the Habgood-Gilmour Spreadsheet (‘HaGiS’) from Microsoft Excel format to the subsequent R package ‘hagis’ and improved upon the analyses readily available to compare pathotypes from different populations of P. sojae (Chapter 3; ‘hagis’ beta-diversity). I then implemented the R package ‘hagis’ in my own P. sojae pathotype and fungicide sensitivity survey in the state of Michigan, identifying effective resistance genes and seed treatment compounds for the management of PRR. This study identified a loss of Rps1c and Rps1k, the two most widely plant Phytophthora sojae resistance genes, as viable management tools in Michigan and an increase in pathotype complexity, as compared to a survey conducted twenty years ago in Michigan (Chapter 4). In Chapter 5 I led a multi-state integrated pest management field trial that was performed in Michigan, Indiana, and Minnesota to study the effects of partial resistance and seed treatments with or without ethaboxam and metalaxyl on soybean stand, plant dry weights, and final yields under P. sojae pressure. This study found that oomicide treated seed protects stand across three locations in the Midwest, but the response of soybean varieties based on seed treatment, was variety and year specific. Significant yield benefits from using oomicide treated seed were only observed in one location and year. The effects of partial resistance were inconclusive and highlighted the need for a more informative and reliable rating system for soybean varieties partial resistance to P. sojae. Finally, in Chapter 6 I present conclusions and impacts on the studies presented in this dissertation. Overall, the studies presented provide an improvement to the detection, virulence data analysis, and integrated pest management recommendations for Phytophthora sojae.
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Title: ASSURING THE ROBUSTNESS AND RESILIENCY OF LEARNING-ENABLED AUTONOMOUS SYSTEMS
Creator: Langford, Michael Austin
Date: 2022
Collection: Electronic Theses & Dissertations
Description: As Learning-Enabled Systems (LESs) have become more prevalent in safety-critical applications, addressing the assurance of LESs has become increasingly important. Because machine learning models in LESs are not explicitly programmed like traditional software, developers typically have less direct control over the inferences learned by LESs, relying instead on semantically valid and complete patterns to be extracted from the system’s exposure to the environment. As such, the behavior of an LES...
Show moreAs Learning-Enabled Systems (LESs) have become more prevalent in safety-critical applications, addressing the assurance of LESs has become increasingly important. Because machine learning models in LESs are not explicitly programmed like traditional software, developers typically have less direct control over the inferences learned by LESs, relying instead on semantically valid and complete patterns to be extracted from the system’s exposure to the environment. As such, the behavior of an LES is strongly dependent on the quality of its training experience. However, run-time environments are often noisy or not well-defined. Uncertainty in the behavior of an LES can arise when there is inadequate coverage of relevant training/test cases (e.g., corner cases). It is challenging to assure safety-critical LESs will perform as expected when exposed to run-time conditions that have never been experienced during training or validation. This doctoral research contributes automated methods to improve the robustness and resilience of an LES. For this work, a robust LES is less sensitive to noise in the environment, and a resilient LES is able to self-adapt to adverse run-time contexts in order to mitigate system failure. The proposed methods harness diversity-driven evolution-based methods, machine learning, and software assurance cases to train robust LESs, uncover robust system configurations, and foster resiliency through self-adaptation and predictive behavior modeling. This doctoral work demonstrates these capabilities by applying the proposed framework to deep learning and autonomous cyber-physical systems.
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Title: Investigating the Role of Sensor Based Technologies to Support Domestic Activities in Sub-Saharan Africa
Creator: Chidziwisano, George Hope
Date: 2022
Collection: Electronic Theses & Dissertations
Description: In sub-Saharan Africa (SSA), homes face various challenges including insecurity, unreliable power supply, and extreme weather conditions. While the use of sensor-based technologies is increasing in industrialized countries, it is unclear how they can be used to support domestic activities in SSA. The availability of low-cost sensors and the widespread adoption of mobile phones presents an opportunity to collect real-time data and utilize proactive methods to monitor these challenges. This...
Show moreIn sub-Saharan Africa (SSA), homes face various challenges including insecurity, unreliable power supply, and extreme weather conditions. While the use of sensor-based technologies is increasing in industrialized countries, it is unclear how they can be used to support domestic activities in SSA. The availability of low-cost sensors and the widespread adoption of mobile phones presents an opportunity to collect real-time data and utilize proactive methods to monitor these challenges. This dissertation presents three studies that build upon each other to explore the role of sensor-based technologies in SSA. I used a technology probes method to develop three sensor-based systems that support domestic security (M-Kulinda), power blackout monitoring (GridAlert) and poultry farming (NkhukuApp). I deployed M-Kulinda in 20 Kenyan homes, GridAlert in 18 Kenyan homes, and NkhukuProbe in 15 Malawian home-based chicken coops for one month. I used interview, observation, diary, and data logging methods to understand participants’ experiences using the probes. Findings from these studies suggest that people in Kenya and Malawi want to incorporate sensor-based technologies into their everyday activities, and they quickly find unexpected ways to use them. Participants’ interactions with the probes prompted detailed reflections about how they would integrate sensor-based technologies in their homes (e.g., monitoring non-digital tools). These reflections are useful for motivating new design concepts in HCI. I use these findings to motivate a discussion about unexplored areas that could benefit from sensor-based technologies. Further, I discuss recommendations for designing sensor-based technologies that support activities in some Kenyan and Malawian homes. This research contributes to HCI by providing design implications for sensor-based applications in Kenyan and Malawian homes, employing a technology probes method in a non-traditional context, and developing prototypes of three novel systems.
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Title: EFFICIENT AND PORTABLE SPARSE SOLVERS FOR HETEROGENEOUS HIGH PERFORMANCE COMPUTING SYSTEMS
Creator: Rabbi, Md Fazlay
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Sparse matrix computations arise in the form of the solution of systems of linear equations, matrix factorization, linear least-squares problems, and eigenvalue problems in numerous computational disciplines ranging from quantum many-body problems, computational fluid dynamics, machine learning and graph analytics. The scale of problems in these scientific applications typically necessitates execution on massively parallel architectures. Moreover, due to the irregular data access patterns and...
Show moreSparse matrix computations arise in the form of the solution of systems of linear equations, matrix factorization, linear least-squares problems, and eigenvalue problems in numerous computational disciplines ranging from quantum many-body problems, computational fluid dynamics, machine learning and graph analytics. The scale of problems in these scientific applications typically necessitates execution on massively parallel architectures. Moreover, due to the irregular data access patterns and low arithmetic intensities of sparse matrix computations, achieving high performance and scalability is very difficult. These challenges are further exacerbated by the increasingly complex deep memory hierarchies of the modern architectures as they typically integrate several layers of memory storage. Data movement is an important bottleneck against efficiency and energy consumption in large-scale sparse matrix computations. Minimizing data movement across layers of the memory and overlapping data movement with computations are keys to achieving high performance in sparse matrix computations. My thesis work contributes towards systematically identifying algorithmic challenges of the sparse solvers and providing optimized and high performing solutions for both shared memory architectures and heterogeneous architectures by minimizing data movements between different memory layers. For this purpose, we first introduce a shared memory task-parallel framework focusing on optimizing the entire solvers rather than a specific kernel. As most of the recent (or upcoming) supercomputers are equipped with Graphics Processing Unit (GPU), we decided to evaluate the efficacy of the directive-based programming models (i.e., OpenMP and OpenACC) in offloading computations on GPU to achieve performance portability. Being inspired by the promising results of this work, we port and optimize our shared memory task-parallel framework on GPU accelerated systems to execute problem sizes that exceed device memory.
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Title: Gender-related effects of advanced placement computer science courses on self-efficacy, belongingness, and persistence
Creator: Good, Jonathon Andrew
Date: 2018
Collection: Electronic Theses & Dissertations
Description: The underrepresentation of women in computer science has been a concern of educators for multiple decades. The low representation of women in the computer science is a pattern from K-12 schools through the university level and profession. One of the purposes of the introduction of the Advanced Placement Computer Science Principles (APCS-P) course in 2016 was to help broaden participation in computer science at the high school level. The design of APCS-P allowed teachers to present computer...
Show moreThe underrepresentation of women in computer science has been a concern of educators for multiple decades. The low representation of women in the computer science is a pattern from K-12 schools through the university level and profession. One of the purposes of the introduction of the Advanced Placement Computer Science Principles (APCS-P) course in 2016 was to help broaden participation in computer science at the high school level. The design of APCS-P allowed teachers to present computer science from a broad perspective, allowing students to pursue problems of personal significance, and allowing for computing projects to take a variety of forms. The nationwide enrollment statistics for Advanced Placement Computer Science Principles in 2017 had a higher proportion of female students (30.7%) than Advanced Placement Computer Science A (23.6%) courses. However, it is unknown to what degree enrollment in these courses was related to students’ plans to enroll in future computer science courses. This correlational study examined how students’ enrollment in Advanced Placement Computer Science courses, along with student gender, predicted students’ sense of computing self-efficacy, belongingness, and expected persistence in computer science. A nationwide sample of 263 students from 10 APCS-P and 10 APCS-A courses participated in the study. Students completed pre and post surveys at the beginning and end of their Fall 2017 semester regarding their computing self-efficacy, belongingness, and plans to continue in computer science studies. Using hierarchical linear modeling analysis due to the nested nature of the data within class sections, the researcher found that the APCS course type was not predictive of self-efficacy, belongingness, or expectations to persist in computer science. The results suggested that female students’ self-efficacy declined over the course of the study. However, gender was not predictive of belongingness or expectations to persist in computer science. Students were found to have entered into both courses with high a sense of self-efficacy, belongingness, and expectation to persist in computer science.The results from this suggests that students enrolled in both Advanced Placement Computer Science courses are already likely to pursue computer science. I also found that the type of APCS course in which students enroll does not relate to students’ interest in computer science. This suggests that educators should look beyond AP courses as a method of exposing students to computer science, possibly through efforts such as computational thinking and cross-curricular uses of computer science concepts and practices. Educators and administrators should also continue to examine whether there are structural biases in how students are directed to computer science courses. As for the drop in self-efficacy related to gender, this in alignment with previous research suggesting that educators should carefully scaffold students’ initial experiences in the course to not negatively influence their self-efficacy. Further research should examine how specific pedagogical practices could influence students’ persistence, as the designation and curriculum of APCS-A or APCS-P alone may not capture the myriad of ways in which teachers may be addressing gender inequity in their classrooms. Research can also examine how student interest in computer science is affected at an earlier age, as the APCS courses may be reaching students after they have already formed their opinions about computer science as a field.
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Title: Non-coding RNA identification in large-scale genomic data
Creator: Yuan, Cheng
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Noncoding RNAs (ncRNAs), which function directly as RNAs without translating into proteins, play diverse and important biological functions. ncRNAs function not only through their primary structures, but also secondary structures, which are defined by interactions between Watson-Crick and wobble base pairs. Common types of ncRNA include microRNA, rRNA, snoRNA, tRNA. Functions of ncRNAs vary among different types. Recent studies suggest the existence of large number of ncRNA genes....
Show moreNoncoding RNAs (ncRNAs), which function directly as RNAs without translating into proteins, play diverse and important biological functions. ncRNAs function not only through their primary structures, but also secondary structures, which are defined by interactions between Watson-Crick and wobble base pairs. Common types of ncRNA include microRNA, rRNA, snoRNA, tRNA. Functions of ncRNAs vary among different types. Recent studies suggest the existence of large number of ncRNA genes. Identification of novel and known ncRNAs becomes increasingly important in order to understand their functionalities and the underlying communities.Next-generation sequencing (NGS) technology sheds lights on more comprehensive and sensitive ncRNA annotation. Lowly transcribed ncRNAs or ncRNAs from rare species with low abundance may be identified via deep sequencing. However, there exist several challenges in ncRNA identification in large-scale genomic data. First, the massive volume of datasets could lead to very long computation time, making existing algorithms infeasible. Second, NGS has relatively high error rate, which could further complicate the problem. Third, high sequence similarity among related ncRNAs could make them difficult to identify, resulting in incorrect output. Fourth, while secondary structures should be adopted for accurate ncRNA identification, they usually incur high computational complexity. In particular, some ncRNAs contain pseudoknot structures, which cannot be effectively modeled by the state-of-the-art approach. As a result, ncRNAs containing pseudoknots are hard to annotate.In my PhD work, I aimed to tackle the above challenges in ncRNA identification. First, I designed a progressive search pipeline to identify ncRNAs containing pseudoknot structures. The algorithms are more efficient than the state-of-the-art approaches and can be used for large-scale data. Second, I designed a ncRNA classification tool for short reads in NGS data lacking quality reference genomes. The initial homology search phase significantly reduces size of the original input, making the tool feasible for large-scale data. Last, I focused on identifying 16S ribosomal RNAs from NGS data. 16S ribosomal RNAs are very important type of ncRNAs, which can be used for phylogenic study. A set of graph based assembly algorithms were applied to form longer or full-length 16S rRNA contigs. I utilized paired-end information in NGS data, so lowly abundant 16S genes can also be identified. To reduce the complexity of problem and make the tool practical for large-scale data, I designed a list of error correction and graph reduction techniques for graph simplification.
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Title: Example-Based Parameterization of Linear Blend Skinning for Skinning Decomposition (EP-LBS
Creator: Hopkins, Kayra M.
Date: 2017
Collection: Electronic Theses & Dissertations
Description: This thesis presents Example-based Parameterization of Linear Blend Skinning for Skinning Decomposition (EP-LBS), a unified and robust method for using example data to simplify and improve the development and parameterization of high quality 3D models for animation. Animation and three-dimensional (3D) computer graphics have quickly become a popular medium for education, entertainment and scientific simulation. In addition to film, gaming and research applications, recent advancements in...
Show moreThis thesis presents Example-based Parameterization of Linear Blend Skinning for Skinning Decomposition (EP-LBS), a unified and robust method for using example data to simplify and improve the development and parameterization of high quality 3D models for animation. Animation and three-dimensional (3D) computer graphics have quickly become a popular medium for education, entertainment and scientific simulation. In addition to film, gaming and research applications, recent advancements in augmented reality (AR) and virtual reality (VR) are driving additional demand for 3D content. However, the success of graphics in these arenas depends greatly on the efficiency of model creation and the realism of the animation or 3D image.A common method for figure animation is skeletal animation using linear blend skinning (LBS). In this method, vertices are deformed based on a weighted sum of displacements due to an embedded skeleton. This research addresses the problem that LBS animation parameter computation, including determining the rig (the skeletal structure), identifying influence bones (which bones influence which vertices), and assigning skinning weights (amounts of influence a bone has on a vertex), is a tedious process that is difficult to get right. Even the most skilled animators must work tirelessly to design an effective character model and often find themselves repeatedly correcting flaws in the parameterization. Significant research, including the use of example-data, has focused on simplifying and automating individual components of the LBS deformation process and increasing the quality of resulting animations. However, constraints on LBS animation parameters makes automated analytic computation of the values equally as challenging as traditional 3D animation methods. Skinning decomposition is one such method of computing LBS animation LBS parameters from example data. Skinning decomposition challenges include constraint adherence and computationally efficient determination of LBS parameters.The EP-LBS method presented in this thesis utilizes example data as input to a least-squares non-linear optimization process. Given a model as a set of example poses captured from scan data or manually created, EP-LBS institutes a single optimization equation that allows for simultaneous computation of all animation parameters for the model. An iterative clustering methodology is used to construct an initial parameterization estimate for this model, which is then subjected to non-linear optimization to improve the fitting to the example data. Simultaneous optimization of weights and joint transformations is complicated by a wide range of differing constraints and parameter interdependencies. To address interdependent and conflicting constraints, parameter mapping solutions are presented that map the constraints to an alternative domain more suitable for nonlinear minimization. The presented research is a comprehensive, data-driven solution for automatically determining skeletal structure, influence bones and skinning weights from a set of example data. Results are presented for a range of models that demonstrate the effectiveness of the method.
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Title: Evolution of distributed behavior
Creator: Knoester, David B.
Date: 2011
Collection: Electronic Theses & Dissertations
Description: In this dissertation, we describe a study in the evolution of distributed behavior, where evolutionary algorithms are used to discover behaviors for distributed computing systems. We define distributed behavior as that in which groups of individuals must both cooperate in working towards a common goal and coordinate their activities in a harmonious fashion. As such, communication among individuals is necessarily a key component of distributed behavior, and we have identified three classes of...
Show moreIn this dissertation, we describe a study in the evolution of distributed behavior, where evolutionary algorithms are used to discover behaviors for distributed computing systems. We define distributed behavior as that in which groups of individuals must both cooperate in working towards a common goal and coordinate their activities in a harmonious fashion. As such, communication among individuals is necessarily a key component of distributed behavior, and we have identified three classes of distributed behavior that require communication: data-driven behaviors, where semantically meaningful data is transmitted between individuals; temporal behaviors, which are based on the relative timing of individuals' actions; and structural behaviors, which are responsible for maintaining the underlying communication network connecting individuals. Our results demonstrate that evolutionary algorithms can discover groups of individuals that exhibit each of these different classes of distributed behavior, and that these behaviors can be discovered both in isolation (e.g., evolving a purely data-driven algorithm) and in concert (e.g., evolving an algorithm that includes both data-driven and structural behaviors). As part of this research, we show that evolutionary algorithms can discover novel heuristics for distributed computing, and hint at a new class of distributed algorithm enabled by such studies.The majority of this research was conducted with the Avida platform for digital evolution, a system that has been proven to aid researchers in understanding the biological process of evolution by natural selection. For this reason, the results presented in this dissertation provide the foundation for future studies that examine how distributed behaviors evolved in nature. The close relationship between evolutionary biology and evolutionary algorithms thus aids our study of evolving algorithms for the next generation of distributed computing systems.
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Title: Algorithms for deep packet inspection
Creator: Patel, Jignesh
Date: 2012
Collection: Electronic Theses & Dissertations
Description: The core operation in network intrusion detection and prevention systems is Deep Packet Inspection (DPI), in which each security threat is represented as a signature, and the payload of each data packet is matched against the set of current security threat signatures. DPI is also used for other networking applications like advanced QoS mechanisms, protocol identification etc.. In the past, attack signatures were specified as strings, and a great deal of research has been done in string...
Show moreThe core operation in network intrusion detection and prevention systems is Deep Packet Inspection (DPI), in which each security threat is represented as a signature, and the payload of each data packet is matched against the set of current security threat signatures. DPI is also used for other networking applications like advanced QoS mechanisms, protocol identification etc.. In the past, attack signatures were specified as strings, and a great deal of research has been done in string matching for network applications. Today most DPI systems use Regular Expression (RE) to represent signatures. RE matching is more diffcult than string matching, and current string matching solutions don't work well for REs. RE matching for networking applications is diffcult for several reasons. First, the DPI application is usually implemented in network devices, which have limited computing resources. Second, as new threats are discovered, size of the signature set grows over time. Last, the matching needs to be done at network speeds, the growth of which out paces improvements in computing speed; so there is a need for novel solutions that can deliver higher throughput. So RE matching for DPI is a very important and active research area.In our research, we investigate the existing methods proposed for RE matching, identify their limitations, and propose new methods to overcome these limitations. RE matching remains a fundamentally challenging problem due to the diffculty in compactly encoding DFA. While the DFA for any one RE is typically small, the DFA that corresponds to the entire set of REs is usually too large to be constructed or deployed. To address this issue, many alternative automata implementations that compress the size of the final automaton have been proposed. However, previously proposed automata construction algorithms employ a “Union then Minimize” framework where the automata for each RE are first joined before minimization occurs. This leads to expensive minimization on a large automata, and a large intermediate memory footprint. We propose a “Minimize then Union” framework for constructing compact alternative automata, which minimizes smaller automata first before combining them. This approach required much less time and memory, allowing us to handle a much larger RE set. Prior hardware based RE matching algorithms typically use FPGA. The drawback of FPGA is that resynthesizing and updating FPGA circuitry to handle RE updates is slow and diffcult. We propose the first hardware-based RE matching approach that uses Ternary Content Addressable Memory (TCAM). TCAMs have already been widely used in modern networking devices for tasks such as packet classification, so our solutions can be easily deployed. Our methods support easy RE updates, and we show that we can achieve very high throughput. The main reason combined DFAs for multiple REs grow exponentially in size is because of replication of states. We developed a new overlay automata model which exploit this replication to compress the size of the DFA. The idea is to group together the replicated DFA structures instead of repeating them multiple times. The result is that we get a final automata size that is close to that of a NFA (which is linear in the size of the RE set), and simultaneously achieve fast deterministic matching speed of a DFA.
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Title: Applying evolutionary computation techniques to address environmental uncertainty in dynamically adaptive systems
Creator: Ramirez, Andres J.
Date: 2013
Collection: Electronic Theses & Dissertations
Description: A dynamically adaptive system (DAS) observes itself and its execution environment at run time to detect conditions that warrant adaptation. If an adaptation is necessary, then a DAS changes its structure and/or behavior to continuously satisfy its requirements, even as its environment changes. It is challenging, however, to systematically and rigorously develop a DAS due to environmental uncertainty. In particular, it is often infeasible for a human to identify all possible combinations of...
Show moreA dynamically adaptive system (DAS) observes itself and its execution environment at run time to detect conditions that warrant adaptation. If an adaptation is necessary, then a DAS changes its structure and/or behavior to continuously satisfy its requirements, even as its environment changes. It is challenging, however, to systematically and rigorously develop a DAS due to environmental uncertainty. In particular, it is often infeasible for a human to identify all possible combinations of system and environmental conditions that a DAS might encounter throughout its lifetime. Nevertheless, a DAS must continuously satisfy its requirements despite the threat that this uncertainty poses to its adaptation capabilities. This dissertation proposes a model-based framework that supports the specification, monitoring, and dynamic reconfiguration of a DAS to explicitly address uncertainty. The proposed framework uses goal-oriented requirements models and evolutionary computation techniques to derive and fine-tune utility functions for requirements monitoring in a DAS, identify combinations of system and environmental conditions that adversely affect the behavior of a DAS, and generate adaptations on-demand to transition the DAS to a target system configuration while preserving system consistency. We demonstrate the capabilities of our model-based framework by applying it to an industrial case study involving a remote data mirroring network that efficiently distributes data even as network links fail and messages are dropped, corrupted, and delayed.
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Title: Multiple kernel and multi-label learning for image categorization
Creator: Bucak, Serhat Selçuk
Date: 2014
Collection: Electronic Theses & Dissertations
Description: "One crucial step towards the goal of converting large image collections to useful information sources is image categorization. The goal of image categorization is to find the relevant labels for a given an image from a closed set of labels. Despite the huge interest and significant contributions by the research community, there remains much room for improvement in the image categorization task. In this dissertation, we develop efficient multiple kernel learning and multi-label learning...
Show more"One crucial step towards the goal of converting large image collections to useful information sources is image categorization. The goal of image categorization is to find the relevant labels for a given an image from a closed set of labels. Despite the huge interest and significant contributions by the research community, there remains much room for improvement in the image categorization task. In this dissertation, we develop efficient multiple kernel learning and multi-label learning algorithms with high prediction performance for image categorization... " -- Abstract.
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Title: Finding optimized bounding boxes of polytopes in d-dimensional space and their properties in k-dimensional projections
Creator: Shahid, Salman (Of Michigan State University)
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Using minimal bounding boxes to encapsulate or approximate a set of points in d-dimensional space is a non-trivial problem that has applications in a variety of fields including collision detection, object rendering, high dimensional databases and statistical analysis to name a few. While a significant amount of work has been done on the three dimensional variant of the problem (i.e. finding the minimum volume bounding box of a set of points in three dimensions), it is difficult to find a...
Show moreUsing minimal bounding boxes to encapsulate or approximate a set of points in d-dimensional space is a non-trivial problem that has applications in a variety of fields including collision detection, object rendering, high dimensional databases and statistical analysis to name a few. While a significant amount of work has been done on the three dimensional variant of the problem (i.e. finding the minimum volume bounding box of a set of points in three dimensions), it is difficult to find a simple method to do the same for higher dimensions. Even in three dimensions existing methods suffer from either high time complexity or suboptimal results with a speed up in execution time. In this thesis we present a new approach to find the optimized minimum bounding boxes of a set of points defining convex polytopes in d-dimensional space. The solution also gives the optimal bounding box in three dimensions with a much simpler implementation while significantly speeding up the execution time for a large number of vertices. The basis of the proposed approach is a series of unique properties of the k-dimensional projections that are leveraged into an algorithm. This algorithm works by constructing the convex hulls of a given set of points and optimizing the projections of those hulls in two dimensional space using the new concept of Simultaneous Local Optimal. We show that the proposed algorithm provides significantly better performances than those of the current state of the art approach on the basis of time and accuracy. To illustrate the importance of the result in terms of a real world application, the optimized bounding box algorithm is used to develop a method for carrying out range queries in high dimensional databases. This method uses data transformation techniques in conjunction with a set of heuristics to provide significant performance improvement.
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Title: EXTENDED REALITY (XR) & GAMIFICATION IN THE CONTEXT OF THE INTERNET OF THINGS (IOT) AND ARTIFICIAL INTELLIGENCE (AI)
Creator: Pappas, Georgios
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The present research develops a holistic framework for and way of thinking about Deep Technologies related to Gamification, eXtended Reality (XR), the Internet of Things (IoT), and Artificial Intelligence (AI). Starting with the concept of gamification and the immersive technology of XR, we create interconnections with the IoT and AI implementations. While each constituent technology has its own unique impact, our approach uniquely addresses the combinational potential of these technologies...
Show moreThe present research develops a holistic framework for and way of thinking about Deep Technologies related to Gamification, eXtended Reality (XR), the Internet of Things (IoT), and Artificial Intelligence (AI). Starting with the concept of gamification and the immersive technology of XR, we create interconnections with the IoT and AI implementations. While each constituent technology has its own unique impact, our approach uniquely addresses the combinational potential of these technologies that may have greater impact than any technology on its own. To approach the research problem more efficiently, the methodology followed includes its initial division into smaller parts. For each part of the research problem, novel applications were designed and developed including gamified tools, serious games and AR/VR implementations. We apply the proposed framework in two different domains: autonomous vehicles (AVs), and distance learning.Specifically, in chapter 2, an innovative hybrid tool for distance learning is showcased where, among others, the fusion with IoT provides a novel pseudomultiplayer mode. This mode may transform advanced asynchronous gamified tools to synchronous by enabling or disabling virtual events and phenomena enhancing the student experience. Next, in Chapter 3, along with gamification, the combination of XR with IoT data streams is presented but this time in an automotive context. We showcase how this fusion of technologies provides low-latency monitoring of vehicle characteristics, and how this can be visualized in augmented and virtual reality using low-cost hardware and services. This part of our proposed framework provides the methodology of creating any type of Digital Twin with near real-time data visualization.Following that, in chapter 4 we establish the second part of the suggested holistic framework where Virtual Environments (VEs), in general, can work as synthetic data generators and thus, be a great source of artificial suitable for training AI models. This part of the research includes two novel implementations the Gamified Digital Simulator (GDS) and the Virtual LiDAR Simulator.Having established the holistic framework, in Chapter 5, we now “zoom in” to gamification exploring deeper aspects of virtual environments and discuss how serious games can be combined with other facets of virtual layers (cyber ranges,virtual learning environments) to provide enhanced training and advanced learning experiences. Lastly, in chapter 6, “zooming out” from gamification an additional enhancement layer is presented. We showcase the importance of human-centered design of via an implementation that tries to simulate the AV-pedestrian interactions in a virtual and safe environment.
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Title: The Evolutionary Origins of Cognition : Understanding the early evolution of biological control systems and general intelligence
Creator: Carvalho Pontes, Anselmo
Date: 2021
Collection: Electronic Theses & Dissertations
Description: In the last century, we have made great strides towards understanding natural cognition and recreating it artificially. However, most cognitive research is still guided by an inadequate theoretical framework that equates cognition to a computer system executing a data processing task. Cognition, whether natural or artificial, is not a data processing system; it is a control system.At cognition's core is a value system that allows it to evaluate current conditions and decide among two or more...
Show moreIn the last century, we have made great strides towards understanding natural cognition and recreating it artificially. However, most cognitive research is still guided by an inadequate theoretical framework that equates cognition to a computer system executing a data processing task. Cognition, whether natural or artificial, is not a data processing system; it is a control system.At cognition's core is a value system that allows it to evaluate current conditions and decide among two or more courses of action. Memory, learning, planning, and deliberation, rather than being essential cognitive abilities, are features that evolved over time to support the primary task of deciding “what to do next”. I used digital evolution to recreate the early stages in the evolution of natural cognition, including the ability to learn. Interestingly, I found cognition evolves in a predictable manner, with more complex abilities evolving in stages, by building upon previous simpler ones. I initially investigated the evolution of dynamic foraging behaviors among the first animals known to have a central nervous system, Ediacaran microbial mat miners. I then followed this up by evolving more complex forms of learning. I soon encountered practical limitations of the current methods, including exponential demand of computational resources and genetic representations that were not conducive to further scaling. This type of complexity barrier has been a recurrent issue in digital evolution. Nature, however, is not limited in the same ways; through evolution, it has created a language to express robust, modular, and flexible control systems of arbitrary complexity and apparently open-ended evolvability. The essential features of this language can be captured in a digital evolution platform. As an early demonstration of this, I evolved biologically plausible regulatory systems for virtual cyanobacteria. These systems regulate the cells' growth, photosynthesis and replication given the daily light cycle, the cell's energy reserves, and levels of stress. Although simple, this experimental system displays dynamics and decision-making mechanisms akin to biology, with promising potential for open-ended evolution of cognition towards general intelligence.
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