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Title: Deep Convolutional Networks for Modeling Geo-Spatio-Temporal Relationships and Extremes
Creator: Wilson, Tyler
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Geo-spatio-temporal data are valuable for a broad range of applications including traffic forecasting, weather prediction, detection of epidemic outbreaks, and crime monitoring. Data driven approaches to these problems must address several fundamental challenges such as handling the %The two we focus on are the importance ofgeo-spatio-temporal relationships and extreme events. Another recent technological shift has been the success of deep learning especially in applications such as computer...
Show moreGeo-spatio-temporal data are valuable for a broad range of applications including traffic forecasting, weather prediction, detection of epidemic outbreaks, and crime monitoring. Data driven approaches to these problems must address several fundamental challenges such as handling the %The two we focus on are the importance ofgeo-spatio-temporal relationships and extreme events. Another recent technological shift has been the success of deep learning especially in applications such as computer vision, speech recognition, and natural language processing. In this work, we argue that deep learning is a promising approach for many geo-spatio-temporal problems and highlight how it can be used to address the challenges of modeling geo-spatio-temporal relationships and extremes. Though previous research has established techniques for modeling spatio-temporal relationships, these approaches are often limited to gridded spatial data with fixed-length feature vectors and considered only spatial relationships among the features, while ignoring the relationships among model parameters.We begin by describing how the spatial and temporal relationships for non-gridded spatial data can be modeled simultaneously by coupling the graph convolutional network with a long short-term memory (LSTM) network. Unlike previous research, our framework treats the adjacency matrix associated with the spatial data as a model parameter that can be learned from data, with constraints on its sparsity and rank to reduce the number of estimated parameters.Further, we show that the learned adjacency matrix may reveal useful information about the dominant spatial relationships that exist within the data. Second, we explore the varieties of spatial relationships that may exist in a geo-spatial prediction task. Specifically, we distinguish between spatial relationships among predictors and the spatial relationships among model parameters at different locations. We demonstrate an approach for modeling spatial dependencies among model parameters using graph convolution and provide guidance on when convolution of each type can be effectively applied. We evaluate our proposed approach on a climate downscaling and weather prediction tasks. Next, we introduce DeepGPD, a novel deep learning framework for predicting the distribution of geo-spatio-temporal extreme events. We draw on research in extreme value theory and use the generalized Pareto distribution (GPD) to model the distribution of excesses over a threshold. The GPD is integrated into our deep learning framework to learn the distribution of future excess values while incorporating the geo-spatio-temporal relationships present in the data. This requires a novel reparameterization of the GPD to ensure that its constraints are satisfied by the outputs of the neural network. We demonstrate the effectiveness of our proposed approach on a real-world precipitation data set. DeepGPD also employs a deep set architecture to handle the variable-sized feature sets corresponding to excess values from previous time steps as its predictors. Finally, we extend the DeepGPD formulation to simultaneously predict the distribution of extreme events and accurately infer their point estimates. Doing so requires modeling the full distribution of the data not just its extreme values. We propose DEMM, a deep mixture model for modeling the distribution of both excess and non-excess values. To ensure the point estimation of DEMM is a feasible value, new constraints on the output of the neural network are introduced, which requires a new reparameterization of the model parameters of the GPD. We conclude by discussing possibilities for further research at the intersection of deep learning and geo-spatio-temporal data.
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Title: IMPROVING THE PREDICTABILITY OF HYDROLOGIC INDICES IN ECOHYDROLOGICAL APPLICATIONS
Creator: Hernandez Suarez, Juan Sebastian
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Monitoring freshwater ecosystems allow us to better understand their overall ecohydrological condition within large and diverse watersheds. Due to the significant costs associated with biological monitoring, hydrological modeling is widely used to calculate ecologically relevant hydrologic indices (ERHIs) for stream health characterization in locations with lacking data. However, the reliability and applicability of these models within ecohydrological frameworks are major concerns....
Show moreMonitoring freshwater ecosystems allow us to better understand their overall ecohydrological condition within large and diverse watersheds. Due to the significant costs associated with biological monitoring, hydrological modeling is widely used to calculate ecologically relevant hydrologic indices (ERHIs) for stream health characterization in locations with lacking data. However, the reliability and applicability of these models within ecohydrological frameworks are major concerns. Particularly, hydrologic modeling’s ability to predict ERHIs is limited, especially when calibrating models by optimizing a single objective function or selecting a single optimal solution. The goal of this research was to develop model calibration strategies based on multi-objective optimization and Bayesian parameter estimation to improve the predictability of ERHIs and the overall representation of the streamflow regime. The research objectives were to (1) evaluate the predictions of ERHIs using different calibration techniques based on widely used performance metrics, (2) develop performance and signature-based calibration strategies explicitly constraining or targeting ERHIs, and (3) quantify the modeling uncertainty of ERHIs using the results from multi-objective model calibration and Bayesian inference. The developed strategies were tested in an agriculture-dominated watershed in Michigan, US, using the Unified Non-dominated Sorting Algorithm III (U-NSGA-III) for multi-objective calibration and the Soil and Water Assessment Tool (SWAT) for hydrological modeling. Performance-based calibration used objective functions based on metrics calculated on streamflow time series, whereas signature-based calibration used ERHIs values for objective functions’ formulation. For uncertainty quantification purposes, a lumped error model accounting for heteroscedasticity and autocorrelation was considered and the multiple-try Differential Evolution Adaptive Metropolis (ZS) (MT-DREAM(ZS)) algorithm was implemented for Markov Chain Monte Carlo (MCMC) sampling. In relation to the first objective, the results showed that using different sets of solutions instead of a single optimal introduces more flexibility in the predictability of various ERHIs. Regarding the second objective, both performance-based and signature-based model calibration strategies were successful in representing most of the selected ERHIs within a +/-30% relative error acceptability threshold while yielding consistent runoff predictions. The performance-based strategy was preferred since it showed a lower dispersion of near-optimal Pareto solutions when representing the selected indices and other hydrologic signatures based on water balance and Flow Duration Curve characteristics. Finally, regarding the third objective, using near-optimal Pareto parameter distributions as prior knowledge in Bayesian calibration generally reduced both the bias and variability ranges in ERHIs prediction. In addition, there was no significant loss in the reliability of streamflow predictions when targeting ERHIs, while improving precision and reducing the bias. Moreover, parametric uncertainty drastically shrank when linking multi-objective calibration and Bayesian parameter estimation. Still, the representation of low flow magnitude and timing, rate of change, and duration and frequency of extreme flows were limited. These limitations, expressed in terms of bias and interannual variability, were mainly attributed to the hydrological model’s structural inadequacies. Therefore, future research should involve revising hydrological models to better describe the ecohydrological characteristics of riverine systems.
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Title: Face Anti-Spoofing : Detection, Generalization, and Visualization
Creator: Liu, Yaojie
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Face anti-spoofing is the process of distinguishing genuine faces and face presentation attacks: attackers presenting spoofing faces (e.g. photograph, digital screen, and mask) to the face recognition system and attempting to be authenticated as the genuine user. In recent years, face anti-spoofing has brought increasing attention to the vision community as it is a crucial step to prevent face recognition systems from a security breach. Previous approaches formulate face anti-spoofing as a...
Show moreFace anti-spoofing is the process of distinguishing genuine faces and face presentation attacks: attackers presenting spoofing faces (e.g. photograph, digital screen, and mask) to the face recognition system and attempting to be authenticated as the genuine user. In recent years, face anti-spoofing has brought increasing attention to the vision community as it is a crucial step to prevent face recognition systems from a security breach. Previous approaches formulate face anti-spoofing as a binary classification problem, and many of them struggle to generalize to different conditions(such as pose, lighting, expressions, camera sensors, and unknown spoof types). Moreover, those methods work as a black box and cannot provide interpretation or visualization to their decision. To address those challenges, we investigate face anti-spoofing in 3 stages: detection, generalization and visualization. In the detection stage, we learn a CNN-RNN model to estimate auxiliary tasks of face depth and rPPG signals estimation, which can bring additional knowledge for the spoof detection. In the generalization stage, we investigate the detection of unknown spoof attacks and propose a novel Deep Tree Network (DTN) to well represent the unknown spoof attacks. In the visualization stage, we find “spoof trace, the subtle image pattern in spoof faces (e.g., color distortion, 3D mask edge, and Moire pattern), is effective to explain why a spoof is a spoof. We provide a proper physical modeling of the spoof traces and design a generative model to disentangle the spoof traces from input faces. In addition, we also show that a proper physical modeling can benefit other face problems, such as face shadow detection and removal. A proper shadow modeling can not only detect the shadow region effectively, but also remove the shadow in a visually plausible manner.
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Title: Towards a Robust Unconstrained Face Recognition Pipeline with Deep Neural Networks
Creator: Shi, Yichun
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Face recognition is a classic problem in the field of computer vision and pattern recognition due to its wide applications in real-world problems such as access control, identity verification, physical security, surveillance, etc. Recent progress in deep learning techniques and the access to large-scale face databases has lead to a significant improvement of face recognition accuracy under constrained and semi-constrained scenarios. Deep neural networks are shown to surpass human performance...
Show moreFace recognition is a classic problem in the field of computer vision and pattern recognition due to its wide applications in real-world problems such as access control, identity verification, physical security, surveillance, etc. Recent progress in deep learning techniques and the access to large-scale face databases has lead to a significant improvement of face recognition accuracy under constrained and semi-constrained scenarios. Deep neural networks are shown to surpass human performance on Labeled Face in the Wild (LFW), which consists of celebrity photos captured in the wild. However, in many applications, e.g. surveillance videos, where we cannot assume that the presented face is under controlled variations, the performance of current DNN-based methods drop significantly. The main challenges in such an unconstrained face recognition problem include, but are not limited to: lack of labeled data, robust face normalization, discriminative representation learning and the ambiguity of facial features caused by information loss.In this thesis, we propose a set of methods that attempt to address the above challenges in unconstrained face recognition systems. Starting from a classic deep face recognition pipeline, we review how each step in this pipeline could fail on low-quality uncontrolled input faces, what kind of solutions have been studied before, and then introduce our proposed methods. The various methods proposed in this thesis are independent but compatible with each other. Experiment on several challenging benchmarks, e.g. IJB-C and IJB-S show that the proposed methods are able to improve the robustness and reliability of deep unconstrained face recognition systems. Our solution achieves state-of-the-art performance, i.e. 95.0\% TAR@FAR=0.001\% on IJB-C dataset and 61.98\% Rank1 retrieval rate on the surveillance-to-booking protocol of IJB-S dataset.
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Title: Coreference Resolution for Downstream NLP Tasks
Creator: Pani, Sushanta Kumar
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Natural Language Processing (NLP) tasks have witnessed a significant improvement in performance by utilizing the power of end-to-end neural network models. An NLP system built for one job can contribute to other closely related tasks. Coreference Resolution (CR) systems work on resolving references and are at the core of many NLP tasks. The coreference resolution refers to the linking of repeated object references in a text. CR systems can boost the performance of downstream NLP tasks, such...
Show moreNatural Language Processing (NLP) tasks have witnessed a significant improvement in performance by utilizing the power of end-to-end neural network models. An NLP system built for one job can contribute to other closely related tasks. Coreference Resolution (CR) systems work on resolving references and are at the core of many NLP tasks. The coreference resolution refers to the linking of repeated object references in a text. CR systems can boost the performance of downstream NLP tasks, such as Text Summarization, Question Answering, Machine Translation, etc. We provide a detailed comparative error analysis of two state-of-the-art coreference resolution systems to understand error distribution in the predicted output. The understanding of error distribution is helpful to interpret the system behavior. Eventually, this will contribute to the selection of an optimal CR system for a specific target task.
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Title: The Evolution of Fundamental Neural Circuits for Cognition in Silico
Creator: Tehrani-Saleh, Ali
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Despite decades of research on intelligence and fundamental components of cognition, we still know very little about the structure and functionality of nervous systems. Questions in cognition and intelligent behavior are addressed by scientists in the fields of behavioral biology, neuroscience, psychology, and computer science. Yet it is difficult to reverse engineer observed sophisticated intelligent behaviors in animals and even more difficult to understand their underlying mechanisms.In...
Show moreDespite decades of research on intelligence and fundamental components of cognition, we still know very little about the structure and functionality of nervous systems. Questions in cognition and intelligent behavior are addressed by scientists in the fields of behavioral biology, neuroscience, psychology, and computer science. Yet it is difficult to reverse engineer observed sophisticated intelligent behaviors in animals and even more difficult to understand their underlying mechanisms.In this dissertation, I use a recently-developed neuroevolution platform -called Markov brain networks- in which Darwinian selection is used to evolve both structure and functionality of digital brains. I use this platform to study some of the most fundamental cognitive neural circuits: 1) visual motion detection, 2) collision-avoidance based on visual motion cues, 3) sound localization, and 4) time perception. In particular, I investigate both the selective pressures and environmental conditions in the evolution of these cognitive components, as well as the circuitry and computations behind them. This dissertation lays the groundwork for an evolutionary agent-based method to study the neural circuits for cognition in silico.
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Title: Optimal Learning of Deployment and Search Strategies for Robotic Teams
Creator: Wei, Lai
Date: 2021
Collection: Electronic Theses & Dissertations
Description: In the problem of optimal learning, the dilemma of exploration and exploitation stems from the fact that gathering information and exploiting it are, in many cases, two mutually exclusive activities. The key to optimal learning is to strike a balance between exploration and exploitation. The Multi-Armed Bandit (MAB) problem is a prototypical example of such an explore-exploit tradeoff, in which a decision-maker sequentially allocates a single resource by repeatedly choosing one among a set of...
Show moreIn the problem of optimal learning, the dilemma of exploration and exploitation stems from the fact that gathering information and exploiting it are, in many cases, two mutually exclusive activities. The key to optimal learning is to strike a balance between exploration and exploitation. The Multi-Armed Bandit (MAB) problem is a prototypical example of such an explore-exploit tradeoff, in which a decision-maker sequentially allocates a single resource by repeatedly choosing one among a set of options that provide stochastic rewards. The MAB setup has been applied in many robotics problems such as foraging, surveillance, and target search, wherein the task of robots can be modeled as collecting stochastic rewards. The theoretical work of this dissertation is based on the MAB setup and three problem variations, namely heavy-tailed bandits, nonstationary bandits, and multi-player bandits, are studied. The first two variations capture two key features of stochastic feedback in complex and uncertain environments: heavy-tailed distributions and nonstationarity; while the last one addresses the problem of achieving coordination in uncertain environments. We design several algorithms that are robust to heavy-tailed distributions and nonstationary environments. Besides, two distributed policies that require no communication among agents are designed for the multi-player stochastic bandits in a piece-wise stationary environment.The MAB problems provide a natural framework to study robotic search problems. The above variations of the MAB problems directly map to robotic search tasks in which a robot team searches for a target from a fixed set of view-points (arms). We further focus on the class of search problems involving the search of an unknown number of targets in a large or continuous space. We view the multi-target search problem as a hot-spots identification problem in which, instead of the global maximum of the field, all locations with a value greater than a threshold need to be identified. We consider a robot moving in 3D space with a downward-facing camera sensor. We model the robot's sensing output using a multi-fidelity Gaussian Process (GP) that systematically describes the sensing information available at different altitudes from the floor. Based on the sensing model, we design a novel algorithm that (i) addresses the coverage-accuracy tradeoff: sampling at a location farther from the floor provides a wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection. We rigorously analyze the algorithm and establish formal guarantees on the target detection accuracy and the detection time.An approach to extend the single robot search policy to multiple robots is to partition the environment into multiple regions such that workload is equitably distributed among all regions and then assign a robot to each region. The coverage control focuses on such equitable partitioning and the workload is equivalent to the so-called service demands in the coverage control literature. In particular, we study the adaptive coverage control problem, in which the demands of robotic service within the environment are modeled as a GP. To optimize the coverage of service demands in the environment, the team of robots aims to partition the environment and achieve a configuration that minimizes the coverage cost, which is a measure of the average distance of a service demand from the nearest robot. The robots need to address the explore-exploit tradeoff: to minimize coverage cost, they need to gather information about demands within the environment, whereas information gathering deviates them from maintaining a good coverage configuration. We propose an algorithm that schedules learning and coverage epochs such that its emphasis gradually shifts from exploration to exploitation while never fully ceasing to learn. Using a novel definition of coverage regret, we analyze the algorithm and characterizes its coverage performance over a finite time horizon.
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Title: Evolving Phenotypically Plastic Digital Organisms
Creator: Lalejini, Alexander
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The ability to dynamically respond to cues from the environment is a fundamental feature of most adaptive systems. In biological systems, changes to an organism based on environmental cues is called phenotypic plasticity. Indeed, phenotypic plasticity underlies many of the adaptive traits and developmental patterns found in nature and serves as a key mechanism for responding to spatially or temporally variable environments. Most computer programs require phenotypic plasticity, as they must...
Show moreThe ability to dynamically respond to cues from the environment is a fundamental feature of most adaptive systems. In biological systems, changes to an organism based on environmental cues is called phenotypic plasticity. Indeed, phenotypic plasticity underlies many of the adaptive traits and developmental patterns found in nature and serves as a key mechanism for responding to spatially or temporally variable environments. Most computer programs require phenotypic plasticity, as they must respond dynamically to stimuli such as user input, sensor data, et cetera. As such, phenotypic plasticity also has practical applications in genetic programming, wherein we apply the natural principles of evolution to automatically synthesize computer programs rather than writing them by hand. In this dissertation, I achieve two synergistic aims: (1) I use populations of self-replicating computer programs (digital organisms) to empirically study the conditions under which adaptive phenotypic plasticity evolves and how its evolution shapes subsequent evolutionary outcomes; and (2) I transfer insights from biology to develop novel genetic programming techniques in order to evolve more responsive (i.e., phenotypically plastic) computer programs. First, I illustrate the importance of mutation rate, environmental change, and partially-plastic building blocks for the evolution of adaptive plasticity. Next, I show that adaptive phenotypic plasticity stabilizes populations against environmental change, allowing them to more easily retain novel adaptive traits. Finally, I improve our ability to evolve phenotypically plastic computer programs with three novel genetic programming techniques: (1) SignalGP, which provides mechanisms to control code expression based on environmental cues, (2) tag-based genetic regulation to adjust code expression based on current context, and (3) tag-accessed memory to provide more dynamic mechanisms for storing data.
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Title: Face Recognition : Representation, Intrinsic Dimensionality, Capacity, and Demographic Bias
Creator: Gong, Sixue
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Face recognition is a widely adopted technology with numerous applications, such as mobile phone unlock, mobile payment, surveillance, social media and law enforcement. There has been tremendous progress in enhancing the accuracy of face recognition systems over the past few decades, much of which can be attributed to deep learning. Despite this progress, several fundamental problems in face recognition still remain unsolved. These problems include finding a salient representation, estimating...
Show moreFace recognition is a widely adopted technology with numerous applications, such as mobile phone unlock, mobile payment, surveillance, social media and law enforcement. There has been tremendous progress in enhancing the accuracy of face recognition systems over the past few decades, much of which can be attributed to deep learning. Despite this progress, several fundamental problems in face recognition still remain unsolved. These problems include finding a salient representation, estimating intrinsic dimensionality, representation capacity, and demographic bias. With growing applications of face recognition, the need for an accurate, robust, compact and fair representation is evident.In this thesis, we first develop algorithms to obtain practical estimates of intrinsic dimensionality of face representations, and propose a new dimensionality reduction method to project feature vectors from ambient space to intrinsic space. Based on the study in intrinsic dimensionality, we then estimate capacity of face representation, casting the face capacity estimation problem under the information theoretic framework of capacity of a Gaussian noise channel. Numerical experiments on unconstrained faces (IJB-C) provide a capacity upper bound of 27,000 for FaceNet and 84,000 for SphereFace representation at 1% FAR. In the second part of the thesis, we address the demographic bias problem in face recognition systems where errors are lower on certain cohorts belonging to specific demographic groups. We propose two de-biasing frameworks that extract feature representations to improve fairness in face recognition. Experiments on benchmark face datasets (RFW, LFW, IJB-A, and IJB-C) show that our approaches are able to mitigate face recognition bias on various demographic groups (biasness drops from 6.83 to 5.07) as well as maintain the competitive performance (i.e., 99.75% on LFW, and 93.70% TAR @ 0.1% FAR on IJB-C). Lastly, we explore the global distribution of deep face representations derived from correlations between image samples of within-class and cross-class to enhance the discriminativeness of face representation of each identity in the embedding space. Our new approach to face representation achieves state-of-the-art performance for both verification and identification tasks on benchmark datasets (99.78% on LFW, 93.40% on CPLFW, 98.41% on CFP-FP, 96.2% TAR @ 0.01% FAR and 95.3% Rank-1 accuracy on IJB-C). Since, the primary techniques we employ in this dissertation are not specific to faces only, we believe our research can be extended to other problems in computer vision, for example, general image classification and representation learning.
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Title: CS1 AND GENDER : UNDERSTANDING EFFECTS OF BACKGROUND AND SELF- EFFICACY ON ACHIEVEMENT AND INTEREST
Creator: Sands, Philip
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Over the past 20 years, the field of computer science has experienced a growth in studentinterest. Despite this increase in participation rates, longstanding gender gaps persist in computer science. Recent research has examined a wide variety of individual factors (e.g., self-efficacy, sense of belonging, etc.) that impact student interest and achievement in computer science; however, these factors are rarely considered in the context of existing learning theories. In this correlational study...
Show moreOver the past 20 years, the field of computer science has experienced a growth in studentinterest. Despite this increase in participation rates, longstanding gender gaps persist in computer science. Recent research has examined a wide variety of individual factors (e.g., self-efficacy, sense of belonging, etc.) that impact student interest and achievement in computer science; however, these factors are rarely considered in the context of existing learning theories. In this correlational study, I explored the relationship between prior knowledge of computer programming, self-efficacy, and the sources of self-efficacy as they differed by gender in a theoretical model of achievement and interest for students in first-year computer science (CS1) courses. This model was based on prior work from Bandura (1997) and others exploring self- efficacy and social cognitive theory in the context of mathematics and science fields. Using cross-sectional data from N=182 CS1 students at two universities, structural regressions were conducted between factors impacting CS1 students across the entire population and for men (N=108) and women (N=70) individually. This data was then used to address the following research questions. (1A) How do prior knowledge of computer programming, the sources of self- efficacy, and self-efficacy for computing predict CS1 achievement and student intentions to continue study in CS? (1B) How does self-efficacy mediate the relationship between student prior knowledge of computer programming and achievement in CS1? (1C) How are thoserelationships moderated by gender? (2) How does feedback in the form of student grades impact intention to continue in CS when considering gender as a moderating factor? For all students, student self-efficacy for CS positively impacted CS1 achievement and post-CS1 interest. Aligning with past research, self-efficacy was derived largely from mastery experiences, with vicarious experiences and social persuasions also contributing to a moderate degree. Social persuasions had a negative effect on self-efficacy, which diverged from research in other fields. The relationship between prior knowledge of computer programming and CS1 achievement was not mediated by self-efficacy and had a small positive effect. For women, vicarious experiences played a stronger role in defining student self-efficacy in CS. Additionally, while the importance of self-efficacy on achievement was similar to that for men, self-efficacy and achievement both played a much stronger role in determining student interest in CS for women. All these findings are in need of further exploration as the analysis was underpowered due to a small, COVID-19 impacted sample size. Future work should focus on the role of feedback on student self-efficacy, the potential misalignment of CS1 feedback and social network feedback, and interventions that address student beliefs about CS abilities to increase opportunities for authentic mastery and vicarious experiences.
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Title: Object Detection from 2D to 3D
Creator: Brazil, Garrick
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Monocular camera-based object detection plays a critical role in widespread applications including robotics, security, self-driving cars, augmented reality and many more. Increased relevancy is often given to the detection and tracking of safety-critical objects like pedestrians, cyclists, and cars which are often in motion and in close association to people. Compared to other generic objects such as animals, tools, food — safety-critical objects in urban scenes tend to have unique challenges...
Show moreMonocular camera-based object detection plays a critical role in widespread applications including robotics, security, self-driving cars, augmented reality and many more. Increased relevancy is often given to the detection and tracking of safety-critical objects like pedestrians, cyclists, and cars which are often in motion and in close association to people. Compared to other generic objects such as animals, tools, food — safety-critical objects in urban scenes tend to have unique challenges. Firstly, such objects usually have a wide range of detection scales such that they may appear anywhere from 5-50+ meters from the camera. Safety-critical objects also tend to have a high variety of textures and shapes, exemplified by the clothing of people and variability of vehicle models. Moreover, the high-density of objects in urban scenes leads to increased levels of self-occlusion compared to general objects in the wild. Off-the-shelf object detectors do not always work effectively due to these traits, and hence special attention is needed for accurate detection. Moreover, even successful detection of safety-critical is not inherently practical for applications designed to function in the real 3D world, without integration of expensive depth sensors. To remedy this, in this thesis we aim to improve the performance of 2D object detection and extend boxes into 3D, while using only monocular camera-based sensors. We first explore how pedestrian detection can be augmented using an efficient simultaneous detection and segmentation technique, while notably requiring no additional data or annotations. We then propose a multi-phased autoregressive network which progressively improves pedestrian detection precision for difficult samples, while critically maintaining an efficient runtime. We additionally propose a single-stage region proposal networks for 3D object detection in urban scenes, which is both more efficient and up to 3x more accurate than comparable state-of-the-art methods. We stabilize our 3D object detector using a highly tailored 3D Kalman filter, which both improves localization accuracy and provides useful byproducts such as ego-motion and per-object velocity. Lastly, we utilize differentiable rendering to discover the underlying 3D structure of objects beyond the cuboids used in detection, and without relying on expensive sensors or 3D supervision. For each method, we provide comprehensive experiments to demonstrate effectiveness, impact and runtime efficiency.
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Title: Contributions to Fingerprint Recognition
Creator: Engelsma, Joshua James
Date: 2021
Collection: Electronic Theses & Dissertations
Description: From the early days of the mid to late nineteenth century when scientific research first began to focus on fingerprints, to the present day fingerprint recognition systems we find deployed on our day to day devices, the science of fingerprint recognition has come a long way. In spite of this progress, there remains challenging problems to be solved. This thesis highlights a few of these problems, and proposes solutions to address them. One area of further research that must be conducted on...
Show moreFrom the early days of the mid to late nineteenth century when scientific research first began to focus on fingerprints, to the present day fingerprint recognition systems we find deployed on our day to day devices, the science of fingerprint recognition has come a long way. In spite of this progress, there remains challenging problems to be solved. This thesis highlights a few of these problems, and proposes solutions to address them. One area of further research that must be conducted on fingerprint recognition systems is that of robust, operational evaluations. In chapter two of this thesis, we show how the current practices of using calibration patterns to evaluate fingerprint readers are limited. We then propose a realistic fake finger called the Universal Target. The Universal Target is a realistic, 3D, fake finger (or phantom) which can be imaged by all major types of fingerprint sensing technologies. We show the entire manufacturing (molding and casting) process for fabricating the Universal Targets. Then, we show a series of evaluations which demonstrate how the Universal Targets can be used to operationally evaluate current commercial fingerprint readers. Our Universal Target is a significant step forward in enabling more realistic, standardized evaluations of fingerprint readers. In our third chapter, we shift gears from improving the evaluation standards of fingerprint readers to instead focus on the security of fingerprint readers. In particular, we turn our attention towards detecting fake fingerprint (spoof) attacks. To do so, we open source a fingerprint reader (built from low-cost ubiquitous components), called RaspiReader. RaspiReader is a high-resolution fingerprint reader customized with both direct-view imaging and FTIR imaging in order to better detect fingerprint spoofs. We show through a number of experiments that RaspiReader enables state-of-the-art fingerprint spoof detection accuracy. We also demonstrate that RaspiReader enables better generalization to what are known as "unseen attacks" (those attacks which were not seen during training of the spoof detector). Finally, we show that fingerprints captured by RaspiReader are completely compatible with images captured by legacy fingerprint readers for matching.In chapter four, we move on to propose a major improvement to the fingerprint feature extraction and matching sub-modules of fingerprint recognition systems. In particular, we propose a deep network, called DeepPrint, to extract a 200 byte fixed-length fingerprint representation. While prevailing fingerprint matchers primarily utilize minutiae points and expensive graph matching algorithms for comparison, two DeepPrint representations can be compared with only 192 multiplications and 191 additions. This is extremely useful for large scale search where potentially billions of pairwise fingerprint comparisons must be made. The DeepPrint representation also enables practical encrypted matching using a fully homomorphic encryption scheme. This enables better protection of the fingerprint templates which are stored in the database. While discriminative fixed-length representations are available for both face and iris recognition, such a representation has eluded fingerprint recognition. This chapter aims to fill that void.Finally, we conclude our thesis by working to extend fingerprint recognition to all ages. While current fingerprint recognition systems are being used by billions of teenagers and adults around the world, the youngest people among us remain disenfranchised. In particular, modern day fingerprint recognition systems do not work well on infants and young children. In this penultimate chapter, we aim to rectify this major shortcoming. To that end, we prototype a high-resolution (1900 ppi) infant fingerprint reader. Then, we track and fingerprint 315 infants (under the age of 3 months at enrollment) at the Dayalbagh Children's Hospital in Agra India over the course of 1 year (4 different sessions). To match the infant fingerprints, we develop our own high-resolution infant fingerprint matcher. Our experimental results demonstrate significant promise for the extension of fingerprint recognition to all ages. This work has the potential for major global good as all young infants and children could be given a verifiable digital identity for better vaccination tracking as a child and for government benefits and assistance as an adult. In summary, this thesis makes major contributions to the entire end-to-end fingerprint recognition system and extends its use case to all ages.
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Title: Towards Robust and Secure Face Recognition : Defense Against Physical and Digital Attacks
Creator: Deb, Debayan
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The accuracy, usability, and touchless acquisition of state-of-the-art automated face recognition systems (AFR) have led to their ubiquitous adoption in a plethora of domains, including mobile phone unlock, access control systems, and payment services. Despite impressive recognition performance, prevailing AFR systems remain vulnerable to the growing threat of face attacks which can be launched in both physical and digital domains. Face attacks can be broadly classified into three attack...
Show moreThe accuracy, usability, and touchless acquisition of state-of-the-art automated face recognition systems (AFR) have led to their ubiquitous adoption in a plethora of domains, including mobile phone unlock, access control systems, and payment services. Despite impressive recognition performance, prevailing AFR systems remain vulnerable to the growing threat of face attacks which can be launched in both physical and digital domains. Face attacks can be broadly classified into three attack categories: (i) Spoof attacks: artifacts in the physical domain (e.g., 3D masks, eye glasses, replaying videos), (ii) Adversarial attacks: imperceptible noises added to probes for evading AFR systems, and (iii) Digital manipulation attacks: entirely or partially modified photo-realistic faces using generative models. Each of these categories is composed of different attack types. For example, each spoof medium, e.g., 3D mask and makeup, constitutes one attack type. Likewise, in adversarial and digital manipulation attacks, each attack model, designed by unique objectives and losses, may be considered as one attack type. Thus, the attack categories and types form a 2-layer tree structure encompassing the diverse attacks. Such a tree will inevitably grow in the future. Given the growing dissemination of ``fake news” and "deepfakes", the research community and social media platforms alike are pushing towards generalizable defense against continuously evolving and sophisticated face attacks. In this dissertation, we first propose a set of defense methods that achieve state-of-the-art performance in detecting attack types within individual attack categories, both physical (e.g., face spoofs) and digital (e.g., adversarial faces and digital manipulation), then introduce a method for simultaneously safeguarding against each attack.First, in an effort to impart generalizability and interpretability to face spoof detection systems, we propose a new face anti-spoofing framework specifically designed to detect unknown spoof types, namely, Self-Supervised Regional Fully Convolutional Network (SSR-FCN), that is trained to learn local discriminative cues from a face image in a self-supervised manner. The proposed framework improves generalizability while maintaining the computational efficiency of holistic face anti-spoofing approaches (< 4 ms on a Nvidia GTX 1080Ti GPU). The proposed method is also interpretable since it localizes which parts of the face are labeled as spoofs. Experimental results show that SSR-FCN can achieve True Detection Rate (TDR) = 65% @ 2.0% False Detection Rate (FDR) when evaluated on a dataset comprising of 13 different spoof types under unknown attacks while achieving competitive performances under standard benchmark face anti-spoofing datasets (Oulu-NPU, CASIA-MFSD, and Replay-Attack).Next, we address the problem of defending against adversarial attacks. We first propose, AdvFaces, an automated adversarial face synthesis method that learns to generate minimal perturbations in the salient facial regions. Once AdvFaces is trained, it can automatically evade state-of-the-art face matchers with attack success rates as high as 97.22% and 24.30% at 0.1% FAR for obfuscation and impersonation attacks, respectively. We then propose a new self-supervised adversarial defense framework, namely FaceGuard, that can automatically detect, localize, and purify a wide variety of adversarial faces without utilizing pre-computed adversarial training samples. FaceGuard automatically synthesizes diverse adversarial faces, enabling a classifier to learn to distinguish them from bona fide faces. Concurrently, a purifier attempts to remove the adversarial perturbations in the image space. FaceGuard can achieve 99.81%, 98.73%, and 99.35% detection accuracies on LFW, CelebA, and FFHQ, respectively, on six unseen adversarial attack types.Finally, we take the first steps towards safeguarding AFR systems against face attacks in both physical and digital domains. We propose a new unified face attack detection framework, namely UniFAD, which automatically clusters similar attacks and employs a multi-task learning framework to learn salient features to distinguish between bona fides and coherent attack types. The proposed UniFAD can detect face attacks from 25 attack types across all 3 attack categories with TDR = 94.73% @ 0.2% FDR on a large fake face dataset, namely GrandFake. Further, UniFAD can identify whether attacks are adversarial, digitally manipulated, or contain spoof artifacts, with 97.37% classification accuracy.
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Title: Online Learning Algorithms for Mining Trajectory data and their Applications
Creator: Wang, Ding
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Trajectories are spatio-temporal data that represent traces of moving objects, such as humans, migrating animals, vehicles, and tropical cyclones. In addition to the geo-location information, a trajectory data often contain other (non-spatial) features describing the states of the moving objects. The time-varying geo-location and state information would collectively characterize a trajectory dataset, which can be harnessed to understand the dynamics of the moving objects. This thesis focuses...
Show moreTrajectories are spatio-temporal data that represent traces of moving objects, such as humans, migrating animals, vehicles, and tropical cyclones. In addition to the geo-location information, a trajectory data often contain other (non-spatial) features describing the states of the moving objects. The time-varying geo-location and state information would collectively characterize a trajectory dataset, which can be harnessed to understand the dynamics of the moving objects. This thesis focuses on the development of efficient and accurate machine learning algorithms for forecasting the future trajectory path and state of a moving object. Although many methods have been developed in recent years, there are still numerous challenges that have not been sufficiently addressed by existing methods, which hamper their effectiveness when applied to critical applications such as hurricane prediction. These challenges include their difficulties in terms of handling concept drifts, error propagation in long-term forecasts, missing values, and nonlinearities in the data. In this thesis, I present a family of online learning algorithms to address these challenges. Online learning is an effective approach as it can efficiently fit new observations while adapting to concept drifts present in the data. First, I proposed an online learning framework called OMuLeT for long-term forecasting of the trajectory paths of moving objects. OMuLeT employs an online learning with restart strategy to incrementally update the weights of its predictive model as new observation data become available. It can also handle missing values in the data using a novel weight renormalization strategy.Second, I introduced the OOR framework to predict the future state of the moving object. Since the state can be represented by ordinal values, OOR employs a novel ordinal loss function to train its model. In addition, the framework was extended to OOQR to accommodate a quantile loss function to improve its prediction accuracy for larger values on the ordinal scale. Furthermore, I also developed the OOR-ε and OOQR-ε frameworks to generate real-valued state predictions using the ε insensitivity loss function.Third, I developed an online learning framework called JOHAN, that simultaneously predicts the location and state of the moving object. JOHAN generates its predictions by leveraging the relationship between the state and location information. JOHAN utilizes a quantile loss function to bias the algorithm towards predicting more accurately large categorical values in terms of the state of the moving object, say, for a high intensity hurricane.Finally, I present a deep learning framework to capture non-linear relationships in trajectory data. The proposed DTP framework employs a TDM approach for imputing missing values, coupled with an LSTM architecture for dynamic path prediction. In addition, the framework was extended to ODTP, which applied an online learning setting to address concept drifts present in the trajectory data.As proof of concept, the proposed algorithms were applied to the hurricane prediction task. Both OMuLeT and ODTP were used to predict the future trajectory path of a hurricane up to 48 hours lead time. Experimental results showed that OMuLeT and ODTP outperformed various baseline methods, including the official forecasts produced by the U.S. National Hurricane Center. OOR was applied to predict the intensity of a hurricane up to 48 hours in advance. Experimental results showed that OOR outperformed various state-of-the-art online learning methods and can generate predictions close to the NHC official forecasts. Since hurricane intensity prediction is a notoriously hard problem, JOHAN was applied to improve its prediction accuracy by leveraging the trajectory information, particularly for high intensity hurricanes that are near landfall.
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Title: Replaying Life's Virtual Tape : Examining the Role of History in Experiments with Digital Organisms
Creator: Bundy, Jason Nyerere
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Evolution is a complex process with a simple recipe. Evolutionary change involves three essential “ingredients” interacting over many generations: adaptation (selection), chance (random variation), and history (inheritance). In 1989’s Wonderful Life, the late paleontologist Stephen Jay Gould advocated for the importance of historical contingency—the way unique events throughout history influence future possibilities—using a clever thought experiment of “replaying life’s tape”. But not...
Show moreEvolution is a complex process with a simple recipe. Evolutionary change involves three essential “ingredients” interacting over many generations: adaptation (selection), chance (random variation), and history (inheritance). In 1989’s Wonderful Life, the late paleontologist Stephen Jay Gould advocated for the importance of historical contingency—the way unique events throughout history influence future possibilities—using a clever thought experiment of “replaying life’s tape”. But not everyone was convinced. Some believed that chance was the primary driver of evolutionary change, while others insisted that natural selection was the most powerful influence. Since then, “replaying life’s tape” has become a core method in experimental evolution for measuring the relative contributions of adaptation, chance, and history. In this dissertation, I focus on the effects associated with history in evolving populations of digital organisms—computer programs that self-replicate, mutate, compete, and evolve in virtual environments. In Chapter 1, I discuss the philosophical significance of Gould’s thought experiment and its influence on experimental methods. I argue that his thought experiment was a challenge to anthropocentric reasoning about natural history that is still popular, particularly outside of the scientific community. In this regard, it was his way of advocating for a “radical” view of evolution. In Chapter 2—Richard Lenski, Charles Ofria, and I describe a two-phase, virtual, “long-term” evolution experiment with digital organisms using the Avida software. In Phase I, we evolved 10 replicate populations, in parallel, from a single genotype for around 65,000 generations. This part of the experiment is similar to the design of Lenski’s E. coli Long-term Evolution Experiment (LTEE). We isolated the dominant genotype from each population around 3,000 generations (shallow history) into Phase I and then again at the end of Phase I (deep history). In Phase II, we evolved 10 populations from each of the genotypes we isolated from Phase I in two new environments, one similar and one dissimilar to the old environment used for Phase I. Following Phase II, we estimated the contributions of adaptation, chance, and history to the evolution of fitness and genome length in each new environment. This unique experimental design allowed us to see how the contributions of adaptation, chance, and history changed as we extended the depth of history from Phase I. We were also able to determine whether the results depended on the extent of environmental change (similar or dissimilar new environment). In Chapter 3, we report an extended analysis of the experiment from the previous chapter to further examine how extensive adaptation to the Phase I environment shaped the evolution of replicates during Phase II. We show how the form of pleiotropy (antagonistic or synergistic) between the old (Phase I) and new (Phase II) habitats was influenced by the depth of history from Phase I (shallow or deep) and the extent of environmental change (similar or dissimilar new environment). In the final chapter Zachary Blount, Richard Lenski, and I describe an exercise we developed using the educational version of Avida (Avida-ED). The exercise features a two-phase, “replaying life’s tape” activity. Students are able to explore how the unique history of founders that we pre-evolved during Phase I influences the acquisition of new functions by descendent populations during Phase II, which the students perform during the activity.
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Title: COMBINING FACE AND IRIS FOR PRIVACY PRESERVATION
Creator: Ledala, Achsah Junia
Date: 2021
Collection: Electronic Theses & Dissertations
Description: With the extensive use of biometrics for authenticating users, the need to ensure privacy of biometric data is greater than ever before. Biometric authentication systems are vulnerable to attacks and the loss of biometric data will lead to loss of privacy of an individual. Multibiometrics refers to the use of multiple biometric modalities simultaneously in order to perform matching. In this work, we introduce a multibiometric fusion technique which can be used to ensure that the original raw...
Show moreWith the extensive use of biometrics for authenticating users, the need to ensure privacy of biometric data is greater than ever before. Biometric authentication systems are vulnerable to attacks and the loss of biometric data will lead to loss of privacy of an individual. Multibiometrics refers to the use of multiple biometric modalities simultaneously in order to perform matching. In this work, we introduce a multibiometric fusion technique which can be used to ensure that the original raw biometric data are unlikely to be compromised and, at the same time, recognition can be performed. The face and the iris biometric modalities are fused at the feature-level to produce discriminative embeddings that can be used for recognition. The original face or the iris cannot be retrieved from the combined representation, thus preserving the privacy of the individual. We present the results of this approach, provide analysis, discuss the challenges, and list possible future directions.
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Title: Iris Recognition : Enhancing Security and Improving Performance
Creator: Sharma, Renu
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Biometric systems recognize individuals based on their physical or behavioral traits, viz., face, iris, and voice. Iris (the colored annular region around the pupil) is one of the most popular biometric traits due to its uniqueness, accuracy, and stability. However, its widespread usage raises security concerns against various adversarial attacks. Another challenge is to match iris images with other compatible biometric modalities (i.e., face) to increase the scope of human identification....
Show moreBiometric systems recognize individuals based on their physical or behavioral traits, viz., face, iris, and voice. Iris (the colored annular region around the pupil) is one of the most popular biometric traits due to its uniqueness, accuracy, and stability. However, its widespread usage raises security concerns against various adversarial attacks. Another challenge is to match iris images with other compatible biometric modalities (i.e., face) to increase the scope of human identification. Therefore, the focus of this thesis is two-fold: firstly, enhance the security of the iris recognition system by detecting adversarial attacks, and secondly, accentuate its performance in iris-face matching.To enhance the security of the iris biometric system, we work over two types of adversarial attacks - presentation and morph attacks. A presentation attack (PA) occurs when an adversary presents a fake or altered biometric sample (plastic eye, cosmetic contact lens, etc.) to a biometric system to obfuscate their own identity or impersonate another identity. We propose three deep learning-based iris PA detection frameworks corresponding to three different imaging modalities, namely NIR spectrum, visible spectrum, and Optical Coherence Tomography (OCT) imaging inputting a NIR image, visible-spectrum video, and cross-sectional OCT image, respectively. The techniques perform effectively to detect known iris PAs as well as generalize well across unseen attacks, unseen sensors, and multiple datasets. We also presented the explainability and interpretability of the results from the techniques. Our other focuses are robustness analysis and continuous update (retraining) of the trained iris PA detection models. Another burgeoning security threat to biometric systems is morph attacks. A morph attack entails the generation of an image (morphed image) that embodies multiple different identities. Typically, a biometric image is associated with a single identity. In this work, we first demonstrate the vulnerability of iris recognition techniques to morph attacks and then develop techniques to detect the morphed iris images.The second focus of the thesis is to improve the performance of a cross-modal system where iris images are matched against face images. Cross-modality matching involves various challenges, such as cross-spectral, cross-resolution, cross-pose, and cross-temporal. To address these challenges, we extract common features present in both images using a multi-channel convolutional network and also generate synthetic data to augment insufficient training data using a dual-variational autoencoder framework. The two focus areas of this thesis improve the acceptance and widespread usage of the iris biometric system.
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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: PALETTEVIZ : A METHOD FOR VISUALIZATION OF HIGH-DIMENSIONAL PARETO-OPTIMAL FRONT AND ITS APPLICATIONS TO MULTI-CRITERIA DECISION MAKING AND ANALYSIS
Creator: Talukder, AKM Khaled Ahsan
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Visual representation of a many-objective Pareto-optimal front in four or more dimensional objective space requires a large number of data points. Moreover, choosing a single point from a large set even with certain preference information is problematic, as it imposes a large cognitive burden on the decision-makers. Therefore, many-objective optimization and decision-making practitioners have been interested in effective visualization methods to en- able them to filter down a large set to a...
Show moreVisual representation of a many-objective Pareto-optimal front in four or more dimensional objective space requires a large number of data points. Moreover, choosing a single point from a large set even with certain preference information is problematic, as it imposes a large cognitive burden on the decision-makers. Therefore, many-objective optimization and decision-making practitioners have been interested in effective visualization methods to en- able them to filter down a large set to a few critical points for further analysis. Most existing visualization methods are borrowed from other data analytics domains and they are too generic to be effective for many-criterion decision making. In this dissertation, we propose a visualization method, using star-coordinate and radial visualization plots, for effectively visualizing many-objective trade-off solutions. The proposed method respects some basic topological, geometric and functional decision-making properties of high-dimensional trade- off points mapped to a three-dimensional space. We call this method Palette Visualization (PaletteViz). We demonstrate the use of PaletteViz on a number of large-dimensional multi- objective optimization test problems and three real-world multi-objective problems, where one of them has 10 objective and 16 constraint functions. We also show the uses of NIMBUS and Pareto-Race concepts from canonical multi-criterion decision making and analysis literature and introduce them into PaletteViz to demonstrate the ease and advantage of the proposed method.
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Title: Towards Robust and Reliable Communication for Millimeter Wave Networks
Creator: Zarifneshat, Masoud
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The future generations of wireless networks benefit significantly from millimeter wave technology (mmW) with frequencies ranging from about 30 GHz to 300 GHz. Specifically, the fifth generation of wireless networks has already implemented the mmW technology and the capacity requirements defined in 6G will also benefit from the mmW spectrum. Despite the attractions of the mmW technology, the mmW spectrum has some inherent propagation properties that introduce challenges. The first is that free...
Show moreThe future generations of wireless networks benefit significantly from millimeter wave technology (mmW) with frequencies ranging from about 30 GHz to 300 GHz. Specifically, the fifth generation of wireless networks has already implemented the mmW technology and the capacity requirements defined in 6G will also benefit from the mmW spectrum. Despite the attractions of the mmW technology, the mmW spectrum has some inherent propagation properties that introduce challenges. The first is that free space pathloss in mmW is more severe than that in the sub 6 GHz band. To make the mmW signal travel farther, communication systems need to use phased array antennas to concentrate the signal power to a limited direction in space at each given time. Directional communication can incur high overhead on the system because it needs to probe the space for finding signal paths. To have efficient communication in the mmW spectrum, the transmitter and the receiver should align their beams on strong signal paths which is a high overhead task. The second is a low diffraction of the mmW spectrum. The low diffraction causes almost any object including the human body to easily block the mmW signal degrading the mmW link quality. Avoiding and recovering from the blockage in the mmW communications, especially in dynamic environments, is particularly challenging because of the fast changes of the mmW channel. Due to the unique characteristics of the mmW propagation, the traditional user association methods perform poorly in the mmW spectrum. Therefore, we propose user association methods that consider the inherent propagation characteristics of the mmW signal. We first propose a method that collects the history of blockage incidents throughout the network and exploits the historical blockage incidents to associate user equipment to the base station with lower blockage possibility. The simulation results show that our proposed algorithm performs better in terms of improving the quality of the links and blockage rate in the network. User association based only on one objective may deteriorate other objectives. Therefore, we formulate a biobjective optimization problem to consider two objectives of load balance and blockage possibility in the network. We conduct Lagrangian dual analysis to decrease time complexity. The results show that our solution to the biobjective optimization problem has a better outcome compared to optimizing each objective alone. After we investigate the user association problem, we further look into the problem of maintaining a robust link between a transmitter and a receiver. The directional propagation of the mmW signal creates the opportunity to exploit multipath for a robust link. The main reasons for the link quality degradation are blockage and link movement. We devise a learning-based prediction framework to classify link blockage and link movement efficiently and quickly using diffraction values for taking appropriate mitigating actions. The simulations show that the prediction framework can predict blockage with close to 90% accuracy. The prediction framework will eliminate the need for time-consuming methods to discriminate between link movement and link blockage. After detecting the reason for the link degradation, the system needs to do the beam alignment on the updated mmW signal paths. The beam alignment on the signal paths is a high overhead task. We propose using signaling in another frequency band to discover the paths surrounding a receiver working in the mmW spectrum. In this way, the receiver does not have to do an expensive beam scan in the mmW band. Our experiments with off-the-shelf devices show that we can use a non-mmW frequency band's paths to align the beams in mmW frequency. In this dissertation, we provide solutions to the fundamental problems in mmW communication. We propose a user association method that is designed for mmW networks considering challenges of mmW signal. A closed-form solution for a biobjective optimization problem to optimize both blockage and load balance of the network is also provided. Moreover, we show that we can efficiently use the out-of-band signal to exploit multipath created in mmW communication. The future research direction includes investigating the methods proposed in this dissertation to solve some of the classic problems in the wireless networks that exist in the mmW spectrum.
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