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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
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
TEACHERS IN SOCIAL MEDIA : A DATA SCIENCE PERSPECTIVE
Creator
Karimi, Hamid
Date
2021
Collection
Electronic Theses & Dissertations
Description
Social media has become an integral part of human life in the 21st century. The number of social media users was estimated to be around 3.6 billion individuals in 2020. Social media platforms (e.g., Facebook) have facilitated interpersonal communication, diffusion of information, the creation of groups and communities, to name a few. As far as education systems are concerned, online social media has transformed and connected traditional social networks within the schoolhouse to a broader and...
Show moreSocial media has become an integral part of human life in the 21st century. The number of social media users was estimated to be around 3.6 billion individuals in 2020. Social media platforms (e.g., Facebook) have facilitated interpersonal communication, diffusion of information, the creation of groups and communities, to name a few. As far as education systems are concerned, online social media has transformed and connected traditional social networks within the schoolhouse to a broader and expanded world outside. In such an expanded virtual space, teachers engage in various activities within their communities, e.g., exchanging instructional resources, seeking new teaching methods, engaging in online discussions. Therefore, given the importance of teachers in social media and its tremendous impact on PK-12 education, in this dissertation, we investigate teachers in social media from a data science perspective. Our investigation in this direction is essentially an interdisciplinary endeavor bridging modern data science and education. In particular, we have made three contributions, as briefly discussed in the following. Current teachers in social media studies suffice to a small number of surveyed teachers while thousands of other teachers are on social media. This hinders us from conducting large-scale data-driven studies pertinent to teachers in social media. Aiming to overcome this challenge and further facilitate data-driven studies related to teachers in social media, we propose a novel method that automatically identifies teachers on Pinterest, an image-based social media popular among teachers. In this framework, we formulate the teacher identification problem as a positive unlabelled (PU) learning where positive samples are surveyed teachers, and unlabelled samples are their online friends. Using our framework, we build the largest dataset of teachers on Pinterest. With this dataset at our disposal, we perform an exploratory analysis of teachers on Pinterest while considering their genders. Our analysis incorporates two crucial aspects of teachers in social media. First, we investigate various online activities of male and female teachers, e.g., topics and sources of their curated resources, the professional language employed to describe their resources. Second, we investigate male and female teachers in the context of the social network (the graph) they belong to, e.g., structural centrality, gender homophily. Our analysis and findings in this part of the dissertation can serve as a valuable reference for many entities concerned with teachers' gender, e.g., principals, state, and federal governments.Finally, in the third part of the dissertation, we shed light on the diffusion of teacher-curated resources on Pinterest. First, we introduce three measures to characterize the diffusion process. Then, we investigate these three measures while considering two crucial characteristics of a resource, e.g., the topic and the source. Ultimately, we investigate how teacher attributes (e.g., the number of friends) affect the diffusion of their resources. The conducted diffusion analysis is the first of its kind and offers a deeper understating of the complex mechanism driving the diffusion of resources curated by teachers on Pinterest.
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Title
Some contributions to semi-supervised learning
Creator
Mallapragada, Paven Kumar
Date
2010
Collection
Electronic Theses & Dissertations
Title
Semi=supervised learning with side information : graph-based approaches
Creator
Liu, Yi
Date
2007
Collection
Electronic Theses & Dissertations
Title
SIGN LANGUAGE RECOGNIZER FRAMEWORK BASED ON DEEP LEARNING ALGORITHMS
Creator
Akandeh, Atra
Date
2021
Collection
Electronic Theses & Dissertations
Description
According to the World Health Organization (WHO, 2017), 5% of the world’s population have hearing loss. Most people with hearing disabilities communicate via sign language, which hearing people find extremely difficult to understand. To facilitate communication of deaf and hard of hearing people, developing an efficient communication system is a necessity. There are many challenges associated with the Sign Language Recognition (SLR) task, namely, lighting conditions, complex background,...
Show moreAccording to the World Health Organization (WHO, 2017), 5% of the world’s population have hearing loss. Most people with hearing disabilities communicate via sign language, which hearing people find extremely difficult to understand. To facilitate communication of deaf and hard of hearing people, developing an efficient communication system is a necessity. There are many challenges associated with the Sign Language Recognition (SLR) task, namely, lighting conditions, complex background, signee body postures, camera position, occlusion, complexity and large variations in hand posture, no word alignment, coarticulation, etc.Sign Language Recognition has been an active domain of research since the early 90s. However, due to computational resources and sensing technology constraints, limited advancement has been achieved over the years. Existing sign language translation systems mostly can translate a single sign at a time, which makes them less effective in daily-life interaction. This work develops a novel sign language recognition framework using deep neural networks, which directly maps videos of sign language sentences to sequences of gloss labels by emphasizing critical characteristics of the signs and injecting domain-specific expert knowledge into the system. The proposed model also allows for combining data from variant sources and hence combating limited data resources in the SLR field.
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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
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
OPTIMIZATION OF LARGE SCALE ITERATIVE EIGENSOLVERS
Creator
Afibuzzaman, Md
Date
2021
Collection
Electronic Theses & Dissertations
Description
Sparse matrix computations, in the form of solvers for systems of linear equations, eigenvalue problem or matrix factorizations constitute the main kernel in problems from fields as diverse as computational fluid dynamics, quantum many body problems, machine learning and graph analytics. Iterative eigensolvers have been preferred over the regular method because the regular method not being feasible with industrial sized matrices. Although dense linear algebra libraries like BLAS, LAPACK,...
Show moreSparse matrix computations, in the form of solvers for systems of linear equations, eigenvalue problem or matrix factorizations constitute the main kernel in problems from fields as diverse as computational fluid dynamics, quantum many body problems, machine learning and graph analytics. Iterative eigensolvers have been preferred over the regular method because the regular method not being feasible with industrial sized matrices. Although dense linear algebra libraries like BLAS, LAPACK, SCALAPACK are well established and some vendor optimized implementation like mkl from Intel or Cray Libsci exist, it is not the same case for sparse linear algebra which is lagging far behind. The main reason behind slow progress in the standardization of sparse linear algebra or library development is the different forms and properties depending on the application area. It is worsened for deep memory hierarchies of modern architectures due to low arithmetic intensities and memory bound computations. Minimization of data movement and fast access to the matrix are critical in this case. Since the current technology is driven by deep memory architectures where we get the increased capacity at the expense of increased latency and decreased bandwidth when we go further from the processors. The key to achieve high performance in sparse matrix computations in deep memory hierarchy is to minimize data movement across layers of the memory and overlap data movement with computations. My thesis work contributes towards addressing the algorithmic challenges and developing a computational infrastructure to achieve high performance in scientific applications for both shared memory and distributed memory architectures. For this purpose, I started working on optimizing a blocked eigensolver and optimized specific computational kernels which uses a new storage format. Using this optimization as a building block, we introduce a shared memory task parallel framework focusing on optimizing the entire solvers rather than a specific kernel. Before extending this shared memory implementation to a distributed memory architecture, I simulated the communication pattern and overheads of a large scale distributed memory application and then I introduce the communication tasks in the framework to overlap communication and computation. Additionally, I also tried to find a custom scheduler for the tasks using a graph partitioner. To get acquainted with high performance computing and parallel libraries, I started my PhD journey with optimizing a DFT code named Sky3D where I used dense matrix libraries. Despite there might not be any single solution for this problem, I tried to find an optimized solution. Though the large distributed memory application MFDn is kind of the driver project of the thesis, but the framework we developed is not confined to MFDn only, rather it can be used for other scientific applications too. The output of this thesis is the task parallel HPC infrastructure that we envisioned for both shared and distributed memory architectures.
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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
Network analysis with negative links
Creator
Derr, Tyler Scott
Date
2020
Collection
Electronic Theses & Dissertations
Description
As we rapidly continue into the information age, the rate at which data is produced has created an unprecedented demand for novel methods to effectively extract insightful patterns. We can then seek to understand the past, make predictions about the future, and ultimately take actionable steps towards improving our society. Thus, due to the fact that much of today's big data can be represented as graphs, emphasis is being taken to harness the natural structure of data through network analysis...
Show moreAs we rapidly continue into the information age, the rate at which data is produced has created an unprecedented demand for novel methods to effectively extract insightful patterns. We can then seek to understand the past, make predictions about the future, and ultimately take actionable steps towards improving our society. Thus, due to the fact that much of today's big data can be represented as graphs, emphasis is being taken to harness the natural structure of data through network analysis. Traditionally, network analysis has focused on networks having only positive links, or unsigned networks. However, in many real-world systems, relations between nodes in a graph can be both positive and negative, or signed networks. For example, in online social media, users not only have positive links such as friends, followers, and those they trust, but also can establish negative links to those they distrust, towards their foes, or block and unfriend users.Thus, although signed networks are ubiquitous due to their ability to represent negative links in addition to positive links, they have been significantly under explored. In addition, due to the rise in popularity of today's social media and increased polarization online, this has led to both an increased attention and demand for advanced methods to perform the typical network analysis tasks when also taking into consideration negative links. More specifically, there is a need for methods that can measure, model, mine, and apply signed networks that harness both these positive and negative relations. However, this raises novel challenges, as the properties and principles of negative links are not necessarily the same as positive links, and furthermore the social theories that have been used in unsigned networks might not apply with the inclusion of negative links.The chief objective of this dissertation is to first analyze the distinct properties negative links have as compared to positive links and towards improving network analysis with negative links by researching the utility and how to harness social theories that have been established in a holistic view of networks containing both positive and negative links. We discover that simply extending unsigned network analysis is typically not sufficient and that although the existence of negative links introduces numerous challenges, they also provide unprecedented opportunities for advancing the frontier of the network analysis domain. In particular, we develop advanced methods in signed networks for measuring node relevance and centrality (i.e., signed network measuring), present the first generative signed network model and extend/analyze balance theory to signed bipartite networks (i.e., signed network modeling), construct the first signed graph convolutional network which learns node representations that can achieve state-of-the-art prediction performance and then furthermore introduce the novel idea of transformation-based network embedding (i.e., signed network mining), and apply signed networks by creating a framework that can infer both link and interaction polarity levels in online social media and constructing an advanced comprehensive congressional vote prediction framework built around harnessing signed networks.
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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
MICROBLOG GUIDED CRYPTOCURRENCY TRADING AND FRAMING ANALYSIS
Creator
Pawlicka Maule, Anna Paula
Date
2020
Collection
Electronic Theses & Dissertations
Description
With 56 million people actively trading and investing in cryptocurrency online and globally, there is an increasing need for an automatic social media analysis tool to help understand trading discourse and behavior. Previous works have shown the usefulness of modeling microblog discourse for the prediction of trading stocks and their price fluctuations, as well as content framing. In this work, I present a natural language modeling pipeline that leverages language and social network behaviors...
Show moreWith 56 million people actively trading and investing in cryptocurrency online and globally, there is an increasing need for an automatic social media analysis tool to help understand trading discourse and behavior. Previous works have shown the usefulness of modeling microblog discourse for the prediction of trading stocks and their price fluctuations, as well as content framing. In this work, I present a natural language modeling pipeline that leverages language and social network behaviors for the prediction of cryptocurrency day trading actions and their associated framing patterns. Specifically, I present two modeling approaches. The first determines if the tweets of a 24-hour period can be used to guide day trading behavior, specifically if a cryptocurrency investor should buy, sell, or hold their cryptocurrencies in order to make a trading profit. The second is an unsupervised deep clustering approach to automatically detect framing patterns. My contributions include the modeling pipeline for this novel task, a new dataset of cryptocurrency-related tweets from influential accounts, and a transaction volume dataset. The experiments executed show that this weakly-supervised trading pipeline achieves an 88.78% accuracy for day trading behavior predictions and reveals framing fluctuations prior to and during the COVID-19 pandemic that could be used to guide investment actions.
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Title
LIDAR AND CAMERA CALIBRATION USING A MOUNTED SPHERE
Creator
Li, Jiajia
Date
2020
Collection
Electronic Theses & Dissertations
Description
Extrinsic calibration between lidar and camera sensors is needed for multi-modal sensor data fusion. However, obtaining precise extrinsic calibration can be tedious, computationally expensive, or involve elaborate apparatus. This thesis proposes a simple, fast, and robust method performing extrinsic calibration between a camera and lidar. The only required calibration target is a hand-held colored sphere mounted on a whiteboard. The convolutional neural networks are developed to automatically...
Show moreExtrinsic calibration between lidar and camera sensors is needed for multi-modal sensor data fusion. However, obtaining precise extrinsic calibration can be tedious, computationally expensive, or involve elaborate apparatus. This thesis proposes a simple, fast, and robust method performing extrinsic calibration between a camera and lidar. The only required calibration target is a hand-held colored sphere mounted on a whiteboard. The convolutional neural networks are developed to automatically localize the sphere relative to the camera and the lidar. Then using the localization covariance models, the relative pose between the camera and lidar is derived. To evaluate the accuracy of our method, we record image and lidar data of a sphere at a set of known grid positions by using two rails mounted on a wall. The accurate calibration results are demonstrated by projecting the grid centers into the camera image plane and finding the error between these points and the hand-labeled sphere centers.
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Title
I AM DOING MORE THAN CODING : A QUALITATIVE STUDY OF BLACK WOMEN HBCU UNDERGRADUATES’ PERSISTENCE IN COMPUTING
Creator
Benton, Amber V.
Date
2020
Collection
Electronic Theses & Dissertations
Description
The purpose of my study is to explore why and how Black women undergraduates at historically Black colleges and universities (HBCUs) persist in computing. By centering the experiences of Black women undergraduates and their stories, this dissertation expands traditional, dominant ways of understanding student persistence in higher education. Critical Race Feminism (CRF) was applied as a conceptual framework to the stories of 11 Black women undergraduates in computing and drew on the small...
Show moreThe purpose of my study is to explore why and how Black women undergraduates at historically Black colleges and universities (HBCUs) persist in computing. By centering the experiences of Black women undergraduates and their stories, this dissertation expands traditional, dominant ways of understanding student persistence in higher education. Critical Race Feminism (CRF) was applied as a conceptual framework to the stories of 11 Black women undergraduates in computing and drew on the small stories qualitative approach to examine the day-to-day experiences of Black women undergraduates at HBCUs as they persisted in their computing degree programs. The findings suggest that: (a) gender underrepresentation in computing affects Black women’s experiences, (b) computing culture at HBCUs directly affect Black women in computing, (c) Black women need access to resources and opportunities to persist in computing, (d) computing-related internships are beneficial professional opportunities but are also sites of gendered racism for Black women, (e) connectedness between Black people is innate but also needs to be fostered, (f) Black women want to engage in computing that contributes to social impact and community uplift, and (g) science identity is not a primary identity for Black women in computing. This paper also argues that disciplinary focused efforts contribute to the persistence of Black women in computing.
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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
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
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
Energy Conservation in Heterogeneous Smartphone Ad Hoc Networks
Creator
Mariani, James
Date
2018
Collection
Electronic Theses & Dissertations
Description
In recent years mobile computing has been rapidly expanding to the point that there are now more devices than there are people. While once it was common for every household to have one PC, it is now common for every person to have a mobile device. With the increased use of smartphone devices, there has also been an increase in the need for mobile ad hoc networks, in which phones connect directly to each other without the need for an intermediate router. Most modern smart phones are equipped...
Show moreIn recent years mobile computing has been rapidly expanding to the point that there are now more devices than there are people. While once it was common for every household to have one PC, it is now common for every person to have a mobile device. With the increased use of smartphone devices, there has also been an increase in the need for mobile ad hoc networks, in which phones connect directly to each other without the need for an intermediate router. Most modern smart phones are equipped with both Bluetooth and Wifi Direct, where Wifi Direct has a better transmission range and rate and Bluetooth is more energy efficient. However only one or the other is used in a smartphone ad hoc network. We propose a Heterogeneous Smartphone Ad Hoc Network, HSNet, a framework to enable the automatic switching between Wifi Direct and Bluetooth to emphasize minimizing energy consumption while still maintaining an efficient network. We develop an application to evaluate the HSNet framework which shows significant energy savings when utilizing our switching algorithm to send messages by a less energy intensive technology in situations where energy conservation is desired. We discuss additional features of HSNet such as load balancing to help increase the lifetime of the network by more evenly distributing slave nodes among connected master nodes. Finally, we show that the throughput of our system is not affected due to technology switching for most scenarios. Future work of this project includes exploring energy efficient routing as well as simulation/scale testing for larger and more diverse smartphone ad hoc networks.
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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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