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Deep Video Understanding With Model Efficiency And Sparse Active Labeling, Aayush Jung Bahadur Rana
Deep Video Understanding With Model Efficiency And Sparse Active Labeling, Aayush Jung Bahadur Rana
Electronic Theses and Dissertations, 2020-
Videos capture the inherently sequential nature of the real world, making automatic video understanding an essential need for automatic understanding of the real world. Due to major advancements in camera, communication, and storage hardware, videos have become a widely used data format for crucial applications such as home automation, security, analysis, robotics, and autonomous driving. Existing methods for video understanding require heavy computation and large training data for good performance, this limits how quick the videos can be processed and how much data can be labeled for training. Real-world video understanding requires analyzing dense scenes and sequential information, which increases …
Annotation Efficient Visual Recognition: From Semi-Supervised To Few-Shot Learning, Mamshad Nayeem Rizve
Annotation Efficient Visual Recognition: From Semi-Supervised To Few-Shot Learning, Mamshad Nayeem Rizve
Electronic Theses and Dissertations, 2020-
In recent years, supervised deep learning has achieved remarkable success in solving a wide range of visual recognition problems. Large-scale labeled datasets have been crucial for this success and the progress has primarily been limited to controlled environments. In this dissertation, we present methods to improve the annotation efficiency of deep visual recognition models and also propose methods to improve the performance of annotation-efficient models in unconstrained open-world settings. To address the annotation bottleneck in supervised learning, we introduce a pseudo-labeling framework for semi-supervised learning. While consistency regularization methods dominate the field, they heavily rely on domain-specific data augmentations, limiting …
Towards Efficient And Effective Representation Learning For Image And Video Understanding, Taojiannan Yang
Towards Efficient And Effective Representation Learning For Image And Video Understanding, Taojiannan Yang
Electronic Theses and Dissertations, 2020-
Deep learning has achieved tremendous success on various computer vision tasks. However, deep learning methods and models are usually computationally expensive, making it hard to train and deploy, especially on resource-constrained devices. In this dissertation, we explore how to improve the efficiency and effectiveness of deep learning methods from various perspectives. We first propose a new learning method to learn computationally adaptive representations. Traditional neural networks are static. However, our method trains adaptive neural networks that can adjust their computational cost during runtime, avoiding the need to train and deploy multiple networks for dynamic resource budgets. Next, we extend our …
Human Recognition Theory And Facial Recognition Technology: A Topic Modeling Approach To Understanding The Ethical Implication Of A Developing Algorithmic Technologies Landscape On How We View Ourselves And Are Viewed By Others, Hajer Albalawi
Electronic Theses and Dissertations, 2020-
The emergence of algorithmic-driven technology has significantly impacted human life in the current century. Algorithms, as versatile constructs, hold different meanings across various disciplines, including computer science, mathematics, social science, and human-artificial intelligence studies. This study defines algorithms from an ethical perspective as the foundation of an information society and focuses on their implications in the context of human recognition. Facial recognition technology, driven by algorithms, has gained widespread use, raising important ethical questions regarding privacy, bias, and accuracy. This dissertation aims to explore the impact of algorithms on machine perception of human individuals and how humans perceive one another …
Efficient Convolutional Neural Networks For Image Classification And Regression, Muhammad Tayyab
Efficient Convolutional Neural Networks For Image Classification And Regression, Muhammad Tayyab
Electronic Theses and Dissertations, 2020-
Neural networks have been a topic of research since 1970s and the Convolutional Neural Networks (CNNs) were first shown to work well for hand written digits recognition in 1998. These early networks were however still shallow and contained only a few layers. Moreover these networks were mostly trained on a small amount of data in contrast to the modern CNNs which contain hundreds of convolution layers and are trained on millions of images. However, this recent shift in machine learning comes at a cost. Modern neural networks have extremely large number of parameters and require huge amount of computations for …
Studying Memes During Covid Lockdown As A Lens Through Which To Understand Video-Mediated Communication Interactions, Tatyana Claytor
Studying Memes During Covid Lockdown As A Lens Through Which To Understand Video-Mediated Communication Interactions, Tatyana Claytor
Electronic Theses and Dissertations, 2020-
The purpose of this study is to analyze image macros about video-mediated communication (VMC) created during the time frame of 2020-2021 when people all over the world started using Zoom and VMC for work and school. It is a unique opportunity to study how users' interactions with themselves and with others were affected at a time when a lot of people started using the technology at the same time. Because the focus is on interactions, I narrowed it down to three topics to analyze the memes: presence, self, and space and place to analyze the memes. I chose memes relating …
Detecting Team Conflict From Multiparty Dialogue, Ayesha Enayet
Detecting Team Conflict From Multiparty Dialogue, Ayesha Enayet
Electronic Theses and Dissertations, 2020-
The emergence of online collaboration platforms has dramatically changed the dynamics of human teamwork, creating a veritable army of virtual teams composed of workers in different physical locations. The global world requires a tremendous amount of collaborative problem solving, primarily virtual, making it an excellent domain for computer scientists and team cognition researchers who seek to understand the dynamics involved in collaborative tasks to provide a solution that can support effective collaboration. Mining and analyzing data from collaborative dialogues can yield insights into virtual teams' thought processes and help develop virtual agents to support collaboration. Good communication is indubitably the …
A Study On Robustness And Semantic Understanding Of Visual Models, Madeline Chantry
A Study On Robustness And Semantic Understanding Of Visual Models, Madeline Chantry
Electronic Theses and Dissertations, 2020-
Vision models have improved in popularity and performance on many tasks since the emergence of large-scale datasets, improved access to computational resources, and new model architectures like the transformer. However, it is still not well understood if these models can be deployed in the real world. Because these models are "blackbox" architectures, we do not fully understand what these models are truly learning. An understanding of what models learn "underneath the hood" would result in better improvements for real-world scenarios. Motivated by this, we benchmark these impressive visual models using newly proposed datasets and tasks on their robustness and their …
Identification And Modeling Social Media Influence Pathways: A Characterization Of A Disinformation Campaign Using The Flooding-The-Zone Strategy Via Transfer Entropy, Jasser Jasser
Electronic Theses and Dissertations, 2020-
The internet has made it easy for narratives to spread quickly and widely without regard for accuracy or the harm they may cause to society. Unfortunately, this has led to the rise of bad actors who use fake and misleading articles to spread harmful misinformation. These actors flood the information space with low-quality articles in an effort to disrupt opposing narratives, sow confusion, and discourage the pursuit of truth. In societies that prioritize free speech, maintaining control over the information space remains a persistent challenge. Achieving this requires strategic planning to protect the dissemination of information in ways that promote …
Mapping The Focal Points Of Wordpress: A Software And Critical Code Analysis, Bryce Jackson
Mapping The Focal Points Of Wordpress: A Software And Critical Code Analysis, Bryce Jackson
Electronic Theses and Dissertations, 2020-
Programming languages or code can be examined through numerous analytical lenses. This project is a critical analysis of WordPress, a prevalent web content management system, applying four modes of inquiry. The project draws on theoretical perspectives and areas of study in media, software, platforms, code, language, and power structures. The applied research is based on Critical Code Studies, an interdisciplinary field of study that holds the potential as a theoretical lens and methodological toolkit to understand computational code beyond its function. The project begins with a critical code analysis of WordPress, examining its origins and source code and mapping selected …
Understanding, Modeling, And Simulating The Discrepancy Between Intended And Perceived Image Appearance On Optical See-Through Augmented Reality Displays, Austin Erickson
Understanding, Modeling, And Simulating The Discrepancy Between Intended And Perceived Image Appearance On Optical See-Through Augmented Reality Displays, Austin Erickson
Electronic Theses and Dissertations, 2020-
Augmented reality (AR) displays are transitioning from being primarily used in research and development settings, to being used by the general public. With this transition, these displays will be used by more people, in many different environments, and in many different contexts. Like other displays, the user's perception of virtual imagery is influenced by the characteristics of the user's environment, creating a discrepancy between the intended appearance and the perceived appearance of virtual imagery shown on the display. However, this problem is much more apparent for optical see-through AR displays, such as the HoloLens. For these displays, imagery is superimposed …
From Human Behavior To Machine Behavior, Zerong Xi
From Human Behavior To Machine Behavior, Zerong Xi
Electronic Theses and Dissertations, 2020-
A core pursuit of artificial intelligence is the comprehension of human behavior. Imbuing intelligent agents with a good human behavior model can help them understand how to behave intelligently and interactively in complex situations. Due to the increase in data availability and computational resources, the development of machine learning algorithms for duplicating human cognitive abilities has made rapid progress. To solve difficult scenarios, learning-based methods must search for solutions in a predefined but large space. Along with implementing a smart exploration strategy, the right representation for a task can help narrow the search process during learning. This dissertation tackles three …
Improving Deep Neural Network Training With Knowledge Distillation, Dongdong Wang
Improving Deep Neural Network Training With Knowledge Distillation, Dongdong Wang
Electronic Theses and Dissertations, 2020-
Knowledge distillation, as a popular compression technique, has been widely used to reduce deep neural network (DNN) size for a variety of applications. However, in recent years, some research had found its potential for improving deep neural network performance. This dissertation focuses on further exploring its power to facilitate accurate and reliable DNN training. First, I explored data-efficient method for blackbox knowledge distillation where the specifics of the DNN for distillation is inaccessible. I integrated active learning and mixup to obtain significant distillation performance gain with limited data. This work reveals the competence of knowledge distillation to facilitate large foundation …
Towards Optimization And Robustification Of Data-Driven Models, Ehsan Kazemi Foroushani
Towards Optimization And Robustification Of Data-Driven Models, Ehsan Kazemi Foroushani
Electronic Theses and Dissertations, 2020-
In the past two decades, data-driven models have experienced a renaissance, with notable success achieved through the use of models such as deep neural networks (DNNs) in various applications. However, complete reliance on intelligent machine learning systems is still a distant dream. Nevertheless, the initial success of data-driven approaches presents a promising path for building trustworthy data-oriented models. This thesis aims to take a few steps toward improving the performance of existing data-driven frameworks in both the training and testing phases. Specifically, we focus on several key questions: 1) How to efficiently design optimization methods for learning algorithms that can …
Machine Learning Algorithms For Molecular Signature Identification With High-Throughput Genome Sequencing Data, Jiao Sun
Electronic Theses and Dissertations, 2020-
Powered by the high-throughput genomic technologies, the RNA sequencing (RNA-Seq) method is capable of measuring transcriptome-wide mRNA expressions and molecular activities in cells. Elucidation of gene expressions at the isoform resolution enables the detection of better molecular signatures for phenotype prediction, and the identified biomarkers may provide insights into the functional consequences of disease. This dissertation research focuses on developing advanced machine learning algorithms for mining large-scale RNA-Seq data in cancer transcriptome analysis. A platform-integrated model for transcript quantification (IntMTQ) is developed to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ provides more precise RNA-Seq-based isoform quantification, and …
Towards A Holistic And Comparative Analysis Of The Free Content Web: Security, Privacy, And Performance, Abdulrahman Alabduljabbar
Towards A Holistic And Comparative Analysis Of The Free Content Web: Security, Privacy, And Performance, Abdulrahman Alabduljabbar
Electronic Theses and Dissertations, 2020-
Free content websites that provide free books, music, games, movies, etc., have existed on the Internet for many years. While it is a common belief that such websites might be different from premium websites providing the same content types in terms of their security, a rigorous analysis that supports this belief is lacking from the literature. In particular, it is unclear if those websites are as safe as their premium counterparts. In this dissertation, we set out to investigate the similarities and differences between free content and premium websites, including their risk profiles. Moreover, we analyze and quantify through measurements …
Algorithms And Variations On The Positional Burrows-Wheeler Transform And Their Applications, Ahsan Sanaullah
Algorithms And Variations On The Positional Burrows-Wheeler Transform And Their Applications, Ahsan Sanaullah
Electronic Theses and Dissertations, 2020-
In this dissertation, we develop algorithms and variations on the Positional Burrows-Wheeler Transform (PBWT). The PBWT is a data structure that stores M binary strings of length N while allowing efficient search. We develop the dynamic-PBWT (d-PBWT). The d-PBWT is a variation of the PBWT that allows its relevant algorithms to run with unchanged time complexity, but also allows efficient insertion and deletion of haplotypes. We provide insertion and deletion algorithms on the PBWT with average case O(N) time complexity. We also improve upon the query algorithms for the PBWT. Durbin described a set maximal match query algorithm on the …
Methodologies For Evaluating Interaction Cues For Virtual Reality, Xinyu Hu
Methodologies For Evaluating Interaction Cues For Virtual Reality, Xinyu Hu
Electronic Theses and Dissertations, 2020-
Virtual reality (VR) games and educational systems commonly employ interaction cues to provide information on how to take appropriate actions at particular moments. Interaction cues can be employed for different purposes, such as informing the user to look, go, pick, and operate. Additionally, different types of interaction cues can directly affect usability and user experiences. In our early research, we conducted two ecologically valid empirical studies with a preexisting VR training application and evaluated the effects of delayed interaction cues, in addition to comparing the purposes of interaction cues for learning and retention. Our results indicated that immediate interaction cues …
Discovering Vulnerabilities And Designing Trustworthy Defenses In Iot Systems And Devices, Bryan Pearson
Discovering Vulnerabilities And Designing Trustworthy Defenses In Iot Systems And Devices, Bryan Pearson
Electronic Theses and Dissertations, 2020-
Internet of Things (IoT) dominates many functions in the modern world, from sensing and reporting temperature, humidity, and air quality, to controlling and automating homes, commercial buildings, and equipment. However, IoT systems have received scrutiny in recent years due to countless security incidents, which can have physical and even deadly consequences. This research provides a comprehensive assessment of the security of IoT systems and devices, including low-cost microcontroller (MCU) based sensors, cloud services, and Building Automation Systems (BAS). We begin by exploring the current landscape of vulnerabilities and defenses in modern IoT applications. We show that many security needs can …
Low-Resource Machine Learning Techniques For The Analysis Of Online Social Media Textual Data, Toktam Amanzadeh Oghaz
Low-Resource Machine Learning Techniques For The Analysis Of Online Social Media Textual Data, Toktam Amanzadeh Oghaz
Electronic Theses and Dissertations, 2020-
Low-resource and label-efficient machine learning methods can be described as the family of statistical and machine learning techniques that can achieve high performance without needing a substantial amount of labeled data. These methods include both unsupervised learning techniques, such as LDA, and supervised methods, such as active learning, each providing different benefits. Thus, this dissertation is devoted to the design and analysis of unsupervised and supervised techniques to provide solutions for the following problems: Unsupervised narrative summary extraction for social media content, Social media text classification with Active Learning (AL), Investigating restrictions and benefits of using Curriculum Learning (CL) for …
Torward Real-World Cross-View Image Geo-Localization, Sijie Zhu
Torward Real-World Cross-View Image Geo-Localization, Sijie Zhu
Electronic Theses and Dissertations, 2020-
Cross-view image geo-localization aims to determine the locations of street-view query images by searching in a GPS-tagged reference image database from aerial view. One fundamental challenge is the dramatic view-point/domain difference between the street-view query images and aerial-view reference images. Recent works have made great progress on bridging the domain gap with advanced deep learning techniques and geometric prior knowledge, i.e. the query is aligned at the center of one aerial-view reference image (spatial alignment) and the orientation relationship between the two views is known (orientation alignment). However, such prior knowledge of the geometry correspondence of the two views is …
Advanced Deep Learning Methodologies For Deepfake Detection, Aminollah Khormali
Advanced Deep Learning Methodologies For Deepfake Detection, Aminollah Khormali
Electronic Theses and Dissertations, 2020-
The recent advances in the field of Artificial Intelligence (AI), particularly Generative Adversarial Networks (GANs) and an abundance of training samples along with robust computational resources have significantly propelled the field of AI-generated fake information in all kinds, e.g., deepfakes. Deepfakes are among the most sinister types of misinformation, posing large-scale and severe security and privacy risks targeting critical governmental institutions and ordinary people across the world. The fact that deepfakes are AI-generated digital content and not actual events captured by a camera implies that they still can be detected using advanced AI models. Although the deepfake detection task has …
Exploring The Privacy Dimension Of Wearables Through Machine Learning-Enabled Inference, Ulku Meteriz Yildiran
Exploring The Privacy Dimension Of Wearables Through Machine Learning-Enabled Inference, Ulku Meteriz Yildiran
Electronic Theses and Dissertations, 2020-
Today's hyper-connected consumers demand convenient ways to tune into information without switching between devices, which led the industry leaders to the wearables. Wearables such as smartwatches, fitness trackers, and augmented reality (AR) glasses can be comfortably worn on the body. In addition, they offer limitless features, including activity tracking, authentication, navigation, and entertainment. Wearables that provide digestible information stimulate even higher consumer demand. However, to keep up with the ever-growing user expectations, developers keep adding new features and interaction methods to augment the use cases without considering their privacy impacts. In this dissertation, we explore the privacy dimension of wearables …
Distance Perception Through Head-Mounted Displays, Sina Masnadi
Distance Perception Through Head-Mounted Displays, Sina Masnadi
Electronic Theses and Dissertations, 2020-
It has been shown in numerous research studies that people tend to underestimate distances while wearing head-mounted displays (HMDs). We investigated various possible factors affecting the perception of distance is HMDs through multiple studies. Many contributing factors has been identified by researchers in the past decades, however, further investigation is required to provide a better understanding of this problem. In order to find a baseline for distance underestimation, we performed a study to compare the distance perception in real world versus a fake headset versus a see-through HMD. Users underestimated distances while wearing the fake headset or the see-through HMD. …
Methods For Defending Neural Networks Against Adversarial Attacks, Sharvil Shah
Methods For Defending Neural Networks Against Adversarial Attacks, Sharvil Shah
Electronic Theses and Dissertations, 2020-
Convolutional Neural Networks (CNNs) have been at the frontier of the revolution within the field of computer vision. Since the advent of AlexNet in 2012, neural networks with CNN architectures have surpassed human-level capabilities for many cognitive tasks. As the neural networks are integrated in many safety critical applications such as autonomous vehicles, it is critical that they are robust and resilient to errors. Unfortunately, it has recently been observed that deep neural network models are susceptible to adversarial perturbations which are imperceptible to human vision. In this thesis, we propose a solution to defend neural networks against white box …
Static Analysis Of The Build System To Accelerate Continuous Testing Of Highly Configurable Software, Necip Fazil Yildiran
Static Analysis Of The Build System To Accelerate Continuous Testing Of Highly Configurable Software, Necip Fazil Yildiran
Electronic Theses and Dissertations, 2020-
Continuous testing is widely used for facilitating fast and reliable software delivery. However, build-time configurability makes such testing harder for configurable software. As configurable software forms the basis of much of our computing infrastructure, there is even more need for better continuous testing for configurable software. In this dissertation, our goal is to improve the quality of configurable software. To this end, we tackle two, previously unsolved problems. The build system of configurable software is one of the biggest reasons why testing configurable software is hard. Therefore, in our solutions, we deal with the build system by using a comprehensive …
Towards Leveraging Sparse Infrared Datasets For Multiple View Synthesis, Few Shot Learning And Background Invariant Recognition, Maliha Arif
Electronic Theses and Dissertations, 2020-
This dissertation presents a study of various machine learning techniques for recognizing vehicular objects in infrared images. State of the art methods for computer vision have not been widely explored for this part of the electromagnetic spectrum (EM). Challenges that arise due to the dearth of infrared training images, terrain clutter, and thermal phenomenology have not been fully addressed. Infrared dataset collection and annotation is both difficult and expensive. What if there is a way we can generate infrared images and diminish the need for collecting data out in the field? Our first research study encompasses an encoder-decoder model that …
Algorithms For The Detection Of Resolved And Unresolved Targets In The Infrared Bands, Bruce Mcintosh
Algorithms For The Detection Of Resolved And Unresolved Targets In The Infrared Bands, Bruce Mcintosh
Electronic Theses and Dissertations, 2020-
This dissertation proposes algorithms for the detection of both resolved and unresolved targets in the infrared bands. Recent breakthroughs in deep learning have spurred major advancements in computer vision, but most of the attention and progress has been focused on RGB imagery from the visual band. The infrared bands such as Long Wave Infrared (LWIR), Medium Wave Infrared (MWIR), Short Wave Infrared (SWIR) and Near Infrared (NIR) each respond differently to physical phenomena, providing information that can be used to better understand the environment. The first task addressed is that of detecting vehicles in heavy clutter in MWIR imagery. A …
Applications For Machine Learning On Readily Available Data From Virtual Reality Training Experiences, Alec Moore
Applications For Machine Learning On Readily Available Data From Virtual Reality Training Experiences, Alec Moore
Electronic Theses and Dissertations, 2020-
The purpose of the research presented in this dissertation is to improve virtual reality (VR) training systems by enhancing their understanding of users. While the field of intelligent tutoring systems (ITS) has seen value in this approach, much research into making use of biometrics to improve user understanding and subsequently training, relies on specialized hardware. Through the presented research, I show that with machine learning (ML), the VR system itself can serve as that specialized hardware for VR training systems. I begin by discussing my explorations into using an ecologically valid, specialized training simulation as a testbed to predict knowledge …
Addressing Human-Centered Artificial Intelligence: Fair Data Generation And Classification And Analyzing Algorithmic Curation In Social Media, Amirarsalan Rajabi
Addressing Human-Centered Artificial Intelligence: Fair Data Generation And Classification And Analyzing Algorithmic Curation In Social Media, Amirarsalan Rajabi
Electronic Theses and Dissertations, 2020-
With the growing impact of artificial intelligence, the topic of fairness in AI has received increasing attention. Artificial intelligence is observed to have caused unanticipated negative consequences. In this dissertation, we address two critical aspects regarding human-centered artificial intelligence (HCAI), a new paradigm for developing artificial intelligence that is ethical, fair, and helps to improve the human condition. In the first part of this dissertation, we investigate the effect that AI curation of contents by social media platforms has on an online discussions, by studying a polarized discussion in the Twitter network. We then develop a network communication model that …