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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 …
Towards More Efficient Collaborative Distributed Data Analysis And Learning, Zixia Liu
Towards More Efficient Collaborative Distributed Data Analysis And Learning, Zixia Liu
Electronic Theses and Dissertations, 2020-
Modern information era gives rise to the persistent generation of large amounts of data with rapid speed and broad geographical distribution. Obtaining knowledge and understanding via analysis and learning from such data have invaluable worth. Features of such data analytical tasks commonly include: data can be large scale and geographically distributed; computing capability demand can be enormous; tasks can be time-critical; some data can be private; participants can have heterogeneous capabilities and non-IID data; and multiple simultaneously submitted data analytical tasks can be possible. These bring challenges to contemporary computing infrastructure and learning models. In view of this, we develop …
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 …
Methodology Of Augmented Reality Chinese Language Articulatory Pronunciation Practice: Game And Study Design, Daria Sinyagovskaya
Methodology Of Augmented Reality Chinese Language Articulatory Pronunciation Practice: Game And Study Design, Daria Sinyagovskaya
Electronic Theses and Dissertations, 2020-
Learning a language can be hard. Learning a language that contains tones to convey meaning is even harder. This dissertation presents a novel methodology for creating a language practice using augmented reality that has never been used before. The design of a new app in AR and non-AR versions can evaluate the same practice methodology. This methodology was applied to new software and was examined in regard to the importance of this software. Although the study results are inconclusive, progress has been made in answering research questions on the effectiveness of AR versus non-AR and the reliability of peer assessment. …
A Human-Centered Approach To Improving Adolescent Online Sexual Risk Detection Algorithms, Afsaneh Razi
A Human-Centered Approach To Improving Adolescent Online Sexual Risk Detection Algorithms, Afsaneh Razi
Electronic Theses and Dissertations, 2020-
Computational risk detection has the potential to protect especially vulnerable populations from online victimization. Conducting a comprehensive literature review on computational approaches for online sexual risk detection led to the identification that the majority of this work has focused on identifying sexual predators after-the-fact. Also, many studies rely on public datasets and third-party annotators to establish ground truth and train their algorithms, which do not accurately represent young social media users and their perspectives to prevent victimization. To address these gaps, this dissertation integrated human-centered approaches to both creating representative datasets and developing sexual risk detection machine learning models to …
Examining Cooperative System Responses Against Grid Integrity Attacks, Alexander D. Parady
Examining Cooperative System Responses Against Grid Integrity Attacks, Alexander D. Parady
Honors Undergraduate Theses
Smart grid technologies are integral to society’s transition to sustainable energy sources, but they do not come without a cost. As the energy sector shifts away from a century’s reliance on fossil fuels and centralized generation, technology that actively monitors and controls every aspect of the power infrastructure has been widely adopted, resulting in a plethora of new vulnerabilities that have already wreaked havoc on critical infrastructure. Integrity attacks that feedback false data through industrial control systems, which result in possible catastrophic overcorrections and ensuing failures, have plagued grid infrastructure over the past several years. This threat is now at …
Comparative Evaluation Of Assemblers For Metagenomic Data Analysis, Matheus Pavini Franco Ferreira
Comparative Evaluation Of Assemblers For Metagenomic Data Analysis, Matheus Pavini Franco Ferreira
Honors Undergraduate Theses
Metagenomics is a cultivation-independent approach for obtaining the genomic composition of microbial communities. Microbial communities are ubiquitous in nature. Microbes which are associated with the human body play important roles in human health and disease. These roles span from protecting us against infections from other bacteria, to being the causes of these diseases. A deeper understanding of these communities and how they function inside our bodies allows for advancements in treatments and preventions for these diseases. Recent developments in metagenomics have been driven by the emergence of Next-Generation Sequencing technologies and Third-Generation Sequencing technologies that have enabled cost-effective DNA sequencing …
Translations To Support Loop Invariant Generation In Jml, Kohei Koja
Translations To Support Loop Invariant Generation In Jml, Kohei Koja
Electronic Theses and Dissertations, 2020-
Software is used in many critical systems in the real world such as autonomous cars and medical devices. Such software must be reliable to protect the general public. One standard way to make reliable software is to use Hoare-style verification techniques. However, for Hoare-style verification of loop correctness, loop invariants are necessary but are difficult for people to write themselves. Since Java is one of the most popular programming languages in the world, it is useful to have a tool to generate loop invariants for Java programs. OpenJML is a widely used program verification tool for Java. However, it does …
Reverse Engineering Of Adversarial Samples By Leveraging Patterns Left By The Attacker, Rahul Ambati
Reverse Engineering Of Adversarial Samples By Leveraging Patterns Left By The Attacker, Rahul Ambati
Electronic Theses and Dissertations, 2020-
Intrinsic susceptibility of deep learning to adversarial examples has led to a plethora of attack techniques with a common broad objective of fooling deep models. However, we find slight compositional differences between the algorithms achieving this objective. These differences leave traces that provide important clues for attacker profiling in real-life scenarios. Inspired by this, we introduce a novel problem of 'Reverse Engineering of aDversarial attacks' (RED). Given an adversarial example, the objective of RED is to identify the attack used to generate it. Under this perspective, we can systematically group existing attacks into different families, leading to the sub-problem of …
Multivariate Cognitive Walkthrough Of Qubitvr: An Educational Quantum Computing, Virtual Reality Application, Pauline Johnson
Multivariate Cognitive Walkthrough Of Qubitvr: An Educational Quantum Computing, Virtual Reality Application, Pauline Johnson
Electronic Theses and Dissertations, 2020-
Quantum computing is a promising field but understanding how it works can be challenging for a beginner. There are also not many educational resources to visualize and learn about quantum computing. To advance knowledge in this area, we have created QubitVR, which employs a Bloch sphere representation of a qubit, and supports trajectory visualizations and state equations in a virtual reality (VR) setting. We also conducted a multivariate cognitive walkthrough with subject matter experts (SMEs) on QubitVR to assess the effectiveness of trajectory visualizations and state equations in learning about quantum gates. The results were that trajectory visualizations aided users …
Towards Automated Data Mining: Reinforcement Intelligence For Self-Optimizing Feature Engineering, Kunpeng Liu
Towards Automated Data Mining: Reinforcement Intelligence For Self-Optimizing Feature Engineering, Kunpeng Liu
Electronic Theses and Dissertations, 2020-
Feature engineering is one of the most important components in data mining and machine learning. One of the key thrusts in data mining is to answer: How should a low-dimensional geometry structure be extracted and reconstructed from high-dimensional data? To solve this issue, researchers proposed feature selection, PCA, sparsity regularization, factorization, embedding, and deep learning. However, existing techniques are limited in achieving full automation, globally optimal, and explainable explicitness. Can I address the automation, optimal, and explainability challenges in data geometry reconstruction? A low-dimensional data geometry structure is crucial for SciML methods (e.g., GP models), and the accuracy of these …
Visual Question Answering: Exploring Trade-Offs Between Task Accuracy And Explainability, Aisha Urooj
Visual Question Answering: Exploring Trade-Offs Between Task Accuracy And Explainability, Aisha Urooj
Electronic Theses and Dissertations, 2020-
Given visual input and a natural language question about it, the visual question answering (VQA) task is to answer the question correctly. To improve a system's reliability and trustworthiness, it is imperative that it links the text (question and answer) to specific visual regions. This dissertation first explores the VQA task in a multi-modal setting where questions are based on video as well as subtitles. An algorithm is introduced to process each modality and their features are fused to solve the task. Additionally, to understand the model's emphasis on visual data, this study collects a diagnostic set of questions which …
How Adolescents In The Child Welfare System Seek Support For Their Sexual Risk Experiences Online, Taylor L. Moraguez
How Adolescents In The Child Welfare System Seek Support For Their Sexual Risk Experiences Online, Taylor L. Moraguez
Honors Undergraduate Theses
Youth in the foster care system experience unique and challenging situations online, such as higher risks of inappropriate messaging (e.g., sexting) and unwanted solicitations from strangers. As a vulnerable group of adolescents, foster youth often use online platforms as a resource to express themselves and seek support over their sexual experiences online. This thesis analyzes how foster youth seek support online for their sexual risk experiences, including sexual abuse, sexting, and sexuality. To understand how adolescents (ages 13-17) in the child welfare system seek support for these experiences, we conducted a thematic analysis of 541 individual posts made by 121 …
Genetic Algorighm Representation Selection Impact On Binary Classification Problems, Stephen V. Maldonado
Genetic Algorighm Representation Selection Impact On Binary Classification Problems, Stephen V. Maldonado
Honors Undergraduate Theses
In this thesis, we explore the impact of problem representation on the ability for the genetic algorithms (GA) to evolve a binary prediction model to predict whether a physical therapist is paid above or below the median amount from Medicare. We explore three different problem representations, the vector GA (VGA), the binary GA (BGA), and the proportional GA (PGA). We find that all three representations can produce models with high accuracy and low loss that are better than Scikit-Learn’s logistic regression model and that all three representations select the same features; however, the PGA representation tends to create lower weights …
Computational Methods To Analyze Next-Generation Sequencing Data In Genomics And Metagenomics, Saidi Wang
Computational Methods To Analyze Next-Generation Sequencing Data In Genomics And Metagenomics, Saidi Wang
Electronic Theses and Dissertations, 2020-
This thesis focuses on two important computational problems in genomics and metagenomics with the public available next-generation sequencing data. One is about gene regulation, for which we explore how distal regulatory elements may interact with the proximal regulatory elements. The other is about metagenomics, in which we study how to reconstruct bacterial strain genomes from shotgun reads. Studying gene regulation, especially distal gene regulation, is important because regulatory elements, including those in distal regulatory regions, orchestrate when, where and how much a gene is activated under every experimental condition. Their dysfunction results in various types of diseases. Moreover, the current …
Leveraging Human-Centered Insights And Vision Transformers To Understand And Detect Online Risks In Teens' Private Social Media Conversations On Instagram, Joshua Gracie
Electronic Theses and Dissertations, 2020-
With the growing ubiquity of the internet and internet enabled devices, the issue of online risk and the need for safety measures against such risks has grown paramount. Many researchers have analyzed the scope and characteristics of online risk, especially with respect to demographics, yet few have studied the risky media itself. This work sets out to move the conversation from surveys and interviews to content analysis and automation through a comprehensive thematic analysis of online multi-media risks (images and videos) sent to and from teens in private messages on Instagram. These messages, along with demographic information such as age …
Balancing User Experience For Mobile One-To-One Interpersonal Telepresence, Kevin Pfeil
Balancing User Experience For Mobile One-To-One Interpersonal Telepresence, Kevin Pfeil
Electronic Theses and Dissertations, 2020-
The COVID-19 virus disrupted all aspects of our daily lives, and though the world is finally returning to normalcy, the pandemic has shown us how ill-prepared we are to support social interactions when expected to remain socially distant. Family members missed major life events of their loved ones; face-to-face interactions were replaced with video chat; and the technologies used to facilitate interim social interactions caused an increase in depression, stress, and burn-out. It is clear that we need better solutions to address these issues, and one avenue showing promise is that of Interpersonal Telepresence. Interpersonal Telepresence is an interaction paradigm …
Effficient Graph-Based Computation And Analytics, Bingbing Rao
Effficient Graph-Based Computation And Analytics, Bingbing Rao
Electronic Theses and Dissertations, 2020-
With data explosion in many domains, such as social media, big code repository, Internet of Things (IoT), and inertial sensors, only 32% of data available to academic and industry is put to work, and the remaining 68% goes unleveraged. Moreover, people are facing an increasing number of obstacles concerning complex analytics on the sheer size of data, which include 1) how to perform dynamic graph analytics in a parallel and robust manner within a reasonable time? 2) How to conduct performance optimizations on a property graph representing and consisting of the semantics of code, data, and runtime systems for big …
Authoring Tools For Augmented Reality Scenario Based Training Experiences, Andres Vargas Gonzalez
Authoring Tools For Augmented Reality Scenario Based Training Experiences, Andres Vargas Gonzalez
Electronic Theses and Dissertations, 2020-
Augmented Reality's (AR) scope and capabilities have grown considerably in the last few years. AR applications can be run across devices such as phones, wearables, and head-mounted displays (HMDs). The increasing research and commercial efforts in HMDs capabilities allow end users to map a 3D environment and interact with virtual objects that can respond to the physical aspects of the scene. Within this context, AR is an ideal format for in-situ training scenarios. However, building such AR scenarios requires proficiency in game engine development environments and programming expertise. These difficulties can make it challenging for domain experts to create training …
Studying The Robustness Of Machine Learning-Based Malware Detection Models: Analysis, Design, And Implementation, Ahmed Abusnaina
Studying The Robustness Of Machine Learning-Based Malware Detection Models: Analysis, Design, And Implementation, Ahmed Abusnaina
Electronic Theses and Dissertations, 2020-
With the rise of the popularity of machine learning (ML), it has been shown that ML-based classifiers are susceptible to adversarial examples and concept drifting, where a small modification in the input space may result in misclassification. The ever-evolving nature of the data, the behavioral and pattern shifting over time not only lessened the trust in the machine learning output but also created a barrier for its usage in critical applications. This dissertation builds toward analyzing machine learning-based malware detection systems, including the detection and mitigation of adversarial malware examples. In particular, we first introduce two black-box adversarial attacks on …
Quantum Graph Parameters, Parisa Darbari Kozekanan
Quantum Graph Parameters, Parisa Darbari Kozekanan
Electronic Theses and Dissertations, 2020-
This dissertation considers some of the advantages, and limits, of applying quantum computing to solve two important graph problems. The first is estimating a graph's quantum chromatic number. The quantum chromatic number is the minimum number of colors necessary in a two-player game where the players cannot communicate but share an entangled state and must convince a referee with probability one that they have a proper vertex coloring. We establish several spectral lower bounds for the quantum chromatic number. These lower bounds extend the well-known Hoffman lower bound for the classical chromatic number. The second is the Pattern Matching on …
Towards Secure And Trustworthy Iot Systems, Lan Luo
Towards Secure And Trustworthy Iot Systems, Lan Luo
Electronic Theses and Dissertations, 2020-
The boom of the Internet of Things (IoT) brings great convenience to the society by connecting the physical world to the cyber world, but it also attracts mischievous hackers for benefits. Therefore, understanding potential attacks aiming at IoT systems and devising new protection mechanisms are of great significance to maintain the security and privacy of the IoT ecosystem. In this dissertation, we first demonstrate potential threats against IoT networks and their severe consequences via analyzing a real-world air quality monitoring system. By exploiting the discovered flaws, we can impersonate any victim sensor device and polluting its data with fabricated data. …
Load Forecasting And Synthetic Data Generation For Smart Home Energy Management System, Mina Razghandi
Load Forecasting And Synthetic Data Generation For Smart Home Energy Management System, Mina Razghandi
Electronic Theses and Dissertations, 2020-
A number of recent trends, such as the increased power consumption in developed and developing countries, the dangers associated with greenhouse gases, the potential shortages of fossil fuels, and the increasing availability of solar and wind energy act as motivating factors for the development of more intelligent and efficient systems both on the power provider as well as the consumer side. One of the most important prerequisites for making efficient energy management decisions is the ability to predict energy production and consumption patterns. While long-term forecasting of average consumption had been extensively used to direct investments in the energy grid, …