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Articles 91 - 120 of 490
Full-Text Articles in Computer Sciences
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 …
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, 2020-2023
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 …
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, 2020-2023
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 …
Towards Leveraging Sparse Infrared Datasets For Multiple View Synthesis, Few Shot Learning And Background Invariant Recognition, Maliha Arif
Electronic Theses and Dissertations, 2020-2023
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 …
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, 2020-2023
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 …
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, 2020-2023
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 …
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 …
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, 2020-2023
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 …
Translations To Support Loop Invariant Generation In Jml, Kohei Koja
Translations To Support Loop Invariant Generation In Jml, Kohei Koja
Electronic Theses and Dissertations, 2020-2023
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 …
Effficient Graph-Based Computation And Analytics, Bingbing Rao
Effficient Graph-Based Computation And Analytics, Bingbing Rao
Electronic Theses and Dissertations, 2020-2023
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 …
Towards Secure And Trustworthy Iot Systems, Lan Luo
Towards Secure And Trustworthy Iot Systems, Lan Luo
Electronic Theses and Dissertations, 2020-2023
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. …
Deep Learning Anomaly Detection Using Edge Ai, William Holdren
Deep Learning Anomaly Detection Using Edge Ai, William Holdren
Electronic Theses and Dissertations, 2020-2023
Deep learning anomaly detection is an evolving field with many real-world applications. As more and more devices continue to be added to the Internet of Things (IoT), there is an increasing desire to make use of the additional computational capacity to run demanding tasks. The increase in devices and amounts of data flooding in have led to a greater need for security and outlier detection. Motivated by those facts, this thesis studies the potential of creating a distributed anomaly detection framework. While there have been vast amounts of research into deep anomaly detection, there has been no research into building …
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, 2020-2023
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 …
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, 2020-2023
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 …
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, 2020-2023
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 …
Quantifiability: Concurrent Correctness From First Principles, Victor Cook
Quantifiability: Concurrent Correctness From First Principles, Victor Cook
Electronic Theses and Dissertations, 2020-2023
Architectural imperatives due to the slowing of Moore's Law, the broad acceptance of relaxed semantics and the O(n!) worst case verification complexity of sequential histories motivate a new approach to concurrent correctness. Desiderata for a new correctness condition are that it be independent of sequential histories, compositional over objects, flexible as to timing, modular as to semantics and free of inherent locking or waiting. This dissertation proposes Quantifiability, a novel correctness condition based on intuitive first principles. Quantifiablity is formally defined with its system model. Useful properties of quantifiability such as compositionality, measurablility and observational refinement are demonstrated. Quantifiability models …
Human Behavior In Domestic Environments: Prediction And Applications, Sharare Zehtabian
Human Behavior In Domestic Environments: Prediction And Applications, Sharare Zehtabian
Electronic Theses and Dissertations, 2020-2023
A longstanding goal of human behavior science is to model and predict how humans interact with each other or with other systems. Such models are beneficial and have many applications, including designing and implementing assistive technologies, improving users' experiences and quality of life and making better decisions to create public policies. Behavior is highly complex due to uncertainties and a lack of scientific tools to measure it. Hence prediction of human behavior cannot be 100% accurate. However, prediction is also not hopeless because the biological needs, as well as cultural conventions (for instance, regarding meal times) set the general patterns …
Efficient Data Structures For Text Processing Applications, Paniz Abedin
Efficient Data Structures For Text Processing Applications, Paniz Abedin
Electronic Theses and Dissertations, 2020-2023
This thesis is devoted to designing and analyzing efficient text indexing data structures and associated algorithms for processing text data. The general problem is to preprocess a given text or a collection of texts into a space-efficient index to quickly answer various queries on this data. Basic queries such as counting/reporting a given pattern's occurrences as substrings of the original text are useful in modeling critical bioinformatics applications. This line of research has witnessed many breakthroughs, such as the suffix trees, suffix arrays, FM-index, etc. In this work, we revisit the following problems: 1. The Heaviest Induced Ancestors problem 2. …
Studying Users Interactions And Behavior In Social Media Using Natural Language Processing, Sultan Alshamrani
Studying Users Interactions And Behavior In Social Media Using Natural Language Processing, Sultan Alshamrani
Electronic Theses and Dissertations, 2020-2023
Social media platforms have been growing at a rapid pace, attracting users' engagement with the online content due to their convenience facilitated by many useful features. Such platforms provide users with interactive options such as likes, dislikes as well as a way of expressing their opinions in the form of text (i.e., comments). As more people engage in different social media platforms, such platforms will increase in both size and importance. This growth in social media data is becoming a vital new area for scholars and researchers to explore this new form of communication. The huge data from social media …
Machine Learning Techniques For Topic Detection And Authorship Attribution In Textual Data, Fereshteh Jafariakinabad
Machine Learning Techniques For Topic Detection And Authorship Attribution In Textual Data, Fereshteh Jafariakinabad
Electronic Theses and Dissertations, 2020-2023
The unprecedented expansion of user-generated content in recent years demands more attempts of information filtering in order to extract high-quality information from the huge amount of available data. In this dissertation, we begin with a focus on topic detection from microblog streams, which is the first step toward monitoring and summarizing social data. Then we shift our focus to the authorship attribution task, which is a sub-area of computational stylometry. It is worth mentioning that determining the style of a document is orthogonal to determining its topic, since the document features which capture the style are mainly independent of its …
Capsule Networks For Video Understanding, Kevin Duarte
Capsule Networks For Video Understanding, Kevin Duarte
Electronic Theses and Dissertations, 2020-2023
With the increase of videos available online, it is more important than ever to learn how to process and understand video data. Although convolutional neural networks have revolutionized the representation learning from images and videos, they do not explicitly model entities within the given input. It would be useful for learned models to be able to represent part-to-whole relationships within a given image or video. To this end, a novel neural network architecture - capsule networks - has been proposed. Capsule networks add extra structure to allow for the modeling of entities and has shown great promise when applied to …
Directional Spectral Solar Energy For Building Performance: From Simulation To Cyber-Physical Prototype, Joseph Del Rocco
Directional Spectral Solar Energy For Building Performance: From Simulation To Cyber-Physical Prototype, Joseph Del Rocco
Electronic Theses and Dissertations, 2020-2023
The original research and development in this dissertation contributes to the field of building performance by actively harnessing a wider spectrum of directional solar radiation for use in buildings. Solar radiation (energy) is often grouped by wavelength measurement into the spectra ultraviolet (UV), visible (light), and short and long-wave infrared (heat) on the electromagnetic spectrum. While some of this energy is directly absorbed or deflected by our atmosphere, most of it passes through, scatters about, and collides with our planet. Modern building performance simulations, tools, and control systems often oversimplify this energy into scalar values for light and heat, when …
The Social And Behavioral Influences Of Interactions With Virtual Dogs As Embodied Agents In Augmented And Virtual Reality, Nahal Norouzi
The Social And Behavioral Influences Of Interactions With Virtual Dogs As Embodied Agents In Augmented And Virtual Reality, Nahal Norouzi
Electronic Theses and Dissertations, 2020-2023
Intelligent virtual agents (IVAs) have been researched for years and recently many of these IVAs have become commercialized and widely used by many individuals as intelligent personal assistants. The majority of these IVAs are anthropomorphic, and many are developed to resemble real humans entirely. However, real humans do not interact only with other humans in the real world, and many benefit from interactions with non-human entities. A prime example is human interactions with animals, such as dogs. Humans and dogs share a historical bond that goes back thousands of years. In the past 30 years, there has been a great …
Human-Computer Interaction Research And Education--Crossing Boundaries Between Academic Research And Industry Practices, Yasushi Akiyama
Human-Computer Interaction Research And Education--Crossing Boundaries Between Academic Research And Industry Practices, Yasushi Akiyama
Interface: The C3 Lab Knowledge Commons
In this paper, I will discuss my own experience and approaches to enhancing students' learning in Human-Computer Interaction (HCI) classes by adopting an interdisciplinary approach that integrates academic research and industry practice. Due to the inherent interdisciplinarity of HCI, and to foster a set of "soft" skills known in Interdisciplinary Studies as the "cognitive toolkit," I invite expertise from the other departments at my university as well as industry professionals to give lectures, facilitate workshops, and oversee projects. These collaborations have produced several insights and have had a positive impact on the participants.
Unsupervised Meta-Learning, Siavash Khodadadeh
Unsupervised Meta-Learning, Siavash Khodadadeh
Electronic Theses and Dissertations, 2020-2023
Deep learning has achieved classification performance matching or exceeding the human one, as long as plentiful labeled training samples are available. However, the performance on few-shot learning, where the classifier had seen only several or possibly only one sample of the class is still significantly below human performance. Recently, a type of algorithm called meta-learning achieved impressive performance for few-shot learning. However, meta-learning requires a large dataset of labeled tasks closely related to the test task. The work described in this dissertation outlines techniques that significantly reduce the need for expensive and scarce labeled data in the meta-learning phase. Our …
Fpga-Augmented Secure Crash-Consistent Non-Volatile Memory, Yu Zou
Fpga-Augmented Secure Crash-Consistent Non-Volatile Memory, Yu Zou
Electronic Theses and Dissertations, 2020-2023
Emerging byte-addressable Non-Volatile Memory (NVM) technology, although promising superior memory density and ultra-low energy consumption, poses unique challenges to achieving persistent data privacy and computing security, both of which are critically important to the embedded and IoT applications. Specifically, to successfully restore NVMs to their working states after unexpected system crashes or power failure, maintaining and recovering all the necessary security-related metadata can severely increase memory traffic, degrade runtime performance, exacerbate write endurance problem, and demand costly hardware changes to off-the-shelf processors. In this thesis, we summarize and expand upon two of our innovative works, ARES and HERMES, to design …
Self-Governed Iot Networks: A Blockchain-Based Framework, Mehrdad Salimitari
Self-Governed Iot Networks: A Blockchain-Based Framework, Mehrdad Salimitari
Electronic Theses and Dissertations, 2020-2023
In the modern-day, IoT-enabled devices and sensors are growingly deployed in a variety of applications in consumer, commercial, industrial, and infrastructure spaces. Thus, decentralization of such networks for reducing the maintenance and repair costs is significantly gaining attention. In this dissertation, we investigate a variety of mathematical tools and technological advancements to design a secure, fully decentralized, and self-governed IoT network. On this path, the main challenge is ensuring the data integrity in IoT networks, securing the devices against a variety of attack scenarios, and preserving the privacy of the users. We begin by proposing a prospect theoretic method for …
Algorithms And Lower Bounds For Ordering Problems On Strings, Daniel Gibney
Algorithms And Lower Bounds For Ordering Problems On Strings, Daniel Gibney
Electronic Theses and Dissertations, 2020-2023
This dissertation presents novel algorithms and conditional lower bounds for a collection of string and text-compression-related problems. These results are unified under the theme of ordering constraint satisfaction. Utilizing the connections to ordering constraint satisfaction, we provide hardness results and algorithms for the following: recognizing a type of labeled graph amenable to text-indexing known as Wheeler graphs, minimizing the number of maximal unary substrings occurring in the Burrows-Wheeler Transformation of a text, minimizing the number of factors occurring in the Lyndon factorization of a text, and finding an optimal reference string for relative Lempel-Ziv encoding.
Binary State Distance Vector Routing: A Protocol For Near-Unicast Forwarding In Partitioned Networks, Ammar Farooq
Binary State Distance Vector Routing: A Protocol For Near-Unicast Forwarding In Partitioned Networks, Ammar Farooq
Electronic Theses and Dissertations, 2020-2023
Ad-hoc networks are highly dynamic and can be disconnected/partitioned during their operations, particularly in delay-tolerant networks (DTNs) where infrastructure support is not entirely available. Even after several decades of research on DTN routing, there is still a need for routing protocols that operate effectively in network environments where disconnections, delays, and resource scarcity are common. Traditionally, DTN routing protocols use an epidemic routing strategy, where multiple copies of packets get forwarded to increase network reachability. However, these flooding-based strategies are seldom suitable in resource-constrained network settings. Other prominent DTN routing designs use distance vectors (DVs) that summarize global network reachability …
Improving Matching And Classification Through Deep Learning Of Structure And Varying Illumination, Sarah Braeger
Improving Matching And Classification Through Deep Learning Of Structure And Varying Illumination, Sarah Braeger
Electronic Theses and Dissertations, 2020-2023
Convolutional networks have driven major advances in computer vision in recent years. The design of deep architectures, loss functions, and the curation of large, diverse datasets have furthered progress in many applied computer vision tasks. How data is represented to a network guides feature discovery and must be carefully considered in order to maximize performance on any applied task. We introduce novel input representations and associated architectural techniques to better utilize them such as complementary loss terms and network structure. We demonstrate the impact of these approaches on classification and matching tasks which involve shape and varied illumination. We show …