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Articles 61 - 90 of 444
Full-Text Articles in Computer Sciences
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 …
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 …
Deep Learning Anomaly Detection Using Edge Ai, William Holdren
Deep Learning Anomaly Detection Using Edge Ai, William Holdren
Electronic Theses and Dissertations, 2020-
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 …
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 …
Identifying Challenges And Opportunities For Designing Social Media Nudges For Adolescents, Oluwatomisin Obajemu
Identifying Challenges And Opportunities For Designing Social Media Nudges For Adolescents, Oluwatomisin Obajemu
Electronic Theses and Dissertations, 2020-
With the prevalence of online risks encountered by youth online, strength-based approaches such as nudges have been recommended as a potential solution to subtly guide teens toward safer decisions. However, most nudging interventions to date have not been designed to cater to teens’ unique needs and online safety concerns. To address this gap, this study aimed to better understand adolescents’ perceptions and feedback on online safety nudges to inform the design of more effective online safety interventions. We conducted 12 semi-structured interviews and 3 focus group sessions with 21 teens (13 – 17 years old) to get their feedback on …
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 …
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, …
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-
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 …
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-
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 …
Efficient Data Structures For Text Processing Applications, Paniz Abedin
Efficient Data Structures For Text Processing Applications, Paniz Abedin
Electronic Theses and Dissertations, 2020-
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. …
Quantifiability: Concurrent Correctness From First Principles, Victor Cook
Quantifiability: Concurrent Correctness From First Principles, Victor Cook
Electronic Theses and Dissertations, 2020-
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-
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 …
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-
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 …
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-
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 …
Capsule Networks For Video Understanding, Kevin Duarte
Capsule Networks For Video Understanding, Kevin Duarte
Electronic Theses and Dissertations, 2020-
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 …
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-
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 …
A Deep Learning Approach For Learning Human Gait Signature, Alexander Matasa
A Deep Learning Approach For Learning Human Gait Signature, Alexander Matasa
Electronic Theses and Dissertations, 2020-
With advancements in biometric securities, focus has increased on utilizing gait as a means of recognition. Gait describes the unique walking pattern present in humans and has shown promising results in person re-identification tasks. Unlike other biometric features, gait is unique in that it is a subconscious behavior minimizing the risk of purposeful obfuscation. In this research, we first cover supervised approaches showing that current methods fail to learn a unique signature that describes the motion of a subject. Rather they extract frame-based feature information which is then aggregated. While these methods have shown to be effective, they do not …
Secure And Trustworthy Hardware And Machine Learning Systems For Internet Of Things, Shayan Taheri
Secure And Trustworthy Hardware And Machine Learning Systems For Internet Of Things, Shayan Taheri
Electronic Theses and Dissertations, 2020-
The advancements on the Internet have enabled connecting more devices into this technology every day. This great connectivity has led to the introduction of the internet of things (IoTs) that is a great bed for engagement of all new technologies for computing devices and systems. Nowadays, the IoT devices and systems have applications in many sensitive areas including military systems. These challenges target hardware and software elements of IoT devices and systems. Integration of hardware and software elements leads to hardware systems and software systems in the IoT platforms, respectively. A recent trend for the hardware systems is making them …
Spatial-Temporal Representation Learning: Concepts, Algorithms And Applications, Pengyang Wang
Spatial-Temporal Representation Learning: Concepts, Algorithms And Applications, Pengyang Wang
Electronic Theses and Dissertations, 2020-
Recent years have witnessed the flourish of Internet-of-Things (IoT), in which sensors connect spatial entities to constitute complex Cyber-Physical Systems (CPSs). In this setting, spatial-temporal data becomes increasingly available. Mining spatial-temporal data can reveal holistic user and system structures, dynamics, and semantics of the underlying CPSs, including identifying trends, forecasting future behavior, and detecting anomalies. However, obtaining effective representations over spatial-temporal data remains a big challenge for the following reasons: (1) on the one hand, traditional manual feature design is labor-intensive and time-consuming facing the complex and huge volumes of spatial-temporal data; (2) on the other hand, as an emerging …
Visual Learning Beyond Human Curated Datasets, Muhammad Abdullah Jamal
Visual Learning Beyond Human Curated Datasets, Muhammad Abdullah Jamal
Electronic Theses and Dissertations, 2020-
The success of deep neural networks in a variety of computer vision tasks heavily relies on large- scale datasets. However, it is expensive to manually acquire labels for large datasets. Given the human annotation cost and scarcity of data, the challenge is to learn efficiently with insufficiently labeled data. In this dissertation, we propose several approaches towards data-efficient learning in the context of few-shot learning, long-tailed visual recognition, and unsupervised and semi-supervised learning. In the first part, we propose a novel paradigm of Task-Agnostic Meta- Learning (TAML) algorithms to improve few-shot learning. Furthermore, in the second part, we analyze the …
Unsupervised Meta-Learning, Siavash Khodadadeh
Unsupervised Meta-Learning, Siavash Khodadadeh
Electronic Theses and Dissertations, 2020-
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 …
Evaluating Augmented Reality Tools For Physics Education, Corey Pittman
Evaluating Augmented Reality Tools For Physics Education, Corey Pittman
Electronic Theses and Dissertations, 2020-
While we are in the midst of a renaissance of interest in augmented reality (AR), there remain a small number of application domains that have seen significant development. One domain that often benefits from additional visualization capabilities is education, specifically physics and other sciences. This paper summarizes interviews with secondary school educators about their experience with AR and their most desired features. Three prototypes were created which were used to collect usability information from students and educators about their preferences for AR applications in their physics courses. Additionally, we introduce the concept of Environmental Integration, a novel method of defining …
Contextual Understanding Of Sequential Data Across Multiple Modalities, Sangwoo Cho
Contextual Understanding Of Sequential Data Across Multiple Modalities, Sangwoo Cho
Electronic Theses and Dissertations, 2020-
In recent years, progress in computing and networking has made it possible to collect large volumes of data for various different applications in data mining and data analytics using machine learning methods. Data may come from different sources and in different shapes and forms depending on their inherent nature and the acquisition process. In this dissertation, we focus specifically on sequential data, which have been exponentially growing in recent years on platforms such as YouTube, social media, news agency sites, and other platforms. An important characteristic of sequential data is the inherent causal structure with latent patterns that can be …
Examining Everyday Literacies: An Autoethnographic Analysis Of Mundane Textualities, Kyle J. Mauter
Examining Everyday Literacies: An Autoethnographic Analysis Of Mundane Textualities, Kyle J. Mauter
Honors Undergraduate Theses
As a way of extending perspectives of writing and learning, this thesis explores everyday literacy activities and their role in function in shaping people's activities. Taking up an autoethnographic approach to studying the mundane literacies of everyday life, this thesis offers a fine-grained analysis of the processes and practices involved in two specific literate activities I have engaged in over the two years: creating a mixtape for a friend and streaming my participation in online video games. As key findings, the analysis of these everyday literate activities suggests that the interactions between people and social contexts figure prominently in the …