Physical Sciences and Mathematics Commons^™

Data To Science With Ai And Human-In-The-Loop, Gustavo Perez Sarabia

Doctoral Dissertations

AI has the potential to accelerate scientific discovery by enabling scientists to analyze vast datasets more efficiently than traditional methods. For example, this thesis considers the detection of star clusters in high-resolution images of galaxies taken from space telescopes, as well as studying bird migration from RADAR images. In these applications, the goal is to make measurements to answer scientific questions, such as how the star formation rate is affected by mass, or how the phenology of bird migration is influenced by climate change. However, current computer vision systems are far from perfect for conducting these measurements directly. They may …

Enabling Daily Tracking Of Individual’S Cognitive State With Eyewear, Soha Rostaminia

Doctoral Dissertations

Research studies show that sleep deprivation causes severe fatigue, impairs attention and decision making, and affects our emotional interpretation of events, which makes it a big threat to public safety, and mental and physical well-being. Hence, it would be most desired if we could continuously measure one’s drowsiness and fatigue level, their emotion while making decisions, and assess their sleep quality in order to provide personalized feedback or actionable behavioral suggestions to modulate sleep pattern and alertness levels with the aim of enhancing performance, well-being, and quality of life. While there have been decades of studies on wearable devices, we …

Unobtrusive Assessment Of Upper-Limb Motor Impairment Using Wearable Inertial Sensors, Brandon R. Oubre

Doctoral Dissertations

Many neurological diseases cause motor impairments that limit autonomy and reduce health-related quality of life. Upper-limb motor impairments, in particular, significantly hamper the performance of essential activities of daily living, such as eating, bathing, and changing clothing. Assessment of impairment is necessary for tracking disease progression, measuring the efficacy of interventions, and informing clinical decision making. Impairment is currently assessed by trained clinicians using semi-quantitative rating scales that are limited by their reliance on subjective, visual assessments. Furthermore, existing scales are often burdensome to administer and do not capture patients' motor performance in home and community settings, resulting in a …

Communicative Information Visualizations: How To Make Data More Understandable By The General Public, Alyxander Burns

Doctoral Dissertations

Although data visualizations have been around for centuries and are encountered frequently by the general public, existing evidence suggests that a significant portion of people have difficulty understanding and interpreting them. It might not seem like a big problem when a reader misreads a weather map and finds themselves without an umbrella in a rainstorm, but for those who lack the means, experience, or ability to make sense of data, misreading a data visualization concerning public health and safety could be a matter of life and death. However, figuring out how to make visualizations truly usable for a diverse audience …

Measuring Network Interference And Mitigating It With Dns Encryption, Seyed Arian Akhavan Niaki

Doctoral Dissertations

The Internet has emerged as one of the most important tools of communication. With around 4.5 billion active users as of July 2020, it provides people the opportunity to access a vast treasure trove of information and express their opinions online. How- ever, some countries consider the Internet as a critical communication medium and attempt to deploy network interference strategies. National governments, in particular, are notorious for their attempts to impose restrictions on online communication. Further, certain Internet service providers (ISPs) have been known to throttle specific applications and violate net neutrality principles. Alongside the proliferation of network interference and …

Control And Calibration Strategies For Quantum Simulation, Paul M. Kairys

Doctoral Dissertations

The modeling and prediction of quantum mechanical phenomena is key to the continued development of chemical, material, and information sciences. However, classical computers are fundamentally limited in their ability to model most quantum effects. An alternative route is through quantum simulation, where a programmable quantum device is used to emulate the phenomena of an otherwise distinct physical system. Unfortunately, there are a number of challenges preventing the widespread application of quantum simulation arising from the imperfect construction and operation of quantum simulators. Mitigating or eliminating deleterious effects is critical for using quantum simulation for scientific discovery. This dissertation develops strategies …

Human Mobility Monitoring Using Wifi: Analysis, Modeling, And Applications, Amee Trivedi

Doctoral Dissertations

Understanding and modeling humans and device mobility has fundamental importance in mobile computing, with implications ranging from network design and location-aware technologies to urban infrastructure planning. Today's users carry a plethora of devices such as smartphones, laptops, tablets, and smartwatches, with each device offering a different set of services resulting in different usage and mobility leading to the research question of understanding and modeling multiple user device trajectories. Additionally, prior research on mobility focuses on outdoor mobility when it is known that users spend 80% of their time indoors resulting in wide gaps in knowledge in the area of indoor …

An Analysis Of Modern Password Manager Security And Usage On Desktop And Mobile Devices, Timothy Oesch

Doctoral Dissertations

Security experts recommend password managers to help users generate, store, and enter strong, unique passwords. Prior research confirms that managers do help users move towards these objectives, but it also identified usability and security issues that had the potential to leak user data or prevent users from making full use of their manager. In this dissertation, I set out to measure to what extent modern managers have addressed these security issues on both desktop and mobile environments. Additionally, I have interviewed individuals to understand their password management behavior.

I begin my analysis by conducting the first security evaluation of the …

Characterization And Benchmarking Of Quantum Computers, Megan L. Dahlhauser

Doctoral Dissertations

Quantum computers are a promising technology expected to provide substantial speedups to important computational problems, but modern quantum devices are imperfect and prone to noise. In order to program and debug quantum computers as well as monitor progress towards more advanced devices, we must characterize their dynamics and benchmark their performance. Characterization methods vary in measured quantities and computational requirements, and their accuracy in describing arbitrary quantum devices in an arbitrary context is not guaranteed. The leading techniques for characterization are based on fine-grain physical models that are typically accurate but computationally expensive. This raises the question of how to …

System Design For Digital Experimentation And Explanation Generation, Emma Tosch

Doctoral Dissertations

Experimentation increasingly drives everyday decisions in modern life, as it is considered by some to be the gold standard for determining cause and effect within any system. Digital experiments have expanded the scope and frequency of experiments, which can range in complexity from classic A/B tests to contextual bandits experiments, which share features with reinforcement learning. Although there exists a large body of prior work on estimating treatment effects using experiments, this prior work did not anticipate the new challenges and opportu- nities introduced by digital experimentation. Novel errors and threats to validity arise at the intersection of software and …

Exploration Of Mid To Late Paleozoic Tectonics Along The Cincinnati Arch Using Gis And Python To Automate Geologic Data Extraction From Disparate Sources, Kenneth Steven Boling

Doctoral Dissertations

Structure contour maps are one of the most common methods of visualizing geologic horizons as three-dimensional surfaces. In addition to their practical applications in the oil and gas and mining industries, these maps can be used to evaluate the relationships of different geologic units in order to unravel the tectonic history of an area. The construction of high-resolution regional structure contour maps of a particular geologic horizon requires a significant volume of data that must be compiled from all available surface and subsurface sources. Processing these data using conventional methods and even basic GIS tools can be tedious and very …

Tools For Tutoring Theoretical Computer Science Topics, Mark Mccartin-Lim

Doctoral Dissertations

This thesis introduces COMPLEXITY TUTOR, a tutoring system to assist in learning abstract proof-based topics, which has been specifically targeted towards the population of computer science students studying theoretical computer science. Existing literature has shown tremendous educational benefits produced by active learning techniques, student-centered pedagogy, gamification and intelligent tutoring systems. However, previously, there had been almost no research on adapting these ideas to the domain of theoretical computer science. As a population, computer science students receive immediate feedback from compilers and debuggers, but receive no similar level of guidance for theoretical coursework. One hypothesis of this thesis is that immediate …

Software-Defined Infrastructure For Iot-Based Energy Systems, Stephen Lee

Doctoral Dissertations

Internet of Things (IoT) devices are becoming an essential part of our everyday lives. These physical devices are connected to the internet and can measure or control the environment around us. Further, IoT devices are increasingly being used to monitor buildings, farms, health, and transportation. As these connected devices become more pervasive, these devices will generate vast amounts of data that can be used to gain insights and build intelligence into the system. At the same time, large-scale deployment of these devices will raise new challenges in efficiently managing and controlling them. In this thesis, I argue that the IoT …

Function And Dissipation In Finite State Automata - From Computing To Intelligence And Back, Natesh Ganesh

Doctoral Dissertations

Society has benefited from the technological revolution and the tremendous growth in computing powered by Moore's law. However, we are fast approaching the ultimate physical limits in terms of both device sizes and the associated energy dissipation. It is important to characterize these limits in a physically grounded and implementation-agnostic manner, in order to capture the fundamental energy dissipation costs associated with performing computing operations with classical information in nano-scale quantum systems. It is also necessary to identify and understand the effect of quantum in-distinguishability, noise, and device variability on these dissipation limits. Identifying these parameters is crucial to designing …

Multidimensional Feature Engineering For Post-Translational Modification Prediction Problems, Norman Mapes Jr.

Doctoral Dissertations

Protein sequence data has been produced at an astounding speed. This creates an opportunity to characterize these proteins for the treatment of illness. A crucial characterization of proteins is their post translational modifications (PTM). There are 20 amino acids coded by DNA after coding (translation) nearly every protein is modified at an amino acid level. We focus on three specific PTMs. First is the bonding formed between two cysteine amino acids, thus introducing a loop to the straight chain of a protein. Second, we predict which cysteines can generally be modified (oxidized). Finally, we predict which lysine amino acids are …

Leveraging Eye Structure And Motion To Build A Low-Power Wearable Gaze Tracking System, Addison Mayberry

Doctoral Dissertations

Clinical studies have shown that features of a person's eyes can function as an effective proxy for cognitive state and neurological function. Technological advances in recent decades have allowed us to deepen this understanding and discover that the actions of the eyes are in fact very tightly coupled to the operation of the brain. Researchers have used camera-based eye monitoring technology to exploit this connection and analyze mental state across across many different metrics of interest. These range from simple things like attention and scene processing, to impairments such as a fatigue or substance use, and even significant mental disorders …

Spatiotemporal Subspace Feature Tracking By Mining Discriminatory Characteristics, Richard D. Appiah

Doctoral Dissertations

Recent advancements in data collection technologies have made it possible to collect heterogeneous data at complex levels of abstraction, and at an alarming pace and volume. Data mining, and most recently data science seek to discover hidden patterns and insights from these data by employing a variety of knowledge discovery techniques. At the core of these techniques is the selection and use of features, variables or properties upon which the data were acquired to facilitate effective data modeling. Selecting relevant features in data modeling is critical to ensure an overall model accuracy and optimal predictive performance of future effects. The …

Stochastic Network Design: Models And Scalable Algorithms, Xiaojian Wu

Doctoral Dissertations

Many natural and social phenomena occur in networks. Examples include the spread of information, ideas, and opinions through a social network, the propagation of an infectious disease among people, and the spread of species within an interconnected habitat network. The ability to modify a phenomenon towards some desired outcomes has widely recognized benefits to our society and the economy. The outcome of a phenomenon is largely determined by the topology or properties of its underlying network. A decision maker can take management actions to modify a network and, therefore, change the outcome of the phenomenon. A management action is an …

Learning From Pairwise Proximity Data, Hamid Dadkhahi

Doctoral Dissertations

In many areas of machine learning, the characterization of the input data is given by a form of proximity measure between data points. Examples of such representations are pairwise differences, pairwise distances, and pairwise comparisons. In this work, we investigate different learning problems on data represented in terms of such pairwise proximities. More specifically, we consider three problems: masking (feature selection) for dimensionality reduction, extension of the dimensionality reduction for time series, and online collaborative filtering. For each of these problems, we start with a form of pairwise proximity which is relevant in the problem at hand. We evaluate the …

Anxiolytic Effects Of Propranolol And Diphenoxylate On Mice And Automated Stretch-Attend Posture Analysis, Kevin Scott Holly

Doctoral Dissertations

The prevention of social anxiety, performance anxiety, and social phobia via the combination of two generic drugs, diphenoxylate HC1 (opioid) plus atropine sulfate (anticholinergic) and propranolol HCl (beta blocker) was evaluated in mice through behavioral studies. A patent published on a September 8, 2011 by Benjamin D. Holly, US 2011/0218215 Al, prompted the research. The drug combination of diphenoxylate and atropine plus propranolol could be an immediate treatment for patients suffering from acute phobic and social anxiety disorders. Demonstrating the anxiolytic effects of the treatment on mice would validate a mouse model for neuroscientist to be used to detect the …

Quantitative Metrics For Comparison Of Hyper-Dimensional Lsa Spaces For Semantic Differences, John Christopher Martin

Doctoral Dissertations

Latent Semantic Analysis (LSA) is a mathematically based machine learning technology that has demonstrated success in numerous applications in text analytics and natural language processing. The construction of a large hyper-dimensional space, a LSA space, is central to the functioning of this technique, serving to define the relationships between the information items being processed. This hyper-dimensional space serves as a semantic mapping system that represents learned meaning derived from the input content. The meaning represented in an LSA space, and therefore the mappings that are generated and the quality of the results obtained from using the space, is completely dependent …

Sensitivity Of Mixed Models To Computational Algorithms Of Time Series Data, Gunaime Nevine

Doctoral Dissertations

Statistical analysis is influenced by implementation of the algorithms used to execute the computations associated with various statistical techniques. Over many years; very important criteria for model comparison has been studied and examined, and two algorithms on a single dataset have been performed numerous times. The goal of this research is not comparing two or more models on one dataset, but comparing models with numerical algorithms that have been used to solve them on the same dataset.

In this research, different models have been broadly applied in modeling and their contrasting which are affected by the numerical algorithms in different …

Epistemological Databases For Probabilistic Knowledge Base Construction, Michael Louis Wick

Doctoral Dissertations

Knowledge bases (KB) facilitate real world decision making by providing access to structured relational information that enables pattern discovery and semantic queries. Although there is a large amount of data available for populating a KB; the data must first be gathered and assembled. Traditionally, this integration is performed automatically by storing the output of an information extraction pipeline directly into a database as if this prediction were the ``truth.'' However, the resulting KB is often not reliable because (a) errors accumulate in the integration pipeline, and (b) they persist in the KB even after new information arrives that could rectify …

Interactive Feature Selection And Visualization For Large Observational Data, Jingyuan Wang

Doctoral Dissertations

Data can create enormous values in both scientific and industrial fields, especially for access to new knowledge and inspiration of innovation. As the massive increases in computing power, data storage capacity, as well as capability of data generation and collection, the scientific research communities are confronting with a transformation of exploiting the advanced uses of the large-scale, complex, and high-resolution data sets in situation awareness and decision-making projects. To comprehensively analyze the big data problems requires the analyses aiming at various aspects which involves of effective selections of static and time-varying feature patterns that fulfills the interests of domain users. …

Social Fingerprinting: Identifying Users Of Social Networks By Their Data Footprint, Denise Koessler Gosnell

Doctoral Dissertations

This research defines, models, and quantifies a new metric for social networks: the social fingerprint. Just as one's fingers leave behind a unique trace in a print, this dissertation introduces and demonstrates that the manner in which people interact with other accounts on social networks creates a unique data trail. Accurate identification of a user's social fingerprint can address the growing demand for improved techniques in unique user account analysis, computational forensics and social network analysis.

In this dissertation, we theorize, construct and test novel software and methodologies which quantify features of social network data. All approaches and methodologies are …

Designing Efficient And Accurate Behavior-Aware Mobile Systems, Abhinav Parate

Doctoral Dissertations

The proliferation of sensors on smartphones, tablets and wearables has led to a plethora of behavior classification algorithms designed to sense various aspects of individual user's behavior such as daily habits, activity, physiology, mobility, sleep, emotional and social contexts. This ability to sense and understand behaviors of mobile users will drive the next generation of mobile applications providing services based on the users' behavioral patterns. In this thesis, we investigate ways in which we can enhance and utilize the understanding of user behaviors in such applications. In particular, we focus on identifying the key challenges in the following three aspects …

Model-Driven Analytics Of Energy Meter Data In Smart Homes, Sean K. Barker

Doctoral Dissertations

The proliferation of smart meter deployments has led to significant interest in analyzing home energy use as part of the emerging 'smart grid'. As buildings account for nearly 40% of society's energy use, data from smart meters provides significant opportunities for both utilities and consumers to optimize energy use, minimize waste, and provide insight into how modern homes and devices use energy. Meter data is often difficult to analyze, however, owing to the aggregation of many disparate and complex loads as well as relatively coarse measurement granularities. At utility scales, analysis is further complicated by the vast quantity of data, …

Dual Channel-Based Network Traffic Authentication, David Irakiza

Doctoral Dissertations

In a local network or the Internet in general, data that is transmitted between two computers (also known as network traffic or simply, traffic) in that network is usually classified as being of a malicious or of a benign nature by a traffic authentication system employing databases of previously observed malicious or benign traffic signatures, i.e., blacklists or whitelists, respectively. These lists typically consist of either the destinations (i.e., IP addresses or domain names) to which traffic is being sent or the statistical properties of the traffic, e.g., packet size, rate of connection establishment, etc. The drawback with the list-based …

Topology Dependence Of Ppm-Based Internet Protocol Traceback Schemes, Ankunda R. Kiremire

Doctoral Dissertations

Multiple schemes that utilize probabilistic packet marking (PPM) have been proposed to deal with Distributed Denial of Service (DDoS) attacks by reconstructing their attack graphs and identifying the attack sources.

In the first part of this dissertation, we present our contribution to the family of PPM-based schemes for Internet Protocol (IP) traceback. Our proposed approach, Prediction-Based Scheme (PBS), consists of marking and traceback algorithms that reduce scheme convergence times by dealing with the problems of data loss and incomplete attack graphs exhibited by previous PPM-based schemes.

Compared to previous PPM-based schemes, the PBS marking algorithm ensures that traceback is possible …

A Knowledge Discovery Approach For The Detection Of Power Grid State Variable Attacks, Nathan Wallace

Doctoral Dissertations

As the level of sophistication in power system technologies increases, the amount of system state parameters being recorded also increases. This data not only provides an opportunity for monitoring and diagnostics of a power system, but it also creates an environment wherein security can be maintained. Being able to extract relevant information from this pool of data is one of the key challenges still yet to be obtained in the smart grid. The potential exists for the creation of innovative power grid cybersecurity applications, which harness the information gained from advanced analytics. Such analytics can be based on the extraction …