Digital Commons Network^™

Reasoning About User Feedback Under Identity Uncertainty In Knowledge Base Construction, Ariel Kobren

Doctoral Dissertations

Intelligent, automated systems that are intertwined with everyday life---such as Google Search and virtual assistants like Amazon’s Alexa or Apple’s Siri---are often powered in part by knowledge bases (KBs), i.e., structured data repositories of entities, their attributes, and the relationships among them. Despite a wealth of research focused on automated KB construction methods, KBs are inevitably imperfect, with errors stemming from various points in the construction pipeline. Making matters more challenging, new data is created daily and must be integrated with existing KBs so that they remain up-to-date. As the primary consumers of KBs, human users have tremendous potential to …

Unifying Chemistry And Machine Learning For The Study Of Noncovalent Interactions, Jacob A. Townsend

Doctoral Dissertations

Gas separations are in great demand for carbon emission reduction, natural gas purification, oxygen isolation, and much more. Many of these separations rely on cost-prohibitive methods such as cryogenic distillation or strong-binding solvents. As a result, novel materials are being developed to subvert the energetic expense of gas separation processes. These studies focus on improving the performance of alternative materials, including (but not limited to) metal-organic frameworks, covalent organic frameworks, dense polymeric membranes, porous polymers, and ionic liquids.

In this work, the atomistic effects of functional units are explored for gas separations processes using electronic structure theory and machine learning. …

Automated Intelligent Cueing Device To Improve Ambient Gait Behaviors For Patients With Parkinson's Disease, Nader Naghavi

Doctoral Dissertations

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods …

Zinc Oxide Based Nanowire Arrays For Selective Detection Of Multiple Gaseous Analytes At Elevated Temperature, Bo Zhang

Doctoral Dissertations

Zinc oxide (ZnO) based nanostructures represent an important class of gas sensor materials, due to their high surface-to-volume ratio, significant surface band structure bending upon gaseous analyte exposures, good mobility of charge carriers, and good structural stability at elevated temperature. Their usually surface-dominant sensing processes entail the important roles played by nanostructure size, defects, morphologies, and surface absorbate energetics and dynamics. In this dissertation, based on ZnO nanowire array as a gas sensing platform, rational decoration of electronic sensitizers, such as Au nanoparticles, and semiconducting oxide, such as Fe₂O₃ nanoparticles, has been employed to boost its electrical …

Predicting Materials Behavior With Atomistic Simulations And Machine Learning, Ayana Ghosh

Doctoral Dissertations

Data driven approaches based on machine learning (ML) algorithms are very popular in the domain of physical sciences for the determination of yet unknown structure- properties-performance relationships for a wide range of different material families. This dissertation focuses on studying a number of such cases where various ML algorithms and statistical techniques, coupled with appropriate materials data obtained from experiments and atomistic simulations, are employed to build comprehensive ML-based frameworks capable of predicting complex materials behavior. The materials spaces investigated encompass isolated organic molecules, polymer crystals, inorganic multiferroics and actinides, while the target system characteristics or functionalities include molecular crystallization …

Bayesian Topological Machine Learning, Christopher A. Oballe

Doctoral Dissertations

Topological data analysis encompasses a broad set of ideas and techniques that address 1) how to rigorously define and summarize the shape of data, and 2) use these constructs for inference. This dissertation addresses the second problem by developing new inferential tools for topological data analysis and applying them to solve real-world data problems. First, a Bayesian framework to approximate probability distributions of persistence diagrams is established. The key insight underpinning this framework is that persistence diagrams may be viewed as Poisson point processes with prior intensities. With this assumption in hand, one may compute posterior intensities by adopting techniques …

A Datacentric Algorithm For Gamma-Ray Radiation Anomaly Detection In Unknown Background Environments, James M. Ghawaly Jr

Doctoral Dissertations

The detection of anomalous radioactive sources in environments characterized by a high level of variation in the background radiation is a challenging problem in nuclear security. A variety of natural and artificial sources contribute to background radiation dynamics including variations in the absolute and relative concentrations of naturally occurring radioisotopes in different materials, the wet-deposition of $^{222}$Rn daughters during precipitation, and background suppression due to physical objects in the detector scene called ``clutter." This dissertation presents a new datacentric algorithm for radiation anomaly detection in dynamic background environments. The algorithm is based on a custom deep neural autoencoder architecture called …

A Framework For Performance-Based Facade Design: Approach For Automated And Multi-Objective Simulation And Optimization, Mahsa Minaei

Doctoral Dissertations

Buildings have a considerable impact on the environment, and it is crucial to consider environmental and energy performance in building design. Buildings account for about 40% of the global energy consumption and contribute over 30% of the CO2 emissions. A large proportion of this energy is used for meeting occupants’ thermal comfort in buildings, followed by lighting. The building facade forms a barrier between the exterior and interior environments; therefore, it has a crucial role in improving energy efficiency and building performance. In this regard, decision-makers are required to establish an optimal solution, considering multi-objective problems that are usually competitive …

The Limits Of Location Privacy In Mobile Devices, Keen Yuun Sung

Doctoral Dissertations

Mobile phones are widely adopted by users across the world today. However, the privacy implications of persistent connectivity are not well understood. This dissertation focuses on one important concern of mobile phone users: location privacy. I approach this problem from the perspective of three adversaries that users are exposed to via smartphone apps: the mobile advertiser, the app developer, and the cellular service provider. First, I quantify the proportion of mobile users who use location permissive apps and are able to be tracked through their advertising identifier, and demonstrate a mark and recapture attack that allows continued tracking of users …

Automatic Depression Screening And Depressive Symptom Prediction Using Smartphone Sensing Data, Shweta Ware

Doctoral Dissertations

Depression is a common, yet serious health problem. It has significant detrimental impacts on both physical and psychological functioning. Current diagnosis techniques rely on physician-administered or patient self-administered interview tools, which are burdensome and suffer from recall bias. Additionally, these techniques incur higher medical costs. There is an urgent need for an accurate, objective and easily accessible depression screening tool for mass usage. In this dissertation, we explore the usage of smartphone sensing data, collected directly on smartphones or meta-data collected from a WiFi infrastructure, for automatic depression screening and depressive symptom prediction.

In the first part of the dissertation, …

Toward More Predictive Models By Leveraging Multimodal Data, Sudarshan Srinivasan

Doctoral Dissertations

Data is often composed of structured and unstructured data. Both forms of data have information that can be exploited by machine learning models to increase their prediction performance on a task. However, integrating the features from both these data forms is a hard, complicated task. This is all the more true for models which operate on time-constraints. Time-constrained models are machine learning models that work on input where time causality has to be maintained such as predicting something in the future based on past data. Most previous work does not have a dedicated pipeline that is generalizable to different tasks …

Learning Latent Characteristics Of Data And Models Using Item Response Theory, John P. Lalor

Doctoral Dissertations

A supervised machine learning model is trained with a large set of labeled training data, and evaluated on a smaller but still large set of test data. Especially with deep neural networks (DNNs), the complexity of the model requires that an extremely large data set is collected to prevent overfitting. It is often the case that these models do not take into account specific attributes of the training set examples, but instead treat each equally in the process of model training. This is due to the fact that it is difficult to model latent traits of individual examples at the …

Noise-Aware Inference For Differential Privacy, Garrett Bernstein

Doctoral Dissertations

Domains involving sensitive human data, such as health care, human mobility, and online activity, are becoming increasingly dependent upon machine learning algorithms. This leads to scenarios in which data owners wish to protect the privacy of individuals comprising the sensitive data, while at the same time data modelers wish to analyze and draw conclusions from the data. Thus there is a growing demand to develop effective private inference methods that can marry the needs of both parties. For this we turn to differential privacy, which provides a framework for executing algorithms in a private fashion by injecting specifically-designed randomization at …

Development Of A System Architecture For The Prediction Of Student Success Using Machine Learning Techniques, Tatiana A. Cardona

Doctoral Dissertations

“ The goals of higher education have evolved through time based on the impact that technology development and industry have on productivity. Nowadays, jobs demand increased technical skills, and the supply of prepared personnel to assume those jobs is insufficient. The system of higher education needs to evaluate their practices to realize the potential of cultivating an educated and technically skilled workforce. Currently, completion rates at universities are too low to accomplish the aim of closing the workforce gap. Recent reports indicate that 40 percent of freshman at four-year public colleges will not graduate, and rates of completion are even …