Physical Sciences and Mathematics Commons^™

Distributed Load Testing By Modeling And Simulating User Behavior, Chester Ira Parrott

LSU Doctoral Dissertations

Modern human-machine systems such as microservices rely upon agile engineering practices which require changes to be tested and released more frequently than classically engineered systems. A critical step in the testing of such systems is the generation of realistic workloads or load testing. Generated workload emulates the expected behaviors of users and machines within a system under test in order to find potentially unknown failure states. Typical testing tools rely on static testing artifacts to generate realistic workload conditions. Such artifacts can be cumbersome and costly to maintain; however, even model-based alternatives can prevent adaptation to changes in a system …

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 …

Global Optimization Algorithms For Image Registration And Clustering, Cuicui Zheng

Dissertations

Global optimization is a classical problem of finding the minimum or maximum value of an objective function. It has applications in many areas, such as biological image analysis, chemistry, mechanical engineering, financial analysis, deep learning and image processing. For practical applications, it is important to understand the efficiency of global optimization algorithms. This dissertation develops and analyzes some new global optimization algorithms and applies them to practical problems, mainly for image registration and data clustering.

First, the dissertation presents a new global optimization algorithm which approximates the optimum using only function values. The basic idea is to use the points …

Learning Health Information From Floor Sensor Data Within A Pervasive Smart Home Environment, Nicholas Brent Burns

Computer Science and Engineering Dissertations

Spatial and temporal gait analysis can provide useful measures for determining a person’s state of health while also identifying deviations in day-to-day activity. The SmartCare project is a multi-discipline health technologies project that aims to provide an unobtrusive and pervasive system that provides in-home health monitoring for the elderly. This research work focuses on the pressure-sensitive smart floor of the SmartCare project by using an experimental floor to develop methods for future use on a floor deployed within a home. This work presents a procedure to automatically calibrate a smart floor’s pressure sensors without specialized physical effort. The calibration algorithm …

Reinforcement Learning In Large, Structured Action Spaces: A Simulation Study Of Decision Support For Spinal Cord Injury Rehabilitation, Nathan Phelps

Electronic Thesis and Dissertation Repository

Reinforcement learning (RL) has helped improve decision-making in several applications. However, applying traditional RL is challenging in some applications, such as rehabilitation of people with a spinal cord injury (SCI). Among other factors, using RL in this domain is difficult because there are many possible treatments (i.e., large action space) and few patients (i.e., limited training data). Treatments for SCIs have natural groupings, so we propose two approaches to grouping treatments so that an RL agent can learn effectively from limited data. One relies on domain knowledge of SCI rehabilitation and the other learns similarities among treatments using an embedding …

Developing Agent-Based Models To Study Financial Markets, Saurav Chakraborty

USF Tampa Graduate Theses and Dissertations

This dissertation presents research that employs agent-based modelling to provide a framework to support simulation as a complement to traditional economic models for policy evaluation. It consists of three studies. The first study employs cluster analysis to capture the different types of banks and the associated business models that define their decision-making. The results from study one will help us get an understanding of how different banks behave and provide an insight into their lending practices. Hence, it would be very helpful in evaluating and analyzing the impact of future policies. Study two develops a fine-grained interbank lending model based …

Machine Learning And Data Mining-Based Methods To Estimate Parity Status And Age Of Wild Mosquito Vectors Of Infectious Diseases From Near-Infrared Spectra, Masabho Peter Milali

Dissertations (1934 -)

Previous studies show that a trained partial least square regresser [sic] (PLSR) from near-infrared spectra classify laboratory and semi-field raised mosquitoes into less than or ≥ to seven days old with an average accuracy of 80%. This dissertation demonstrates that training models on near-infrared spectra (NIRS) using artificial neural network (ANN) as an architecture yields models with higher accuracies than training models using partial least squares (PLS) as an architecture. In addition, irrespective of the model architecture used, direct training of a binary classifier scores higher accuracy than training a regresser and interpreting it as a binary classifier. Furthermore, for …

Optimizing Cluster Sets For The Scan Statistic Using Local Search, James Shulgan

Graduate Research Theses & Dissertations

In recent years, scattering sensors to produce wireless sensor networks (WSN) has been proposed for detecting localized events in large areas. Because sensor measurements are noisy, the WSN needs to use statistical methods such as the scan statistic. The scan statistic groups measurements into various clusters, computes a cluster statistic for each cluster, and decides that an event has happened if any of the statistics exceeds a threshold. Previous researchers have investigated the performance of the scan statistic to detect events; however, little attention was given to the optimization of which clusters the scan statistic should use. Using the scan …

Development Of A Modeling Algorithm To Predict Lean Implementation Success, Richard Charles Barclay

Doctoral Dissertations

”Lean has become a common term and goal in organizations throughout the world. The approach of eliminating waste and continuous improvement may seem simple on the surface but can be more complex when it comes to implementation. Some firms implement lean with great success, getting complete organizational buy-in and realizing the efficiencies foundational to lean. Other organizations struggle to implement lean. Never able to get the buy-in or traction needed to really institute the sort of cultural change that is often needed to implement change. It would be beneficial to have a tool that organizations could use to assess their …