Digital Commons Network^™

Decomposing The Hamiltonian Of Quantum Circuits Using Machine Learning, Jordan Burns, Yih Sung, Colby Wight

Physics Capstone Projects

Quantum computing is one of the most promising techniques for simulating physical systems that cannot be simulated on classical computers[1]. A significant drawback of this approach is the inherent difficulty in designing circuits that can represent these systems on quantum computers. Every quantum circuit is built out of small components called quantum gates. Each of these gates manipulate the quantum system in a specific way. When used in combination, a finite subset of these gates, the set of universal gates, can be used to construct any possible quantum circuit[2].

Approxml: Efficient Approximate Ad-Hoc Ml Models Through Materialization And Reuse, Faezeh Ghaderi

Computer Science and Engineering Theses - Archive

Machine Learning (ML) has become an essential tool in answering complex predictive analytic queries. Model building for large scale datasets is one of the most time-consuming parts of the data science pipeline. Often data scientists are willing to sacrifice some accuracy in order to speed up this process during the exploratory phase. In this report, we aim to demonstrate ApproxML, a system that efficiently constructs approximate ML models for new queries from previously constructed ML models using the concepts of model materialization and reuse. ApproxML supports a wide variety of ML models such as generalized linear models for supervised learning …

Social Media Text Analysis Using Multi-Kernel Convolutional Neural Network, Anna Philips

Computer Science and Engineering Theses - Archive

Transportation planners and ride hailing platforms such as Uber and Lyft use their riders feedback to assess their services and monitor customer satisfaction. Social media websites such as Facebook, Instagram, LinkedIn and in particular Twitter provides a large dataset of micro-texts by users who regularly post to their social media accounts about their grievances with their ride experience. This data is often unorganized and intractable to process because of it’s extremely large size which is continuously increasing daily. In this project, we collected ride hailing service relevant text data from Twitter around New York and developed a novel Convolutional Neural …

Use Of Word Embedding To Generate Similar Words And Misspellings For Training Purpose In Chatbot Development, Sanjay Thapa

Computer Science and Engineering Theses - Archive

The advancement in the field of Natural Language Processing and Machine Learning has played a significant role in the huge improvement of conversational Artificial Intelligence (AI). The use of text-based conversation AI such as chatbots have increased significantly for the everyday purpose to communicate with real people for a variety of tasks. Chatbots are deployed in almost all popular messaging platforms and channels. The rise of chatbot development frameworks based on machine learning is helping to deploy chatbot easily and promptly. These chatbot development frameworks use machine learning and natural language understanding (NLU) to understand users' messages and intents and …

Learning Robot Manipulation Tasks Via Observation, Michail Theofanidis

Computer Science and Engineering Dissertations - Archive

The coexistence of humans and robots has been the aspiration of many scientific endeavors in the past century. Most anthropomorphic or industrial robots are highly articulated and complex machines, which are designed to carry out tasks that often involve the manipulation of physical objects. Traditionally, robots learn how to perform such tasks with the aid of a human programmer or operator. In this regard, the human acts as a teacher who provides a demonstration of a task. From the data of the demonstration, the robot must learn a state-action mapping that accomplishes the task. This state-action mapping is often addressed …

Feature Extraction In Noise-Diverse Environments For Human Activities Recognition Using Wi-Fi, Sheheryar Arshad

Computer Science and Engineering Dissertations - Archive

With the rapid development of 802.11 standard and Internet of Things (IoT) applications, Wi-Fi (IEEE 802.11) has emerged as the most widely used wireless communication technology. Wi-Fi based sensing has found widespread use cases involving activity recognition, indoor localization, design of smart spaces and in healthcare applications. This dissertation presents the study of human activities’ sensing and recognition using channel state information (CSI) of Wi-Fi. We highlight the limitations of existing methods and consequently design the frameworks for collecting stable CSI and monitoring different indoor and outdoor environments for human activities. Specifically, this dissertation provide means to define and extract …

Think2act: Using Multimodal Data To Assess Human Cognitive And Physical Performance, Maher Abujelala

Computer Science and Engineering Dissertations - Archive

As computers become more advanced, affordable, and smaller in size, we start to use them in almost every aspect of our daily life. Nowadays, the use of computers is not just limited to accomplish work-related tasks. Instead, we use computers for education, entertainment, healthcare, and in many other areas to facilitate our daily life activities. From here, the Human-Computer Interaction (HCI) field emerged. HCI is a multidisciplinary field of study that focuses on utilizing computers and technology to interact with humans, improve their quality of life, and enhance their performance. The rapid advancements in other related research fields, such as …

Deep Representation Learning For Clustering And Domain Adaptation, Mohsen Kheirandishfard

Computer Science and Engineering Dissertations - Archive

Representation learning is a fundamental task in the area of machine learning which can significantly influence the performance of the algorithms used in various applications. The main goal of this task is to capture the relationships between the input data and learn feature representations that contain the most useful information of the original data. Such representations can be further leveraged in many machine learning applications such as clustering, natural language analysis, recommender systems, etc. In this dissertation, we first present a theoretical framework for solving a broad class of non-convex optimization problems. The proposed method is applicable to various tasks …

Multivariate Time Series Pattern Recognition Using Machine Learning And Deep Learning Methods, Sai Abhishek Devar

Industrial, Manufacturing, and Systems Theses - Archive

In this research work, we have implemented machine learning & deep-learning algorithms on real-time multivariate time series datasets in the manufacturing & health care fields. The research work is organized in two case-studies. The case study-1 is about rare event classification in multivariate time series in a pulp and paper manufacturing industry, data was collected of multiple sensors at each stage of production line, the data contains a rare event of paper break that commonly occurs in the industry. For preprocessing we have implemented sliding window approach for calculating first order difference method to capture the variation in the data …

Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque

School of Computing: Dissertations, Theses, and Student Research

Landing an unmanned aerial vehicle (UAV) on a moving platform is a challenging task that often requires exact models of the UAV dynamics, platform characteristics, and environmental conditions. In this thesis, we present and investigate three different machine learning approaches with varying levels of domain knowledge: dynamics randomization, universal policy with system identification, and reinforcement learning with no parameter variation. We first train the policies in simulation, then perform experiments both in simulation, making variations of the system dynamics with wind and friction coefficient, then perform experiments in a real robot system with wind variation. We initially expected that providing …

Countering Cybersecurity Vulnerabilities In The Power System, Fengli Zhang

Graduate Theses and Dissertations

Security vulnerabilities in software pose an important threat to power grid security, which can be exploited by attackers if not properly addressed. Every month, many vulnerabilities are discovered and all the vulnerabilities must be remediated in a timely manner to reduce the chance of being exploited by attackers. In current practice, security operators have to manually analyze each vulnerability present in their assets and determine the remediation actions in a short time period, which involves a tremendous amount of human resources for electric utilities. To solve this problem, we propose a machine learning-based automation framework to automate vulnerability analysis and …

Toward Self-Reconfigurable Parametric Systems: Reinforcement Learning Approach, Ting-Yu Mu

Dissertations

For the ongoing advancement of the fields of Information Technology (IT) and Computer Science, machine learning-based approaches are utilized in different ways in order to solve the problems that belong to the Nondeterministic Polynomial time (NP)-hard complexity class or to approximate the problems if there is no known efficient way to find a solution. Problems that determine the proper set of reconfigurable parameters of parametric systems to obtain the near optimal performance are typically classified as NP-hard problems with no efficient mathematical models to obtain the best solutions. This body of work aims to advance the knowledge of machine learning …

Characterizing Dryland Ecosystems Using Remote Sensing And Dynamic Global Vegetation Modeling, Abdolhamid Dashtiahangar

Boise State University Theses and Dissertations

Drylands include all terrestrial regions where the production of crops, forage, wood and other ecosystem services are limited by water. These ecosystems cover approximately 40% of the earth terrestrial surface and accommodate more than 2 billion people (Millennium Ecosystem Assessment, 2005). Moreover, the interannual variability of the global carbon budget is strongly regulated by vegetation dynamics in drylands. Understanding the dynamics of such ecosystems is significant for assessing the potential for and impacts of natural or anthropogenic disturbances and mitigation planning, and a necessary step toward enhancing the economic and social well-being of dryland communities in a sustainable manner (Global …

Pixel-Level Deep Multi-Dimensional Embeddings For Homogeneous Multiple Object Tracking, Mateusz Mittek

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, …

Machine Learning For Robust Understanding Of Scene Materials In Hyperspectral Images, Utsav B. Gewali

Theses

The major challenges in hyperspectral (HS) imaging and data analysis are expensive sensors, high dimensionality of the signal, limited ground truth, and spectral variability. This dissertation develops and analyzes machine learning based methods to address these problems. In the first part, we examine one of the most important HS data analysis tasks-vegetation parameter estimation. We present two Gaussian processes based approaches for improving the accuracy of vegetation parameter retrieval when ground truth is limited and/or spectral variability is high. The first is the adoption of covariance functions based on well-established metrics, such as, spectral angle and spectral correlation, which are …

Comparison Of Rl Algorithms For Learning To Learn Problems, Adolfo Gonzalez Iii

Theses and Dissertations

Machine learning has been applied to many different problems successfully due to the expressiveness of neural networks and simplicity of first order optimization algorithms. The latter being a vital piece needed for training large neural networks efficiently. Many of these algorithms were produced with behavior produced by experiments and intuition. An interesting question that comes to mind is that rather than observing and then designing algorithms with beneficial behaviors, can these algorithms be learned through a reinforcement learning by modeling optimization as a game. This paper explores several reinforcement learning algorithms which are applied to learn policies suited for optimization.

Materials Prediction Using High-Throughput And Machine Learning Techniques, Chandramouli Nyshadham

Theses and Dissertations

Predicting new materials through virtually screening a large number of hypothetical materials using supercomputers has enabled materials discovery at an accelerated pace. However, the innumerable number of possible hypothetical materials necessitates the development of faster computational methods for speedier screening of materials reducing the time of discovery. In this thesis, I aim to understand and apply two computational methods for materials prediction. The first method deals with a computational high-throughput study of superalloys. Superalloys are materials which exhibit high-temperature strength. A combinatorial high-throughput search across 2224 ternary alloy systems revealed 102 potential superalloys of which 37 are brand new, all …

Sleep Stage Prediction With Raw Acceleration And Photoplethysmography Heart Rate Data Derived From A Consumer Wearable Device, Olivia Walch, Yitong Huang, Daniel Forger, Cathy Goldstein

Mathematics Sciences: Faculty Publications

Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple …

Mdt Geolocation Through Machine Learning: Evaluation Of Supervised Regression Ml Algorithms, Aria Canadell Solana

Theses and Dissertations

Minimizing Drive Test is a statistical protocol used to evaluate the network performance. It provides several benefits with respect to traditional drive test analysis; however, multiple inconveniences exist that prevent cell companies from precisely retrieving most of the locations of these reports. . MATLAB and Jupyter Notebook were used to prepare the data and create the models. Multiple supervised regression algorithms were tested and evaluated. The best predictions were obtained from the K-Nearest Neighbor algorithm with one ‘k’ and distance-weighted predictions. The UE geolocation was predicted with a median accuracy of 5.42 meters, a mean error of 61.62 meters, and …

Augmenting Education: Ethical Considerations For Incorporating Artificial Intelligence In Education, Dana Remian

Instructional Design Capstones Collection

Artificial intelligence (AI) has existed in theory and practice for decades, but applications have been relatively limited in most domains. Recent developments in AI and computing have placed AI-enhanced applications in various industries and a growing number of consumer products. AI platforms and services aimed at enhancing educational outcomes and taking over administrative tasks are becoming more prevalent and appearing in more and more classrooms and offices. Conversations about the disruption and ethical concerns created by AI are occurring in many fields. The development of the technology threatens to outpace academic discussion of its utility and pitfalls in education, however. …

“Where’S The I-O?” Artificial Intelligence And Machine Learning In Talent Management Systems, Manuel F. Gonzalez, John F. Capman, Frederick L. Oswald, Evan R. Theys, David L. Tomczak

Personnel Assessment and Decisions

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; …

Machine Learning To Quantitate Neutrophil Netosis, Laila Elsherif, Noah Sciaky, Carrington A. Metts, Md. Modasshir, Ioannis Rekleitis, Christine A. Burris, Joshua A. Walker, Nadeem Ramadan, Tina M. Leisner, Stephen P. Holly, Martis W. Cowles, Kenneth I. Ataga, Joshua N. Cooper, Leslie V. Parise

Faculty Publications

We introduce machine learning (ML) to perform classifcation and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered process involved in multiple human diseases. CNNs achieved >94% in performance accuracy in diferentiating NETotic from non-NETotic cells and vastly facilitated dose-response analysis and screening of the NETotic response in neutrophils from patients. Using only features learned from nuclear morphology, CNNs can distinguish between NETosis and necrosis and between distinct NETosis signaling pathways, making them a precise tool for …

Algorithms For Multi-Objective Mixed Integer Programming Problems, Alvaro Miguel Sierra Altamiranda

USF Tampa Graduate Theses and Dissertations

This thesis presents a total of 3 groups of contributions related to multi-objective optimization. The first group includes the development of a new algorithm and an open-source user-friendly package for optimization over the efficient set for bi-objective mixed integer linear programs. The second group includes an application of a special case of optimization over the efficient on conservation planning problems modeled with modern portfolio theory. Finally, the third group presents a machine learning framework to enhance criterion space search algorithms for multi-objective binary linear programming.

In the first group of contributions, this thesis presents the first (criterion space search) algorithm …

Virtual Wrap-Up Presentation: Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

School of Computing: Conference and Workshop Papers

Includes framing, overview, and discussion of the explorations pursued as part of the Digital Libraries, Intelligent Data Analytics, and Augmented Description demonstration project, pursued by members of the Aida digital libraries research team at the University of Nebraska-Lincoln through a research services contract with the Library of Congress. This presentation covered: Aida research team and background for the demonstration project; broad outlines of “Digital Libraries, Intelligent Data Analytics, and Augmented Description”; what changed for us as a research team over the collaboration and why; deliverables of our work; thoughts toward “What next”; and deep-dives into the explorations. The machine learning …

Habitat Associations And Reproduction Of Fishes On The Northwestern Gulf Of Mexico Shelf Edge, Elizabeth Marie Keller

LSU Doctoral Dissertations

Several of the northwestern Gulf of Mexico (GOM) shelf-edge banks provide critical hard bottom habitat for coral and fish communities, supporting a wide diversity of ecologically and economically important species. These sites may be fish aggregation and spawning sites and provide important habitat for fish growth and reproduction. Already designated as habitat areas of particular concern, many of these banks are also under consideration for inclusion in the expansion of the Flower Garden Banks National Marine Sanctuary. This project aimed to gain a more comprehensive understanding of the communities and fish species on shelf-edge banks by way of gonad histology, …

Using Uncertainty To Interpret Supervised Machine Learning Predictions, Michael C. Darling

Electrical and Computer Engineering ETDs

Traditionally, machine learning models are assessed using methods that estimate an average performance against samples drawn from a particular distribution. Examples include the use of cross-validation or hold0out to estimate classification error, F-score, precision, and recall.

While these measures provide valuable information, they do not tell us a model's certainty relative to particular regions of the input space. Typically there are regions where the model can differentiate the classes with certainty, and regions where the model is much less certain about its predictions.

In this dissertation we explore numerous approaches for quantifying uncertainty in the individual predictions made by supervised …

Word Importance Modeling To Enhance Captions Generated By Automatic Speech Recognition For Deaf And Hard Of Hearing Users, Sushant Kafle

Theses

People who are deaf or hard-of-hearing (DHH) benefit from sign-language interpreting or live-captioning (with a human transcriptionist), to access spoken information. However, such services are not legally required, affordable, nor available in many settings, e.g., impromptu small-group meetings in the workplace or online video content that has not been professionally captioned. As Automatic Speech Recognition (ASR) systems improve in accuracy and speed, it is natural to investigate the use of these systems to assist DHH users in a variety of tasks. But, ASR systems are still not perfect, especially in realistic conversational settings, leading to the issue of trust and …

Groundwater Level Mapping Tool: Development Of A Web Application To Effectively Characterize Groundwater Resources, Steven William Evans

Theses and Dissertations

Groundwater is used worldwide as a major source for agricultural irrigation, industrial processes, mining, and drinking water. An accurate understanding of groundwater levels and trends is essential for decision makers to effectively manage groundwater resources throughout an aquifer, ensuring its sustainable development and usage. Unfortunately, groundwater is one of the most challenging and expensive water resources to characterize, quantify, and monitor on a regional basis. Data, though present, are often limited or sporadic, and are generally not used to their full potential to aid decision makers in their groundwater management.This thesis presents a solution to this under-utilization of available data …

What Do You Mean? Research In The Age Of Machines, Arthur J. Boston

Faculty & Staff Research and Creative Activity

What Do You Mean?” was an undeniable bop of its era in which Justin Bieber explores the ambiguities of romantic communication. (I pinky promise this will soon make sense for scholarly communication librarians interested in artificial intelligence [AI].) When the single hit airwaves in 2015, there was a meta-debate over what Bieber meant to add to public discourse with lyrics like “What do you mean? Oh, oh, when you nod your head yes, but you wanna say no.” It is unlikely Bieber had consent culture in mind, but the failure of his songwriting team to take into account that some …

Detecting Digitally Forged Faces In Online Videos, Neilesh Sambhu

USF Tampa Graduate Theses and Dissertations

We use Rossler’s FaceForensics dataset of 1004 online videos and their corresponding forged counterparts [1] to investigate the ability to distinguish digitally forged facial images from original images automatically with deep learning. The proposed convolutional neural network is much smaller than the current state-of-the-art solutions. Nevertheless, the network maintains a high level of accuracy (99.6%), all while using the entire FaceForensics dataset and not including any temporal information. We implement majority voting and show the impact on accuracy (99.67%), where only 1 video of 300 is misclassified. We examine why the model misclassified this one video. In terms of tuning …