Digital Commons Network^™

Poly-Gan: A Multi-Conditioned Gan For Multiple Tasks, Nilesh Pandey

Theses

We present Poly-GAN, a novel conditional GAN architecture that is motivated by different Image generation and manipulation applications like Fashion Synthesis, an application where garments are automatically placed on images of human models at an arbitrary pose, image inpainting, an application where we try to recover a damaged image using the edges or a rough sketch of the image. While different applications use different GAN setup for image generation, we propose only one architecture for multiple applications with little to no change in the pipeline. Poly-GAN allows conditioning on multiple inputs and is suitable for many different tasks. Our novel …

Spatially-Explicit Snap Bean Flowering And Disease Prediction Using Imaging Spectroscopy From Unmanned Aerial Systems, Ethan W. Hughes

Theses

Sclerotinia sclerotiorum, or white mold, is a fungus that infects the flowers of snap bean plants and causes a subsequent reduction in snap bean pods, which adversely impacts yield. Timing the application of white mold fungicide thus is essential to preventing the disease, and is most effective when applied during the flowering stage. However, most of the flowers are located beneath the canopy, i.e., hidden by foliage, which makes spectral detection of flowering via the leaf/canopy spectra paramount. The overarching objectives of this research therefore are to i) identify spectral signatures for the onset of flowering to optimally time the …

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 …

Critiquing The New Autonomy Of Immaterial Labour: An Analysis Of Work In The Artificial Intelligence Industry, James Steinhoff

Electronic Thesis and Dissertation Repository

Karl Marx theorized capitalism as a relation between labour, capital and machines. For Marx, capital, the process of self-augmenting value appropriated from human labour, is inherently driven by competition to replace labour in production with machines. Marx goes as far as to describe machines as capital’s “most powerful weapon” for suppressing working class revolt. Marx, however, could not have predicted the computing machines – such as artificial intelligence – which now form the basis for an increasingly cybernetic capital. Since Marx’s time, many Marxist thinkers have sought to apply or update his approach to the cybernetic era. The influential post-operaismo …

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 …

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 …

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 …

Neural Models For Information Retrieval Without Labeled Data, Hamed Zamani

Doctoral Dissertations

Recent developments of machine learning models, and in particular deep neural networks, have yielded significant improvements on several computer vision, natural language processing, and speech recognition tasks. Progress with information retrieval (IR) tasks has been slower, however, due to the lack of large-scale training data as well as neural network models specifically designed for effective information retrieval. In this dissertation, we address these two issues by introducing task-specific neural network architectures for a set of IR tasks and proposing novel unsupervised or \emph{weakly supervised} solutions for training the models. The proposed learning solutions do not require labeled training data. Instead, …

Extracting And Representing Entities, Types, And Relations, Patrick Verga

Doctoral Dissertations

Making complex decisions in areas like science, government policy, finance, and clinical treatments all require integrating and reasoning over disparate data sources. While some decisions can be made from a single source of information, others require considering multiple pieces of evidence and how they relate to one another. Knowledge graphs (KGs) provide a natural approach for addressing this type of problem: they can serve as long-term stores of abstracted knowledge organized around concepts and their relationships, and can be populated from heterogeneous sources including databases and text. KGs can facilitate higher level reasoning, influence the interpretation of new data, and …

Essays On The Minimum Wage, Immigration, And Privatization, Doruk Cengiz

Doctoral Dissertations

This dissertation empirically examines effects of the minimum wage, immigration, and privatization; three of the most crucial policies that impact workers worldwide using recent advances in statistics and econometrics to provide causally interpretable results, and to reconcile controversies in the literature. In the first chapter, titled “Seeing Beyond the Trees: Using machine learning to estimate the impact of minimum wages on affected individuals”, I identify minimum wage workers prior to estimating its effects using machine learning tools, and provide highly representative demographically-based groups that capture as much as 73.4% of all likely minimum wage workers. I find that there is …

Study Of Human Hand-Eye Coordination Using Machine Learning Techniques In A Virtual Reality Setup, Kamran Binaee

Theses

Theories of visually guided action are characterized as closed-loop control in the presence of reliable sources of visual information, and predictive control to compensate for visuomotor delay and temporary occlusion. However, prediction is not well understood. To investigate, a series of studies was designed to characterize the role of predictive strategies in humans as they perform visually guided actions, and to guide the development of computational models that capture these strategies. During data collection, subjects were immersed in a virtual reality (VR) system and were tasked with using a paddle to intercept a virtual ball. To force subjects into a …

Adaptive Feature Engineering Modeling For Ultrasound Image Classification For Decision Support, Hatwib Mugasa

Doctoral Dissertations

Ultrasonography is considered a relatively safe option for the diagnosis of benign and malignant cancer lesions due to the low-energy sound waves used. However, the visual interpretation of the ultrasound images is time-consuming and usually has high false alerts due to speckle noise. Improved methods of collection image-based data have been proposed to reduce noise in the images; however, this has proved not to solve the problem due to the complex nature of images and the exponential growth of biomedical datasets. Secondly, the target class in real-world biomedical datasets, that is the focus of interest of a biopsy, is usually …

Development Of A National-Scale Big Data Analytics Pipeline To Study The Potential Impacts Of Flooding On Critical Infrastructures And Communities, Nattapon Donratanapat

Theses and Dissertations

With the rapid development of the Internet of Things (IoT) and Big data infrastructure, crowdsourcing techniques have emerged to facilitate data processing and problem solving particularly for flood emergences purposes. A Flood Analytics Information System (FAIS) has been developed as a Python Web application to gather Big data from multiple servers and analyze flooding impacts during historical and real-time events. The application is smartly designed to integrate crowd intelligence, machine learning (ML), and natural language processing of tweets to provide flood warning with the aim to improve situational awareness for flood risk management and decision making. FAIS allows the user …

Machine Learning Based Ultra High Carbon Steel Image Segmentation, Sumith Kuttiyil Suresh

Theses and Dissertations

Mechanical and structural properties of ultra-high carbon steel are determined by their microstructures composed of constituents such as pearlite and spheroidites. Locating micro constituents and quantitatively measuring its presence is key for material researchers to study the physical properties of the carbon steel materials. This micrograph analysis is currently done manually and subjectively by material scientists, which is tedious and time-consuming. Here we propose to apply the image segmentation algorithm called U-Net to achieve automated labeling of steel microstructures on a subset of ultra- high carbon steel image dataset containing pearlite and spheroidite as the primary micro constituents. Our work …

Vector Spaces For Multiple Modal Embeddings, Sabarish Gopalakrishnan

Theses

Deep learning has enabled great advances in the field of natural language processing, computer vision and pattern recognition in general. Deep learning frameworks have been very successful in performing classification, object detection, segmentation and translation. Before objects can be processed, a vector representation of that object needs to be created. For example, sentences and images can be encoded with a sent2vec and image2vec function respectively in preparation for input to a machine learning framework. Neural networks are able to learn efficient vector representation of images, text, audio, videos and 3D point clouds. However, the transfer of knowledge from one modality …

Discovery Of Materials Through Applied Machine Learning, Travis Williams

Theses and Dissertations

Advances in artificial intelligence technology, specifically machine learning, have cre- ated opportunities in the material sciences to accelerate material discovery and gain fundamental understanding of the interaction between certain the constituent ele- ments of a material and the properties expressed by that material. Application of machine learning to experimental materials discovery is slow due to the monetary and temporal cost of experimental data, but parallel techniques such as continuous com- positional gradients or high-throughput characterization setups are capable of gener- ating larger amounts of data than the typical experimental process, and therefore are suitable for combination with machine learning. A …

Demonstration Of Visible And Near Infrared Raman Spectrometers And Improved Matched Filter Model For Analysis Of Combined Raman Signals, Alexander Matthew Atkinson

Electrical & Computer Engineering Theses & Dissertations

Raman spectroscopy is a powerful analysis technique that has found applications in fields such as analytical chemistry, planetary sciences, and medical diagnostics. Recent studies have shown that analysis of Raman spectral profiles can be greatly assisted by use of computational models with achievements including high accuracy pure sample classification with imbalanced data sets and detection of ideal sample deviations for pharmaceutical quality control. The adoption of automated methods is a necessary step in streamlining the analysis process as Raman hardware becomes more advanced. Due to limits in the architectures of current machine learning based Raman classification models, transfer from pure …

Machine Learning-Based Models For Assessing Impacts Before, During And After Hurricane Events, Julie L. Harvey

Electronic Theses and Dissertations

Social media provides an abundant amount of real-time information that can be used before, during, and after extreme weather events. Government officials, emergency managers, and other decision makers can use social media data for decision-making, preparation, and assistance. Machine learning-based models can be used to analyze data collected from social media. Social media data and cloud cover temperature as physical sensor data was analyzed in this study using machine learning techniques. Data was collected from Twitter regarding Hurricane Florence from September 11, 2018 through September 20, 2018 and Hurricane Michael from October 1, 2018 through October 18, 2018. Natural language …

Semi-Supervised Regression With Generative Adversarial Networks Using Minimal Labeled Data, Greg Olmschenk

Dissertations, Theses, and Capstone Projects

This work studies the generalization of semi-supervised generative adversarial networks (GANs) to regression tasks. A novel feature layer contrasting optimization function, in conjunction with a feature matching optimization, allows the adversarial network to learn from unannotated data and thereby reduce the number of labels required to train a predictive network. An analysis of simulated training conditions is performed to explore the capabilities and limitations of the method. In concert with the semi-supervised regression GANs, an improved label topology and upsampling technique for multi-target regression tasks are shown to reduce data requirements. Improvements are demonstrated on a wide variety of vision …

The Application Of Synthetic Signals For Ecg Beat Classification, Elliot Morgan Brown

Theses and Dissertations

A brief overview of electrocardiogram (ECG) properties and the characteristics of various cardiac conditions is given. Two different models are used to generate synthetic ECG signals. Domain knowledge is used to create synthetic examples of 16 different heart beat types with these models. Other techniques for synthesizing ECG signals are explored. Various machine learning models with different combinations of real and synthetic data are used to classify individual heart beats. The performance of the different methods and models are compared, and synthetic data is shown to be useful in beat classification.

Analyzing Evolution Of Rare Events Through Social Media Data, Xiaoyu Lu

Dissertations

Recently, some researchers have attempted to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of those studies can accurately deduce a time point when social media activities are most highly affected by a rare event because producing an accurate temporal pattern of social media during the evolution of a rare event is very difficult. This work expands the current studies along three directions. Firstly, we focus on the intensity of information volume and propose an innovative clustering …

Parkinsonian Speech And Voice Quality: Assessment And Improvement, Amr Gaballah

Electronic Thesis and Dissertation Repository

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Statistics show that nearly 90% of people impaired with PD develop voice and speech disorders. Speech production impairments in PD subjects typically result in hypophonia and consequently, poor speech signal-to-noise ratio (SNR) in noisy environments and inferior speech intelligibility and quality. Assessment, monitoring, and improvement of the perceived quality and intelligibility of Parkinsonian voice and speech are, therefore, paramount. In the first study of this thesis, the perceived quality of sustained vowels produced by PD patients was assessed through objective predictors. Subjective quality ratings of sustained vowels were collected from …

Data Analytics And Performance Enhancement In Edge-Cloud Collaborative Internet Of Things Systems, Tianqi Yu

Electronic Thesis and Dissertation Repository

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets …

Classification With Measurement Error In Covariates Or Response, With Application To Prostate Cancer Imaging Study, Kexin Luo

Electronic Thesis and Dissertation Repository

The research is motivated by the prostate cancer imaging study conducted at the University of Western Ontario to classify cancer status using multiple in-vivo images. The prostate cancer histological image and the in-vivo images are subject to misalignment in the co-registration procedure, which can be viewed as measurement error in covariates or response. We investigate methods to correct this problem.

The first proposed method corrects the predicted class probability when the data has misclassified labels. The correction equation is derived from the relationship between the true response and the error-prone response. The probability for the observed class label is adjusted …

Prediction Enhancement Through Machine Learning Of North Atlantic Tropical Cyclone Rapid Intensification: Diagnostics, Model Development, And Independent Verification, Alexandria Grimes

Theses and Dissertations

Forecasting rapid intensification (RI) of tropical cyclones (TCs) is considered one of the most challenging problems for the TC operational and research communities and remains a top priority for the National Hurricane Center. Upon landfall, these systems can have detrimental impacts to life and property. To support continued improvement of TC RI forecasts, this study investigated large-scale TC environments undergoing RI in the North Atlantic basin, specifically identifying important diagnostic variables in three-dimensional space. These results were subsequently used in the development of prognostic machine learning algorithms designed to predict RI 24 hours prior to occurrence. Using three RI definitions, …

Predicting Vulnerability For Requirements: A Data-Driven Approach, Sayem Mohammad Imtiaz

Theses and Dissertations

Being software security one of the primary concerns in the software engineering community, researchers are coming up with many preemptive approaches which are primarily designed to detect vulnerabilities in the post-implementation stage of the software development life-cycle (SDLC). While they have been shown to be effective in detecting vulnerabilities, the consequences are often expensive. Accommodating changes after detecting a bug or vulnerability in late stages of the SDLC is costly. On that account, in this thesis, we propose a novel framework to provide an additional measure of predicting vulnerabilities at earlier stages of the SDLC. To that end, we leverage …

A Machine Learning Approach To Genome Assessment, Charles Adam Thrash

Theses and Dissertations

A key use of high throughput sequencing technology is the sequencing and assembly of full genome sequences. These genome assemblies are commonly assessed using statistics relating to contiguity of the assembly. Measures of contiguity are not strongly correlated with information about the biological completion or correctness of the assembly, and a commonly reported metric, N50, can be misleading. Over the past ten years, multiple research groups have rejected the overuse of N50 and sought to develop more informative metrics. This research seeks to create a ranking method that includes biologically relevant information about the genome, such as completeness and correctness …