Physical Sciences and Mathematics Commons^™

Cloud Container Security’ Next Move, Vishakha Sadhwani

Dissertations and Theses

In the last few years, it is apparent to cybersecurity experts everywhere that the proverbial container tech genie is out of the bottle, and has been widely embraced across multiple organizations. To achieve the flexibility of building and deploying applications anywhere and everywhere, cloud native environments have gained great momentum and made the development lifecycle simpler than ever. However, container environments brings with them a range of cybersecurity issues that includes images, containers, hosts, runtimes, registries, and orchestration platforms, which needs the necessity to focus on investing in securing your container stack.

According to this report[1], released by cloud-native …

Scaling Epa-Rimm With Multicore System Management Interrupt Handlers, Alexander K. Freed

Dissertations and Theses

Continuous runtime integrity measurement mechanisms (RIMMs) can be used for timely detection of kernel and hypervisor rootkits. Researchers have proposed running RIMMs in privileged execution environments, such as the x86 architecture’s System Management Mode (SMM), to detect interference from rootkits that have gained control of the host operating system. However, the extended amount of time in SMM required to perform inspections can cause severe disruption to the host. A previously proposed RIMM design called EPA-RIMM addresses this by decomposing long inspections across multiple System Management Interrupts (SMI), the interrupt used to invoke SMM.

EPA-RIMM is intended for deployment on server-class …

Learning From Machines: Insights In Forest Transpiration Using Machine Learning Methods, Morgan Tholl

Dissertations and Theses

Machine learning has been used as a tool to model transpiration for individual sites, but few models are capable of generalizing to new locations without calibration to site data. Using the global SAPFLUXNET database, 95 tree sap flow data sites were grouped using three clustering strategies: by biome, by tree functional type, and through use of a k-means unsupervised clustering algorithm. Two supervised machine learning algorithms, a random forest algorithm and a neural network algorithm, were used to build machine learning models that predicted transpiration for each cluster. The performance and feature importance in each model were analyzed and compared …

Toward Analyzing The Diversity Of Extractive Summaries, Aaron David Hudson

Dissertations and Theses

As the amount of text generated across the internet continues to increase, developing methods for processing that text to glean valuable insights is paramount. Automatic text summarization is one such method that aims to provide a concise and representative summary of input text, allowing users access to the most salient points from a large amount of textual data. However, in working with these summaries, especially those generated from social media data, questions arise about not only the quality of a summary, but also its ability to reflect the diversity of user perspectives. This work examines the quality of summaries with …

Unpaired Style Transfer Conditional Generative Adversarial Network For Scanned Document Generation, David Jonathan Hawbaker

Dissertations and Theses

Neural networks are a powerful machine learning tool, especially when trained on a large dataset of relevant high-quality data. Generative adversarial networks, image super resolution and most other image manipulation neural networks require a dataset of images and matching target images for training. Collecting and compiling that data can be time consuming and expensive. This work explores an approach for building a dataset of paired document images with a matching scanned version of each document without physical printers or scanners. A dataset of these document image pairs could be used to train a generative adversarial network or image super resolution …

Making Curry With Rice: An Optimizing Curry Compiler, Steven Libby

Dissertations and Theses

In this dissertation we present the RICE optimizing compiler for the functional logic language Curry. This is the first general optimizing compiler for a functional logic language. Our work is based on the idea of compiling through program transformations, which we have adapted from the functional language compiler community. We also present the GAS system for generating new program transformations, which uses the power of functional logic programming to provide a flexible framework for describing transformations. This allows us to describe and implement a wide range of optimizations including inlining, shortcut deforestation, unboxing, and case shortcutting, a new optimization we …

Scenario Acceleration Through Automated Modelling: A Method And System For Creating Traceable Quantitative Future Scenarios Based On Fcm System Modeling And Natural Language Processing, Christopher W.H. Davis

Dissertations and Theses

Scenario planning is used extensively in strategic planning because it helps leaders broaden their perspectives and make better decisions by presenting possible futures in story form. Some of the benefits of using scenarios include breaking away from groupthink, creating better products, acceleration of organization learning and reducing bias. Product development teams, particularly for digital products, are gaining more autonomy in organizations and tend to manage risk by undergoing very short development iterations on their products while leaning on their consumers for feedback -- a process known as agile development. This method tends to limit the perspective of the team and …

Using Intrinsically-Typed Definitional Interpreters To Verify Compiler Optimizations In A Monadic Intermediate Language, Dani Barrack

Dissertations and Theses

Compiler optimizations are critical to the efficiency of modern functional programs. At the same time, optimizations that unintentionally change the semantics of programs can systematically introduce errors into programs that pass through them. The question of how to best verify that optimizations and other program transformations preserve semantics is an important one, given the potential for error introduction. Dependent types allow us to prove that properties about our programs are correct, as well as to design data types and interpreters in such a way that they are correct-by-construction. In this thesis, we explore the use of dependent types and intrinsically-typed …

An Automated Zoom Class Session Analysis Tool To Improve Education, Jack Arlo Cannon Ii

Dissertations and Theses

The recent shift towards remote education has presented new challenges for instructors with respect to teaching evaluation. Students in traditional classrooms send signals to instructors which provide feedback for the effectiveness of a given lecture. Virtual learning environments lack some of these communication channels and require new ways of collecting feedback. This work presents a suite of analysis tools for the virtual instructor. Given the transcript and video files for a Zoom meeting, this tool summarizes student sentiment and speaking characteristics. Sentiment scores are derived using state of the art Natural Language Processing (NLP) models. The video file is used …

2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice

Dissertations and Theses

In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients (MFCC), …

A Real-Time 3d Object Detection, Recognition And Presentation System On A Mobile Device For Assistive Navigation, Jin Chen

Dissertations and Theses

This thesis proposes an integrated solution for 3D object detection, recognition, and presentation to increase accessibility for various user groups in indoor areas through a mobile application. The system has three major components: a 3D object detection module, an object tracking and update module, and a voice and AR-enhanced interface. The 3D object detection module consists of pre-trained 2D object detectors and 3D bounding box estimation methods to detect the 3D poses and sizes of the objects in each camera frame. This module can easily adapt to various 2D object detectors (e.g., YOLO, SSD, Mask RCNN) based on the requested …

A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir

Dissertations and Theses

Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches toward the comprehension of urban flood risk using data science techniques on crowd-sourced data. This is accomplished by developing a series of data-driven models to identify flood factors of significance and localized areas of flood vulnerability in New York City (NYC). First, the infrastructural (catch basin clogs, …

Automated Chest X-Ray Analysis: Biomedical/Non-Biomedical Foreign Object Detection, Shotabdi Roy

Dissertations and Theses

The presence of non-biomedical foreign objects (NBFO) such as coins, buttons, jewelry, etc. and biomedical foreign objects (BFO) such as medical tubes, and devices in Chest X-Rays (CXRs) make accurate interpretation difficult as they do not indicate known biological abnormalities like excess fluids, Tuberculosis (TB) or cysts. Accurate diagnosis and screening, require these NBFO and BFO to be detected, categorized as either NBFO or BFO, and removed from CXR or highlighted in CXR for effective abnormality analysis. During an automated CXR screening process, NBFOs can adversely impact the process as typical machine learning algorithms would consider these objects to be …

Deep Features To Analyze Pulmonary Abnormalities In Chest X-Rays Due To Covid-19, Supriti Ghosh

Dissertations and Theses

Artificial Intelligence (AI) has contributed a lot since the beginning. Healthcare is no exception. Detecting anomaly/abnormality in (bio)medical image is crucial. In this thesis, we aim at detecting/screening pulmonary abnormalities due to Covid-19 in chest X-rays using deep features. We study CheXNet, DenseNet169, ResNet50 and VggNet16 to analyze CXRs to detect the evidence of Covid-19 in this research. CheXNet was primarily designed for radiologist-level pneumonia detection in Chest X-rays (CXRs). We created a benchmark dataset size of 4,716 CXRs (2,358 Covid-19 positive cases and 2,358 non-Covid cases (Healthy and Pneumonia cases)) and with k(=5) fold cross-validation technique, using the DenseNet, …

Analyzing Cough Sounds For The Evidence Of Covid-19 Using Deep Learning Models, Muntasir Mamun

Dissertations and Theses

Early detection of infectious disease is the must to prevent/avoid multiple infections, and Covid-19 is an example. When dealing with Covid-19 pandemic, Cough is still ubiquitously presented as one of the key symptoms in both severe and non-severe Covid-19 infections, even though symptoms appear differently in different sociodemographic categories. By realizing the importance of clinical studies, analyzing cough sounds using AI-driven tools could help add more values when it comes to decision-making. Moreover, for mass screening and to serve resource constrained regions, AI-driven tools are the must. In this thesis, Convolutional Neural Network (CNN) tailored deep learning models are studied …