Physical Sciences and Mathematics Commons^™

Ferris Bakes, Ferris Dehart

Honors Theses

No abstract provided.

Memory Forensics Comparison Of Apple M1 And Intel Architecture Using Volatility Framework, Joshua Duke

LSU Master's Theses

Memory forensics allows an investigator to get a full picture of what is occurring on-device at the time that a memory sample is captured and is frequently used to detect and analyze malware. Malicious attacks have evolved from living on disk to having persistence mechanisms in the volatile memory (RAM) of a device and the information that is captured in memory samples contains crucial information for full forensic analysis by cybersecurity professionals. Recently, Apple unveiled computers containing a custom designed system on a chip (SoC) called the M1 that is based on ARM architecture. Our research focused on the differences …

System Design And Optimization For Efficient Flash-Based Caching In Data Centers, Jian Liu

LSU Doctoral Dissertations

Modern data centers are the backbone of today’s Internet-based services and applications. With the explosive growth of the Internet data and a wider range of data-intensive applications being deployed, it is increasingly challenging for data centers to satisfy the ever-increasing demand for high-quality data services. To relieve the heavy burden on data center systems and accelerate data processing, a popular cost-efficient solution is to deploy high-speed, large-capacity flash-based cache systems. However, we are facing multiple critical challenges from device hardware, systems, to application workloads. In this dissertation, we focus on designing highly efficient caching solutions to cope with the explosive …

Laser Surface Treatment And Laser Powder Bed Fusion Additive Manufacturing Study Using Custom Designed 3d Printer And The Application Of Machine Learning In Materials Science, Hao Wen

LSU Doctoral Dissertations

Selective Laser Melting (SLM) is a laser powder bed fusion (L-PBF) based additive manufacturing (AM) method, which uses a laser beam to melt the selected areas of the metal powder bed. A customized SLM 3D printer that can handle a small quantity of metal powders was built in the lab to achieve versatile research purposes. The hardware design, electrical diagrams, and software functions are introduced in Chapter 2. Several laser surface engineering and SLM experiments were conducted using this customized machine which showed the functionality of the machine and some prospective fields that this machine can be utilized. Chapter 3 …

Learning To Interpret Fluid Type Phenomena Via Images, Simron Thapa

LSU Doctoral Dissertations

Learning to interpret fluid-type phenomena via images is a long-standing challenging problem in computer vision. The problem becomes even more challenging when the fluid medium is highly dynamic and refractive due to its transparent nature. Here, we consider imaging through such refractive fluid media like water and air. For water, we design novel supervised learning-based algorithms to recover its 3D surface as well as the highly distorted underground patterns. For air, we design a state-of-the-art unsupervised learning algorithm to predict the distortion-free image given a short sequence of turbulent images. Specifically, we design a deep neural network that estimates the …

Characterizing And Optimizing Asynchronous Event-Driven Architecture For Modern Cloud Systems, Shungeng Zhang

LSU Doctoral Dissertations

Achieving good performance and high efficiency simultaneously is an essential requirement for emerging modern cloud systems such as e-commerce due to their business impact. For example, Akamai reported that every 100ms delay in website load time could lead to a 6% drop in sales. Unfortunately, achieving good performance (e.g., low latency) for modern cloud systems at high resource utilization is significantly challenging. Despite continuing efforts by cloud professionals, however, they have consistently experienced performance degradation problems (e.g., the long-tail latency problem) due to the bursty workload in the cloud. To resolve the performance degradation problems, many previous research efforts have …

Machine Learning Methods For Depression Detection Using Smri And Rs-Fmri Images, Marzieh Sadat Mousavian

LSU Doctoral Dissertations

Major Depression Disorder (MDD) is a common disease throughout the world that negatively influences people’s lives. Early diagnosis of MDD is beneficial, so detecting practical biomarkers would aid clinicians in the diagnosis of MDD. Having an automated method to find biomarkers for MDD is helpful even though it is difficult. The main aim of this research is to generate a method for detecting discriminative features for MDD diagnosis based on Magnetic Resonance Imaging (MRI) data.

In this research, representational similarity analysis provides a framework to compare distributed patterns and obtain the similarity/dissimilarity of brain regions. Regions are obtained by either …

Quantifying Feature Overlaps In Deep Neural Networks And Their Applications In Unsupervised Learning And Generative Adversarial Networks, Edward Collier

LSU Doctoral Dissertations

Deep neural network learn a wide range of features from the input data. These features take many different forms from, structural to textural, and can be very scale invariant. The complexity of these features also differs from layer to layer. Much like the human brain, this behavior in deep neural networks can also be used to cluster and separate classes. Applicability in deep neural networks is the quantitative measurement of the networks ability to differentiate between clusters in feature space. Applicability can measure the differentiation between clusters of sets of classes, single classes, or even within the same class. In …

Musical Gesture Through The Human Computer Interface: An Investigation Using Information Theory, Michael Vincent Blandino

LSU Doctoral Dissertations

This study applies information theory to investigate human ability to communicate using continuous control sensors with a particular focus on informing the design of digital musical instruments. There is an active practice of building and evaluating such instruments, for instance, in the New Interfaces for Musical Expression (NIME) conference community. The fidelity of the instruments can depend on the included sensors, and although much anecdotal evidence and craft experience informs the use of these sensors, relatively little is known about the ability of humans to control them accurately. This dissertation addresses this issue and related concerns, including continuous control performance …

Static Analysis Of Haskell For Suitable Metrics For Grading, Christian Fontenot

Honors Theses

No abstract provided.

Integration And Analysis Of Gaze Behavior In Augmented Reality, George Villaume

Honors Theses

No abstract provided.

Improving Memory Forensics Through Emulation And Program Analysis, Ryan Dominick Maggio

LSU Doctoral Dissertations

Memory forensics is an important tool in the hands of investigators. However, determining if a computer is infected with malicious software is time consuming, even for experts. Tasks that require manual reverse engineering of code or data structures create a significant bottleneck in the investigative workflow. Through the application of emulation software and symbolic execution, these strains have been greatly lessened, allowing for faster and more thorough investigation. Furthermore, these efforts have reduced the barrier for forensic investigation, so that reasonable conclusions can be drawn even by non-expert investigators. While previously Volatility had allowed for the detection of malicious hooks …

Evaluation Of Algorithms For Randomizing Key Item Locations In Game Worlds, Caleb Johnson

LSU Master's Theses

In the past few years, game randomizers have become increasingly popular. In general, a game randomizer takes some aspect of a game that is usually static and shuffles it somehow. In particular, in this paper we will discuss the type of randomizer that shuffles the locations of items in a game where certain key items are needed to traverse the game world and access some of these locations. Examples of these types of games include series such as The Legend of Zelda and Metroid.

In order to accomplish this shuffling in such a way that the player is able to …

The Dna Cloud: Is It Alive?, Theodoros Bargiotas

LSU Doctoral Dissertations

In this analysis, I will firstly be presenting the current knowledge concerning the materiality of the internet based Cloud, which I will henceforth be referring to as simply the Cloud. For organisation purposes I have created two umbrella categories under which I place the ongoing research in the field. Scholars have been addressing the issue of Cloud materiality through broadly two prisms: sociological materiality and geopolitical materiality. The literature of course deals with the intricacies of the Cloud based on its present ferromagnetic storage functionality. However, developments in synthetic biology have caused private tech companies and University spin-offs to flirt …

Choosing Isds As A Major: Predictive Analysis, Sarah Johnson

Honors Theses

No abstract provided.

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 …

Development Of Reduced Order Models Using Reservoir Simulation And Physics Informed Machine Learning Techniques, Mark V. Behl Jr

LSU Master's Theses

Reservoir simulation is the industry standard for prediction and characterization of processes in the subsurface. However, simulation is computationally expensive and time consuming. This study explores reduced order models (ROMs) as an appropriate alternative. ROMs that use neural networks effectively capture nonlinear dependencies, and only require available operational data as inputs. Neural networks are a black box and difficult to interpret, however. Physics informed neural networks (PINNs) provide a potential solution to these shortcomings, but have not yet been applied extensively in petroleum engineering.

A mature black-oil simulation model from Volve public data release was used to generate training data …

Adaptive Data Migration In Load-Imbalanced Hpc Applications, Parsa Amini

LSU Doctoral Dissertations

Distributed parallel applications need to maximize and maintain computer resource utilization and be portable across different machines. Balanced execution of some applications requires more effort than others because their data distribution changes over time. Data re-distribution at runtime requires elaborate schemes that are expensive and may benefit particular applications.

This dissertation discusses a solution for HPX applications to monitor application execution with APEX and use AGAS migration to adaptively redistribute data and load balance applications at runtime to improve application performance and scaling behavior. This dissertation provides evidence for the practicality of using the Active Global Address Space as is …

Quantum Criticality In Strongly Correlated Electron Systems, Samuel Obadiah Kellar

LSU Doctoral Dissertations

The study of the Hubbard model in three dimensions contains a variety of phases dependent upon the chosen parameters. This thesis shows that there is the indication of a zero temperature phase transition at a finite doping. The Hubbard model has been used to identify a similar quantum critical point in two dimensions. The presented results continue these investigations. The system demonstrates a strange metal phase at finite temperature which cannot be described in term of the conventional Fermi liquid. While there have been extensive studies over the past three decades for such materials in two dimensions, there are few …

Evolution Of Computational Thinking Contextualized In A Teacher-Student Collaborative Learning Environment., John Arthur Underwood

LSU Doctoral Dissertations

The discussion of Computational Thinking as a pedagogical concept is now essential as it has found itself integrated into the core science disciplines with its inclusion in all of the Next Generation Science Standards (NGSS, 2018). The need for a practical and functional definition for teacher practitioners is a driving point for many recent research endeavors. Across the United States school systems are currently seeking new methods for expanding their students’ ability to analytically think and to employee real-world problem-solving strategies (Hopson, Simms, and Knezek, 2001). The need for STEM trained individuals crosses both the vocational certified and college degreed …

Predictive Modeling Of Asynchronous Event Sequence Data, Jin Shang

LSU Doctoral Dissertations

Large volumes of temporal event data, such as online check-ins and electronic records of hospital admissions, are becoming increasingly available in a wide variety of applications including healthcare analytics, smart cities, and social network analysis. Those temporal events are often asynchronous, interdependent, and exhibiting self-exciting properties. For example, in the patient's diagnosis events, the elevated risk exists for a patient that has been recently at risk. Machine learning that leverages event sequence data can improve the prediction accuracy of future events and provide valuable services. For example, in e-commerce and network traffic diagnosis, the analysis of user activities can be …

Information Retrieval-Based Optimization Approaches For Requirement Traceability Recovery, Danissa Victoria Rodriguez Caraballo

LSU Doctoral Dissertations

Requirements traceability provides support for important software engineering activities. Requirements traceability recovery (RTR) is becoming increasingly important due to the numerous benefits to the overall quality of software. Improving the RTR problem has become an active topic of research for software engineers; researchers have proposed a number of approaches for improving and automating RTR across the requirements and the source code of the system. Textual analysis and Information Retrieval (IR) techniques have been applied to the RTR problem for many years; however, most of the existing IR-based methodologies applied to the RTR problem are semiautomatic or time-consuming, even though many …

Applications And Implementation Of A Satellite-Based Quantum Internet, Renèe Desporte

Honors Theses

No abstract provided.

Automated Extraction Of Network Activity From Memory Resident Code, Austin Nicholas Sellers

LSU Master's Theses

Advancements in malware development, including the use of file-less and memory-only payloads, have led to a significant interest in the use of volatile memory analysis by digital forensics practitioners. Memory analysis can uncover a wealth of information not available via traditional analysis, such as the discovery of injected code, hooked APIs, and more. Unfortunately, the process of analyzing such malicious code is largely left to analysts who must manually reverse engineer the code to discover its intent. This task is not only slow and error-prone, but is also generally left only to senior-level analysts to perform, given that significant reverse …

Finding Music In Chaos: Designing And Composing With Virtual Instruments Inspired By Chaotic Equations, Landon P. Viator

LSU Doctoral Dissertations

Using chaos theory to design novel audio synthesis engines has been explored little in computer music. This could be because of the difficulty of obtaining harmonic tones or the likelihood of chaos-based synthesis engines to explode, which then requires re-instantiating of the engine to proceed with sound production. This process is not desirable when composing because of the time wasted fixing the synthesis engine instead of the composer being able to focus completely on the creative aspects of composition. One way to remedy these issues is to connect chaotic equations to individual parts of the synthesis engine instead of relying …

Improving Ocr Accuracy Of Damaged Pictures With Generative Adversarial Networks, Pu Du

LSU Master's Theses

In this thesis, we focus on resolving the inpainting problem and improving Optical Character Recognition (OCR) accuracy of damaged text images at character level. We present a Generative Adversarial Network (GAN)-based model conditioned on class labels for image inpainting. This model is a deep convolutional neural network with encoder-decoder style architecture which can process images with holes at random locations. Experiments on the character images dataset demonstrate that our proposed model generates promising inpainting results and significantly improve OCR accuracy by reconstructing missing parts of damaged character images.

@Houstonpolice: An Exploratory Case Of Twitter During Hurricane Harvey, Seungwon Yang, Brenton Stewart

Faculty Publications

Abstract

Purpose

The purpose of this paper is to examine the Houston Police Department (HPD)’s public engagement efforts using Twitter during Hurricane Harvey, which was a large-scale urban crisis event.

Design/methodology/approach

This study harvested a corpus of over 13,000 tweets using Twitter’s streaming API, across three phases of the Hurricane Harvey event: preparedness, response and recovery. Both text and social network analysis (SNA) techniques were employed including word clouds, n-gram analysis and eigenvector centrality to analyze data.

Findings

Findings indicate that departmental tweets coalesced around topics of protocol, reassurance and community resilience. Twitter accounts of governmental agencies, such as …

Managing Overheads In Asynchronous Many-Task Runtime Systems, Bibek Wagle

LSU Doctoral Dissertations

Asynchronous Many-Task (AMT) runtime systems are based on the idea of dividing an algorithm into small units of work, known as tasks. The runtime system is then responsible for scheduling and executing these tasks in an efficient manner by taking into account the resources provided to it and the associated data dependencies between the tasks. One of the primary challenges faced by AMTs is managing such fine-grained parallelism and the overheads associated with creating, scheduling and executing tasks. This work develops methodologies for assessing and managing overheads associated with fine-grained task execution in HPX, our exemplar Asynchronous Many-Task runtime system. …

High Performance Fuzz Testing Of Memory Forensics Frameworks, Arian Dokht Shahmirza

LSU Master's Theses

The analysis of the volatile memory (RAM) of a computer system, known as memory forensics, is a critical component of modern digital forensics investigations. Since the evidence provided by memory forensics is vital, it is necessary for there to be automated solutions that implement the analysis. Volatility is the most widely used memory forensics framework and also contains the most functionality of all tools publicly available. Volatility, as well as all other memory forensics frameworks, are extremely complex software systems as they must parse a substantial number of in-memory data structures and their associated values. Given the reliance on memory …

A Study On Large-Scale Deep Learning In Bioinformatics And Biomedical Applications, Shayan Shams

LSU Doctoral Dissertations

Recent advances in Artificial Intelligence and deep learning have provided researchers in various fields insights into the analysis of multiple datasets. These applications include image analysis, text analysis, and many more. However, the effectiveness of deep learning in some areas, such as biomedical imaging and genomic research, has been overshadowed by the variance in the types and complexity of data. This is in addition to the expensive labeling process and the limited size of datasets in these fields. These challenges require advanced deep learning models capable of learning from a small dataset and also from a small number of labeled …