Digital Commons Network^™

Storage Systems For Mobile-Cloud Applications, Nafize R. Paiker

Dissertations

Mobile devices have become the major computing platform in todays world. However, some apps on mobile devices still suffer from insufficient computing and energy resources. A key solution is to offload resource-demanding computing tasks from mobile devices to the cloud. This leads to a scenario where computing tasks in the same application run concurrently on both the mobile device and the cloud.

This dissertation aims to ensure that the tasks in a mobile app that employs offloading can access and share files concurrently on the mobile and the cloud in a manner that is efficient, consistent, and transparent to locations. …

Deep Learning Methods For Mining Genomic Sequence Patterns, Xin Gao

Dissertations

Nowadays, with the growing availability of large-scale genomic datasets and advanced computational techniques, more and more data-driven computational methods have been developed to analyze genomic data and help to solve incompletely understood biological problems. Among them, deep learning methods, have been proposed to automatically learn and recognize the functional activity of DNA sequences from genomics data. Techniques for efficient mining genomic sequence pattern will help to improve our understanding of gene regulation, and thus accelerate our progress toward using personal genomes in medicine.

This dissertation focuses on the development of deep learning methods for mining genomic sequences. First, we compare …

Polya Db3: A Database Cataloging Polyadenation Sites(Pas) Across Different Species And Their Conservation, Ram Mohan Nambiar

Theses

Polyadenation is an important process occurring in the messenger RNA that involves cleavage of 3 end nascent mRNAs and addition of poly(A) tails. For this thesis,I present PolyA DB3 ,a database cataloging cleavage and polyadenylation sites (PASs) in several genomes specifically for human,mouse,rat and chicken. This database is based on deep sequencing data. PASs are mapped by the 3’ region extraction and deep sequencing (3’READS) method, ensuring unequivocal PAS identification. Large volume of data based on diverse biological samples is used to increase PAS coverage and provide PAS usage information. Strand-specific RNA-seq data were used to extend annotated 3’ ends …

Computational Intelligence In Steganography: Adaptive Image Watermarking, Xin Zhong

Dissertations

Digital image watermarking, as an extension of traditional steganography, refers to the process of hiding certain messages into cover images. The transport image, called marked-image or stego-image, conveys the hidden messages while appears visibly similar to the cover-image. Therefore, image watermarking enables various applications such as copyright protection and covert communication. In a watermarking scheme, fidelity, capacity and robustness are considered as crucial factors, where fidelity measures the similarity between the cover- and marked-images, capacity measures the maximum amount of watermark that can be embedded, and robustness concerns the watermark extraction under attacks on the marked-image. Watermarking techniques are often …

Application Of Graphical Models In Protein-Protein Interactions And Dynamics, Amir Vajdi Hoojghan

Graduate Doctoral Dissertations

Every organism contains a few hundred to thousands of proteins. A protein is made of a sequence of molecular building blocks named amino acids. Amino acids will be referred to as residues. Every protein performs one or more functions in the cell. In order for a protein to do its job, it requires to bind properly to other partner proteins. Many genetic diseases such as cancer are caused by mutations (changes) of specific residues which cause disturbances in the functions of those proteins.

The problem of prediction of protein binding site is a crucial topic in computational biology. A protein …

Predicting Post-Procedural Complications Using Neural Networks On Mimic-Iii Data, Namratha Mohan

LSU Master's Theses

The primary focus of this paper is the creation of a Machine Learning based algorithm for the analysis of large health based data sets. Our input was extracted from MIMIC-III, a large Health Record database of more than 40,000 patients. The main question was to predict if a patient will have complications during certain specified procedures performed in the hospital. These events are denoted by the icd9 code 996 in the individuals' health record. The output of our predictive model is a binary variable which outputs the value 1 if the patient is diagnosed with the specific complication or 0 …

Remote Sensing Of Icebergs In Greenland's Fjords And Coastal Waters, Jessica Scheick

Electronic Theses and Dissertations

Increases in ocean water temperature are implicated in driving recent accelerated rates of mass loss from the Greenland Ice Sheet. Icebergs provide a key tool for gaining insight into ice-ocean interactions and until recently have been relatively understudied. Here we develop several methods that exploit icebergs visible in optical satellite imagery to provide insight on the ice--ocean environment and explore how iceberg datasets can be used to examine the physics of iceberg decay and parent glacier properties. First, a semi-automated algorithm, which includes a machine learning-based cloud mask, is applied to six years (2000-2002 and 2013-2015) of the Landsat archive …

Assessment Of Two Pedagogical Tools For Cybersecurity Education, Pranita Deshpande

University of New Orleans Theses and Dissertations

Cybersecurity is an important strategic areas of computer science, and a difficult discipline to teach effectively. To enhance and provide effective teaching and meaningful learning, we develop and assess two pedagogical tools: Peer instruction, and Concept Maps. Peer instruction teaching methodology has shown promising results in core computer science courses by reducing failure rates and improving student retention in computer science major. Concept maps are well-known technique for improving student-learning experience in class. This thesis document presents the results of implementing and evaluating the peer instruction in a semester-long cybersecurity course, i.e., introduction to computer security. Development and evaluation of …

Feasible Form Parameter Design Of Complex Ship Hull Form Geometry, Thomas L. Mcculloch

University of New Orleans Theses and Dissertations

This thesis introduces a new methodology for robust form parameter design of complex hull form geometry via constraint programming, automatic differentiation, interval arithmetic, and truncated hierarchical B- splines. To date, there has been no clearly stated methodology for assuring consistency of general (equality and inequality) constraints across an entire geometric form parameter ship hull design space. In contrast, the method to be given here can be used to produce guaranteed narrowing of the design space, such that infeasible portions are eliminated. Furthermore, we can guarantee that any set of form parameters generated by our method will be self consistent. It …

Leveraging Relocations In Elf-Binaries For Linux Kernel Version Identification, Manish Bhatt

University of New Orleans Theses and Dissertations

In this paper, we present a working research prototype codeid-elf for ELF binaries based on its Windows counterpart codeid, which can identify kernels through relocation entries extracted from the binaries. We show that relocation-based signatures are unique and distinct and thus, can be used to accurately determine Linux kernel versions and derandomize the base address of the kernel in memory (when kernel Address Space Layout Randomization is enabled). We evaluate the effectiveness of codeid-elf on a subset of Linux kernels and find that the relocations in kernel code have nearly 100\% code coverage and low similarity (uniqueness) across various kernels. …

Proposing An Optimized Algorithm For Consolidating Electric-Powered Shared Scooters Into Hubs For Efficiently Managing Their Charging And Maintenance Operations, Ojen Goshtasb

Master's Projects

The use of vehicles other than ones containing combustion engines have been adopted significantly over the past few years and the direction it’s taking seems to be the future of urban transportation. The hottest vehicle of choice currently is the electric scooter. They are small and portable, fast, and less costly compared to getting in a cab from Lyft or Uber to get around town. The goal of this paper is to make a proposal to drive the creation of a safe, efficient system for these scooters’ management. This must be beneficial to all parties involved; the rider, non-riders, and …

Management And Security Of Iot Systems Using Microservices, Tharun Theja Kammara

Master's Projects

Devices that assist the user with some task or help them to make an informed decision are called smart devices. A network of such devices connected to internet are collectively called as Internet of Things (IoT). The applications of IoT are expanding exponentially and are becoming a part of our day to day lives. The rise of IoT led to new security and management issues. In this project, we propose a solution for some major problems faced by the IoT devices, including the problem of complexity due to heterogeneous platforms and the lack of IoT device monitoring for security and …

Fine-Grained Topic Models Using Anchor Words, Jeffrey A. Lund

Theses and Dissertations

Topic modeling is an effective tool for analyzing the thematic content of large collections of text. However, traditional probabilistic topic modeling is limited to a small number of topics (typically no more than hundreds). We introduce fine-grained topic models, which have large numbers of nuanced and specific topics. We demonstrate that fine-grained topic models enable use cases not currently possible with current topic modeling techniques, including an automatic cross-referencing task in which short passages of text are linked to other topically related passages. We do so by leveraging anchor methods, a recent class of topic model based on non-negative matrix …

Predicting Software Fault Proneness Using Machine Learning, Sanjay Ghanathey

Electronic Thesis and Dissertation Repository

Context: Continuous Integration (CI) is a DevOps technique which is widely used in practice. Studies show that its adoption rates will increase even further. At the same time, it is argued that maintaining product quality requires extensive and time consuming, testing and code reviews. In this context, if not done properly, shorter sprint cycles and agile practices entail higher risk for the quality of the product. It has been reported in literature [68], that lack of proper test strategies, poor test quality and team dependencies are some of the major challenges encountered in continuous integration and deployment.

Objective: The objective …

Partitioning And Offloading For Iot And Video Streaming Applications That Utilize Computing Resources At The Network Edge, Navid Bayat

Electronic Thesis and Dissertation Repository

The Internet of Things (IoT) is a concept in which physical objects embedded with sensors, actuators, and network connectivity can communicate and react to their surroundings. IoT applications connect physical objects for the purpose of decision making by sensing and analysing generated data from the embedded sensors in physical objects. IoT applications are growing rapidly as sensors become less expensive. Sensors generate large amounts of data that may meaningless unless the data is used to derive knowledge with in a certain period of time. Stream processing paradigm is used by IoT to provide requirements of IoT applications. In a stream …

Virtual Robot Climbing Using Reinforcement Learning, Ujjawal Garg

Master's Projects

Reinforcement Learning (RL) is a field of Artificial Intelligence that has gained a lot of attention in recent years. In this project, RL research was used to design and train an agent to climb and navigate through an environment with slopes. We compared and evaluated the performance of two state-of-the-art reinforcement learning algorithms for locomotion related tasks, Deep Deterministic Policy Gradients (DDPG) and Trust Region Policy Optimisation (TRPO). We observed that, on an average, training with TRPO was three times faster than DDPG, and also much more stable for the locomotion control tasks that we experimented. We conducted experiments and …

Deep Visual Recommendation System, Raksha Sunil

Master's Projects

Recommendation system is a filtering system that predicts ratings or preferences that a user might have. Recommendation system is an evolved form of our trivial information retrieval systems. In this paper, we present a technique to solve new item cold start problem. New item cold start problem occurs when a new item is added to a shopping website like Amazon.com. There is no metadata for this item, no ratings and no reviews because it’s a new item in the system. Absence of data results in no recommendation or bad recommendations. Our approach to solve new item cold start problem requires …

Pantry: A Macro Library For Python, Derek Pang

Master's Projects

Python lacks a simple way to create custom syntax and constructs that goes outside of its own syntax rules. A paradigm that allows for these possibilities to exist within languages is macros. Macros allow for a shorter set of syntax to expand into a longer set of instructions at compile-time. This gives the capability to evolve the language to fit personal needs.

Pantry, implements a hygienic text-substitution macro system for Python. Pantry achieves this through the introduction of an additional preparsing step that utilizes parsing and lexing of the source code. Pantry proposes a way to simply declare a pattern …

Application Of Machine Learning Techniques In Credit Card Fraud Detection, Ronish Shakya

UNLV Theses, Dissertations, Professional Papers, and Capstones

Credit card fraud is an ever-growing problem in today’s financial market. There has been a rapid increase in the rate of fraudulent activities in recent years causing a substantial financial loss to many organizations, companies, and government agencies. The numbers are expected to increase in the future, because of which, many researchers in this field have focused on detecting fraudulent behaviors early using advanced machine learning techniques. However, the credit card fraud detection is not a straightforward task mainly because of two reasons: (i) the fraudulent behaviors usually differ for each attempt and (ii) the dataset is highly imbalanced, i.e., …

Combinatorial Ant Optimization And The Flowshop Problem, Tasmin Chowdhury

UNLV Theses, Dissertations, Professional Papers, and Capstones

Researchers have developed efficient techniques, meta-heuristics to solve many Combinatorial Optimization (CO) problems, e.g., Flow shop Scheduling Problem, Travelling Salesman Problem (TSP) since the early 60s of the last century. Ant Colony Optimization (ACO) and its variants were introduced by Dorigo et al. [DBS06] in the early 1990s which is a technique to solve CO problems. In this thesis, we used the ACO technique to find solutions to the classic Flow shop Scheduling Problem and proposed a novel method for solution improvement. Our solution is composed of two phases; in the first phase, we solved TSP using ACO technique which …

Efficient And Practical Composition Of Lock-Free Data Structures, Neha Bajracharya

UNLV Theses, Dissertations, Professional Papers, and Capstones

A concurrent data object is lock-free if it guarantees that at least one, among all concurrent operations, finishes after a finite number of steps. In other words, a lock free technique guarantees that some thread always makes progress. Lock-free data objects offer several advantages over their blocking counterparts, such as being immune to deadlocks and priority inversion, and typically provide high scalability and performance, especially in shared memory multiprocessor architectures.

Composition of data structures is a powerful approach to combine simple data structures to create more complex ones. It works as a building block for many advanced useful data structures. …

Scheduling Two Machines With Dissimilar Costs, Madhurupa Moitra

UNLV Theses, Dissertations, Professional Papers, and Capstones

We consider two devices, which has states ON and OFF. In the ON state, the devices use their full power whereas in the OFF state the devices consume no energy but a constant cost is associated with switching back to ON. Such two devices are configured with different run and power-up costs on which a sequence of jobs must be processed. The object is to minimize the cost. Such systems are widely used to conserve energy, for example, to speed scale CPUs, to control data centers, or to manage renewable energy.

The problems are studied in the framework of online …

A Snowball's Chance: Debt Snowball Vs. Debt Avalanche, Evan Mcallister

Senior Honors Projects, 2010-2019

Traditional mathematical analysis states that the most efficient way to pay off interest-bearing consumer debt is to pay the individual debts in order from largest to smallest interest rate. In doing this, the debtor will eliminate the largest sources of interest first, thus shortening the overall time-to-pay. This method is known as the “Debt Avalanche.” The “Debt Snowball” method, popularized in large part by investor-author David Ramsey, recommends that consumers pay debts in order from smallest to largest, regardless of interest rate. In this paper, I conduct an empirical analysis of the Federal Reserve’s Survey of Consumer Finance (SCF), calculating …

Computational Explorations Of Information And Mechanism Design In Markets, Zhuoshu Li

McKelvey School of Engineering Theses & Dissertations

Markets or platforms assemble multiple selfishly-motivated and strategic agents. The outcomes of such agent interactions depend heavily on the rules, regulations, and norms of the platform, as well as the information available to agents. This thesis investigates the design and analysis of mechanisms and information structures through the ``computational lens'' in both static and dynamic settings. It both addresses the outcome of single platforms and fills a gap in the study of the dynamics of multiple platform interactions.

In static market settings, we are particularly interested in the role of information, because mechanisms are harder to change than the information …

Nanopower Analog Frontends For Cyber-Physical Systems, Kenji Aono

McKelvey School of Engineering Theses & Dissertations

In a world that is increasingly dominated by advances made in digital systems, this work will explore the exploiting of naturally occurring physical phenomena to pave the way towards a self-powered sensor for Cyber-Physical Systems (CPS). In general, a sensor frontend can be broken up into a handful of basic stages: transduction, filtering, energy conversion, measurement, and interfacing. One analog artifact that was investigated for filtering was the physical phenomenon of hysteresis induced in current-mode biquads driven near or at their saturation limit. Known as jump resonance, this analog construct facilitates a higher quality factor to be brought about without …

Fast Objective Coupled Planar Illumination Microscopy, Cody Jonathan Greer

Arts & Sciences Electronic Theses and Dissertations

Among optical imaging techniques light sheet fluorescence microscopy stands out as one of the most attractive for capturing high-speed biological dynamics unfolding in three dimensions. The technique is potentially millions of times faster than point-scanning techniques such as two-photon microscopy. This potential is especially poignant for neuroscience applications due to the fact that interactions between neurons transpire over mere milliseconds within tissue volumes spanning hundreds of cubic microns. However current-generation light sheet microscopes are limited by volume scanning rate and/or camera frame rate. We begin by reviewing the optical principles underlying light sheet fluorescence microscopy and the origin of these …

Learning About Large Scale Image Search: Lessons From Global Scale Hotel Recognition To Fight Sex Trafficking, Abby Stylianou

McKelvey School of Engineering Theses & Dissertations

Hotel recognition is a sub-domain of scene recognition that involves determining what hotel is seen in a photograph taken in a hotel. The hotel recognition task is a challenging computer vision task due to the properties of hotel rooms, including low visual similarity between rooms in the same hotel and high visual similarity between rooms in different hotels, particularly those from the same chain. Building accurate approaches for hotel recognition is important to investigations of human trafficking. Images of human trafficking victims are often shared by traffickers among criminal networks and posted in online advertisements. These images are often taken …

The Evolution Of Computational Propaganda: Trends, Threats, And Implications Now And In The Future, Holly Schnader

Senior Honors Projects, 2010-2019

Computational propaganda involves the use of selected narratives, social networks, and complex algorithms in order to develop and conduct influence operations (Woolley and Howard, 2017). In recent years the use of computational propaganda as an arm of cyberwarfare has increased in frequency. I aim to explore this topic to further understand the underlying forces behind the implementation of this tactic and then conduct a futures analysis to best determine how this topic will change over time. Additionally, I hope to gain insights on the implications of the current and potential future trends that computational propaganda has.

My preliminary assessment shows …

Uas-Based Object Tracking Via Deep Learning, Marc Dinh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Tracking is the task of identifying an object of interest and detect its position over time, and has numerous applications like surveillance, security and traffic control. In present times, unmanned aerial vehicles (UAV) have been more and more common which provides us with a new and less explored domain, with an ideal vantage point for surveillance and monitoring applications.. Aerial tracking is a particularly challenging task as it introduces new environmental variables such as rapid motion in 3D space. We propose a new deep learned tracker architecture catered to aerial tracking.

First, a study of six state-of-the-art deep learned trackers …

Secured Data Masking Framework And Technique For Preserving Privacy In A Business Intelligence Analytics Platform, Osama Ali

Electronic Thesis and Dissertation Repository

The main concept behind business intelligence (BI) is how to use integrated data across different business systems within an enterprise to make strategic decisions. It is difficult to map internal and external BI’s users to subsets of the enterprise’s data warehouse (DW), resulting that protecting the privacy of this data while maintaining its utility is a challenging task. Today, such DW systems constitute one of the most serious privacy breach threats that an enterprise might face when many internal users of different security levels have access to BI components. This thesis proposes a data masking framework (iMaskU: Identify, Map, Apply, …