Physical Sciences and Mathematics Commons^™

A Comparison Of Instructional Efficiency Models In Third Level Education, Murali Rajendran

Dissertations

This study investigates the validity and sensitivity of a novel model of instructional efficiency: the parabolic model. The novel model is compared against state-of-the-art models present in instructional design today; Likelihood model, Deviational model and Multidimensional model. This models is based on the assumption that optimal mental workload and high performance leads to high efficiency, while other models assume that low mental workload and high performance leads to high efficiency. The investigation makes use of two instructional design conditions: a direct instructions approach to learning and its extension with a collaborative activity. A control group received the former instructional design …

Improving A Network Intrusion Detection System’S Efficiency Using Model-Based Data Augmentation, Vinicius Waterkemper Lodetti

Dissertations

A network intrusion detection system (NIDS) is one important element to mitigate cybersecurity risks, the NIDS allow for detecting anomalies in a network which may be a cyberattack to a corporate network environment. A NIDS can be seen as a classification problem where the ultimate goal is to distinguish between malicious traffic among a majority of benign traffic. Researches on NIDS are often performed using outdated datasets that don’t represent the actual cyberspace. Datasets such as the CICIDS2018 address this gap by being generated from attacks and an infrastructure that reflects an up-to-date scenario.

A problem may arise when machine …

Exploiting Bert And Roberta To Improve Performance For Aspect Based Sentiment Analysis, Gagan Reddy Narayanaswamy

Dissertations

Sentiment Analysis also known as opinion mining is a type of text research that analyses people’s opinions expressed in written language. Sentiment analysis brings together various research areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is fast becoming of major importance to companies and organizations as it is started to incorporate online commerce data for analysis. Often the data on which sentiment analysis is performed will be reviews. The data can range from reviews of a small product to a big multinational corporation. The goal of performing sentiment analysis is to extract information from those …

An Evaluation On The Performance Of Code Generated With Webassembly Compilers, Raymond Phelan

Dissertations

WebAssembly is a new technology that is revolutionizing the web. Essentially it is a low-level binary instruction set that can be run on browsers, servers or stand-alone environments. Many programming languages either currently have, or are working on, compilers that will compile the language into WebAssembly. This means that applications written in languages like C++ or Rust can now be run on the web, directly in a browser or other environment. However, as we will highlight in this research, the quality of code generated by the different WebAssembly compilers varies and causes performance issues. This research paper aims to evaluate …

Hemingway, Sartre, And Secularization: Finding Religion Without God, Marcos Antonio Norris

Dissertations

This dissertation intervenes in the critical debate over Ernest Hemingway’s religious orientation. One camp of Hemingway scholars argues that he was a practicing Catholic, while the other camp argues that Hemingway was an existential atheist. Rather than side with either camp in this binary debate, my research offers a third option that bridges the gap between these opposing positions. Examining Hemingway’s life and works through the eyes of contemporary political philosopher Giorgio Agamben, I argue that Hemingway is, properly speaking, neither a secularist nor a theist, but a secularized theist, whose “religion” takes the form of masculine volition, or sovereign, …

Stellar Classification Of Folded Spectra Using The Mk Classification Scheme And Convolutional Neural Networks, John Magee

Dissertations

The year 1943 saw the introduction of the Morgan-Keenan (MK) classification scheme and this replaced the existing Harvard Classification scheme. Both stellar classification scheme are fundamentally grounded in the field of spectroscopy. The Harvard Classification scheme classified stars based on stellar surface temperature. The MK Classification scheme introduced the concept of a luminosity class that is intrinsically linked to the surface gravity of a star. Temperature and luminosity class values are estimated directly from the stellar spectrum.

Machine learning is a well-established technique in astronomy. Traditionally, a spectrum is treated as a one-dimensional sequence of data. Techniques such as artificial …

Event-Driven Servers Using Asynchronous, Non-Blocking Network I/O: Performance Evaluation Of Kqueue And Epoll, Lorcan Leonard

Dissertations

This research project evaluates the performance of kqueue and epoll in the context of event-driven servers. The evaluation is done through benchmarking and tracing which are used to measure throughput and execution time respectively. The experiment is repeated for both a virtualised and native server environment. The results from the experiment are statistically analysed and compared. These results show significant differences between kqueue and epoll, and a profound impact of virtualisation as a variable.

Secularism, The Oxford Movement, And Religious Aesthetics In John Keble, Christina Rossetti, Adelaide Anne Procter, And Gerard Manley Hopkins, Mary Harmon

Dissertations

This dissertation examines the relationship between secularism and the development of Oxford Movement poetry. The members of the movement sought to restore pre-Reformation religious practices in the Anglican Church while maintaining its distinctiveness from the Roman Catholic and Orthodox traditions, which led to the development of unique liturgy, architecture, hymns, and poetry, among other art forms. I argue that secularism simultaneously made the movement more possible while also making it more unstable. Secularism gave the theologians and poets of the movement enough freedom to borrow from older Christian traditions, yet the movement failed to revive the Anglican Church (several members …

Identifying Roles Of Software Developers From Their Answers On Stack Overflow, Dean Power

Dissertations

Stack Overflow is the world’s largest community of software developers. Users ask and answer questions on various tagged topics of software development. The set of questions a site user answers is representative of their knowledge base, or “wheelhouse”. It is proposed that clustering users by their wheelhouse yields communities of similar software developers by skill-set. These communities represent the different roles within software development and could be used as the basis to define roles at any point in time in an ever-evolving landscape of software development. A network graph of site users, linked if they answered questions on the same …

Combination Of Facebook Prophet And Attention-Based Lstm With Multi- Source Data For Indian Stock Market Prediction, Pavan Nagesh

Dissertations

The stock market prediction has been the subject of interest to various researchers and analysts due to its highly unpredictable nature and serves as a perfect example for time series forecasting. Over the years deep learning models such as Long-Term Short-Term Memory and statistical models such as Autoregressive Integrated Moving Average have shown promising results in predicting future stock prices. But the results from these models cannot be generalized as they fail to incorporate the dynamics of the market and influence of several external factors such as political, social, investor's emotion, etc on stock markets. Recently Facebook’s creation of the …

Feature Augmentation For Improved Topic Modeling Of Youtube Lecture Videos Using Latent Dirichlet Allocation, Nakul Srikumar

Dissertations

Application of Topic Models in text mining of educational data and more specifically, the text data obtained from lecture videos, is an area of research which is largely unexplored yet holds great potential. This work seeks to find empirical evidence for an improvement in Topic Modeling by pre- extracting bigram tokens and adding them as additional features in the Latent Dirichlet Allocation (LDA) algorithm, a widely-recognized topic modeling technique. The dataset considered for analysis is a collection of transcripts of video lectures on Machine Learning scraped from YouTube. Using the cosine similarity distance measure as a metric, the experiment showed …

Performance Comparison Between A Distributed Particle Swarm Algorithm And A Centralised Algorithm, Ciarán O’Loughlin

Dissertations

Particle Swarm optimisation (PSO) is a particular form of swarm intelligence, which itself is an innovative intelligent paradigm for solving optimization problems. PSO is generally used to find a global optimum in a single optimisation function. This typically occurs on one node(machine) but there has been a significant body of research into creating distributed implementations of the PSO algorithm. Such research has often focused on the creation and performance of the distributed implementation in an isolated manner or compared to different distributed algorithms.

This research piece aims to bridge a gap in the existing literature, by testing a distributed implementation …

A Hybrid Neural Network For Stock Price Direction Forecasting, Daniel Devine

Dissertations

The volatility of stock markets makes them notoriously difficult to predict and is the reason that many investors sell out at the wrong time. Contrary to the efficient market hypothesis (EMH) and the random walk theory, contribution to the study of machine learning models for stock price forecasting has shown evidence of stock markets predictability with varying degrees of success. Contemporary approaches have sought to use a hybrid of convolutional neural network (CNN) for its feature extraction capabilities and long short-term memory (LSTM) neural network for its time series prediction. This comparative study aims to determine the predictability of stock …

Human Age And Gender Classification Using Convolutional Neural Networks, Eamon Kelliher

Dissertations

In a world relying ever more on human classification, this papers aims to improve on age and gender image classification through the use of Convolutional Neural Networks (CNN). Age and gender classification has become a popular area of study in the past number of years however there are still improvements to be made, particularly in the area of age classification. This research paper aims to test the currently accepted fact that CNN models are the superior model type for image classification by comparing CNN performance against Support Vector Machine performance on the same dataset. Using the Adience image classification dataset, …

Identifying Significant Features For Player Evaluation In Nfl Comparing Anns And Traditional Models, Ronan Walsh

Dissertations

The evaluation of player performance in sports is popular and important in modern sports, enabling teams to use real data in the construction of their rosters. This dissertation proposes to apply machine learning algorithms to predicting the player evaluations from a leading NFL analytics company who use a combination of statistics and expert evaluation. In addition, it will investigate what features are significant in the evaluation of a position. Data for the dissertation is obtained from multiple online sources - Pro Football Reference and Pro Football Focus (the the NFL analytics company). These data sets are combined and analysed before …

Evaluating The Performance Of Transformer Architecture Over Attention Architecture On Image Captioning, Deepti Balasubramaniam

Dissertations

Over the last few decades computer vision and Natural Language processing has shown tremendous improvement in different tasks such as image captioning, video captioning, machine translation etc using deep learning models. However, there were not much researches related to image captioning based on transformers and how it outperforms other models that were implemented for image captioning. In this study will be designing a simple encoder-decoder model, attention model and transformer model for image captioning using Flickr8K dataset where will be discussing about the hyperparameters of the model, type of pre-trained model used and how long the model has been trained. …

Finetuning Bert And Xlnet For Sentiment Analysis Of Stock Market Tweets Using Mixout And Dropout Regularization, Shubham Jangir

Dissertations

Sentiment analysis is also known as Opinion mining or emotional mining which aims to identify the way in which sentiments are expressed in text and written data. Sentiment analysis combines different study areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is quickly becoming a key concern for businesses and organizations, especially as online commerce data is being used for analysis. Twitter is also becoming a popular microblogging and social networking platform today for information among people as they contribute their opinions, thoughts, and attitudes on social media platforms over the years. Because of the large …

Molecular Mechanism Of Cyanobacteria Circadian Clock Oscillator And Effect Of Co Factors On Its Oscillation, Manpreet Kaur

Dissertations

The circadian rhythms arise as an adaptation to the environmental 24-hour day and night cycle due to Earth's rotation. These rhythms prepare organisms to align their internal biological activities and day to day behavior or events with the environmental change of the 24-hour day and night cycle. Circadian rhythms are found widely in all living kingdoms of life on Earth. Cyanobacteria are photosynthetic prokaryotes which first used to study these circadian rhythms. Among cyanobacterial species, Synechococcus elongatus PCC 7942 (henceforth, S. Elongatus) is the simplest organism with a durable and sturdy circadian clock and is study as a model organism. …

Coordination, Adaptation, And Complexity In Decision Fusion, Weiqiang Dong

Dissertations

A parallel decentralized binary decision fusion architecture employs a bank of local detectors (LDs) that access a commonly-observed phenomenon. The system makes a binary decision about the phenomenon, accepting one of two hypotheses (H0 (“absent”) or H1 (“present”)). The k 1 LD uses a local decision rule to compress its local observations yk into a binary local decision uk; uk = 0 if the k 1 LD accepts H0 and uk = 1 if it accepts H1. The k 1 LD sends its decision uk over a noiseless dedicated channel to a Data Fusion Center (DFC). The DFC combines the …

Drone-Assisted Emergency Communications, Di Wu

Dissertations

Drone-mounted base stations (DBSs) have been proposed to extend coverage and improve communications between mobile users (MUs) and their corresponding macro base stations (MBSs). Different from the base stations on the ground, DBSs can flexibly fly over and close to MUs to establish a better vantage for communications. Thus, the pathloss between a DBS and an MU can be much smaller than that between the MU and MBS. In addition, by hovering in the air, the DBS can likely establish a Line-of-Sight link to the MBS. DBSs can be leveraged to recover communications in a large natural disaster struck area …

A Deep Machine Learning Approach For Predicting Freeway Work Zone Delay Using Big Data, Abdullah Shabarek

Dissertations

The introduction of deep learning and big data analytics may significantly elevate the performance of traffic speed prediction. Work zones become one of the most critical factors causing congestion impact, which reduces the mobility as well as traffic safety. A comprehensive literature review on existing work zone delay prediction models (i.e., parametric, simulation and non-parametric models) is conducted in this research. The research shows the limitations of each model. Moreover, most previous modeling approaches did not consider user delay for connected freeways when predicting traffic speed under work zone conditions. This research proposes Deep Artificial Neural Network (Deep ANN) and …

Supporting User Interaction And Social Relationship Formation In A Collaborative Online Shopping Context, Yu Xu

Dissertations

The combination of online shopping and social media allow people with similar shopping interests and experiences to share, comment, and discuss about shopping from anywhere and at any time, which also leads to the emergence of online shopping communities. Today, more people turn to online platforms to share their opinions about products, solicit various opinions from their friends, family members, and other customers, and have fun through interactions with others with similar interests. This dissertation explores how collaborative online shopping presents itself as a context and platform for users' interpersonal interactions and social relationship formation through a series of studies. …

Dances And Escape Of The Vortex Quartet, Brandon Behring

Dissertations

This dissertation considers the linear stability of a one-parameter family of periodic solutions of the four-vortex problem known as 'leapfrogging' orbits. These solutions, which consist of two pairs of identical yet oppositely-signed vortices, were known to W. Gröbli (1877) and A. E. H. Love (1883) and can be parameterized by a dimensionless parameter related to the geometry of the initial configuration. Simulations by Acheson and numerical Floquet analysis by Tophøj and Aref both indicate, to many digits, that the bifurcation occurs at a value related to the inverse square of the golen ratio. Acheson observed that, after an initial period …

Characterizing Reactive Iron Mineral Coatings And Their Roles In Natural Attenuation At A Site With Historical Contamination, Han Hua

Dissertations

Reactive iron mineral coatings in redox transition zones play an important role in contaminant attenuation. These mineral coatings include poorly crystalline to crystalline iron sulfides, carbonates, and oxyhydroxides, and are a signature of the biogeochemical processes occurring. To better understand these processes, reactive iron mineral coatings are characterized in an 18-m Anaerobic Core collected from a contaminated industrial site. This study targets redox transition zones uncovered in the core. A suite of complementary analyses is applied to distinguish the surface coating mineralogy using X-ray Diffraction, X-ray fluorescence, and field-emission scanning electron microscopy (FESEM) with energy dispersive X-ray analyzer (EDX). In …

The Aging And Impacts Of Atmospheric Soot: Closing The Gap Between Experiments And Models, Ogochukwu Yvonne Enekwizu

Dissertations

The main goal of this dissertation is to generate data and parameterizations to accurately represent soot aerosols in atmospheric models. Soot from incomplete combustion of fossil fuels and biomass burning is a major air pollutant and a significant contributor to climate warming. The environmental impacts of soot are strongly dependent on the particle morphology and mixing state, which evolve continuously during atmospheric transport via a process known as aging. To make predictions of soot impacts on the environment, most atmospheric models adopt simplifications of particle structure and mixing state, which lead to substantial uncertainties. Using an experimentally constrained modeling approach, …

Performance Optimization Of Big Data Computing Workflows For Batch And Stream Data Processing In Multi-Clouds, Huiyan Cao

Dissertations

Workflow techniques have been widely used as a major computing solution in many science domains. With the rapid deployment of cloud infrastructures around the globe and the economic benefits of cloud-based computing and storage services, an increasing number of scientific workflows have migrated or are in active transition to clouds. As the scale of scientific applications continues to grow, it is now common to deploy various data- and network-intensive computing workflows such as serial computing workflows, MapReduce/Spark-based workflows, and Storm-based stream data processing workflows in multi-cloud environments, where inter-cloud data transfer oftentimes plays a significant role in both workflow performance …

Modeling Mass Transfer And Chemical Reaction In Industrial Nitrocellulose Manufacturing Processes, Francis Patrick Sullivan

Dissertations

A series of models are proposed to describe the production of military grade nitrocellulose from dense cellulose materials in mixtures of nitric acid, sulfuric acid, and water. This effort is conducted to provide a predictive capability for analyzing the rate and extent of reaction achieved under a range of reaction conditions used in the industrial nitrocellulose manufacturing process for sheeted cellulose materials. Because this capability does not presently exist, nitrocellulose producers have historically relied on a very narrow range of cellulose raw materials and resorted to trial and error methods to develop processing conditions for new materials. This tool enables …

Small-Scale Dynamics Of Photospheric Magnetic Activities And Their Chromospheric Responses, Jiasheng Wang

Dissertations

The evolution of photospheric magnetic fields is considered as the fundamental source of forming atmospheric structures and triggering most solar activities, including flares and mass ejections on various scales (CMEs, jets, etc.). With the implementation of high-resolution observational instruments, small-scale details of magnetic features are recognized that can provide important information regarding the evolution in active regions and the connection between photospheric magnetic reconnection and jet-like ejections in the quiet Sun. This research takes advantage of the exceptionally high-resolution measurements of vector magnetic field and imaging observations by the Goode Solar Telescope, and UV/EUV imaging observations from space-based instruments. The …

Countering Internet Packet Classifiers To Improve User Online Privacy, Sina Fathi-Kazerooni

Dissertations

Internet traffic classification or packet classification is the act of classifying packets using the extracted statistical data from the transmitted packets on a computer network. Internet traffic classification is an essential tool for Internet service providers to manage network traffic, provide users with the intended quality of service (QoS), and perform surveillance. QoS measures prioritize a network's traffic type over other traffic based on preset criteria; for instance, it gives higher priority or bandwidth to video traffic over website browsing traffic. Internet packet classification methods are also used for automated intrusion detection. They analyze incoming traffic patterns and identify malicious …

Novel Applications Of Mass Spectrometry For Quantitation And Reaction Mechanism Elucidation, Pengyi Zhao

Dissertations

Mass spectrometry (MS) has been growing as one of the most widely used tools in the field of analytical chemistry. Various applications have been developed to harness the high sensitivity and specificity of mass spectrometric analysis. In this dissertation, two major challenges are addressed. By developing mass spectrometric-based methods, absolute quantitation of proteins/peptides have been achieved. Elucidation of various reaction mechanisms are also enabled. These are the focuses of this dissertation.

In Chapters 2 to 4, a novel quantitation method is developed, titled as coulometric mass spectrometry (CMS). The strength of this method is that no reference standard or isotope-labeled …