Physical Sciences and Mathematics Commons^™

Evaluating Cranial Nonmetric Traits In Mummies From Pachacamac, Peru: The Utility Of Semi-Automated Image Segmentation In Paleoradiology, Cameron J. Beason

Electronic Thesis and Dissertation Repository

Anthropologists employ biodistance analysis to understand past population interactions and relatedness. The objectives of this thesis are twofold: to determine whether a sample of five mummies from the pilgrimage centre, Pachacamac, on the Central Coast of Peru comprised local or non-local individuals through an analysis of cranial nonmetric traits using comparative samples from the North and Central Coasts of Peru and Chile; and to test the utility of machine-learning-based image segmentation in the image analysis software, Dragonfly, to automatically segment CT scans of the mummies such that the cranial nonmetric traits are visible. Results show that while fully automated segmentation …

A Generative-Discriminative Approach To Human Brain Mapping, Deepanshu Wadhwa

Electronic Thesis and Dissertation Repository

During everyday behaviours, the brain shows complex spatial patterns of activity. These activity maps are very replicable within an individual, but vary significantly across individuals, even though they are evoked by the same behaviour. It is unknown how differences in these spatial patterns relate to differences in behavior or function. More fundamentally, the structural, developmental, and genetic factors that determine the spatial organisation of these brain maps in each individual are unclear. Here we propose a new quantitative approach for uncovering the basic principles by which functional brain maps are organized. We propose to take an generative-discriminative approach to human …

Leveraging Machine Learning Techniques Towards Intelligent Networking Automation, Cesar A. Gomez

Electronic Thesis and Dissertation Repository

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the …

Exploratory Search With Archetype-Based Language Models, Brent D. Davis

Electronic Thesis and Dissertation Repository

This dissertation explores how machine learning, natural language processing and information retrieval may assist the exploratory search task. Exploratory search is a search where the ideal outcome of the search is unknown, and thus the ideal language to use in a retrieval query to match it is unavailable. Three algorithms represent the contribution of this work. Archetype-based Modeling and Search provides a way to use previously identified archetypal documents relevant to an archetype to form a notion of similarity and find related documents that match the defined archetype. This is beneficial for exploratory search as it can generalize beyond standard …

Predicting Stock Market Sector Sentiment Through News Article Based Textual Analysis, William A. Beldman

Electronic Thesis and Dissertation Repository

Investors seek to take advantage of computer technology to gain an edge on their investments. This can be done through quantitative (historical number-based) analysis or qualitative (natural language-based) analysis. Subject matter experts have been known to make predictions between 70 and 79% accuracy at best and less than 50% accuracy on average. Sophisticated algorithms through qualitative analysis are known to demonstrate more successful market predictions for specific stocks. It stands to reason that the same technique could work just as well or better for attempting to predict entire sectors of the stock market. By using indices and exchange traded funds, …

Improving Reader Motivation With Machine Learning, Tanner A. Bohn

Electronic Thesis and Dissertation Repository

This thesis focuses on the problem of increasing reading motivation with machine learning (ML). The act of reading is central to modern human life, and there is much to be gained by improving the reading experience. For example, the internal reading motivation of students, especially their interest and enjoyment in reading, are important factors in their academic success.

There are many topics in natural language processing (NLP) which can be applied to improving the reading experience in terms of readability, comprehension, reading speed, motivation, etc. Such topics include personalized recommendation, headline optimization, text simplification, and many others. However, to the …

Generating Effective Sentence Representations: Deep Learning And Reinforcement Learning Approaches, Mahtab Ahmed

Electronic Thesis and Dissertation Repository

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Many Natural Language applications are powered by machine learning models performing a large variety of underlying tasks. Recently, deep learning approaches have obtained very high performance across many NLP tasks. In order to achieve this high level of performance, it is crucial for computers to have an appropriate representation of sentences. The tasks addressed in the thesis are best approached having shallow semantic representations. These representations are vectors that are then embedded in …

A Lightweight And Explainable Citation Recommendation System, Juncheng Yin

Electronic Thesis and Dissertation Repository

The increased pressure of publications makes it more and more difficult for researchers to find appropriate papers to cite quickly and accurately. Context-aware citation recommendation, which can provide users suggestions mainly based on local citation contexts, has been shown to be helpful to alleviate this problem. However, previous works mainly use RNN models and their variance, which tend to be highly complicated with heavy-weight computation. In this paper, we propose a lightweight and explainable model that is quick to train and obtains high performance. Our model is based on a pre-trained sentence embedding model and trained with triplet loss. Quantitative …

Calibration Between Eye Tracker And Stereoscopic Vision System Employing A Linear Closed-Form Perspective-N-Point (Pnp) Algorithm, Mohammad Karami

Electronic Thesis and Dissertation Repository

In many advanced driver assistance systems (ADAS) applications, it is essential to figure out where gaze of driver locates in image area of stereoscopic vision system. This problem, which involves a cross calibration between the stereo vision system and eye tracker, is a challenging task since the two systems are not consistent in modality and do not share a common image area. The eye tracker system provides a 3D gaze vector which describes the direction of driver’s 3D line of gaze, while the stereoscopic vision system provides a depth image frame. In this thesis, this crosscalibration was performed with a …

A Deep Topical N-Gram Model And Topic Discovery On Covid-19 News And Research Manuscripts, Yuan Du

Electronic Thesis and Dissertation Repository

Topic modeling with the latent semantic analysis (LSA), the latent Dirichlet allocation (LDA) and the biterm topic model (BTM) has been successfully implemented and used in many areas, including movie reviews, recommender systems, and text summarization, etc. However, these models may become computationally intensive if tested on a humongous corpus. Considering the wide acceptance of machine learning based on deep neural networks, this research proposes two deep neural network (NN) variants, 2-layer NN and 3-layer NN of the LDA modeling techniques. The primary goal is to deal with problems with a large corpus using manageable computational resources.

This thesis analyze …

Load Balancing And Resource Allocation In Smart Cities Using Reinforcement Learning, Aseel Alorbani

Electronic Thesis and Dissertation Repository

Today, smart city technology is being adopted by many municipal governments to improve their services and to adapt to growing and changing urban population. Implementing a smart city application can be one of the most challenging projects due to the complexity, requirements and constraints. Sensing devices and computing components can be numerous and heterogeneous. Increasingly, researchers working in the smart city arena are looking to leverage edge and cloud computing to support smart city development. This approach also brings a number of challenges. Two of the main challenges are resource allocation and load balancing of tasks associated with processing data …

Building Effective Network Security Frameworks Using Deep Transfer Learning Techniques, Harsh Dhillon

Electronic Thesis and Dissertation Repository

Network traffic is growing at an outpaced speed globally. According to the 2020 Cisco Annual Report, nearly two-thirds of the global population will have internet connectivity by the year 2023. The number of devices connected to IP networks will also triple the total world population's size by the same year. The vastness of forecasted network infrastructure opens opportunities for new technologies and businesses to take shape, but it also increases the surface of security vulnerabilities. The number of cyberattacks are growing worldwide and are becoming more diverse and sophisticated. Classic network intrusion detection architectures monitor a system to detect malicious …