Computer Sciences Commons^™

Accurate And Justifiable : New Algorithms For Explainable Recommendations., Behnoush Abdollahi

Electronic Theses and Dissertations

Websites and online services thrive with large amounts of online information, products, and choices, that are available but exceedingly difficult to find and discover. This has prompted two major paradigms to help sift through information: information retrieval and recommender systems. The broad family of information retrieval techniques has given rise to the modern search engines which return relevant results, following a user's explicit query. The broad family of recommender systems, on the other hand, works in a more subtle manner, and do not require an explicit query to provide relevant results. Collaborative Filtering (CF) recommender systems are based on algorithms …

A Data Science Pipeline For Educational Data : A Case Study Using Learning Catalytics In The Active Learning Classroom., Asuman Cagla Acun Sener

Electronic Theses and Dissertations

This thesis presents an applied data science methodology on a set of University of Louisville, Speed School of Engineering student data. We used data mining and classic statistical techniques to help educational researchers quickly see the data trends and peculiarities. Our data includes scores and information about two Engineering Fundamental Class. The format of these classes is called an inverted classroom model or flipped class. The purpose of this study is to analyze the data in order to uncover potentially hidden information, tell interesting stories about the data, examine student learning behavior and learning performance in an active learning environment, …

Vertex Weighted Spectral Clustering, Mohammad Masum

Electronic Theses and Dissertations

Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the vertex is close to …

Using A Multi Variate Pattern Analysis (Mvpa) Approach To Decode Fmri Responses To Fear And Anxiety., Sajjad Torabian Esfahani

Electronic Theses and Dissertations

This study analyzed fMRI responses to fear and anxiety using a Multi Variate Pattern Analysis (MVPA) approach. Compared to conventional univariate methods which only represent regions of activation, MVPA provides us with more detailed patterns of voxels. We successfully found different patterns for fear and anxiety through separate classification attempts in each subject’s representational space. Further, we transformed all the individual models into a standard space to do group analysis. Results showed that subjects share a more common fear response. Also, the amygdala and hippocampus areas are more important for differentiating fear than anxiety.

Peeking Into The Other Half Of The Glass : Handling Polarization In Recommender Systems., Mahsa Badami

Electronic Theses and Dissertations

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users' interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate …

An Investigation Into The Performance Evaluation Of Connected Vehicle Applications: From Real-World Experiment To Parallel Simulation Paradigm, Md Salman Ahmed

Electronic Theses and Dissertations

A novel system was developed that provides drivers lane merge advisories, using vehicle trajectories obtained through Dedicated Short Range Communication (DSRC). It was successfully tested on a freeway using three vehicles, then targeted for further testing, via simulation. The failure of contemporary simulators to effectively model large, complex urban transportation networks then motivated further research into distributed and parallel traffic simulation. An architecture for a closed-loop, parallel simulator was devised, using a new algorithm that accounts for boundary nodes, traffic signals, intersections, road lengths, traffic density, and counts of lanes; it partitions a sample, Tennessee road network more efficiently than …

On Barrier Graphs Of Sensor Networks, Kirk Anthony Boyer

Electronic Theses and Dissertations

The study of sensor networks begins with a model, which usually has a geometric component. This thesis focuses on networks of sensors modeled as collections of rays in the plane whose use is to detect intruders, and in particular a graph derived from this geometry, called the barrier graph of the network, which captures information about the network's coverage. Every such ray-barrier sensor network corresponds to a barrier graph, but not every graph is the barrier graph of some network.

We show that any barrier graph is not just tripartite, but perfect. We describe how to find networks which have …

Parallel, Cross-Platform Unit Testing For Real-Time Embedded Systems, Tosapon Pankumhang

Electronic Theses and Dissertations

Embedded systems are used in a wide variety of applications (e.g., automotive, agricultural, home security, industrial, medical, military, and aerospace) due to their small size, low-energy consumption, and the ability to control real-time peripheral devices precisely. These systems, however, are different from each other in many aspects: processors, memory size, develop applications/OS, hardware interfaces, and software loading methods. Unit testing is a fundamental part of software development and the lowest level of software testing, as it tests individual or groups of functions, methods, and classes, to increase confidence that the developed software satisfies both software specifications and user requirements. Although …

Machine Learning And Natural Language Methods For Detecting Psychopathy In Textual Data, Andrew Stephen Henning

Electronic Theses and Dissertations

Among the myriad of mental conditions permeating through society, psychopathy is perhaps the most elusive to diagnose and treat. With the advent of natural language processing and machine learning, however, we have ushered in a new age of technology that provides a fresh toolkit for analyzing text and context. Because text remains the medium of choice for most personal and professional interactions, it may be possible to use textual samples from psychopaths as a means for understanding and ultimately classifying similar individuals based on the content of their language usage. This paper aims to investigate natural language processing and supervised …

On The Security Of Nosql Cloud Database Services, Mohammad Ahmadian

Electronic Theses and Dissertations

Processing a vast volume of data generated by web, mobile and Internet-enabled devices, necessitates a scalable and flexible data management system. Database-as-a-Service (DBaaS) is a new cloud computing paradigm, promising a cost-effective and scalable, fully-managed database functionality meeting the requirements of online data processing. Although DBaaS offers many benefits it also introduces new threats and vulnerabilities. While many traditional data processing threats remain, DBaaS introduces new challenges such as confidentiality violation and information leakage in the presence of privileged malicious insiders and adds new dimension to the data security. We address the problem of building a secure DBaaS for a …

Designing Light Filters To Detect Skin Using A Low-Powered Sensor, Muhammad Uzair Tariq

Electronic Theses and Dissertations

Detection of nudity in photos and videos, especially prior to uploading to the internet, is vital to solving many problems related to adolescent sexting, the distribution of child pornography, and cyber-bullying. The problem with using nudity detection algorithms as a means to combat these problems is that: 1) it implies that a digitized nude photo of a minor already exists (i.e., child pornography), and 2) there are real ethical and legal concerns around the distribution and processing of child pornography. Once a camera captures an image, that image is no longer secure. Therefore, we need to develop new privacy-preserving solutions …

Adversarial Attacks On Vision Algorithms Using Deep Learning Features, Andy Michel

Electronic Theses and Dissertations

Computer vision algorithms, such as those implementing object detection, are known to be susceptible to adversarial attacks. Small barely perceptible perturbations to the input can cause vision algorithms to incorrectly classify inputs that they would have otherwise classified correctly. A number of approaches have been recently investigated to generate such adversarial examples for deep neural networks. Many of these approaches either require grey-box access to the deep neural net being attacked or rely on adversarial transfer and grey-box access to a surrogate neural network. In this thesis, we present an approach to the synthesis of adversarial examples for computer vision …

Resource Allocation And Pricing In Secondary Dynamic Spectrum Access Networks, Enas Khairullah

Electronic Theses and Dissertations

The paradigm shift from static spectrum allocation to a dynamic one has opened many challenges that need to be addressed for the true vision of Dynamic Spectrum Access (DSA) to materialize. This dissertation proposes novel solutions that include: spectrum allocation, routing, and scheduling in DSA networks. First, we propose an auction-based spectrum allocation scheme in a multi-channel environment where secondary users (SUs) bid to buy channels from primary users (PUs) based on the signal to interference and noise ratio (SINR). The channels are allocated such that i) the SUs get their preferred channels, ii) channels are re-used, and iii) there …

Enhanced Breast Cancer Classification With Automatic Thresholding Using Support Vector Machine And Harris Corner Detection, Mohammad Taheri

Electronic Theses and Dissertations

Image classification and extracting the characteristics of a tumor are the powerful tools in medical science. In case of breast cancer medical treatment, the breast cancer classification methods can be used to classify input images as benign and malignant classes for better diagnoses and earlier detection with breast tumors. However, classification process can be challenging because of the existence of noise in the images, and complicated structures of the image. Manual classification of the images is timeconsuming, and need to be done only by medical experts. Hence using an automated medical image classification tool is useful and necessary. In addition, …

Adaptive Audio Classification Framework For In-Vehicle Environment With Dynamic Noise Characteristics, Haitham Alsaadan

Electronic Theses and Dissertations

With ever-increasing number of car-mounted electric devices that are accessed, managed, and controlled with smartphones, car apps are becoming an important part of the automotive industry. Audio classification is one of the key components of car apps as a front-end technology to enable human-app interactions. Existing approaches for audio classification, however, fall short as the unique and time-varying audio characteristics of car environments are not appropriately taken into account. Leveraging recent advances in mobile sensing technology that allows for an active and accurate driving environment detection, in this thesis, we develop an audio classification framework for mobile apps that categorizes …

Optimized Multilayer Perceptron With Dynamic Learning Rate To Classify Breast Microwave Tomography Image, Chulwoo Pack

Electronic Theses and Dissertations

Most recently developed Computer Aided Diagnosis (CAD) systems and their related research is based on medical images that are usually obtained through conventional imaging techniques such as Magnetic Resonance Imaging (MRI), x-ray mammography, and ultrasound. With the development of a new imaging technology called Microwave Tomography Imaging (MTI), it has become inevitable to develop a CAD system that can show promising performance using new format of data. The platform can have a flexibility on its input by adopting Artificial Neural Network (ANN) as a classifier. Among the various phases of CAD system, we have focused on optimizing the classification phase …

A Probabilistic Approach To Multiple-Instance Learning, Silu Zhang

Electronic Theses and Dissertations

This study introduced a probabilistic approach to the multiple-instance learning (mil) problem. In particular, two bayes classication algorithms were proposed where posterior probabilities were estimated under dierent assumptions. The rst algorithm, named instance-vote, assumes that the probability of a bag being positive or negative depends upon the percentage of its instances being positive or negative. This probability is estimated using a k-nn classication of instances. In the second approach, embedded kernel density estimation (ekde), bags are represented in an instance induced (very high dimensional) space. A parametric stochastic neighbor embedding method is applied to learn a mapping that projects bags …

Accelerating The Discontinuous Galerkin Cell-Vertex Scheme (Dg-Cvs) Solver On Cpu-Gpu Heterogeneous Systems, Xiaoqi Hu

Electronic Theses and Dissertations

Dg-Cvs (Discontinuous Galerkin Cell-Vertex Scheme) is an efficient, accurate and robust numerical solver for general hyperbolic conservation laws. It can solve a broad range of conservation laws such as the shallow water equation and Magnetohydrodynamics equations. Dg-Cvs is a Riemann-Solver-free high order space-time method for arbitrary space conservation laws. It fuses the discontinuous Galerkin (dg) method and the conservation element/solution element (ce/se) method to take advantage of the best features of both methods. Thanks to the ce/se method, the time derivative of the solution is treated as an independent unknown, which is amendable to gpu's parallel execution. In this thesis, …

Deployment, Coverage And Network Optimization In Wireless Video Sensor Networks For 3d Indoor Monitoring, Tisha Lafaye Brown

Electronic Theses and Dissertations

As a result of extensive research over the past decade or so, wireless sensor networks (wsns) have evolved into a well established technology for industry, environmental and medical applications. However, traditional wsns employ such sensors as thermal or photo light resistors that are often modeled with simple omni-directional sensing ranges, which focus only on scalar data within the sensing environment. In contrast, the sensing range of a wireless video sensor is directional and capable of providing more detailed video information about the sensing field. Additionally, with the introduction of modern features in non-fixed focus cameras such as the pan, tilt …

Rationalizing The Band Gap Tunability Of Semiconductors Via Electronic Structure Calculations, Matthew N. Srnec

Electronic Theses and Dissertations

The polymorphs of titanium dioxide and various diamond-like semiconductor materials are promising candidates in photovoltaic solar cell applications. Several of these polymorphs have been studied with experimental and computational methods, which often aim at tuning the electronic structure, particularly the band gap value of the crystalline solid. Prior studies report that the addition of a substituent into the structure of titanium dioxide decreases its band gap value, but the reasons for this are unknown. Possible explanations for the change in band gap involve the substituent atom's crystal radius, electronegativity, and ionization energy. Understanding the cause of these changes will provide …

Exploring The Multi-Touch Interaction Design Space For 3d Virtual Objects To Support Procedural Training Tasks, Sarah Holderness

Electronic Theses and Dissertations

Multi-touch interaction has the potential to be an important input method for realistic training in 3D environments. However, multi-touch interaction has not been explored much in 3D tasks, especially when trying to leverage realistic, real-world interaction paradigms. A systematic inquiry into what realistic gestures look like for 3D environments is required to understand how users translate real-world motions to multi-touch motions. Once those gestures are defined, it is important to see how we can leverage those gestures to enhance training tasks. In order to explore the interaction design space for 3D virtual objects, we began by conducting our first study …

Code Park: A New 3d Code Visualization Tool And Ide, Pooya Khaloo

Electronic Theses and Dissertations

We introduce Code Park, a novel tool for visualizing codebases in a 3D game-like environment. Code Park aims to improve a programmer's understanding of an existing codebase in a manner that is both engaging and fun to be appealing especially for novice users such as students. It achieves these goals by laying out the codebase in a 3D park-like environment. Each class in the codebase is represented as a 3D room-like structure. Constituent parts of the class (variable, member functions, etc.) are laid out on the walls, resembling a syntax-aware "wallpaper". The users can interact with the codebase using an …

Data Representation In Machine Learning Methods With Its Application To Compilation Optimization And Epitope Prediction, Yevgeniy Sher

Electronic Theses and Dissertations

In this dissertation we explore the application of machine learning algorithms to compilation phase order optimization, and epitope prediction. The common thread running through these two disparate domains is the type of data being dealt with. In both problem domains we are dealing with categorical data, with its representation playing a significant role in the performance of classification algorithms. We first present a neuroevolutionary approach which orders optimization phases to generate compiled programs with performance superior to those compiled using LLVM's -O3 optimization level. Performance improvements calculated as the speed of the compiled program's execution ranged from 27% for the …

Novel Computational Methods For Integrated Circuit Reverse Engineering, Travis Meade

Electronic Theses and Dissertations

Production of Integrated Circuits (ICs) has been largely strengthened by globalization. System-on-chip providers are capable of utilizing many different providers which can be responsible for a single task. This horizontal structure drastically improves to time-to-market and reduces manufacturing cost. However, untrust of oversea foundries threatens to dismantle the complex economic model currently in place. Many Intellectual Property (IP) consumers become concerned over what potentially malicious or unspecified logic might reside within their application. This logic which is inserted with the intention of causing harm to a consumer has been referred to as a Hardware Trojan (HT). To help IP consumers, …

Network Partitioning In Distributed Agent-Based Models, Antoniya Petkova

Electronic Theses and Dissertations

Agent-Based Models (ABMs) are an emerging simulation paradigm for modeling complex systems, comprised of autonomous, possibly heterogeneous, interacting agents. The utility of ABMs lies in their ability to represent such complex systems as self-organizing networks of agents. Modeling and understanding the behavior of complex systems usually occurs at large and representative scales, and often obtaining and visualizing of simulation results in real-time is critical. The real-time requirement necessitates the use of in-memory computing, as it is difficult and challenging to handle the latency and unpredictability of disk accesses. Combining this observation with the scale requirement emphasizes the need to use …

Online, Supervised And Unsupervised Action Localization In Videos, Khurram Soomro

Electronic Theses and Dissertations

Action recognition classifies a given video among a set of action labels, whereas action localization determines the location of an action in addition to its class. The overall aim of this dissertation is action localization. Many of the existing action localization approaches exhaustively search (spatially and temporally) for an action in a video. However, as the search space increases with high resolution and longer duration videos, it becomes impractical to use such sliding window techniques. The first part of this dissertation presents an efficient approach for localizing actions by learning contextual relations between different video regions in training. In testing, …

Complex Affect Recognition In The Wild, Behnaz Nojavanasghari

Electronic Theses and Dissertations

Artificial social intelligence is a step towards human-like human-computer interaction. One important milestone towards building socially intelligent systems is enabling computers with the ability to process and interpret the social signals of humans in the real world. Social signals include a wide range of emotional responses from a simple smile to expressions of complex affects. This dissertation revolves around computational models for social signal processing in the wild, using multimodal signals with an emphasis on the visual modality. We primarily focus on complex affect recognition with a strong interest in curiosity. In this dissertation,we ?rst present our collected dataset, EmoReact. …

Pervasive Secure Content Delivery Networks Implementation, Hector Lugo-Cordero

Electronic Theses and Dissertations

Over the years, communication networks have been shifting their focus from providing connectivity in a client/server model to providing a service or content. This shift has led to topic areas like Service-Oriented Architecture (SOA), Heterogeneous Wireless Mesh Networks, and Ubiquitous Computing. Furthermore, probably the broadest of these areas which embarks all is the Internet of Things (IoT). The IoT is defined as an Internet where all physical entities (e.g., vehicles, appliances, smart phones, smart homes, computers, etc.), which we interact daily are connected and exchanging data among themselves and users. The IoT has become a global goal for companies, researchers, …

Learning Dynamic Network Models For Complex Social Systems, Alireza Hajibagheri

Electronic Theses and Dissertations

Human societies are inherently complex and highly dynamic, resulting in rapidly changing social networks, containing multiple types of dyadic interactions. Analyzing these time-varying multiplex networks with approaches developed for static, single layer networks often produces poor results. To address this problem, our approach is to explicitly learn the dynamics of these complex networks. This dissertation focuses on five problems: 1) learning link formation rates; 2) predicting changes in community membership; 3) using time series to predict changes in network structure; 4) modeling coevolution patterns across network layers and 5) extracting information from negative layers of a multiplex network. To study …

Implementation Of Refining Statements In Openjml And Verification Of Higher Order Methods With Model Program Specifications, Sai Chandesh Gurramkonda

Electronic Theses and Dissertations

The Java Modeling Language (JML) describes the functional behavior of Java classes and methods using pre- and postconditions. However, standard pre- and postcondition specifications cannot verify calls to higher order methods (HOMs). JML uses model program specifications to reason about HOMs. This thesis describes the implementation of model programs in the OpenJML tool. The implementation includes parsing, type checking, and matching of model program specifications against the code.