Computer Sciences Commons^™

Comparison Of Major Cloud Providers, Justin Berman

Other Student Works

This paper will compare the following major cloud providers: Microsoft Azure, Amazon AWS, Google Cloud, and IBM Cloud. An introduction to the companies and their history, fundamentals and services, strengths and weaknesses, costs, and their security will be discussed throughout this writing.

High Performance Document Store Implementation In Rust, Ishaan Aggarwal

Master's Projects

Databases are a core part of any application which requires persistence of data. The performance of applications involving the use of database systems is directly proportional to how fast their database read-write operations are. The aim of this project was to build a high- performance document store which can support variety of applications which require data storage and retrieval of some kind. This document store can be used as an independently running backend service which can be utilized by search engines, applications which deal with keeping records, etc. We used Rust to make this document store which is fast, robust, …

Identifying Bots On Twitter With Benford’S Law, Sanmesh Bhosale

Master's Projects

Over time Online Social Networks (OSNs) have grown exponentially in terms of active users and have now become an influential factor in the formation of public opinions. Due to this, the use of bots and botnets for spreading misinformation on OSNs has become a widespread concern. The biggest example of this was during the 2016 American Presidential Elections, where Russian bots on Twitter pumped out fake news to influence the election results.

Identifying bots and botnets on Twitter is not just based on visual analysis and can require complex statistical methods to score a profile based on multiple features and …

Analysis Of Camera Trap Footage Through Subject Recognition, Nirnayak Bhardwaj

Master's Projects

Motion-sensitive cameras, otherwise known as camera traps, have become increasingly popular amongst ecologists for studying wildlife. These cameras allow scientists to remotely observe animals through an inexpensive and non-invasive approach. Due to the lenient nature of motion cameras, studies involving them often generate excessive amounts of footage with many photographs not containing any animal subjects. Thus, there is a need for a system that is capable of analyzing camera trap footage to determine if a picture holds value for researchers. While research into automated image recognition is well documented, it has had limited applications in the field of ecology. This …

Employee Churn Prediction Using Logistic Regression And Support Vector Machine, Rajendra Maharjan

Master's Projects

It is a challenge for Human Resource (HR) team to retain their existing employees than to hire a new one. For any company, losing their valuable employees is a loss in terms of time, money, productivity, and trust, etc. This loss could be possibly minimized if HR could beforehand find out their potential employees who are planning to quit their job hence, we investigated solving the employee churn problem through the machine learning perspective. We have designed machine learning models using supervised and classification-based algorithms like Logistic Regression and Support Vector Machine (SVM). The models are trained with the IBM …

Deep Convolutional Neural Networks For Accurate Diagnosis Of Covid-19 Patients Using Chest X-Ray Image Databases From Italy, Canada, And The Usa, Amgad A. Salama, Samy H. Darwish, Samir M. Abdel-Mageed, Radwa A. Meshref, Ehab I. Mohamed

The University of Louisville Journal of Respiratory Infections

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), famously known as COVID-19, has quickly become a global pandemic. Chest X-ray (CXR) imaging has proven reliable, fast, and cost-effective for identifying COVID-19 infections, which proceeds to display atypical unilateral patchy infiltration in the lungs like typical pneumonia. We employed the deep convolutional neural network (DCNN) ResNet-34 to detect and classify CXR images from patients with COVID-19 and Viral Pneumonia and Normal Controls.

Methods: We created a single database containing 781 source CXR images from four different international sub-databases: the Società Italiana di Radiologia Medica e Interventistica (SIRM), the GitHub Database, the …

Utilizing Software Engineering And Cloud Computing Principles To Develop The Revised Self-Report Assessment Of Functional Visual Performance (R-Srafvp) Application, Kirk Hedlich

Masters Projects

Can principles from software engineering and concepts from cloud computing be applied to and aid in the development of a small project, specifically improving the use of the Revised Self-Report Assessment of Functional Visual Performance (R-SRAFVP)? The target for this project is to create a new application and improve on existing attempts to move the R-SRAFVP assessment from an electronic document format to a web-based application. The new application should provide better ease of use, simplicity in design and understanding for use, and hopefully increased access and adoption by Occupation Therapists who specialize in low vision rehabilitation. The benefits of …

Extending The Quality Of Secure Service Model To Multi-Hop Networks, Paul M. Simon, Scott R. Graham

Faculty Publications

Rarely are communications networks point-to-point. In most cases, transceiver relay stations exist between transmitter and receiver end-points. These relay stations, while essential for controlling cost and adding flexibility to network architectures, reduce the overall security of the respective network. In an effort to quantify that reduction, we extend the Quality of Secure Service (QoSS) model to these complex networks, specifically multi-hop networks. In this approach, the quantification of security is based upon probabilities that adversarial listeners and disruptors gain access to or manipulate transmitted data on one or more of these multi-hop channels. Message fragmentation and duplication across available channels …

Markdown To Question & Test Interoperability, Su Kim

Master's Projects

As the classroom setting shifted to a virtual one as a result of Covid-19, numerous software are readily available to accommodate for the change, including Canvas, the online course management system. Canvas has a core feature that allows teachers to generate and administer quizzes for students through their interface, but it does not fully utilize the potential with online exams. The first step to exploring this potential is this project, known as Markdown to Question & Test Interoperability (M2QTI). Based on the QTI specifications, this tool lets users to plan and write quizzes in Markdown format. Combined with Canvas’s ability …

Node.Js Based Document Store For Web Crawling, David Bui

Master's Projects

WARC files are central to internet preservation projects. They contain the raw resources of web crawled data and can be used to create windows into the past of web pages at the time they were accessed. Yet there are few tools that manipulate WARC files outside of basic parsing. The creation of our tool WARC-KIT gives users in the Node.js JavaScript environment, a tool kit to interact with and manipulate WARC files.

Included with WARC-KIT is a WARC parsing tool known as WARCFilter that can be used standalone tool to parse, filter, and create new WARC files. WARCFilter can also, …

Tooth Position Determination By Automatic Cutting And Marking Of Dental Panoramic X-Ray Film In Medical Image Processing, Yen-Cheng Huang, Chiung-An Chen, Tsung-Yi Chen, He-Sheng Chou, Wei-Chi Lin, Tzu-Chien Li, Jia-Jun Yuan, Szu-Yin Lin, Chun-Wei Li, Shih-Lun Chen, Yi-Cheng Mao, Patricia Angela R. Abu, Wei-Yuan Chiang, Wen-Shen Lo

Department of Information Systems & Computer Science Faculty Publications

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, …

Introduction To Using Python In The Digital Humanities, Elisabeth Shook

Library Faculty Publications and Presentations

The materials here are from the Python for Digital Humanities Workshop taught on December 13, 2021 for the Boise State University Digital Humanities Group. This 3-hour workshop was created to provide both a very brief introduction to the various capabilities of Python and a small lesson in using Python to pull meaningful insight out of text files.

Secure And Privacy-Preserving Crowdsensing Using Smart Contracts: Issues And Solutions, Alfredo J. Perez, Sherali Zeadally

Computer Science Faculty Publications

The advent of Blockchain and smart contracts is empowering many technologies and systems to automate commerce and facilitate the exchange, tracking and the provision of goods, data and services in a reliable and auditable way. Crowdsensing systems is one type of systems that have been receiving a lot of attention in the past few years. In crowdsensing systems consumer devices such as mobile phones and Internet of Things devices are used to deploy wide-scale sensor networks. We identify some of the major security and privacy issues associated with the development of crowdsensing systems based on smart contracts and Blockchain. We …

Real-Time Voice Biometric Speaker Verification, Inderbir Dhillon, Jason Rupp, Aniketh Vankina, Robert Slater

SMU Data Science Review

Abstract. Automated speaker verification has been an area of increased research in the last few years, with a special interest in metric learning approaches that compute distances between speaker voiceprints. In this paper, three metric learning systems are built and compared in a one-shot speaker verification task using contrastive max-margin loss, triplet loss, and quadruplet loss. For all the models, spectrograms are created from speaker audio. Convolutional Neural Network embedding layers are trained to produce compact voiceprints that allow users to be distinguished using distance calculations. Performances of the three models were similar, but the model with the best EER …

Eating Detection With A Head-Mounted Video Camera, Shengjie Bi, David Kotz

Computer Science Technical Reports

In this paper, we present a computer-vision based approach to detect eating. Specifically, our goal is to develop a wearable system that is effective and robust enough to automatically detect when people eat, and for how long. We collected video from a cap-mounted camera on 10 participants for about 55 hours in free-living conditions. We evaluated performance of eating detection with four different Convolutional Neural Network (CNN) models. The best model achieved accuracy 90.9% and F1 score 78.7% for eating detection with a 1-minute resolution. We also discuss the resources needed to deploy a 3D CNN model in wearable or …

A Longitudinal Analysis Of Pathways To Computing Careers: Defining Broadening Participation In Computing (Bpc) Success With A Rearview Lens, Mercy Jaiyeola

Theses and Dissertations

Efforts to increase the participation of groups historically underrepresented in computing studies, and in the computing workforce, are well documented. It is a national effort with funding from a variety of sources being allocated to research in broadening participation in computing (BPC). Many of the BPC efforts are funded by the National Science Foundation (NSF) but as existing literature shows, the growth in representation of traditionally underrepresented minorities and women is not commensurate to the efforts and resources that have been directed toward this aim.

Instead of attempting to tackle the barriers to increasing representation, this dissertation research tackles the …

A Framework For Phrase Based Smt In The Technical Translation Domain, Hisham Farouk Khodeir

Archived Theses and Dissertations

No abstract provided.

Using Parallel Primary Caches To Improve Capacity And Bandwidth, John Rubena Wani

Archived Theses and Dissertations

No abstract provided.

Error Correction In Quantum Cryptography, Mohamed Salah El Ashmawy

Archived Theses and Dissertations

No abstract provided.

Resource Optimization Property Manager For Autonomic Computing, Hazem A. M. Sharaf El Din

Archived Theses and Dissertations

No abstract provided.

Moment-Preserving Piecewise Approximation For 1-D And 2-D Signals, Soha M. A. A. Seif

Archived Theses and Dissertations

No abstract provided.

Spatio-Temporal Relation Modeling For Few-Shot Action Recognition, Anirudh Thatipelli, Sanath Narayan, Salman Hameed Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, Bernard Ghanem

Computer Vision Faculty Publications

We propose a novel few-shot action recognition framework, STRM, which enhances class-specific feature discriminability while simultaneously learning higher-order temporal representations. The focus of our approach is a novel spatio-temporal enrichment module that aggregates spatial and temporal contexts with dedicated local patch-level and global frame-level feature enrichment sub-modules. Local patch-level enrichment captures the appearance-based characteristics of actions. On the other hand, global framelevel enrichment explicitly encodes the broad temporal context, thereby capturing the relevant object features over time. The resulting spatio-temporally enriched representations are then utilized to learn the relational matching between query and support action sub-sequences. We further introduce a …

Analyzing And Detecting Android Malware And Deepfake, Md Shohel Rana

Dissertations

Rapid advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) over the past several decades have produced a variety of technologies and tools that, among numerous cybersecurity issues, have enticed cybercriminals and hackers to design malware for the Android operating systems and/or manipulate multimedia. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people; these manipulated, high-quality and realistic videos became known recently as Deepfake. There has been much work done in recent years on malware analysis and …

Machine Learning And Radiomic Features To Predict Overall Survival Time For Glioblastoma Patients, Lina Chato, Shahram Latifi

Electrical & Computer Engineering Faculty Research

Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A …

Exploring The Impact Of Social Influence Mechanisms And Network Density On Societal Polarization, Justin Mittereder

Departmental Honors & Graduate Capstone Projects

I present an agent-based model, inspired by the opinion dynamics
(OD) literature, to explore the underlying behaviors that may induce
societal polarization. My agents interact on a social network, in which
adjacent nodes can influence each other, and each agent holds an array
of continuous opinion values (on a 0-1 scale) on a number of separate
issues. I use three measures as a proxy for the virtual society’s “po-
larization:” the average assortativity of the graph with respect to the
agents’ opinions, the number of non-uniform issues, and the number
of distinct opinion buckets in which agents have the same …

Proquest Tdm Studio: A Text And Data Mining Solution, Anamika Megwalu, Anne Marie Engelsen

Faculty Research, Scholarly, and Creative Activity

TDM Studio is an integrated platform offered by ProQuest for data and text mining. TDM stands for text and data mining. This cloud-based, all-in-one innovative product is designed to offer researchers a clean interface with rights-cleared content, Jupyter notebook, and data visualization tools. As a result, researchers can now search Pro-Quest databases, create large datasets, import data to Jupyter notebook for analysis, and download results within a day.

Computer Program Simulation Of A Quantum Turing Machine With Circuit Model, Shixin Wu

Mathematical Sciences Technical Reports (MSTR)

Molina and Watrous present a variation of the method to simulate a quantum Turing machine employed in Yao’s 1995 publication “Quantum Circuit Complexity”. We use a computer program to implement their method with linear algebra and an additional unitary operator defined to complete the details. Their method is verified to be correct on a quantum Turing machine.

Hyperseed: An End-To-End Method To Process Hyperspectral Images Of Seeds, Tian Gao, Anil Kumar Nalini Chandran, Puneet Paul, Harkamal Walia, Hongfeng Yu

School of Computing: Faculty Publications

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each …

Hyperseed: An End-To-End Method To Process Hyperspectral Images Of Seeds, Tian Gao, Anil Kumar Nalini Chandran, Puneet Paul, Harkamal Walia, Hongfeng Yu

School of Computing: Faculty Publications

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each …

Aerial Flight Paths For Communication, Alisha Bevins, Brittany Duncan

School of Computing: Faculty Publications

This article presents an understanding of naive users’ perception of the communicative nature of unmanned aerial vehicle (UAV) motions refined through an iterative series of studies. This includes both what people believe the UAV is trying to communicate, and how they expect to respond through physical action or emotional response. Previous work in this area prioritized gestures from participants to the vehicle or augmenting the vehicle with additional communication modalities, rather than communicating without clear definitions of the states attempting to be conveyed. In an attempt to elicit more concrete states and better understand specific motion perception, this work includes …