Other Computer Engineering Commons^™

Public Blockchain Scalability: Advancements, Challenges And The Future, Amritraj .

Master of Science in Software Engineering Theses

In the last decade, blockchain has emerged as one of the most influential innovations in software architecture and technology. Ideally, blockchains are designed to be architecturally and politically decentralized, similar to the Internet. But recently, public and permissionless blockchains such as Bitcoin and Ethereum have faced stumbling blocks in the form of scalability. Both Bitcoin and Ethereum process fewer than 20 transactions per second, which is significantly lower than their centralized counterpart such as VISA that can process approximately 1,700 transactions per second. In realizing this hindrance in the wide range adoption of blockchains for building advanced and large scalable …

Predicting Unethical Physician Behavior At Scale: A Distributed Computing Framework, Quinn Keck, Robert Sandor, Miguel Romero, Diane Woodbridge, Paul Intrevado

Creative Activity and Research Day - CARD

As the amount of publicly shared data increases,

developing a robust pipeline to stream, store and process data is

critical, as the casual user often lacks the technology, hardware

and/or skills needed to work with such voluminous data. In

this research, the authors employ Amazon EC2 and EMR,

MongoDB, and Spark MLlib to explore 28.5 gigabytes of CMS

Open Payments data in an attempt to identify physicians who

may have a high propensity to act unethically, owing to significant

transfers of wealth from medical companies. A Random Forest

Classifier is employed to predict the top decile of physicians who

have …

Quantum Criticism, Ashwini Badgujar, Paul Intrevado, Andrew Wang, Kai Yu, David Guy Brizan

Creative Activity and Research Day - CARD

Quantum Criticism scrapes data from the News Articles and performs Sentiment Analysis.

Automated Segmentation Of Brain Ventricles Using 3d U-Net, Robert Sandor, Anish P. Dalal

Creative Activity and Research Day - CARD

Neuro-radiologists currently use qualitative volumetric change of brain ventricles after surgery to assess the safety of removing a ventriculoperitoneal (VP) shunt which is a medical device that relieves pressure on the brain caused by fluid accumulation. Following safe removal of the VP shunt, patients can be released from the hospital. The need for accurate and quick measurement of brain ventricular volumetric change makes automatic 3D segmentation software an ideal candidate to aid decisions after surgery. In this paper, we propose an approach to estimate the ventricular volume variation using segmentation in brain MRI and CT images. Our approach consists of …

An Application Of Artificial General Intelligence In Board Games, Nathan Skalka

Computer Science Graduate Research Workshop

No abstract provided.

A Grammar Based Approach To Distributed Systems Fault Diagnosis Using Log Files, Stephen Hanka

Computer Science and Engineering Theses and Dissertations

Diagnosing and correcting failures in complex, distributed systems is difficult. In a network of perhaps dozens of nodes, each of which is executing dozens of interacting applications, sometimes from different suppliers or vendors, finding the source of a system failure is a confusing, tedious piece of detective work. The person assigned this task must trace the failing command, event, or operation through the network components and find a deviation from the correct, desired interaction sequence. After a deviation is identified, the failing applications must be found, and the fault or faults traced to the incorrect source code.

Often the primary …

Bridgr: An Ios Application For Organizing And Discussing Long-Distance Carpooling, Harrison Engoren, Erik Zorn

Senior Theses

Bridgr is an iOS application that facilitates long distance carpooling. This application allows drivers to post destinations on an interactable map so that they can be linked with students that need a ride to a location within close proximity of the posted destination. The riders and driver are linked in a common chat board where they can discuss ride details among themselves. The goal of Bridgr is to allow drivers to utilize extra space in their car in turn for fellowship and/or gas money.

A Strategic Audit Of Microsoft Azure, Lee Fitchett

Honors Program: Senior Projects (Public)

This paper looks at Microsoft Azure's current strategies and proposes possible options for the future. It looks at several competitors and explores how Azure will affect and react to Microsoft’s vision.

Distributed Wireless Algorithms For Rfid Systems: Grouping Proofs And Cardinality Estimation, Vanya D. Cherneva

LSU Doctoral Dissertations

The breadth and depth of the use of Radio Frequency Identification (RFID) are becoming more substantial. RFID is a technology useful for identifying unique items through radio waves. We design algorithms on RFID-based systems for the Grouping Proof and Cardinality Estimation problems.

A grouping-proof protocol is evidence that a reader simultaneously scanned the RFID tags in a group. In many practical scenarios, grouping-proofs greatly expand the potential of RFID-based systems such as supply chain applications, simultaneous scanning of multiple forms of IDs in banks or airports, and government paperwork. The design of RFID grouping-proofs that provide optimal security, privacy, and …

Machine Translation With Image Context From Mandarin Chinese To English, Brooke E. Johnson

Theses and Dissertations

Despite ongoing improvements in machine translation, machine translators still lack the capability of incorporating context from which source text may have been derived. Machine translators use text from a source language to translate it into a target language without observing any visual context. This work aims to produce a neural machine translation model that is capable of accepting both text and image context as a multimodal translator from Mandarin Chinese to English. The model was trained on a small multimodal dataset of 700 images and sentences, and compared to a translator trained only on the text associated with those images. …

Hyper-Parameter Optimization Of A Convolutional Neural Network, Steven H. Chon

Theses and Dissertations

In the world of machine learning, neural networks have become a powerful pattern recognition technique that gives a user the ability to interpret high-dimensional data whereas conventional methods, such as logistic regression, would fail. There exists many different types of neural networks, each containing its own set of hyper-parameters that are dependent on the type of analysis required, but the focus of this paper will be on the hyper-parameters of convolutional neural networks. Convolutional neural networks are commonly used for classifications of visual imagery. For example, if you were to build a network for the purpose of predicting a specific …

A Shared-Memory Algorithm For Updating Single-Source Shortest Paths In Large Weighted Dynamic Networks, Sriram Srinivasan

UNO Student Research and Creative Activity Fair

In the last decade growth of social media, increased the interest of network algorithms for analyzing large-scale complex systems. The networks are highly unstructured and exhibit poor locality, which has been a challenge for developing scalable parallel algorithms. The state-of-the-art network algorithms such as Prim's algorithm for Minimum Spanning Tree, Dijkstra's algorithm for Single Source Shortest Path and ISPAN algorithm for detecting strongly connected components are designed and optimized for static networks. The networks which change with time i.e. the dynamic networks such as social networks, the above-mentioned approaches can only be utilized if they are recomputed from scratch each …

Mobility-Based Models For Advancing Diagnostic/Predictive Healthcare, Elham Rastegari

UNO Student Research and Creative Activity Fair

Functional ability has been always considered as one of the important determining factors of individuals’ health and quality of life. Traditional movement analysis systems require expensive facilities and frequent visits for patients to specialized laboratories. Portability and affordability of wearable sensors along with their improved accuracy and capability of monitoring movement during daily activities make them a potential alternative for analyzing mobility patterns for clinical and health assessment purposes. Wearable-based movement data, when combined with other relevant clinical or laboratory data, could enhance evidence-based healthcare and data-driven Clinical Decision Support Systems (CDSS). Utilizing the data from wearable devices, many researchers …

Machine Shop Instruction Tool, John Sullivan, Junfei Yu, Tao Wang, Yuteng Mei

ENGS 89/90 Reports

This team has developed a digital learning solution to supplement the machine shop’s training curriculum that students will be able to access remotely anytime and anywhere. This solution will improve how efficiently the machine tools are taught, reduce the time needed to educate each student, and possibly give more students time to learn in the machine shop. In addition, the team has delivered a code repository and documentation to the machine shop. In the future, this solution can be extended to other machines and courses at Thayer. Students will be able to apply their knowledge to their future engineering projects.

My Baseball Collection App, Nicolas A. Parra

Computer Science and Software Engineering

My Baseball Collection is an iOS application that aims to simplify the management and expansion of physical baseball trading card collections. The app allows users to digitize their baseball card collection by uploading images of cards they possess, creating a wishlist of cards they are seeking, and viewing the collections and wishlists of other users. This project seeks to provide quality of life improvements to those within the baseball card trading community and to further facilitate communication and trading in an online world.

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead

Engineering Faculty Articles and Research

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …

Finding Truth In Fake News: Reverse Plagiarism And Other Models Of Classification, Matthew Przybyla, David Tran, Amber Whelpley, Daniel W. Engels

SMU Data Science Review

As the digital age creates new ways of spreading news, fake stories are propagated to widen audiences. A majority of people obtain both fake and truthful news without knowing which is which. There is not currently a reliable and efficient method to identify “fake news”. Several ways of detecting fake news have been produced, but the various algorithms have low accuracy of detection and the definition of what makes a news item ‘fake’ remains unclear. In this paper, we propose a new method of detecting on of fake news through comparison to other news items on the same topic, as …

Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, Heidi Nguyen, Aravind Veluchamy, Mamadou Diop, Rashed Iqbal

SMU Data Science Review

In this paper, we present a comparative study of text sentiment classification models using term frequency inverse document frequency vectorization in both supervised machine learning and lexicon-based techniques. There have been multiple promising machine learning and lexicon-based techniques, but the relative goodness of each approach on specific types of problems is not well understood. In order to offer researchers comprehensive insights, we compare a total of six algorithms to each other. The three machine learning algorithms are: Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting. The three lexicon-based algorithms are: Valence Aware Dictionary and Sentiment Reasoner (VADER), Pattern, …

Sr Education Group, A Leading Education Research Publisher, Ranked Nova Southeastern University (Nsu) Within Their 2019 Lists Of Best Online Colleges, Nova Southeastern University

College of Computing and Engineering News Archive

SR Education Group, a leading education research publisher, ranked Nova Southeastern University (NSU) within their 2019 lists of best online colleges. The group recognized NSU’s College of Computing and Engineering for its Master of Science in Computer Science and Engineering program, ranking it 13 out of 19 in “Best Online Master's in Computer Science Programs.” The college was also ranked 6 out of 8 for “Best Online Master's in Information Technology (IT) Degrees.”

Car Image Classification Using Deep Neural Networks, Mingchen Li

Honors Theses

Image classification is widely used in many fields of study. Deep neural networks are proven to be effective classifier structure due to its massive parameters and training capability. This paper outlines the development of Deep Neural Network in recent years and applied them on a Car image data set in order to compare their performances.