The Internet has made it possible to exchange information at a rapid rate. With this extraordinary ability, media companies and various other organizations have been able to communicate thoughts and information to an extremely large audience. As a result, news subscribers are overwhelmed with biased information, which makes it very easy to be misinformed. Unfortunately, there is currently no way to stay truly informed without spending countless hours searching the Internet for different viewpoints and ultimately using that information to formulate a sound understanding. This project (nicknamed "Newsie") solves this problem by providing news subscribers with many news sources to every topic, thereby saving them time and ultimately paving a way to a more informed society. Since one of the main goals of this project is to provide information to the largest number of people, Newsie is designed with availability in mind. Unsurprisingly, the most accessible method of communication is the Internet \u2014 more specifically, a website. Users will be able to access Newsie via a webpage, and easily view to most recent headlines with their corresponding articles from several sources. Another goal of the project is to classify different articles and sources based on their bias. After reading articles, users will be able to vote on their biases. This provides a crowdsourced method of determining bias.

Even in the largest public university in the country, computer related degrees such as Computer Science, Computer Systems Engineering and Software Engineering have low enrollment rates and high dropout rates. This is interesting because the careers that require these degrees are marketed as the highest paying and most powerful. The goal of this project was to find out what the students of Arizona State University (ASU) thought about these majors and why they did or did not pick them. A total of 206 students were surveyed from a variety of sources including upper level classes, lower level classes and Barrett, the Honors College. Survey questions asked why the students picked their current major, if they had a previous major and why did they switch, and if the students had considered one of the three computer related degrees. Almost all questions were open ended, meaning the students did not have multiple choice answers and instead could write as short or as long of a response as needed. Responses were grouped based on a set of initial hypotheses and any emerging trends. These groups were displayed in several different bar graphs broken down by gender, grade level and category of student (stayed in a computer related degree, left one, joined one or picked a non-computer related degree). Trends included students of all grade levels picking their major because they were passionate or interested in the subject. This may suggest that college students are set in their path and will not switch majors easily. Students also reported seeing computer related degrees as too difficult and intimidating. However, given the low (when compared to all of ASU) number of students surveyed, the conclusions and trends given cannot be representative of ASU as a whole. Rather, they are just representative of this sample population. Further work on this study, if time permitted, would be to try to survey more students and question some of the trends established to find more specific answers.

The current trend of interconnected devices, or the internet of things (IOT) has led to the popularization of single board computers (SBC). This is primarily due to their form-factor and low price. This has led to unique networks of devices that can have unstable network connections and minimal processing power. Many parallel program- ming libraries are intended for use in high performance computing (HPC) clusters. Unlike the IOT environment described, HPC clusters will in general look to obtain very consistent network speeds and topologies. There are a significant number of software choices that make up what is referred to as the HPC stack or parallel processing stack. My thesis focused on building an HPC stack that would run on the SCB computer name the Raspberry Pi. The intention in making this Raspberry Pi cluster is to research performance of MPI implementations in an IOT environment, which had an impact on the design choices of the cluster. This thesis is a compilation of my research efforts in creating this cluster as well as an evaluation of the software that was chosen to create the parallel processing stack.

Students learn in various ways \u2014 visualization, auditory, memorizing, or making analogies. Traditional lecturing in engineering courses and the learning styles of engineering students are inharmonious causing students to be at a disadvantage based on their learning style (Felder & Silverman, 1988). My study analyzes the traditional approach to learning coding skills which is unnatural to engineering students with no previous exposure and examining if visual learning enhances introductory computer science education. Visual and text-based learning are evaluated to determine how students learn introductory coding skills and associated problem solving skills. My study was conducted to observe how the two types of learning aid the students in learning how to problem solve as well as how much knowledge can be obtained in a short period of time. The application used for visual learning was Scratch and Repl.it was used for text-based learning. Two exams were made to measure the progress made by each student. The topics covered by the exam were initialization, variable reassignment, output, if statements, if else statements, nested if statements, logical operators, arrays/lists, while loop, type casting, functions, object orientation, and sorting. Analysis of the data collected in the study allow us to observe whether the traditional method of teaching programming or block-based programming is more beneficial and in what topics of introductory computer science concepts.

Cryptocurrencies have become one of the most fascinating forms of currency and economics due to their fluctuating values and lack of centralization. This project attempts to use machine learning methods to effectively model in-sample data for Bitcoin and Ethereum using rule induction methods. The dataset is cleaned by removing entries with missing data. The new column is created to measure price difference to create a more accurate analysis on the change in price. Eight relevant variables are selected using cross validation: the total number of bitcoins, the total size of the blockchains, the hash rate, mining difficulty, revenue from mining, transaction fees, the cost of transactions and the estimated transaction volume. The in-sample data is modeled using a simple tree fit, first with one variable and then with eight. Using all eight variables, the in-sample model and data have a correlation of 0.6822657. The in-sample model is improved by first applying bootstrap aggregation (also known as bagging) to fit 400 decision trees to the in-sample data using one variable. Then the random forests technique is applied to the data using all eight variables. This results in a correlation between the model and data of 9.9443413. The random forests technique is then applied to an Ethereum dataset, resulting in a correlation of 9.6904798. Finally, an out-of-sample model is created for Bitcoin and Ethereum using random forests, with a benchmark correlation of 0.03 for financial data. The correlation between the training model and the testing data for Bitcoin was 0.06957639, while for Ethereum the correlation was -0.171125. In conclusion, it is confirmed that cryptocurrencies can have accurate in-sample models by applying the random forests method to a dataset. However, out-of-sample modeling is more difficult, but in some cases better than typical forms of financial data. It should also be noted that cryptocurrency data has similar properties to other related financial datasets, realizing future potential for system modeling for cryptocurrency within the financial world.

This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.

Machine learning for analytics has exponentially increased in the past few years due to its ability to identify hidden insights in data. It also has a plethora of applications in healthcare ranging from improving image recognition in CT scans to extracting semantic meaning from thousands of medical form PDFs. Currently in the BioElectrical Systems and Technology Lab, there is a biosensor in development that retrieves and analyzes data manually. In a proof of concept, this project uses the neural network architecture to automatically parse and classify a cardiac disease data set as well as explore health related factors impacting cardiac disease in patients of all ages.

This project is a full integrated development environment implementing the LEGv8 assembly language standard, to be used in classroom settings. The LEGv8 assembly language is defined by the ARM edition of "Computer Organization and Design: The Hardware/Software Interface" by David A. Patterson and John L. Hennessy as a more approachable alternative to the full ARMv8 instruction set. The MIPS edition of that same book is used in the Computer Organization course at ASU. This class makes heavy use of the "MARS" MIPS simulator, which allows students to write and run their own MIPS assembly programs. Writing assembly language programs is a key component of the course, as assembly programs have many design difficulties as compared to a high-level language. This project is a fork of the MARS project. The interface and functionality remain largely the same aside from the change to supporting the LEGv8 syntax and instruction set. Faculty used to the MARS environment from teaching Computer Organization should only have to adjust to the new language standard, as the editor and environment will be familiar. The available instructions are basic arithmetic/logical operations, memory interaction, and flow control. Both floating-point and integer operations are supported, with limited support of conditional execution. Only branches can be conditionally executed, per LEGv8. Directives remain in the format supported by MARS, as documentation on ARM-style directives is both sparse and agreeable to this standard. The operating system functions supported by the MARS simulator also remain, as there is no generally standardized requirements for operating system interactions.

As the need for data concerning the health of the world's oceans increases, it becomes necessary to develop large, networked communication systems underwater. This research involves the development of an embedded operating system that is suited for optically-linked underwater wireless sensor networks (WSNs). Optical WSNs are unique in that large sums of data may be received relatively infrequently, and so an operating system for each node must be very responsive. Additionally, the volatile nature of the underwater environment means that the operating system must be accurate, while still maintaining a low profile on a relatively small microprocessor core. The first part of this research concerns the actual implementation of the operating system's task scheduler and additional libraries to maintain synchronization, and the second part involves testing the operating system for responsiveness to interrupts and overall performance.