Matching Items (37)

Bridging the Gap Between Secondary Education and College Level S.T.E.M. Education

Description

This research ventures to adjust the Algebra 2 Core Standards set by the Arizona Department of Education so that computer science concepts may be taught in parallel with the mathematical

This research ventures to adjust the Algebra 2 Core Standards set by the Arizona Department of Education so that computer science concepts may be taught in parallel with the mathematical concepts in Algebra 2 in order to facilitate a better understanding of both subjects. The close relation to computer science and mathematics make this course possible. Students will be more prepared for university level education when they understand how technology works rather than simply how to use it. The solution is to create an online set of modules that can be taught alongside the high school mathematics course, Algebra 2. The solution contains a set of five modules that parallel with the Arizona core standards of the class. There are several obstacles that needed to be overcome in order to create online modules that would fit the needs of schools, students and teachers. This solution will reach students quickly as the hope is that it will become a requirement according to the Arizona Department of Education core standards. The course will be easily accessible to students as it is online and the course will fit into the existing education system, which would not require state laws to be passed in order to require the teaching of computer science. The goal is to bridge the gap between secondary education and college level S.T.E.M. education specifically in reference to computer science so that students start college with a strong understanding of how technology works in order to help them become more successful in the future.

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Created

Date Created
  • 2016-12

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Voice Reconfigurable Networks

Description

The software element of home and small business networking solutions has failed to keep pace with annual development of newer and faster hardware. The software running on these devices is

The software element of home and small business networking solutions has failed to keep pace with annual development of newer and faster hardware. The software running on these devices is an afterthought, oftentimes equipped with minimal features, an obtuse user interface, or both. At the same time, this past year has seen the rise of smart home assistants that represent the next step in human-computer interaction with their advanced use of natural language processing. This project seeks to quell the issues with the former by exploring a possible fusion of a powerful, feature-rich software-defined networking stack and the incredible natural language processing tools of smart home assistants. To accomplish these ends, a piece of software was developed to leverage the powerful natural language processing capabilities of one such smart home assistant, the Amazon Echo. On one end, this software interacts with Amazon Web Services to retrieve information about a user's speech patterns and key information contained in their speech. On the other end, the software joins that information with its previous session state to intelligently translate speech into a series of commands for the separate components of a networking stack. The software developed for this project empowers a user to quickly make changes to several facets of their networking gear or acquire information about it with just their language \u2014 no terminals, java applets, or web configuration interfaces needed, thus circumventing clunky UI's or jumping from shell to shell. It is the author's hope that showing how networking equipment can be configured in this innovative way will draw more attention to the current failings of networking equipment and inspire a new series of intuitive user interfaces.

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Created

Date Created
  • 2016-12

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Wi-Fi Enabled Message Transmission: An Implementation

Description

The Internet of Things has spread Wi-Fi connectivity to household and business devices everywhere. It is important that we understand IoT's risks and capabilities as its popularity continues to grow,

The Internet of Things has spread Wi-Fi connectivity to household and business devices everywhere. It is important that we understand IoT's risks and capabilities as its popularity continues to grow, and that we recognize new and exciting uses for it. In this project, the ESP8266 Wi-Fi controller, powered by a lithium battery, is used to transmit messages from a user's browser or mobile phone to an OLED display. The ESP8266 is a system on a chip (SOC) which boasts impressive features such as full TCP/IP stack, 1 MB of flash memory, and a 32-bit CPU. A web server is started on the ESP8266 which listens at a specific port and relays any strings from the client back to the display, acting as a simple notification system for a busy individual such as a professor. The difficulties with this project stemmed from the security protocol of Arizona State University's Wi-Fi network and from the limitations of the Wi-Fi chip itself. Several solutions are suggested, such as utilizing a personal cellular broadband router and polling a database for stored strings through a service such as Data.Sparkfun.com.

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Created

Date Created
  • 2016-12

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Data Management Behind Machine Learning

Description

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

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.

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Created

Date Created
  • 2018-05

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LEGv8 Runtime Simulator

Description

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

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.

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Created

Date Created
  • 2017-12

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Machine Learning Enabled Analytics for Health-Related Demographics: a Case Study Identifying Important Factors in Cardiac Disease

Description

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

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.

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Created

Date Created
  • 2018-05

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Machine Learning: A Sentiment Analysis of Customer Reviews

Description

Machine learning is the process of training a computer with algorithms to learn from data and make informed predictions. In a world where large amounts of data are constantly collected,

Machine learning is the process of training a computer with algorithms to learn from data and make informed predictions. In a world where large amounts of data are constantly collected, machine learning is an important tool to analyze this data to find patterns and learn useful information from it. Machine learning applications expand to numerous fields; however, I chose to focus on machine learning with a business perspective for this thesis, specifically e-commerce.

The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.

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Created

Date Created
  • 2020-05

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Prediction of Binding Affinity of T cell Receptor and Antigens using Deep Neural Networks

Description

Immunotherapy is an effective treatment for cancer which enables the patient's immune system to recognize tumor cells as pathogens. In order to design an individualized treatment, the t cell receptors

Immunotherapy is an effective treatment for cancer which enables the patient's immune system to recognize tumor cells as pathogens. In order to design an individualized treatment, the t cell receptors (TCR) which bind to a tumor's unique antigens need to be determined. We created a convolutional neural network to predict the binding affinity between a given TCR and antigen to enable this.

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Created

Date Created
  • 2020-12

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Gram-ART Applied to Music Recommendation Services

Description

In this paper we explore the design, implementation, and analysis of two different approaches for providing music recommendations to targeted users by implementing the Gram-ART unsupervised learning algorithm. We provide

In this paper we explore the design, implementation, and analysis of two different approaches for providing music recommendations to targeted users by implementing the Gram-ART unsupervised learning algorithm. We provide a content filtering approach using a dataset of one million songs which include various metadata tags and a collaborative filtering approach using the listening histories of over one million users. The two methods are evaluated by their results from Million Song Dataset Challenge. While both placed near the top third of the 150 challenge participants, the knowledge gained from the experiments will help further refine the process and likely produced much higher results in a system with the potential to scale several magnitudes.

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Created

Date Created
  • 2015-05

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The Vulnerabilities of Using Passwords and Username Based Systems

Description

One of the major sources of authentication is through the use of username and password systems. Ideally, each password is a unique identifier known by a single individual. In reality

One of the major sources of authentication is through the use of username and password systems. Ideally, each password is a unique identifier known by a single individual. In reality however, exposed passwords from past data breaches reveal vulnerabilities that are traceable to passwords created today. Vulnerabilities include repetitions of characters, words, character sequences, and phrases that are used in a password. This project was observed in English to highlight the vulnerabilities that can come from utilizing the English language. However, the vulnerabilities highlighted in this project can also be applicable in languages across the world. It was observed that through the common types of digital attacks, brute force attack and dictionary attack work effectively against weak passwords. Brute force attack revealed that a user could expose an alphanumeric password of length eight in as little as one and a half days. In addition, dictionary attacks revealed that an alphanumeric password of length eight can be exposed in a shorter amount of time if the password contains a single long word or phrase thought to be secure. During this attack analysis, it found that passwords become significantly more secure in the utilization of alphanumeric passwords of minimal length of eight. In addition, the password must also not be a particular phrase or word with simplistic characteristics for adequate strength against dictionary attack. The solution to using username and password systems is to create a password utilizing as many characters as possible while still retaining memorability. If creating a password of this type is not feasible, there is a need to use technological solutions to keep the current system of username and passwords as secure as possible under daily life. Otherwise, there will be a need to replace the username and password system altogether before it becomes insecure by technology.

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Created

Date Created
  • 2020-05