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Developing Inventory Control and Build Management Software for Spacecraft Engineering

Description

Engineering an object means engineering the process that creates the object. Today, software can make the task of tracking these processes robust and straightforward. When engineering requirements are strict and strenuous, software custom-built for such processes can prove essential. The

Engineering an object means engineering the process that creates the object. Today, software can make the task of tracking these processes robust and straightforward. When engineering requirements are strict and strenuous, software custom-built for such processes can prove essential. The work for this project was developing ICDB, an inventory control and build management system created for spacecraft engineers at ASU to record each step of their engineering processes. In-house development means ICDB is more precisely designed around its users' functionality and cost requirements than most off-the-shelf commercial offerings. By placing a complex relational database behind an intuitive web application, ICDB enables organizations and their users to create and store parts libraries, assembly designs, purchasing and location records for inventory items, and more.

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2018-05

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Prescription Information Extraction from Electronic Health Records using BiLSTM-CRF and Word Embeddings

Description

Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records

Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of information, they require significant effort to develop hand-crafted rules and conduct effective feature engineering. This paper presents the use of a bidirectional LSTM with CRF tagging model initialized with precomputed word embeddings for extracting prescription information from sentences without requiring significant feature engineering. The experimental results, run on the i2b2 2009 dataset, achieve an F1 macro measure of 0.8562, and scores above 0.9449 on four of the six categories, indicating significant potential for this model.

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2018-05

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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 its impact from a data management perspective on both the

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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2018-05

Behavior Trees + Finite State Machines: A Hybrid Game AI Framework

Description

One of the core components of many video games is their artificial intelligence. Through AI, a game can tell stories, generate challenges, and create encounters for the player to overcome. Even though AI has continued to advance through the implementation

One of the core components of many video games is their artificial intelligence. Through AI, a game can tell stories, generate challenges, and create encounters for the player to overcome. Even though AI has continued to advance through the implementation of neural networks and machine learning, game AI tends to implement a series of states or decisions instead to give the illusion of intelligence. Despite this limitation, games can still generate a wide range of experiences for the player. The Hybrid Game AI Framework is an AI system that combines the benefits of two commonly used approaches to developing game AI: Behavior Trees and Finite State Machines. Developed in the Unity Game Engine and the C# programming language, this AI Framework represents the research that went into studying modern approaches to game AI and my own attempt at implementing the techniques learned. Object-oriented programming concepts such as inheritance, abstraction, and low coupling are utilized with the intent to create game AI that's easy to implement and expand upon. The final goal was to create a flexible yet structured AI data structure while also minimizing drawbacks by combining Behavior Trees and Finite State Machines.

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2018-05

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Why Students in Computer Science Courses Cheat?

Description

The goal of this study is to equip administrators and instructors with a deeper understanding of the apparent cheating problem in Computer Science courses, with proposed solutions to lower academic dishonesty from the students’ perspective.

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2019-05

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Using Machine Learning to Predict the NBA

Description

Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and

Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and outcomes are easily measurable. Predicting the outcomes of sports events may also be easily profitable, predictions can be taken to a sportsbook and wagered on. A successful prediction model could easily turn a profit. The goal of this project was to build a model using machine learning to predict the outcomes of NBA games.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.

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2019-05

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Empowering Women in Zambia through Computational Thinking Curriculum

Description

The nonprofit organization, I Am Zambia, works to give supplemental education to young women in Lusaka. I Am Zambia is creating sustainable change by educating these females, who can then lift their families and communities out of poverty. The ultimate

The nonprofit organization, I Am Zambia, works to give supplemental education to young women in Lusaka. I Am Zambia is creating sustainable change by educating these females, who can then lift their families and communities out of poverty. The ultimate goal of this thesis was to explore and implement high level systematic problem solving through basic and specialized computational thinking curriculum at I Am Zambia in order to give these women an even larger stepping stool into a successful future.

To do this, a 4-week long pilot curriculum was created, implemented, and tested through an optional class at I Am Zambia, available to women who had already graduated from the year-long I Am Zambia Academy program. A total of 18 women ages 18-24 chose to enroll in the course. There were a total of 10 lessons, taught over 20 class period. These lessons covered four main computational thinking frameworks: introduction to computational thinking, algorithmic thinking, pseudocode, and debugging. Knowledge retention was tested through the use of a CS educational tool, QuizIt, created by the CSI Lab of School of Computing, Informatics and Decision Systems Engineering at Arizona State University. Furthermore, pre and post tests were given to assess the successfulness of the curriculum in teaching students the aforementioned concepts. 14 of the 18 students successfully completed the pre and post test.

Limitations of this study and suggestions for how to improve this curriculum in order to extend it into a year long course are also presented at the conclusion of this paper.

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2019-05

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Twitch Streamer-Game Recommender System

Description

Abstract
Matrix Factorization techniques have been proven to be more effective in recommender systems than standard user based or item based methods. Using this knowledge, Funk SVD and SVD++ are compared by the accuracy of their predictions of Twitch streamer

Abstract
Matrix Factorization techniques have been proven to be more effective in recommender systems than standard user based or item based methods. Using this knowledge, Funk SVD and SVD++ are compared by the accuracy of their predictions of Twitch streamer data.

Introduction
As watching video games is becoming more popular, those interested are becoming interested in Twitch.tv, an online platform for guests to watch streamers play video games and interact with them. A streamer is an person who broadcasts them-self playing a video game or some other thing for an audience (the guests of the website.) The site allows the guest to first select the game/category to view and then displays currently active streamers for the guest to select and watch. Twitch records the games that a streamer plays along with the amount of time that a streamer spends streaming that game. This is how the score is generated for a streamer’s game. These three terms form the streamer-game-score (user-item-rating) tuples that we use to train out models.
The our problem’s solution is similar to the purpose of the Netflix prize; however, as opposed to suggesting a user a movie, the goal is to suggest a user a game. We built a model to predict the score that a streamer will have for a game. The score field in our data is fundamentally different from a movie rating in Netflix because the way a user influences a game’s score is by actively streaming it, not by giving it an score based off opinion. The dataset being used it the Twitch.tv dataset provided by Isaac Jones [1]. Also, the only data used in training the models is in the form of the streamer-game-score (user-item-rating) tuples. It will be known if these data points with limited information will be able to give an accurate prediction of a streamer’s score for a game. SVD and SVD++ are the baseis of the models being trained and tested. Scikit’s Surprise library in Python3 is used for the implementation of the models.

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2019-05

Detecting Propaganda Bots on Twitter Using Machine Learning

Description

Propaganda bots are malicious bots on Twitter that spread divisive opinions and support political accounts. This project is based on detecting propaganda bots on Twitter using machine learning. Once I began to observe patterns within propaganda followers on

Propaganda bots are malicious bots on Twitter that spread divisive opinions and support political accounts. This project is based on detecting propaganda bots on Twitter using machine learning. Once I began to observe patterns within propaganda followers on Twitter, I determined that I could train algorithms to detect these bots. The paper focuses on my development and process of training classifiers and using them to create a user-facing server that performs prediction functions automatically. The learning goals of this project were detailed, the focus of which was to learn some form of machine learning architecture. I needed to learn some aspect of large data handling, as well as being able to maintain these datasets for training use. I also needed to develop a server that would execute these functionalities on command. I wanted to be able to design a full-stack system that allowed me to create every aspect of a user-facing server that can execute predictions using the classifiers that I design.
Throughout this project, I decided on a number of learning goals to consider it a success. I needed to learn how to use the supporting libraries that would help me to design this system. I also learned how to use the Twitter API, as well as create the infrastructure behind it that would allow me to collect large amounts of data for machine learning. I needed to become familiar with common machine learning libraries in Python in order to create the necessary algorithms and pipelines to make predictions based on Twitter data.
This paper details the steps and decisions needed to determine how to collect this data and apply it to machine learning algorithms. I determined how to create labelled data using pre-existing Botometer ratings, and the levels of confidence I needed to label data for training. I use the scikit-learn library to create these algorithms to best detect these bots. I used a number of pre-processing routines to refine the classifiers’ precision, including natural language processing and data analysis techniques. I eventually move to remotely-hosted versions of the system on Amazon web instances to collect larger amounts of data and train more advanced classifiers. This leads to the details of my final implementation of a user-facing server, hosted on AWS and interfacing over Gmail’s IMAP server.
The current and future development of this system is laid out. This includes more advanced classifiers, better data analysis, conversions to third party Twitter data collection systems, and user features. I detail what it is I have learned from this exercise, and what it is I hope to continue working on.

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2019-05

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Technology Transformations: SmartAid - An Intelligent First Aid Kit

Description

SmartAid aims to target a small, yet relevant issue in a cost effective, easily replicable, and innovative manner. This paper outlines how to replicate the design and building process to create an intelligent first aid kit. SmartAid utilizes Alexa Voice

SmartAid aims to target a small, yet relevant issue in a cost effective, easily replicable, and innovative manner. This paper outlines how to replicate the design and building process to create an intelligent first aid kit. SmartAid utilizes Alexa Voice Service technologies to provide a new and improved way to teach users about the different types of first aid kit items and how to treat minor injuries, step by step. Using Alexa and RaspberryPi, SmartAid was designed as an added attachment to first aid kits. Alexa Services were installed into a RaspberryPi to create a custom Amazon device, and from there, using the Alexa Interaction Model and the Lambda function services, SmartAid was developed. After the designing and coding of the application, a user guide was created to provide users with information on what items are included in the first aid kit, what types of injuries can be treated through first aid, and how to use SmartAid. The
application was tested for its usability and practicality by a small sample of students. Users provided suggestions on how to make the application more versatile and functional, and confirmed that the application made first aid easier and was something that they could see themselves using. While this application is not aimed to replace the current physical guide solution completely, the findings of this project show that SmartAid has potential to stand in as an improved, easy to use, and convenient alternative for first aid guidance.

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2019-05