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An in depth look at the rhetoric behind the campus carry debate at the University of Texas at Austin. This thesis researched and examined primary sources from The Daily Texan and The Austin-American Statesman attempting to analyze what was at stake for both sides of the argument and what the most effective rhetorical tool was.
Backcountry Broadcasts is a multimedia project that aims to empower women in the great outdoors. This platform serves to inspire, encourage and appreciate women in the wilderness through photography, personal stories and more. Through our passion for the outdoors, we're incorporating our own female experience with the voices of others to bring light to the importance of gender inclusivity in the backcountry.
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In a COVID-19 world, student engagement has suffered drastically as organizations and universities shifted to an online format. Yet, there is still an opportunity and a space for digital content creation to bridge the gap in a virtual and hybrid university lifestyle. This project looks at how student groups can still engage students at ASU Tempe through digital content creation and which tools to use to enter the space.
This project is a series of two YouTube videos that follow me learning new skills. The first is soldering, and the second is jumping a bicycle. The goal of this project is to use it to hone my cinematography skills and to inspire other beginners to try new things by highlighting my own trials and tribulations and being vulnerable.
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Designing these agents to cover every case of human interaction is difficult, and usually
imperfect, as human players are capable of learning to overcome these agents in unintended
ways. Artificial intelligence is a growing field that seeks to solve problems by simulating
learning in specific environments. The aim of this paper is to explore the applications that the
self play learning branch of artificial intelligence may pose on game development in the future,
and to attempt to implement a working version of a self play agent learning to play a Pokemon
battle. Originally designed Pokemon battle behavior is often suboptimal, getting stuck making
ineffective or incorrect choices, so training a self play model to learn the strategy and structure of
Pokemon battles from a clean slate would result in an organic agent that would outperform the
original behavior of the computer controlled agents. Though unsuccessful in my implementation,
this paper serves as a record of the exploration of this field, and a log of what worked and what
did not, in order to benefit any future person interested in the same topics.
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Since it doesn’t hurt to attempt to utilize feature extracted values to improve a model (if things don’t work out, one can always use their original features), the question may arise: how could the results of feature extraction on values such as sentiment affect a model’s ability to predict the movement of the stock market? This paper attempts to shine some light on to what the answer could be by deriving TextBlob sentiment values from Twitter data, and using Granger Causality Tests and logistic and linear regression to test if there exist a correlation or causation between the stock market and features extracted from public sentiment.
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Planning coordination between robots in a multi-agent system requires each robot to know the position of the other robots. To address this, the localization server tracked visual fiducial markers attached to the robots and relayed their pose to every robot at a rate of 20Hz using the MQTT communication protocol. The robots used this data to inform a potential fields path planning algorithm and navigate to their target position.
This project was unable to address all of the challenges facing true distributed multi-agent coordination and needed to make concessions in order to meet deadlines. Further research would focus on shoring up these deficiencies and developing a more robust system.
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