The right to cast a meaningful vote, equal in value to other votes, is a fundamental tenet US elections. Despite the 1964 Supreme Court decision formally establishing the one person, one vote principle as a legal requirement of elections, our democracy consistently falls short of it. With mechanisms including the winner-take-all format in the Electoral College, disproportioned geographic allocation of senators, extreme partisan gerrymandering in the House of Representatives, and first-past-the-post elections, many voters experience severe vote dilution. <br/><br/>In order to legitimize our democratic structures, American elections should be reformed so every person’s vote has equal weight, ensuring that the election outcomes reflect the will of the people. Altering the current election structure to include more proportional structures including rank choice voting and population-based representation, will result in a democracy more compatible with the one person, one vote principle.

Descriptive representation is important to building and maintaining a fair court system, especially within a context of historical oppression by race or gender. Using official government biographies, voter rolls, news articles, and press releases, I collected demographic information on the judges of Arizona and compared it to Census data, to show how under representative the state courts of Arizona currently are. Through the use of non-attorney judges, the Justice Court of Arizona has become the most representative level of the state court. Almost all of the BIPOC judges of the Justice Court are not attorneys. Allowing non-attorney Justices of the Peace has made it possible for the court to be more representative of Arizonans. However, even though it is the most representative state court, the Justice Court vastly under represents women and BIPOC as judges. As racial tension and movements for fairness under the law increase, it is important to challenge how the courts could better serve Arizona.

Affective computing allows computers to monitor and influence people’s affects, in other words emotions. Currently, there is a lot of research exploring what can be done with this technology. There are many fields, such as education, healthcare, and marketing, that this technology can transform. However, it is important to question what should be done. There are unique ethical considerations in regards to affective computing that haven't been explored. The purpose of this study is to understand the user’s perspective of affective computing in regards to the Association of Computing Machinery (ACM) Code of Ethics, to ultimately start developing a better understanding of these ethical concerns. For this study, participants were required to watch three different videos and answer a questionnaire, all while wearing an Emotiv EPOC+ EEG headset that measures their emotions. Using the information gathered, the study explores the ethics of affective computing through the user’s perspective.

Cryptojacking is a process in which a program utilizes a user’s CPU to mine cryptocurrencies unknown to the user. Since cryptojacking is a relatively new problem and its impact is still limited, very little has been done to combat it. Multiple studies have been conducted where a cryptojacking detection system is implemented, but none of these systems have truly solved the problem. This thesis surveys existing studies and provides a classification and evaluation of each detection system with the aim of determining their pros and cons. The result of the evaluation indicates that it might be possible to bypass detection of existing systems by modifying the cryptojacking code. In addition to this classification, I developed an automatic code instrumentation program that replaces specific instructions with functionally similar sequences as a way to show how easy it is to implement simple obfuscation to bypass detection by existing systems.

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.