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.

The NCAA is changing the current rules and regulations around a student-athlete’s name, image, and likeness. Previously, student-athletes were not allowed to participate in business activities or noninstitutional promotional activities. With the new rule changes, student-athletes will be able to engage in business activities related to their own name, image, and likeness. The goal of the team was to help “prepare athletes to understand and properly navigate the evolving restrictions and guidelines around athlete name, image, and likeness”. In order to accomplish this, the team had to understand the problems student-athletes face with these changing rules and regulations. The team conducted basic market research to identify the problem. The problem discovered was the lack of communication between student-athletes and businesses. In order to verify this problem, the team conducted several interviews with Arizona State University Athletic Department personnel. From the interviews, the team identified that the user is the student-athletes and the buyer is the brands and businesses. Once the problem was verified and the user and buyer were identified, a solution that would best fit the customers was formulated. The solution is a platform that assists student-athletes navigate the changing rules of the NCAA by providing access to a marketplace optimized to working with student-athletes and offering an ease of maintaining relationships between student-athletes and businesses. The solution was validated through meetings with interested brands. The team used the business model and market potential to pitch the business idea to the brands. Finally, the team gained traction by initiating company partnerships.