For decades, understanding the complexity of behaviors, motivations, and values has interested researchers across various disciplines. So much so that there are numerous terms, frameworks, theories, and studies devoted to understanding these complexities and how they interact and evolve into actions. However, little research has examined how employee behaviors translate into the work environment, particularly regarding perceived organizational success. This study advances research by quantitatively assessing how a greater number of individual employees’ pro-environmental behaviors are related to the perceived success of environmentally sustainable workplace activities. We have concluded that the more pro-environmental behaviors an employee embodies, the more positively they perceive the success of their local government's sustainable purchasing policy. Additionally, other factors matter, including organizational behaviors, like training, innovation, and reduction of red tape.

BACKGROUND: The City of Phoenix initiated the HeatReady program in 2018 to prepare for extreme heat, as there was no official tool, framework, or mechanism at the city level to manage extreme heat. The current landscape of heat safety culture in schools, which are critical community hubs, has received less illumination. HeatReady Schools—a critical component of a HeatReady City—are those that are increasingly able to identify, prepare for, mitigate, track, and respond to the negative impacts of schoolgrounds heat. However, minimal attention has been given to formalize heat preparedness in schools to mitigate high temperatures and health concerns in schoolchildren, a heat-vulnerable population. This study set out to understand heat perceptions, (re)actions, and recommendations of key stakeholders and to identify critical themes around heat readiness. METHODS: An exploratory sequential mixed-methods case study approach was used. These methods focused on acquiring new insight on heat perceptions at elementary schools through semi-structured interviews using thematic analysis and the Delphi panel. Participants included public health professionals and school community members at two elementary schools—one public charter, one public—in South Phoenix, Arizona, a region that has been burdened historically with inequitable distribution of heat resources due to environmental racism and injustices. RESULTS: Findings demonstrated that 1) current heat safety resources are available but not fully utilized within the school sites, 2) expert opinions support that extreme heat readiness plans must account for site-specific needs, particularly education as a first step, and 3) students are negatively impacted by the effects of extreme heat, whether direct or indirect, both inside and outside the classroom. CONCLUSIONS: From key informant interviews and a Delphi panel, a list of 30 final recommendations were developed as important actions to be taken to become “HeatReady.” Future work will apply these recommendations in a HeatReady School Growth Tool that schools can tailor be to their individual needs to improve heat safety and protection measures at schools.

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.