Since the global financial crisis of 2007-8, interest in worker-cooperatives and alternative forms of organization has surged. Mondragon, located in the Basque region of Spain, represents the largest federation of worker-cooperatives around the world, consisting of 98 cooperatives and 143 subsidiaries, which earned a total revenue of $14.5 billion in 2019. While previous attempts to establish a similar model have historically reached limited success, Mondragon has achieved a unique balance of remaining economically viable, on the one hand, and staying true to its founding principles of democratic governance, on the other. This paper sets out to analyze the democratic structure and the cooperative culture at the heart of the Mondragon model, as well as the new type of human relationship that it fosters. In particular, this relationship is one in which individual well-being is bound up with communal well-being that avoids the antagonistic clash between the capital and labor.

This thesis will bring together students to engage in entrepreneurship by finding, measuring and sharing strategic market opportunities. From a student’s perspective, it will take a deep dive into the world of startup ecosystems, markets and trends utilizing both qualitative and quantitative market research techniques. The information gathered has been curated into a productive, meaningful manner, through a report titled “The State of Startups: A Student Perspective.” <br/> The first key theme of this thesis is that market intelligence can be a powerful tool. The second key theme is the power of knowledge implementation towards competitive strategies. The first section of the thesis will focus on identifying and understanding the current “startup” landscape as a basis on which to build strategic and impactful business decisions. This will be accomplished as the team conducts a landscape analysis focused on the student perspective of the student-based North American “entrepreneurial” ecosystem. The second section of the thesis will focus specifically on the personal experiences of student startup founders. This will be accomplished through the analysis of interviews with founders of the startups researched from the first section of the thesis. This will provide us with a direct insight into the student perspective of the student-based North American “entrepreneurial” ecosystem.

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.

The contemporary world is motivated by data-driven decision-making. Small 501(c)3 nonprofit organizations are often limited in their reach due to their size, lack of funding, and a lack of data analysis expertise. In an effort to increase accessibility to data analysis for such organizations, a Founders Lab team designed a product to help them understand and utilize geographic information systems (GIS) software. This product – You Got GIS – strikes the balance between highly technical documentation and general overviews, benefiting 501(c)3 nonprofits in their pursuit of data-driven decision-making. Through the product’s use of case studies and methodologies, You Got GIS serves as a thought experiment platform to start answering questions regarding GIS. The product aims to continuously build partnerships in an effort to improve curriculum and user engagement.

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.