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- All Subjects: Finance
- All Subjects: Machine Learning
- Creators: Department of Finance
- Creators: Computer Science and Engineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
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Frontier markets is a section of the business world where a lot of money could be made but is often overlooked for different reasons. A big reason is that there are a lot of unknowns about investing in these markets. With any business investments comes risk, but through proven years of research and following trends a lot of that risk can become hedged. With knowledge there comes power and, in this context, with taking the time to learn about underdog markets such as frontier markets comes great investment opportunities. This thesis will look to analyze three Sub-Sahara African countries of Tanzania, Kenya, and Ghana; and will answer the questions of why to invest in frontier economies in Africa, and how investors can minimize risk and maximize returns.
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In this paper, I introduce the fake news problem and detail how it has been exacerbated<br/>through social media. I explore current practices for fake news detection using natural language<br/>processing and current benchmarks in ranking the efficacy of various language models. Using a<br/>Twitter-specific benchmark, I attempt to reproduce the scores of six language models<br/>demonstrating their effectiveness in seven tweet classification tasks. I explain the successes and<br/>challenges in reproducing these results and provide analysis for the future implications of fake<br/>news research.
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This thesis will be exploring the situation of one of the most vulnerable groups during the COVID-19 pandemic, low-income renters. As businesses and whole states were shutdown, jobs and wages were lost and the over 100 million renters in the United States, many of whom spend a significant chunk of their income on their rent, were forced into a precarious situation. <br/><br/>The Federal Rent Moratorium that is currently in effect bars any evictions for missed rent payments, but these are expenses that if left unpaid, are just continuously accruing. These large sums of rent payments are currently scheduled to be dropped on struggling individuals at the end of the recently extended date of June 30th, 2021. As these renters are unable to pay for their housing, landlords lose the revenue streams from their investment properties, and are in turn unable to cover the debt service on the financing they utilized to acquire the property. In turn, financial institutions can then face widespread defaults on these loans.<br/><br/>The rental property market is massive, as roughly 34% of the American population consist of renters. If left unaddressed, this situation has the potential to cause cataclysmal consequences on the economy, including mass homelessness and foreclosures of rental properties and complexes. Everyone, from the tenants to the bankers and beyond, are stakeholders in this dire situation and this paper will seek to explore the issues, desires, and potential solutions applicable to all parties involved. Beginning with the pre-pandemic outlook of the rental housing market, then examining the impact of the coronavirus and the resulting federal actions, to finally explore solutions that may prevent or mitigate this potential disaster.
Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.
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The COVID-19 pandemic has and will continue to radically shift the workplace. An increasing percentage of the workforce desires flexible working options and, as such, firms are likely to require less office space going forward. Additionally, the economic downturn caused by the pandemic provides an opportunity for companies to secure favorable rent rates on new lease agreements. This project aims to evaluate and measure Company X’s potential cost savings from terminating current leases and downsizing office space in five selected cities. Along with city-specific real estate market research and forecasts, we employ a four-stage model of Company X’s real estate negotiation process to analyze whether existing lease agreements in these cities should be renewed or terminated.
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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.
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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.
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