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.

The NBA yields billions of dollars each year and serves as a pastime and hobby for millions of Americans. However, many people do not have the time to watch several 2-hour games every week, especially when only a fraction of the game is actually exciting footage. The goal of Sports Summary is to take the ``fluff'' out of these games and create a distilled summary that includes only the most exciting and relevant events. The Sports Summary model records visual and auditory data, camera angles, and game clock readings and correlates it with the game's play-by-play data. On average, a game of more than 2 hours long is shortened to a summary of less than 20 minutes. This summary is then uploaded to the Sports Summary website, where users can filter by the type of event, giving more autonomy and a more comprehensive viewing experience than highlight reels. Additionally, the website allows for users to submit footage they would like to watch for processing and later viewing. Sports Summary creates an enjoyable and accessible way to watch games.

Generating an astounding $110.7 billion annually in domestic revenue alone [1], the world of accounting is one deceptively lacking automation of its most business-critical processes. While accounting tools do exist for the common person, especially when it is time to pay their taxes, such innovations scarcely exist for many larger industrial tasks. Exceedingly common business events, such as Business Combinations, are surprisingly manual tasks despite their $1.1 trillion valuation in 2020 [2]. This work presents the twin accounting solutions TurboGAAP and TurboIFRS: an unprecedented leap into these murky waters in an attempt to automate and streamline these gigantic accounting tasks once entrusted only to teams of experienced accountants.
A first-to-market approach to a trillion-dollar problem, TurboGAAP and TurboIFRS are the answers for years of demands from the accounting sector that established corporations have never solved.