## What it does

When you are in uptime mode, it will (optionally) disable certain sites (ex. social media) of your choosing in order to promote productivity as well as track your usage of active programs (so you can see where your time is going!).

When you are in downtime mode, it will disable tracking as well as return access to any sites that were restricted in uptime mode.

## Built with

The magic of Electron, NodeJS, HTML, CSS, Canvas

## What we learned

Learned electron for the first time!

## What's next for uptrack

* reminders
* pick colors of graph based on the main color from the icon representing the process
* filter out background process
* optional tracking of specific chrome websites to see more detailed view (ex. Social media websites)
## Inspiration

As university students with little time management and an overwhelming amount of work, it is crucial to correctly manage our time on the internet.

## What it does

MonitUr-time is a Chrome extension with the purpose of tracking your daily Chrome activity. Measurements like taxonomy, how long you frequent each tab, and comparisons between time intervals are all tracked in the background of our everyday browsing which we can open in one simple popup.

## How we built it

We used HTML for the extension popup, and JavaScript to interact with Chrome's API, Manifest. Manifest was able to gather the current URL the user is on, and by manipulating the string, we are able to convert it into the company's name. By gathering the time step of each iteration, we are also able to calculate how long the user stays on each tab.

## Challenges we ran into

The main challenge we rain into was understanding the Manifest API to implement into our specific needs. Manifest was extremely thorough, but also very extensive. Picking the correct API calls took a long time, and learning to efficiently use the API was the main struggle of our assignment.

The time spent on learning Manifest led into less time to develop our database. However, this experience was valuable in expanding our skillsets in API and back-end development.

## Accomplishments and Takeaways that we're proud of

Overall, we were all very proud to finish a working product and glad to learn so much from Manifest.

## What's next for MonitUr-time

More time would have been helpful to flush out our front-end UI as well as our database structure. For future, we can emphasize on analyzing time trends, like weekly/monthly/yearly trends, frequency of websites per hour, and specific types of websites at different time periods (productivity during the day).
## Inspiration

Each year, more people are diagnosed with skin cancer than all other cancer's combined. It is also estimated that one in five people will develop skin cancer by the time they reach 70 years of age. Skin cancer is also one of the easiest to cure, if detected early. With this in mind, we were inspired to develop a solution that empowers users to analyze and monitor the health of their skin.

## What it does

Möle allows users to take pictures of freckles, blemishes and moles on their skin. The application then uses a Machine Learning image processing model to compare it to more than ten thousand images of various skin conditions. The resulting analysis informs the user whether the blemish is benign or malignant, and if it resembles Melanoma, Bowens Disease, Basal Cell Carcinoma, Benign lesions, Dermatofibroma, or Melanocytic Nevi.

The application also allows users to track blemishes over time, providing updates on their health as they develop and change.

## How we built it

Möle is built for Android devices, using Kotlin/Java and a Realm database. The image processing agent was built using Azure's Custom Vision Model, trained using HAM10000, a neural network training dataset published in the Harvard Dataverse, with over ten thousand images of common skin lesions.

For more information, please visit: <https://bit.ly/2UlvD7P>

## Challenges we ran into

All team members were unfamiliar with the projects technical stack at the beginning of this competition. The learning curve, coupled with the limited development time, proved to be our biggest challenge. Sorting, tagging and uploading over 10 000 images for our computer vision model was also a challenge on its own, which required a fair amount of scripting.

## Accomplishments that we're proud of

All members of our team undertook a large part of this project, working with tech we were not familiar with, and managed to complete a project within 24 hours! On top of this, we feel that the product we created could be used to help educate and inform millions of people worldwide, who may not have access to dermatologists or skin care professionals.

## What we learned

We would struggle to fit everything we learned over the past 24 hours onto one page, but we feel the biggest lessons were that...

* To always focus on the minimum viable product when time is limited
* It's more fun to work on projects you're excited about
* Wear more sunscreen, skin care is no joke!

## What's next for Möle

Training our model with larger datasets to improve accuracy, and fine tuning the interface to create a more seamless user experience. We also intend to better integrate users age, sex, and geographical location into the analysis in order to produce more accurate results.