## Inspiration

As recent or soon to be graduates, we personally understand the desire to relocate and expand our world views. There is so much potential out there, but it's hard to know what city is best as we all have unique needs and wants.

## What it does

By gathering aspects that students care about when researching a city, we visualize the data based on selected preferences and suggest potential cities. Clicking on a city shows more information about that city and how it compares to others.

## How I built it

We initially narrowed our focus to a set of users: recently graduated students. Then, we discussed several user journeys and sought out specific pain points. We conducted some research to find out what type of criterias people look into when deciding where to move, and then found open datasets from statscan and other online sources to support these criteria.

We pulled the 2016 Canadian Census Data information on the biggest cities in Canada. We sorted this data into specific categories, and compiled static JSON files of the cities. We then fed this information into our web app powered by React where we visualized it using Mapbox and different graphing techniques.

## Challenges I ran into

Going from a well designed static prototype to an implemented version is a big jump as the data had to be manipulated to fit the visualization library we used. The Stats Canada data was also unreliable and oddly formatted, leading to a lot of difficulties.

## Accomplishments that I'm proud of

We managed to design a data visualization that makes use of multiple datasets and combined them in a cohesive way that helps students make an informed decision.

## What I learned

We learned that the quality of the data sets was not only dependent on the source it came from, but also the richness of the data in providing value in visualization. In certain fields the data was especially shallow which made it difficult to draw any useful visualizations.

## What's next for LeaveTheNest

We would love to explore how students can learn from our visualization and where to expand next. Right now we focused on Canadian data, but the next step would be to include American cities and beyond. We would also love to explore more intricate data visualizations that can dig deeper into the data and provide more value.
## Inspiration

Many people were left unprepared to effectively express their ideas over Zoom. As students we’ve noticed that with remote learning, instructors seem to feel very distant, teaching through either slides or drawing on a tablet, without us being able to see their face. We know body language is an important part of being a presenter, so why should Zoom limit us like this? Aside from instructional content, Zoom meetings are boring so we ought to spice it up. Introducing AirCanvas!

## What it does

AirCanvas allows you to have a virtual whiteboard powered by Deep Learning and Computer Vision! Simply stick out your index finger as the pen, and let your imagination unfold in the air that is your canvas. This adds an entirely new dimension of communication that is not possible in person. You can also erase the drawings, drag the canvas around and draw with both hands simultaneously!

## How we built it

We begin with OpenPose, a project by researchers at Carnegie Mellon which performs pose estimation on a video stream in real time. With it, we are able to extract information about one’s poses, including the locations of joints. With this information, we have developed our own algorithm to recognize gestures that indicate the user’s desired actions. We additionally employ motion smoothing, which we will discuss a bit later. Combining these, we can render the user’s drawings onto the webcam stream, as if the user is drawing in mid-air.

## Challenges we ran into

One of the major challenges was, due to the low framerates of OpenPose, the paths generated are choppy, where the lines go back and forth. To solve this issue, we employed motion smoothing, and we can do this rather efficiently in O(1) runtime with our own data structure. This way, we get smooth lines without much performance penalty. In addition, we spent many hours testing our model and refining hyperparameters to ensure it works intuitively.

## What's next for AirCanvas

We were inexperienced with this technology, and we wish we had more time to add the following features:
Support for multiple people drawing with one camera simultaneously
A friendlier interface for user to customize pen settings such as color, thickness etc.
Tools you’d see in note taking apps, such as ruler
## Inspiration

Provide an easy and safe way to combine both online and offline social network
Encourage people with different personalities to make new friends in a safe environment
Making the online transaction more convenient and secure
AR is the next generation technology, this app is easier to be used on different platforms and devices such as AR glasses.

## What it does

Identify a person using a camera and showing three options including social media contact, online transfer, and personality tags.

## How we built it

UI: Apple ARKit
Backend: Swift 4 with Microsoft Cognitive Service Face API

## Challenges we ran into

FrontEnd: allocating Object, facing tracking
BackEnd:
 a. How to connect to Microsoft Face API, and implementing uses Swift 4
 b. How to use Google drive API to upload local image to cloud

## Accomplishments that we're proud of

Achieving all the accepted criteria, and able to do a short demo

## What we learned

ARKit, Swift4, Rest APIs

## What's next for FaceTag

Connect with more online banking service to achieve face indication and online transaction.
Training machine to be able to recognization more people