## Slooth

Slooth.tech was born from the combined laziness and frustration towards long to navigate school websites of four Montréal based hackers.

When faced with the task of creating a hack for McHacks 2016, the creators of Slooth found the perfect opportunity to solve a problem they faced for a long time: navigating tediously complicated school websites.
Inspired by Natural Language Processing technologies and personal assistants such as Google Now and Siri, Slooth was aimed at providing an easy and modern way to access important documents on their school websites.

The Chrome extension Slooth was built with two main features in mind: customization and ease of use.

# Customization:

Slooth is based on user recorded macros. Each user will record any actions they which to automate using the macro recorder and associate an activation phrase to it.

# Ease of use:

Slooth is intended to simplify its user's workflow. As such, it was implemented as an easily accessible Chrome extension and utilizes voice commands to lead its user to their destination.

# Implementation:

Slooth is a Chrome extension built in JS and HTML.
The speech recognition part of Slooth is based on the Nuance ASR API kindly provided to all McHacks attendees.

# Features:

-Fully customizable macros
-No background spying. Slooth's speech recognition is done completely server side and notifies the user when it is recording their speech.
-Minimal server side interaction. Slooth's data is stored entirely locally, never shared with any outside server. Thus you can be confident that your personal browsing information is not publicly available.
-Minimal UI. Slooth is designed to simplify one's life. You will never need a user guide to figure out Slooth.

# Future

While Slooth reached its set goals during McHacks 2016, it still has room to grow.
In the future, the Slooth creators hope to implement the following:
-Full compatibility with single page applications
-Fully encrypted autofill forms synched with the user's Google account for cross platform use.
-Implementation of the Nuance NLU api to add more customization options to macros (such as verbs with differing parameters).

# Thanks

Special thanks to the following companies for their help and support in providing us with resources and APIs:
-Nuance
-Google
-DotTech
## Inspiration

As victims, bystanders and perpetrators of cyberbullying, we felt it was necessary to focus our efforts this weekend on combating an issue that impacts 1 in 5 Canadian teens. As technology continues to advance, children are being exposed to vulgarities online at a much younger age than before.

## What it does

**Prof**(ani)**ty** searches through any webpage a child may access, censors black-listed words and replaces them with an appropriate emoji. This easy to install chrome extension is accessible for all institutional settings or even applicable home devices.

## How we built it

We built a Google chrome extension using JavaScript (JQuery), HTML, and CSS. We also used regular expressions to detect and replace profanities on webpages. The UI was developed with Sketch.

## Challenges we ran into

Every member of our team was a first-time hacker, with little web development experience. We learned how to use JavaScript and Sketch on the fly. We’re incredibly grateful for the mentors who supported us and guided us while we developed these new skills (shout out to Kush from Hootsuite)!

## Accomplishments that we're proud of

Learning how to make beautiful webpages.
Parsing specific keywords from HTML elements.
Learning how to use JavaScript, HTML, CSS and Sketch for the first time.

## What we learned

The manifest.json file is not to be messed with.

## What's next for PROFTY

Expand the size of our black-list.
Increase robustness so it parses pop-up messages as well, such as live-stream comments.
## Inspiration

We were inspired by hard working teachers and students. Although everyone was working hard, there was still a disconnect with many students not being able to retain what they learned. So, we decided to create both a web application and a companion phone application to help target this problem.

## What it does

The app connects students with teachers in a whole new fashion. Students can provide live feedback to their professors on various aspects of the lecture, such as the volume and pace. Professors, on the other hand, get an opportunity to receive live feedback on their teaching style and also give students a few warm-up exercises with a built-in clicker functionality.
The web portion of the project ties the classroom experience to the home. Students receive live transcripts of what the professor is currently saying, along with a summary at the end of the lecture which includes key points. The backend will also generate further reading material based on keywords from the lecture, which will further solidify the students’ understanding of the material.

## How we built it

We built the mobile portion using react-native for the front-end and firebase for the backend. The web app is built with react for the front end and firebase for the backend. We also implemented a few custom python modules to facilitate the client-server interaction to ensure a smooth experience for both the instructor and the student.

## Challenges we ran into

One major challenge we ran into was getting and processing live audio and giving a real-time transcription of it to all students enrolled in the class. We were able to solve this issue through a python script that would help bridge the gap between opening an audio stream and doing operations on it while still serving the student a live version of the rest of the site.

## Accomplishments that we’re proud of

Being able to process text data to the point that we were able to get a summary and information on tone/emotions from it. We are also extremely proud of the

## What we learned

We learned more about React and its usefulness when coding in JavaScript. Especially when there were many repeating elements in our Material Design. We also learned that first creating a mockup of what we want will facilitate coding as everyone will be on the same page on what is going on and all thats needs to be done is made very evident. We used some API’s such as the Google Speech to Text API and a Summary API. We were able to work around the constraints of the API’s to create a working product. We also learned more about other technologies that we used such as: Firebase, Adobe XD, React-native, and Python.

## What's next for Gradian

The next goal for Gradian is to implement a grading system for teachers that will automatically integrate with their native grading platform so that clicker data and other quiz material can instantly be graded and imported without any issues. Beyond that, we can see the potential for Gradian to be used in office scenarios as well so that people will never miss a beat thanks to the live transcription that happens.