## Inspiration

A couple of years ago the American elections took the world by surprise when the unexpected happened. In the aftermath of the election, a lot of people talked about fraud in the polls, and that the number might have been tampered with. From those events and with the growing popularity of blockchain, we came up with the idea to provide a secure and private voting system without the possibility of tampering.

## What it does

Allows you to vote, using a blockchain system to give it an immutable property

## How we built it

We used a pre-configured, private blockchain, that is meant to aid speed up initial setup time. Through coding a contract onto the chain we were able to allow a single transaction i.e. vote, per user. Each of the users was given an address based on the MetaMask Chrome extension. This allowed for us to keep an immutable record of all the users' votes. In doing so we are able to flag and identify a person that tries to vote more than once. In addition, to get to vote users go through an authentication process that is set up through an online webpage. Information required in this process involves full name, phone number, email, and their province, as well as face recognition through the use of the IBM Watson API. Mulitple Sample picture of the user would be needed to be submitted during the signup procedure in order to train Watson to identify them by creating a special private classifier for each user
.

## Challenges we ran into

-One of the major challenges was learning how to code for a blockchain as that all of us never had experience with blockchain

* Having to reset the classifiers every couple of hours due to them being taken off the server
* Trying to interact with the blockchain through the browser
* Getting the HTML code for the web page to store the value's of name, email, phone and province

## Accomplishments that we're proud of

We are proud that we were able to accomplish two of the major milestones of this idea which are actually having a working blockchain and Being able to implement IBM Watson into this project and training to recognize specific people instead of just another face.

## What we learned

Through the last three days (Nov. 17 - Nov 19) we have learned a great deal about our weaknesses and strength when it comes to learning new programming concepts such as BlockChain and how the Watson API functions. Also, we learned that blockchain can have a variety of powerful applications beyond the transaction of cryptocurrency.
## Inspiration

We explored IBM Watson and realized its potentials and features that enable people to make anything they want using its cloud skills. We all read and we always want to read books/articles which suites our taste. We made this easier using our web app. Just upload the pdf file and get detailed entities, keywords, concepts, and emotions visually in our dashboard.

## What it does

Our web app analyzes the content of the articles using IBM NLU and displays entities, keywords, concepts and emotions graphically

## How I built it

Our backend is developed using Springboot and Java while the front end is designed using bootstrap and HTML.
We used d3.js for displaying graphical representation of data.
The content of the article is read using the Apache Tika framework.

## Challenges I ran into

Completing a project within 24 hours was a big challenge. We also struggled connecting front end and backend.
Fortunately, we found a template and we leveraged it to develop our project.

## Accomplishments that I'm proud of

We are proud to say that we worked as a team aiming for a specific prize and we were able to finish the project with pretty much all the features we wanted.

## What I learned

We learned the potential of IBM Watson NLU and other IBM Cloud technologies . We also learned different technologies like d3.js, springboot which we were not familiar with.

## What's next for Know before you read

We want this app accessible to more people and we are planning to deploy it after finishing up the UI.
## Inspiration

We as a team shared the same interest in knowing more about Machine Learning and its applications. upon looking at the challenges available, we were immediately drawn to the innovation factory and their challenges, and thought of potential projects revolving around that category. We started brainstorming, and went through over a dozen design ideas as to how to implement a solution related to smart cities. By looking at the different information received from the camera data, we landed on the idea of requiring the raw footage itself and using it to look for what we would call a distress signal, in case anyone felt unsafe in their current area.

## What it does

We have set up a signal that if done in front of the camera, a machine learning algorithm would be able to detect the signal and notify authorities that maybe they should check out this location, for the possibility of catching a potentially suspicious suspect or even being present to keep civilians safe.

## How we built it

First, we collected data off the innovation factory API, and inspected the code carefully to get to know what each part does. After putting pieces together, we were able to extract a video footage of the nearest camera to us. A member of our team ventured off in search of the camera itself to collect different kinds of poses to later be used in training our machine learning module. Eventually, due to compiling issues, we had to scrap the training algorithm we made and went for a similarly pre-trained algorithm to accomplish the basics of our project.

## Challenges we ran into

Using the Innovation Factory API, the fact that the cameras are located very far away, the machine learning algorithms unfortunately being an older version and would not compile with our code, and finally the frame rate on the playback of the footage when running the algorithm through it.

## Accomplishments that we are proud of

Ari: Being able to go above and beyond what I learned in school to create a cool project

Donya: Getting to know the basics of how machine learning works

Alok: How to deal with unexpected challenges and look at it as a positive change

Sudhanshu: The interesting scenario of posing in front of a camera while being directed by people recording me from a mile away.

## What I learned

Machine learning basics, Postman, working on different ways to maximize playback time on the footage, and many more major and/or minor things we were able to accomplish this hackathon all with either none or incomplete information.

## What's next for Smart City SOS

hopefully working with innovation factory to grow our project as well as inspiring individuals with similar passion or desire to create a change.