## Inspiration

To introduce the most impartial and ensured form of voting submission in response to controversial democratic electoral polling following the 2018 US midterm elections. This event involved several encircling clauses of doubt and questioned authenticity of results by citizen voters. This propelled the idea of bringing enforced and much needed decentralized security to the polling process.

## What it does

Allows voters to vote through a web portal on a blockchain. This web portal is written in HTML and Javascript using the Bootstrap UI framework and JQuery to send Ajax HTTP requests through a flask server written in Python communicating with a blockchain running on the ARK platform. The polling station uses a web portal to generate a unique passphrase for each voter. The voter then uses said passphrase to cast their ballot anonymously and securely. Following this, their vote alongside passphrase go to a flask web server where it is properly parsed and sent to the ARK blockchain accounting it as a transaction. Is transaction is delegated by one ARK coin represented as the count. Finally, a paper trail is generated following the submission of vote on the web portal in the event of public verification.

## How we built it

The initial approach was to use Node.JS, however, Python with Flask was opted for as it proved to be a more optimally implementable solution. Visual studio code was used as a basis to present the HTML and CSS front end for visual representations of the voting interface. Alternatively, the ARK blockchain was constructed on the Docker container. These were used in a conjoined manner to deliver the web-based application.

## Challenges I ran into

* Integration for seamless formation of app between front and back-end merge
* Using flask as an intermediary to act as transitional fit for back-end
* Understanding incorporation, use, and capability of blockchain for security in the purpose applied to

## Accomplishments that I'm proud of

* Successful implementation of blockchain technology through an intuitive web-based medium to address a heavily relevant and critical societal concern

## What I learned

* Application of ARK.io blockchain and security protocols
* The multitude of transcriptional stages for encryption involving pass-phrases being converted to private and public keys
* Utilizing JQuery to compile a comprehensive program

## What's next for Block Vote

Expand Block Vote’s applicability in other areas requiring decentralized and trusted security, hence, introducing a universal initiative.
## Inspiration

There are 1.1 billion people without Official Identity (ID). Without this proof of identity, they can't get access to basic financial and medical services, and often face many human rights offences, due the lack of accountability.

The concept of a Digital Identity is extremely powerful.
In Estonia, for example, everyone has a digital identity, a solution was developed in tight cooperation between the public and private sector organizations.

Digital identities are also the foundation of our future, enabling:

* P2P Lending
* Fractional Home Ownership
* Selling Energy Back to the Grid
* Fan Sharing Revenue
* Monetizing data
* bringing the unbanked, banked.

## What it does

Our project starts by getting the user to take a photo of themselves. Through the use of Node JS and AWS Rekognize, we do facial recognition in order to allow the user to log in or create their own digital identity. Through the use of both S3 and Firebase, that information is passed to both our dash board and our blockchain network!

It is stored on the Ethereum blockchain, enabling one source of truth that corrupt governments nor hackers can edit.

From there, users can get access to a bank account.

## How we built it

Front End: | HTML | CSS | JS

APIs: AWS Rekognize | AWS S3 | Firebase

Back End: Node JS | mvn

Crypto: Ethereum

## Challenges we ran into

Connecting the front end to the back end!!!! We had many different databases and components. As well theres a lot of accessing issues for APIs which makes it incredibly hard to do things on the client side.

## Accomplishments that we're proud of

Building an application that can better the lives people!!

## What we learned

Blockchain, facial verification using AWS, databases

## What's next for CredID

Expand on our idea.
## Inspiration

A lot of the technology that we use today will become obsolete in future ( as it has been in the past ). However; with the current advancements in the field of cloud technology and AI, old hardware can now be transformed into their intelligent form. Which is what inspired us to build Jarvis as it would allow us to not only avoid a national waste crisis but to also to provide intelligent systems for the betterment of the world.

## What it does

Jarvis allows us to use a combination of existing hardware which may be obsolete (or soon to be) such as webcams, speakers, microphones, security systems and transform them into intelligent hardware. The platform uses the groundbreaking Google-Cloud-Vision APIs as the underlying engine which is then used to intelligently make use of the incoming input from the connected hardware. We also had to use a custom Natural - Language model in order to make accurate predictions. Further on, we are also making use of the Blockchain Technology to keep the data secure.

## How we built it

The underlying Google-Cloud engine was built entirely with Python 3, whereas the front-end was built with the standard web-development tools ( HTML, JS, CSS). Google Firebase was the only API that was used on both sides of the platform which improved the flow of data significantly. Since Python and JavaScript execute under different systems, we had to use the Flask API to make a connection between the two. The platform primarily focuses on connecting system video hardware (webcam, security systems) so we also had to use OpenCV and other streaming pythons libraries to configure a multi-camera stream. Further on, we also used GETH and Web3 APIs to spin up a private blockchain network, which is being used to secure the data produced by the engine.

## Challenges we ran into

One of the biggest challenges we ran into was in making the connection between the Python and Js portion of the project, which was mostly because our team had little knowledge on how the API worked (As it hosted its own server in-order to talk with the JS side of the project). Secondly, we also had trouble configuring multiple camera streams as the Linux OS is very specific on the default of the system. Thirdly, we also had trouble figuring out how exactly did the Google-Cloud-Vision works as some of the results were generalized.

## Accomplishments that we are proud of

Our major accomplishment is that we were able to produce a working version of the platform that would allow different camera streams to be combined and be accurately analyzed with the Google-Cloud Engine. Along with it, we also have an object analyzer that uses the Google-Cloud-Vision and Natural-Language Engine to spot specific items.

## What we learned

The most valuable that thing we learned is that an image can produce different sets of information, which if used correctly can predict its environment. Google-Cloud-Vision happens to be one of those tools that provided us with that technology and services.

## What's next for Jarvis - Scalable Platform

Jarvis has an infinite number of use cases, which is why the team will continue to build modules that will provide the user with useful usage of Google-Cloud services. We plan on integrating the compatibility of other hardware(s) that might become obsolete in the foreseeable future.