## Inspiration

Are you out in public but scared about people standing too close? Do you want to catch up on the social interactions at your cozy place but do not want to endanger your guests? Or you just want to be notified as soon as you have come in close contact to an infected individual? With this app, we hope to provide the tools to users to navigate social distancing more easily amidst this worldwide pandemic.

## What it does

The Covid Resource App aims to bring a one-size-fits-all solution to the multifaceted issues that COVID-19 has spread in our everyday lives.

Our app has 4 features, namely:

- A social distancing feature which allows you to track where the infamous "6ft" distance lies

- A visual planner feature which allows you to verify how many people you can safely fit in an enclosed area

- A contact tracing feature that allows the app to keep a log of your close contacts for the past 14 days

- A self-reporting feature which enables you to notify your close contacts by email in case of a positive test result

## How we built it

We made use primarily of Android Studio, Java, Firebase technologies and XML. Each collaborator focused on a task and bounced ideas off of each other when needed.

The social distancing feature functions based on a simple trigonometry concept and uses the height from ground and tilt angle of the device to calculate how far exactly is 6ft.

The visual planner adopts a tactile and object-oriented approach, whereby a room can be created with desired dimensions and the touch input drops 6ft radii into the room.

The contact tracing functions using Bluetooth connection and consists of phones broadcasting unique ids, in this case, email addresses, to each other. Each user has their own sign-in and stores their keys on a Firebase database.

Finally, the self-reporting feature retrieves the close contacts from the past 14 days and launches a mass email to them consisting of quarantining and testing recommendations.

## Challenges we ran into

Only two of us had experience in Java, and only one of us had used Android Studio previously. It was a steep learning curve but it was worth every frantic google search.

## What we learned

* Android programming and front-end app development
* Java programming
* Firebase technologies

## Challenges we faced

* No unlimited food
**In times of disaster, there is an outpouring of desire to help from the public. We built a platform which connects people who want to help with people in need.**

## Inspiration

Natural disasters are an increasingly pertinent global issue which our team is quite concerned with. So when we encountered the IBM challenge relating to this topic, we took interest and further contemplated how we could create a phone application that would directly help with disaster relief.

## What it does

**Stronger Together** connects people in need of disaster relief with local community members willing to volunteer their time and/or resources. Such resources include but are not limited to shelter, water, medicine, clothing, and hygiene products. People in need may input their information and what they need, and volunteers may then use the app to find people in need of what they can provide. For example, someone whose home is affected by flooding due to Hurricane Florence in North Carolina can input their name, email, and phone number in a request to find shelter so that this need is discoverable by any volunteers able to offer shelter. Such a volunteer may then contact the person in need through call, text, or email to work out the logistics of getting to the volunteer’s home to receive shelter.

## How we built it

We used Android Studio to build the Android app. We deployed an Azure server to handle our backend(Python). We used Google Maps API on our app. We are currently working on using Twilio for communication and IBM watson API to prioritize help requests in a community.

## Challenges we ran into

Integrating the Google Maps API into our app proved to be a great challenge for us. We also realized that our original idea of including a blood donation as one of the resources would require some correspondence with an organization such as the Red Cross in order to ensure the donation would be legal. Thus, we decided to add a blood donation to our future aspirations for this project due to the time constraint of the hackathon.

## Accomplishments that we're proud of

We are happy with our design and with the simplicity of our app. We learned a great deal about writing the server side of an app and designing an Android app using Java (and Google Map’s API” during the past 24 hours. We had huge aspirations and eventually we created an app that can potentially save people’s lives.

## What we learned

We learned how to integrate Google Maps API into our app. We learn how to deploy a server with Microsoft Azure. We also learned how to use Figma to prototype designs.

## What's next for Stronger Together

We have high hopes for the future of this app. The goal is to add an AI based notification system which alerts people who live in a predicted disaster area. We aim to decrease the impact of the disaster by alerting volunteers and locals in advance. We also may include some more resources such as blood donations.
## Inspiration

We created ASL Bridgify to address the need for an interactive real time pose-estimation based learning model for ASL. In a post-pandemic world, we foresee that working from home and more remote experiences signals the need to communicate with individuals with hearing disabilities. It is a feature that is missing from various video conferencing, learning, and entertainment - based platforms. Shockingly, Duolingo the number 1 language learning platform does not teach ASL.

## What it does

ASLBridgify is an educational platform that specifically focuses on the learning of ASL. We provide comprehensive modules that help you learn languages in scientifically proven ways. We provide easy to follow UI and personalized AI assistance in your learning journey. We realize that the future of AI comes in more than chatbot form, so our AI models are integrated within video to track hand-movement using Media pipe and TensorFlow.

## How we built it

We created an educational platform by leveraging many technologies. Our frontend uses Next.js, Tailwind and Supabase. Our backend used Python libraries such as PyTorch, TensorFlow, and Keras to train our LLMs with the use of Intel Developer Cloud GPU and CPU to expedite the training. We connected the Frontend with the Backend with Flask. Moreover, we combined our trained models with Google Search API OpenAI API for Retrival-Augemented-Generation (RAG)

## Challenges we ran into

The biggest challenge was time. The time it took to train one Large Language Model, even when using Intel Developer Cloud GPU capabilities was immense. It was a roadblock because we couldn't test any other code on one computer until the LLM was done training. Initially we tried to preprocess both words and sentences using hand pose to map ASL and using encoder decoder architecture, but we were not able to complete this because of the time constraint. ASL sentences is something we want to incorporate in the future.

## Accomphishments we are proud of

We successfully trained preliminary large language models using PyTorch GPUs on the Intel Cloud from scratch. We're thrilled to integrate this accomplishment into our frontend. Through implementing three AI tools—each using different methods such as calling an API and building with IPEX—we've gained valuable insights into AI. Our excitement grows as we introduce our one-of-a-kind educational platform for ASL to the world.